Paulo César Pérez

Daza Rapporteur

Sophie Dupuy-Chessa

Philippe Rosse

Yudith Cardinale

Dalmau Marc

Dominique Masson

Professor Nadine Couture

Proxemic Interactions with Mobile Devices

I give thanks to my loving parents, my mother on Earth, and my father in Heaven. My father always believed that

Chapter 1 Introduction

Nowadays, we are surrounded by smart device technologies in different aspects of our daily life. Recent advances on mobile technology boost the development of a wide variety of applications to support users in their activities (e.g., sports, health care, on-line services, business) or to help them in unexpected or dangerous situations (e.g., first aid, natural disasters) [START_REF] Boulos | How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from ecaalyx[END_REF][START_REF] Cross | Red cross: Mobile apps for emergencies gain in popularity | mobihealthnews[END_REF]. The use of mobile technologies in our ordinary life is increasing in a way without precedent. People can interact with different contexts through electronic devices (e.g., mobile phones, tablets, wearable technologies, and smartwatches) to accomplish their common tasks. Many of these tasks require a specific Human-Computer Interaction (HCI). In the last decade, researchers have demonstrated that the concept of proxemics can support HCI in several uses such as remote controls and interaction for smart environments. Proxemic interaction describes how people use interpersonal distances to interact with digital devices applying the five physical proxemic dimensions [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF][START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF]: Distance, Identity, Location, Movement, and Orientation (DILMO).

Proxemic interaction is derived from the social theory of proxemics proposed in 1963 by the anthropologist Edward T. Hall [START_REF] Hall | The Hidden Dimension: An anthropologist examines man's use of space in private and public[END_REF]. Hall describes how individuals perceive their personal space relative to the distance among themselves and others. Social relationships are essential in the life of human beings and can be expressed as how people allow contact and interaction among each other in a physical space. Thus, people interactions are based on physical distances or face orientation of others. Both factors describe the level of engagement among people to establish communication. Similarly, there are relationships among people and digital objects in a cyber environment, in which the physical distance is the main factor that determines the interactions as response of such digital objects.

Edward T. Hall established the proxemics as a concept used mainly to describe the human use of space. He pointed out proxemics as "the interrelated observations and theories of how the humans use of space as a specialized elaboration of culture" [START_REF] Hall | The Hidden Dimension: An anthropologist examines man's use of space in private and public[END_REF]. His research illustrates Introduction the proxemic behavior of people using a system notation that is based on communication factors among people such as: eye contact, body language (i.e., posture), tone of voice, gesture, and facial expression.

This social science theory has inspired researchers to create seamless interactions between users and digital surfaces and devices in which their physical affect the interactive behaviour. Additionally, current studies in this area focus on developing proxemic applications for specific functionalities [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF][START_REF] Brudy | Eagleview: A video analysis tool for visualising and querying spatial interactions of people and devices[END_REF][START_REF] Grønbaek | Proxemics play: Exploring the interplay between mobile devices and interiors[END_REF][START_REF] Ledo | Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies[END_REF], such as controlling digital devices in smart environments for implementing interactive proxemic applications (e.g., media players, video analysis, and remote control applications). Some other works propose frameworks and toolkits in order to help developers to build systems based on proxemic interactions [START_REF] Cardenas | Proximithings: Implementing proxemic interactions in the internet of things[END_REF][START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF]. Also, proxemic applications have been introduced for improving the daily life of blind people [START_REF] Brock | Change blindness in proximityaware mobile interfaces[END_REF][START_REF] Mojgan | Increasing passersby engagement with public large interactive displays: A study of proxemics and conation[END_REF][START_REF] Rector | Eyes-free art: Exploring proxemic audio interfaces for blind and low vision art engagement[END_REF].

Nowadays, the vast majority of smartphones and mobile devices are equipped with powerful hardware capabilities. These capabilities allow devices to process and obtain proxemic information from the measurement of the DILMO parameters; for example, by using sensors and cameras embedded in a smartphone, it is possible to estimate physical distances between objects or people by means of the smart-mobile camera. In turn, we can implement proxemic-based mobile applications aiming to facilitate users' contact and interaction with other people and devices within proxemic environments, composed of indoor and outdoor spaces.

This current trend of using proxemic interaction with the aim to improve mobile HCI, has raised the need for frameworks and tools to assist the development of such proxemic applications based on mobile devices and wearable technologies. However, there exists a lack of more general approaches able to support the whole implementation process (starting from the design phase) of proxemic applications for smart environments. In such a way, end-users, without programming knowledge, can easily design such proxemic applications, taking into account how to make better use of social distance based on theory of proxemics and implement their designs in mobile devices. Thus, the need for standardization and a more powerful, flexible, and general approach emerges. We use the term "Flexible interaction" as used by Groenbak et al in [START_REF] Grønbaek | Proxemics beyond proximity: Designing for flexible social interaction through cross-device interaction[END_REF], for designing flexible cross-device systems based on proxemic interactions.

Accordingly, to overcome those limitations, we propose a framework to support the development of proxemic applications and the creation of proxemic environments with mobile devices and wearable technologies. More precisely, the main goals of this research are:

• To demonstrate that proxemics can improve people's daily lives using mobile devices based on proxemic interactions.

Contributions

3

• To propose the modelling of proxemic environments based on graphical notation with the purpose of designing and implementing proxemic applications. Thus, non-expert users can easily design proxemic environments and represent proxemic behaviors, which allow developers to implement the proposed design.

• To create a framework-like toolkit, along with a graphical Domain Specific Language (DSL) and an API in order to allow developers to build proxemic mobile applications based on a combination of the DILMO dimensions, which determine interactions either between a person and a device or among devices.

Contributions

Responding the proposed objectives, the main contributions of this study can be summarized as follows:

• A mobile application based on proxemic interactions to help rescuers identify an unconscious person using social interaction, in which the unconscious person's device can share information without explicit interaction (see Chapter 4).

• A graphical DSL, with graphic symbols and formal notation that allows non-specialist modeling of proxemic interactions and their integration into proxemic applications for a better HCI. We develop a prototype of the DSL, which simulates proxemic environments, including entities, proxemic zones, DILMO combinations, and conditions to define entity's behaviors and reactions (see Chapter 5).

• An API toolkit aiming to help developers to build mobile applications that can be implemented in mobile devices by means of exclusively employing sensors available in such devices, in order to obtain proxemic information (see Chapter 6).

• An integral framework, with the DSL and the API, that provides an approach for defining proxemic zones, as well as managing DILMO proxemic dimensions and technical components required for creating proxemic environments based on the use of both mobile appliances (see Chapter 6).

• Mobile applications developed with the proposed framework. We offer mobile applications that allow people to use social distances without compromising users' intimate space to control a video player, to control a musical notes player, and to reduce the spread of nosocomial infections in healthcare workers (HCWs), according to several combinations of DILMO dimensions (see Chapter 7). Introduction

Publications

From the results of the contributions of this research, the following publications were produced and support the material presented in this thesis:

• Pérez, P., Roose, P., Cardinale, Y. The structure of this dissertation is given as follows:

• In Chapter 2, we describe the theory of proxemics and how this social theory has been adopted in ubiquitous computing (ubicomp) environments for improving the HCI. We describes fundamental concepts of HCI and proxemic interaction.

• In Chapter 3, we outline related work based on proxemic interaction. We illustrate the process of developing proxemic systems, including tools for design and frameworks architecture that allows developers to built proxemic applications. Besides, we make reference to technologies for implementing proxemic systems.

• In Chapter 4, we explore the proxemic concept to manage effectively social distance implementing a first aid mobile application that identifies unconscious people, generates alerts, and shares medical identification in a secure way. In this context, we develop a First Aid Mobile Application (FAMA) that can emit emergency information to other smartphones based on proxemic interactions.

• In Chapter 5, we describe our proposed approach for modelling proxemic environments based on a DSL, as well as the formal notation that assist designers to represent proxemic behaviors of entities that are part of the proxemic environment. Moreover, we show the suitability and functionality of our proposed graphical DSL by means of the implementation and the description of a prototype exemplifying the design of proxemic behaviors in different scenarios.

• In Chapter 6, we proposed a framework to implement mobile applications, which allows adapting combinations of DILMO proxemic dimensions to the required scenario. We illustrate our framework that allows developers to build proxemic mobile applications supporting the process of proxemic information.

• In Chapter 7, we describe the use of interpersonal distances and DILMO proxemic dimensions for implementing HCI with mobile devices that reduces their touchability.

We propose an architecture, to stop spreading of nosocomial infection. To show the usability and suitability of our proposal, we present two prototypes of apps for mobile devices as proof-of-concept.

• Chapter 8 summarizes and concludes this thesis work, by stating the key contributions. Finally, ideas and implications concerning future work and its directions are considered.

Chapter 2 Proxemic Interactions in Human-computer interaction: preliminaries

To study the proxemic interaction in the context of computer applications is important to know and understand the social theory of proxemics and how it has been adopted to improve HCI. This chapter is dedicated to explain these aspects.

Theory of Proxemics

Social distances are present in our daily existence. Theory of proxemics is a concept used mainly to describe the human use of space. Edward T. Hall proposed the first definition, who pointed out proxemics as "the interrelated observations and theories of humans use of space as a specialized elaboration of culture" [START_REF] Hall | The Hidden Dimension: An anthropologist examines man's use of space in private and public[END_REF]. He presented how people perceive, interpret, and use space, specially related to distance among people [START_REF] Evans | Cross-cultural differences in tolerance for crowding: Fact or fiction[END_REF]. Theory of proxemics describes how people from different cultures speak not only diverse languages but also inhabit different sensory worlds. For example, Hall's theory demonstrated that human cultures use differences physical distance of communication. In some cultures, people make more use of olfaction and touch senses than others. In this respect, the distance has an indispensable role in proxemics in order to establish a region around the person that serves to maintain proper spacing among individuals.

According to theory of proxemics, the interaction zones have been classified as four proxemic zones, as shown in Figure 2.1. The four distance zones have been collected from Proxemic Interactions in Human-computer interaction: preliminaries Fig. 2.1 Edward Hall's proxemic theory: interpersonal distances of people, showing radius in meters [START_REF] Hall | The Hidden Dimension: An anthropologist examines man's use of space in private and public[END_REF] observations and studies of the human behaviour in different environments of the world. Below, we describe the properties of each proxemic zone:

• Intimate (0-50 cm): This space is reserved for close relationships between people.

The physical contact of both persons is possible in this zone. Usually, people can access to the zone if the other person allows it (Figure 2.2(a)). However, there are exceptions according to environments, such as public transportation, lifts where a person's intimate zone can be compromised without his permission.

• Personal (0.5 cm-1 m): In this zone, people could have a natural interaction with other people, and it is barely possible to reach contact with their arms in which physical domination is limited (Figure 2.2(b)). Beyond it, a person can not freely "get his hands on" someone else.

• Social space (1-4 m): This area can be related to space where the people could maintain communication without touching each other (e.g., a meeting table). In this zone people keep physical distancing among individuals. For example, a business meeting can describe the zone in which people have to speak louder to address others in order to catch their attention (Figure 2.2(c)).

Theory of Proxemics

• Public (>4 m): It describes the distribution of people in urban spaces such as concert hall, public meeting, cinema, where the people's attention is focused on the moderator (Figure 2.2(d)). The person's identity is unknown among individuals that share the same space. Theory of proxemics also describes how people use the space and their territories for specific activities such as sleeping, eating, and working. The territory is in all respects of the word, an extension of the organism, which has been characterized by visual, vocal, and olfactory signs [START_REF] Hall | A system for the notation of proxemic behavior[END_REF]. Hall also describes that any culture's characteristic produces a set of patterns such as postural, touch code, and voice loudness relative to proxemic behavior in the environment [START_REF] Hall | A system for the notation of proxemic behavior[END_REF]. He created a notation system to describe such behaviours. Proxemic behaviour details if a person is standing, sitting, squatting, or lying down in the physical space.

Hall classified the territory as a fixed-feature space and a semifixed-feature space, which we describe below.

Fixed-Feature Space

Edward Hall describes how people design their urban area in the physical space according to a cultural context; for example, houses, office buildings. He emphasizes the lack of congruence in the design of civil infrastructure, which must be designed based on users' physical characteristics such as height and body volume. He highlights that characteristics arrangement of the rooms is different among cultures. According to this theory, people's behavior is influenced by fixed-feature space characteristics.

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Proxemic Interactions in Human-computer interaction: preliminaries

Semifixed-Feature Space

Semi fixed-feature space indicates the arrangement of objects in the physical space like home, conference room, office. Generally, these types of areas may include furniture, plants, screens, paintings, decorations. Edward Hall details how the arrangement of objects can influence on human relationships. For example, "the tables at a French sidewalk cafe, tend to bring people together". On Figure 2.3, we illustrate two examples of seating arrangements in the space which can affect interaction and communication among people. Next, we explain how theory of proxemics has been implemented in ubiquitous computing for improving HCI.

Human-Computer Interactions

Nowadays, we are surrounded by technology. The HCI is present in diverse aspects of our daily life using digital devices, such as mobile phones, tablets, game consoles, vending machines, laptops, among many others, that require different interaction modalities. The interaction modalities have been described as the communication channels between humans and computers (i.e., a set of transformations of raw data from input devices). It is generated by input/output peripherals [START_REF] Lalanda | Multimodal interactions in dynamic, service-oriented pervasive environments[END_REF]. HCI is mostly supported by computer vision, which is the field of computer science that provides new ways of interaction and new applications [START_REF] Abawajy | Human-computer interaction in ubiquitous computing environments[END_REF]. In the next section, we describe technologies that assist computing environments based on HCI.

Human-Computer Interactions

Movement Recognition

Computer vision in the 21st century is widely used in HCI for movement recognition, allowing users to interact with an invisible computer in the environment. [START_REF] Abawajy | Human-computer interaction in ubiquitous computing environments[END_REF]. Vision-based HCI allows obtaining a more extensive range of input capabilities by using computer vision techniques in order to process data from cameras. Visual-based HCI is associated with movement recognition technology, which is produced by sophisticated sensors (e.g., camera with embedded vision processor, depth camera with Microsoft Kinect sensor) [START_REF] Jeanne | Realtime face detection on a dual-sensor smart camera using smooth-edges technique[END_REF][START_REF] Zhang | Microsoft kinect sensor and its effect[END_REF]. Motion capture is the method of recording actors' movement through skeletal tracking in which the human body is represented by several joints representing body parts such as head, neck, shoulders, and arms [START_REF] Jaimes | Multimodal human computer interaction: A survey[END_REF][START_REF] Karray | Human-computer interaction: Overview on state of the art[END_REF][START_REF] Zhang | Microsoft kinect sensor and its effect[END_REF]. Computer vision is implemented to support face detection, proximity between the camera and object [START_REF] Jaimes | Multimodal human computer interaction: A survey[END_REF][START_REF] Jeanne | Realtime face detection on a dual-sensor smart camera using smooth-edges technique[END_REF][START_REF] Karray | Human-computer interaction: Overview on state of the art[END_REF]. Microsoft Kinect sensor is an input device that allows users to interact with electronic devices based on hand gesture recognition (touchless interaction) [START_REF] Gallo | Controller-free exploration of medical image data: Experiencing the kinect[END_REF]. The current Kinect sensor technology provides flexible, naturalistic interfaces that support HCI. Although conventional input devices such as the keyboard, mouse, and touch screen already involve bodily actions, new interaction technologies utilizing human movements that provide more flexible, naturalistic interfaces and support the ubiquitous computing paradigm.

In the next section, we describe the computing ubiquitous computing that has been incorporated into our environment to help us in our daily lives.

Ubiquitous Computing

Mark Weiser coined the first definition of ubiquitous computing (ubicomp) in 1988 when he was a principal scientist of the Computer Science Laboratory at Xerox PARC [START_REF] Weiser | The computer for the 21 st century[END_REF]. Ubiquitous computing assumes the disappearance of technologies into the everyday smart environment, creating them invisible to a user [START_REF] Lyytinen | Ubiquitous computing[END_REF]. Ubiquitous computing has included different services and smart technologies (Internet, operating system, sensors, microprocessors, interfaces, networks, and mobile protocols), which allow people interacting with the environment in a more natural and more personalized way. His approach was focused on the construction of new computing artifacts for use in everyday life. He took his inspiration from objects from home and office, particularly objects that transmit or capture information. He classified the devices that were part of ubiquitous computing environment according to three sizes:

• Wall-size interactive surface similar to the whiteboard office which is a big size and characterizes semifixed-feature spaces.

• Office desk and desk accessories, such as note pad, desk lamp, pends, etc., that use an individual person on his daily work.

• Portable devices like personal digital assistants in a way that each person can have a least one device in one office.

Ubiquitous computing environment has explored a group of devices intending to evaluate the autonomy of computing artefacts for use in everyday life. to the user when users interact with multiple wireless devices interconnected.

Ubiquitous computing provides new ways to explore HCI in which one user has many computing devices and can be employed according to the context required by the user. At this point, proxemic interaction emerges as a strategy to mediate people's interaction in ubicomp environments and facilitate communication between the user and digital devices.

Once the concept of ubiquitous computing is clear, in the following section, we describe proxemic interaction and how it has been implemented in HCI for ubicomp environments.

Proxemic Interactions

Sensing technologies are continually evolving by advancements in technology allowing new techniques for HCI. The earliest interaction between a human and a machine was purely physical before the introduction of computing systems [START_REF] Nardi | Activity theory and human-computer interaction[END_REF]. Over the past two decades, HCI emerged as an area of computer science that studies the user interfaces between humans and machines [START_REF] Carroll | Designing interaction: Psychology at the humancomputer interface[END_REF]. HCI evoked many challenging problems, such as direct manipulation interfaces, user interface management systems, and collaborative work. It is a discipline that has been characterized as the visible part that impacts human lives for supporting computer use. The basic interactions were based on direct manipulation, where visible objects on the screen are directly manipulated with a peripheral device [START_REF] Myers | A brief history of human-computer interaction technology[END_REF]. Peripheral interaction describes how people can perceive information from and physically interact with computing technology [START_REF] Bakker | Peripheral Interaction: Challenges and Opportunities for HCI in the Periphery of Attention[END_REF]. Context-Aware interaction describes a system state as implicit interaction according to inputs of itself [START_REF] Schmidt | Implicit human computer interaction through context[END_REF].

In this regard, implicit HCI captures environmental information that allows implementing context-aware of digital devices [START_REF] Schmidt | Implicit human computer interaction through context[END_REF]. Since the last decade, proxemics in HCI has developed the social theory based on physical distance between people and digital devices. Proxemic interaction has been implemented to enhance HCI in ubiquitous environments [START_REF] Ledo | Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies[END_REF], where the physical distances and contextual factors have been used as a user's interface among the users and ubicomp devices [START_REF] Roussel | Mirrorspace: using proximity as an interface to video-mediated communication[END_REF]. Proxemic interaction is focused on improving social interaction through intuitive implicit communication and fully integrated into everyday activities [START_REF] Ledo | Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies[END_REF]. HCI methods need to be designed to adapt to the interaction of different contexts in which combine two or more modes of input. Therefore, proxemic interaction raises awareness of the environment to indicate situations and establish communication channels between users and devices, allowing interaction without physical contact. In this regard, proxemic interaction is becoming an influential approach to implement HCI in ubiquitous computing [START_REF] Brudy | Cross-device taxonomy: Survey, opportunities and challenges of interactions spanning across multiple devices[END_REF][START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF], where the proximity has been used as a user's interface.

Theory of proxemics describes how individuals perceive their personal space relative to the distance between themselves and others, which are based on physical, social, and cultural contextual factors that influence and regulate interpersonal interactions [START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF][START_REF] Wolf | Proxemic zones of exhibits and their manipulation using floor projection[END_REF]. In order to know how the factors should be applied designing proxemic-aware ubicomp systems Greenberg identified five dimensions [START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF]: Distance, Identity, Location, Movement, and Orientation (we call them DILMO as an abbreviation), which are associated with people, digital devices, and non-digital things. Based on these concepts, Ballendat illustrates a ubiquitous computing ecology based on proxemic interactions that describe how people interact with digital devices (see Figure 2.5). Fig. 2.5 Proxemic interactions associate people to digital devices; digital devices to digital devices; and non-digital physical objects to both people and digital devices [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF].

Proxemic interactions allow enhancing connectivity and interaction possibilities when in proximity to people, other devices, or objects for building context-aware systems [START_REF] Marquardt | Proxemic interactions: From theory to practice[END_REF]. Proxemic interaction empowers the user to control digital devices, reducing physical contact with hardware without losing semantic interactions [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF][START_REF] Ledo | Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies[END_REF][START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF]. However, the proxemic interactions are not widely implemented in developing mobile applications. The adoption of mobile technologies in our daily life is growing in a way without precedent. Users can interact with diverse contexts through electronic devices (e.g., personal mobile phones, tablets, and wearable technologies) to accomplish their daily activities. Many of these activities require a specific HCI. In our dissertation, we implement proxemic interactions on mobile devices for improving the user experience. In the following, we describe the DILMO proxemic dimensions that have been proposed by Greenberg [START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF].

Proxemic DILMO Dimensions

Proxemic DILMO dimensions allow determining relationships among people and entities (digital devices and non-digital devices). Each DILMO dimension can also be analysed in a variety of ways according to the measures that can vary by accuracy and the values they return (i.e., discrete or continuous). In the following, we describe the concept of DILMO dimensions, proposed by Greenberg [START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF].

Distance

Distance is a physical measure of separation between two entities, according to how they interact [START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF][START_REF] Marquardt | Cross-device interaction via micro-mobility and f-formations[END_REF] (see Figure 2.6(a)). The distance is used as a discrete parameter with the purpose to assign a proxemic zone based on Hall's theory. The zone allows the users to interact with the display or devices in different proximity [START_REF] Vogel | Interactive public ambient displays: transitioning from implicit to explicit, public to personal, interaction with multiple users[END_REF]. Typically, short distances allow high-level interactions and the user could select multiple options to interact with the surface (see Figure 2.6 (a), while long distances reduce interaction between the user and a device (see Figure 2.6 (b)).

Identity

Identity is a term that mainly describes the individuality or role of a person or a particular object that distinguishes one entity from another one in a space [START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF][START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF]. The identity is used for controlling spatial interactions between a person's handheld device and all contiguous appliances in order to generate an effective appliance control interface [START_REF] Ledo | Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies[END_REF] (see Figure 2.7).

Proxemic Interactions in Human-computer interaction: preliminaries Fig. 2.7 Identities in proxemic interaction: Appliances control base on user's identity [START_REF] Ledo | Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies[END_REF].

Location

Location defines the physical context in which the entities reside. The location allows relationships of entities with objects which are categorized as fixed (e.g., room layout, doors, and windows) and semi-fixed objects that are changeable, such as chairs, desks, lamps [START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF][START_REF] Ledo | Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies[END_REF] (see Figure 2.8). It is an important factor because other measures may depend on the contextual location. Location provides the entity's positions in the space, that can be assessed at any time.

Movement

Movement is defined as entity's change of positions over the time [START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF][START_REF] Ledo | Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies[END_REF]. Figure 2.9 shows when a user walks towards the screen or approaches it the content of the screen is adjusted according to the speed user's movements. The movement includes the directionality allowing interaction between the user and the application. Fig. 2.9 User's movement towards the screen.

Orientation

Orientation provides the information related to the direction in which entities are facing between each other (see Figure 2.10). It is only possible if an entity has a "front face" and the entity can be detected in the visual field of another entity. Orientation can be continuous (e.g., the pitch/roll/ yaw angle of one object relative to another) or discrete (e.g., facing toward or away from the other object) [START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF].

Conclusion

This chapter explained the theory of proxemics, proposed by the anthropologist Edward T Hall, which describes the human use of space and the people's behavior in a cultural context. Hall demonstrated the importance of his approach for effective communication among people using four distance zones (intimate, personal, social, and public). Furthermore, he characterized the territory as fixed-feature space and semi fixed-feature, where locations allow people to do activities of daily life. According to the theory of proxemics, human Proxemic Interactions in Human-computer interaction: preliminaries behavior can be affected by the design of fixed-feature space or semi fixed-feature space, that govern every human interaction.

We explained how the HCI allows information transmitting through communication channels between humans and computers and how they have been implemented in ubicomp environments for improving the HCI. In this sense, the social theory of proxemics has inspired researches to design features to provide fluid interactions between digital devices and people. Proxemic interaction contributes to more natural experiences for HCI. Through this study, we have explored the concept of proxemic interactions and each proxemic dimensions, called DILMO (Distance, Identity, Location, Movement, and Orientation), whose measurements define the behaviors of entities in ubicomp environments.

In the next chapter, we continue describing how proxemic interaction has been implemented by previous studies and tools that help the development of proxemic systems.

Chapter 3 Related Work

In this chapter, we first present a review of studies that show how DILMO proxemic dimensions have been addressed with the purpose of implementing interactions among digital devices and among devices and people. Then, we describe studies that support the software engineering for developing proxemic systems -i.e., tools and frameworks for the design and implementation of proxemic applications. Finally, we introduce technologies that provide a wide range of possibilities for sensing proxemic information.

Systems based on DILMO Proxemic Dimensions

Proxemic interaction is a remarkable interaction technique that allows users to control digital devices in a natural way [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF][START_REF] Ledo | Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies[END_REF][START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF]. Previous proposals have implemented multiple proxemic applications based on DILMO and focused on developing proxemic applications for specific functionalities. From this review, we want to highlight that these proposals manage all DILMO proxemic dimensions or a subset of them.

Regarding the proxemic dimensions, the majority of works use all DILMO dimensions, while other works use just a subset of them. DILMO proxemic environments, in which all dimensions are considered are described in [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF][START_REF] Bhagya | Proxemics and approach evaluation by service robot based on user behavior in domestic environment[END_REF][START_REF] Brudy | Eagleview: A video analysis tool for visualising and querying spatial interactions of people and devices[END_REF][START_REF] Cardenas | Proximithings: Implementing proxemic interactions in the internet of things[END_REF][START_REF] Dostal | Spidereyes: designing attention-and proximity-aware collaborative interfaces for wall-sized displays[END_REF][START_REF] Garcia-Macias | Uasisi: a modular and adaptable wearable system to assist the visually impaired[END_REF][START_REF] Kim | ministudio: Designers' tool for prototyping ubicomp space with interactive miniature[END_REF][START_REF] Ledo | Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies[END_REF][START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF][START_REF] Mentis | Interaction proxemics and image use in neurosurgery[END_REF][START_REF] Mojgan | Increasing passersby engagement with public large interactive displays: A study of proxemics and conation[END_REF][START_REF] Rector | Eyes-free art: Exploring proxemic audio interfaces for blind and low vision art engagement[END_REF]. The work presented in [START_REF] Cardenas | Proximithings: Implementing proxemic interactions in the internet of things[END_REF], illustrates how the proxemic dimensions can support interaction among entities (people and objects), with a context-aware framework. The approach introduced in [START_REF] Rector | Eyes-free art: Exploring proxemic audio interfaces for blind and low vision art engagement[END_REF] proposes the use of the body-tracking capabilities of kinect sensors to obtain the distance and react according to the location, movements, and orientation.

DIMO proxemic environments are considered in the studies presented in [START_REF] Brock | Change blindness in proximityaware mobile interfaces[END_REF][START_REF] Wang | Proxemic peddler: a public advertising display that captures and preserves the attention of a passerby[END_REF]. The work applied computer vision and OptiTrack camera to measure the mobile device's distances between the user and device, allowing interaction with a proximity-aware mobile navigation. application [START_REF] Brock | Change blindness in proximityaware mobile interfaces[END_REF]. Proxemic Peddler [START_REF] Wang | Proxemic peddler: a public advertising display that captures and preserves the attention of a passerby[END_REF] is a display that reacts to users' movement monitoring the passerby distance and orientation with respect to the display at all times. Additionally, DLMO proxemic environments are presented in [START_REF] Grønbaek | Proxemics play: Exploring the interplay between mobile devices and interiors[END_REF][START_REF] Grønbaek | Proxemics beyond proximity: Designing for flexible social interaction through cross-device interaction[END_REF] in the context of cross-device games and cross-device interactions. Games' actions are defined according to distance, location, movements, and orientation of devices manipulated by children [START_REF] Grønbaek | Proxemics play: Exploring the interplay between mobile devices and interiors[END_REF]; while in [START_REF] Grønbaek | Proxemics beyond proximity: Designing for flexible social interaction through cross-device interaction[END_REF] crossdevice interactions are conducted by these dimensions with the purpose to share information among digital devices. In [START_REF] Cho | Deep thermal imaging: Proximate material type recognition in the wild through deep learning of spatial surface temperature patterns[END_REF], a proxemic environment is described in the context of an application that allows the recognition of materials based on low-cost mobile thermal camera integrated into a smartphone. This application measures the physical distance between the camera and the material and recognizes a specific texture (identity) a. The application provides a tool that records the location of potholes in a road based on DIL. Works based on DILM environments are presented in [START_REF] Dingler | Interaction proxemics: Combining physical spaces for seamless gesture interaction[END_REF][START_REF] Sørensen | Proxemic interaction in a multi-room music system[END_REF]. The Multi-Room Music System proposed in [START_REF] Sørensen | Proxemic interaction in a multi-room music system[END_REF] is a mobile app based on proxemic interactions that lets the user hearing the same songs playlist, while he changes his distance and location through the speaker arrangement in the house.In [START_REF] Dingler | Interaction proxemics: Combining physical spaces for seamless gesture interaction[END_REF], entities have been implicitly managed distance, movement, and location dimensions have been used to implement interaction between user and screen based on interaction zones. Proxemic environments reacting to distance, identity, and orientation (i.e., DIO proxemic environments) are presented in [START_REF] Dostal | Multi-view proxemics: distance and position sensitive interaction[END_REF][START_REF] Grønbaek | Built-in device orientation sensors for ad-hoc pairing and spatial awareness[END_REF][START_REF] Wolf | Proxemic zones of exhibits and their manipulation using floor projection[END_REF]. Multi-View distinct [START_REF] Dostal | Multi-view proxemics: distance and position sensitive interaction[END_REF] views from a single display compared to the angle of orientation between two users. This work used computer vision technology that allows user identification. In [START_REF] Grønbaek | Built-in device orientation sensors for ad-hoc pairing and spatial awareness[END_REF] shows the use of a built-in compass in smart-phones to support pairing them based on the orientation. Distance and location DL of people are considered in [START_REF] Vermeulen | Proxemic flow: Dynamic peripheral floor visualizations for revealing and mediating large surface interactions[END_REF] to define different actions in an interactive display. Distance and location are also used for adapting visualizations on displays based on the users' distance relative to the screen and generate multiple views of the information displayed.

Table 3.1 summarizes the comparison of the described studies according to how DILMO dimensions have been addressed. Distance dimensions has been frequently used to implement interaction between a user and a screen where the physical distance has been implemented to define the proxemic zones. In all of these applications, the interaction objects (i.e., people and devices) are considered entities; when the Identity dimension is implemented, these entities are explicitly identified in a particular role. Orientation provides information related to the direction in which an entity is facing. It allows identifying the front of an entity. Previous proposals have demonstrated how a user's orientation related to a display can improve user interaction. Location and movement dimensions are less used in the previous work. The location has been associated with the position of an object in the environment. The movement allows applications to adjusted screen views according to the user's movements.

Software Engineering for the Development of Proxemic Interactions

Software Engineering allow designers and developers to focus on developing useful and accurate software. It is a discipline that organizes the systems development, separated into states to improve the design and implementation phases. Software engineering supports developing complex systems in a systematic way, including the development and building of computer systems software on a second hand. In order to increase the productivity of software engineering, tools to support the different phases of the software development process have been proposed, such Domain-Specific Languages (DSL) [START_REF] Visser | Webdsl: A case study in domain-specific language engineering, generative and transformational techniques in software engineering ii: International summer school, gttse 2007[END_REF], tools to support the design of applications, and frameworks or the development. Next sections provide a brief review of these three aspects.

Domain-Specific Languages

DSL is an executable specification language that provides the appropriate notations for a specific problem domain [START_REF] Van Deursen | Domain-specific languages: An annotated bibliography[END_REF]. It can be viewed as specification languages, as well as programming languages. DSLs can be supported by a compiler which generates applications. A DSL can be textual or graphical that allow modeling a particular system. DSLs are a code structure designed to make it easy for humans to understand particular domain [START_REF] Fowler | Domain-specific languages[END_REF]. DSLs are focused on four key elements to solve a problem for particular domains:

• Computer programming language: Humans use a DSL to instruct a computer to do something.

• Language nature: DSL is a programming language that makes sense when the expressiveness comes from composed expressions and individual expressions.

• Limited expressiveness: A general-purpose programming language is to provide support abstract structures in a specific domain that helps to build aspects of the computer system.

• Domain focus: A limited language is useful only if it has a clear focus on a small domain.

Hundreds of DSLs are currently used for different areas such as financial products, software architectures, web computing, robot control, and digital hardware design. The DSLs are used in software engineering to increase the productivity of the development process [START_REF] Visser | Webdsl: A case study in domain-specific language engineering, generative and transformational techniques in software engineering ii: International summer school, gttse 2007[END_REF].

A DSL provides a collection of sentences in a textual or visual notation with a formally defined syntax and semantics. The structure of the language sentences must be based on a meta-model with abstract mathematical semantics. Therefore, the properties and behavior of a DSL model should be predictable [START_REF] Visser | Webdsl: A case study in domain-specific language engineering, generative and transformational techniques in software engineering ii: International summer school, gttse 2007[END_REF].

In the context of ubicomp environments, we did not found many studies proposing DSL to support the development of ubicomp applications and we did not find any DSL helping to modelling proxemic applications. Only the wok proposed in [START_REF] Giner | A framework for the reconfiguration of ubicomp systems[END_REF] shows the modeling of a system that describe the user requirements about the different services based on DSL for specifying pervasive systems [START_REF] Giner | A framework for the reconfiguration of ubicomp systems[END_REF]. Thus, there is a lack of DSL that allows designer to describe proxemic interactions, in order to improve HCI in ubicomp environments.

In previous studies were limited to the implementation of applications for specific task which have been described in section 3.1. Concerning design tools, only two of them aim to design proxemic interactions [START_REF] Dostal | Spidereyes: designing attention-and proximity-aware collaborative interfaces for wall-sized displays[END_REF][START_REF] Kim | ministudio: Designers' tool for prototyping ubicomp space with interactive miniature[END_REF] which will be described in next section 3.2.2 . There is also two frameworks in order to develop applications based on proxemic interactions [START_REF] Cardenas | Proximithings: Implementing proxemic interactions in the internet of things[END_REF][START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF] that will be described in section 3.2.3.

Software Engineering for the Development of Proxemic Interactions

Tools for Designing Proxemic Applications

Software design is the process to convert the user requirements into some suitable form, which supports the developer in software coding and implementation. Table 3.2 summarizes the tools for supporting the design of proxemic environments. SpiderEyes [START_REF] Dostal | Spidereyes: designing attention-and proximity-aware collaborative interfaces for wall-sized displays[END_REF] is a system that offers a visual tool that allows designers to create collaborative proxemic applications for Wall-Sized Displays. This work develops proxemic applications for adjusting visualizations on displays. The visualization design tool is a web application that requires setting the values to distinguishes active users passing by in the background and foreground based on whether their visual attention is on display or not and only displays visualizations for active users (see Figure 3.1).

Related Work

This system shows how people's orientation and distance to the display can support collaborative activities around large wall-sized displays. Nevertheless, it does not propose a graphical design to enable end-users the general modelling of proxemic behavior.

Ministudio [START_REF] Kim | ministudio: Designers' tool for prototyping ubicomp space with interactive miniature[END_REF] is a tool for designers without technical implementation skills, which can be used to build miniature prototypes for proxemic interactions in the design phase. This tool illustrates how the proxemic dimensions can be used to define spatial relationships and interactions among people, devices, and objects using designers' familiar software and materials. Ministudio is based on tangible interaction interface, which uses miniature models on paper and projected images. The tool is composed of three functional components (see Figure 3. (c) Camera-projector system for projecting images on physical miniatures.

This work shows that MiniStudio supports rapid designing of large and complex ideas with multiple connected components for prototyping ubicomp environments in the design phase. However, the implementation of the tool needs specific hardware and material for creating miniature models such as Augmented Reality marker and camera-projector and it does not offer a graphical notation to allow the designer to model proxemic behaviors. Therefore, this tool is limited to be implemented on a large scale. Specific hardware requirements limit the use of SpiderEyes and Ministudio for the design of mobile proxemic applications Next, we discuss frameworks proposed to build proxemic systems based on processing proxemic information from an ample range of sensors.

Frameworks to build Proxemic Systems

Some frameworks have been proposed to support the development process and complement the software engineering [START_REF] Cardenas | Proximithings: Implementing proxemic interactions in the internet of things[END_REF][START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF]. In [START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF], a framework called Proximity Toolkit, used to discover novel proxemic-aware interaction techniques is proposed. The framework is a guide on how to apply proxemic interaction design for domestic ubiquitous computing environments. It is used to discover novel proxemic aware interaction techniques. The framework manages DILMO dimensions, which allow to determine relationships between entities and people. The framework architecture has four main components (see Figure 3 The Tracking plug-in contains two plugins: the marker based VICON and the KINECT sensor, that tracking of skeletal bodies and stream the raw input data of tracked entities to the server. The Visual Monitoring tool permits developers and designers to visualize tracked entities and their proxemic relationships among digital devices.

The API is a collection of libraries developed in C that use spatial information and relations among objects and space for processing proxemic information from ubicomp environment. This framework allows rapid prototyping of innovative interfaces processing proxemic information from sensors. However, the implementation of this framework requires a hardware architecture based on fixed devices (e.g., a Kinect Depth sensor and a client-server architecture) for allowing the server to process the proxemic information from appliances. This solution does not offer the mobility and portability required for implementing proxemic interactions on mobile devices or wearable technologies [START_REF] Bâce | Collocated multi-user gestural interactions with unmodified wearable devices[END_REF]. With the current Proximity Toolkit version, it is not possible to obtain proxemic information from the new smartphone's sensors capabilities and ensuring that users can use proxemic mobile applications on their smartphones in any place.

The work presented in [START_REF] Cardenas | Proximithings: Implementing proxemic interactions in the internet of things[END_REF], illustrates how the proxemic dimensions can support interaction among entities (people or objects), with a proposed context-aware framework. This framework used mobile sensors that provide portability for sensing DILMO proxemic dimensions. However, this framework needs an active internet connection to process proxemic information from mobile sensors. This framework does not offer methods that allow developers to implement proxemic interactions based on mobile computer vision. Furthermore, neither of the two current frameworks provides an API that enables developers to process proxemic information from mobile devices sensors. Both frameworks are limited to built proxemic mobile applications, while ProximiThings [START_REF] Cardenas | Proximithings: Implementing proxemic interactions in the internet of things[END_REF] cannot process DILMO dimensions without a server connection. Table 3.3 summarizes technologies used by previous frameworks and also describes constraints for building proxemic mobile applications. We described the hardware and software that previous frameworks have been used for sensing proxemic information. All these works demonstrate the current interest for researchers to develop tools that support the design and implementation of proxemic applications. Nowadays, smartphones and mobile technologies are powerful and offer a wide range of possibilities to improve user interaction. There are about 2 billion people with smartphones, which represents one-quarter of the global population in the world, and this trend is on the rise [START_REF] Gao | Understanding consumers' continuance intention towards mobile purchase: A theoretical framework and empirical study-a case of china[END_REF]. It is therefore of great importance to provide tools for developing mobile apps and improve HCI on mobile devices.

Technologies for sensing DILMO Proxemic Dimentions

In this section, we describe the technologies used in ubiquitous environments for sensing each DILMO from a variety of sensors. We describe the technologies that have been used for the development of proxemic systems by previous studies. Table 3.4 summarizes the sensors used by proposals to obtain proxemic information listed in Table 3.1.

Table 3.4 List of sensors for obtaining DILMO proxemic dimensions

Sensor D I L M O Microsoft kinect (Computer Vision) ✔ ✔ ✔ ✔ ✔ Vicon/OptiTrac Motion Capture ✔ ✔ ✔ ✔ Leap Motion ✔ LV-MaxSonar-EZ1 ✔ SHARP GP2Y0A02YK0F ✔ Bluetooth BLE ✔ ✔ Mobile Sensors ( accelerometer, gyroscope, magnetometer) ✔ ✔ ✔ Mobile Thermal Imaging ✔ ✔
Distance is defined in Chapter 2.3.2. The work presented in [START_REF] Rector | Eyes-free art: Exploring proxemic audio interfaces for blind and low vision art engagement[END_REF] proposes the use of the body-tracking capabilities of Microsoft Kinect Sensors to obtain the distance. Authors demonstrate the suitability of their proposal in an application that measures the distance between blind people and paintings, according to which it provides different background music experiences. In [START_REF] Brock | Change blindness in proximityaware mobile interfaces[END_REF][START_REF] Mentis | Interaction proxemics and image use in neurosurgery[END_REF], computer vision is used for measuring the distance from the device hosting the program to the user. Vicon tracking system has been used to estimate distance between the user and a wall display in [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF]. However, the Vicon tracking system is expensive and thus it is not a realistic mobile platform. In work used, [START_REF] Garcia-Macias | Uasisi: a modular and adaptable wearable system to assist the visually impaired[END_REF] the user wearing the ultrasonic sensor LV-MaxSonar-EZ1 that allows the detection of objects at distances between 30 cm to 6.45 meters. The Sharp GP2Y0A02YK0F is employed for measuring the distance between the visitors and an exhibit which is around of interaction zones [START_REF] Wolf | Proxemic zones of exhibits and their manipulation using floor projection[END_REF].

Related Work

Bluetooth Low Energy (BLE) technologies allow the device to estimate the proximity among entities by Received Signal Strength Indicator (RSSI) and Broadcasting Power value (TX power), as in [START_REF] Zidek | Bellrock: Anonymous proximity beacons from personal devices[END_REF][START_REF] Wang | Bluetooth positioning using rssi and triangulation methods[END_REF]. The BLE technology is currently used with Beacons which are small Bluetooth radio transmitters that broadcast BLE signal frequently employed in proximity marketing (see Figure 3.4). Furthermore, these are used to support human interaction. The work presented in [START_REF] Cheraghi | Guidebeacon: Beaconbased indoor wayfinding for the blind, visually impaired, and disoriented[END_REF], shows how blind people or visually impaired users equipped with smartphones can interact with beacons in order to receive assistance. In that work, the resolving of proximity detection (distance) takes great relevance. This study shows the advantage of implementing BLE Beacon. The work described in [START_REF] Desjardins | Collaboration surrounding beacon use during companion avalanche rescue[END_REF], implements beacon to rescue persons caught in an avalanche (skiers). Authors propose the use of two physical beacons: transmitter and receiver, one for victim and the other for the rescuer. Beacon transmits an electromagnetic signal, allowing achievement of the goal. This work has demonstrated how the beacon technology has contributed to save human lives. Mobile Thermal Imaging allows to estimate proximity which is based on the use of mobile thermal imaging and deep learning [START_REF] Cho | Deep thermal imaging: Proximate material type recognition in the wild through deep learning of spatial surface temperature patterns[END_REF]. However this is not a standard sensor for all smartphone. Mobile Computer Vision is implemented to determine the distance between the user and the display using mobile device's camera [START_REF] Brock | Change blindness in proximityaware mobile interfaces[END_REF].

Identity is defined in Chapter 2.3.3. SpiderEyes [START_REF] Dostal | Spidereyes: designing attention-and proximity-aware collaborative interfaces for wall-sized displays[END_REF] uses a Kinect Depth Camera that allows identification of entities. In this particular regard, it was indispensable to have user identification. User's interaction is based on their identity and distances with the display. Kinect combines three kinds of information to recognize users' identity such as face, clothes, and body skeleton using an RGB camera which is based on the three primary colors red, green, and blue (RGB) [START_REF] Leyvand | Kinect identity: Technology and experience[END_REF]. The user identity is verified using a "truth-table". This table provides a final result based on a sequential process that start from face detection to height estimation, in which face detection is the most reliable value. Table 3.5 [START_REF]Kinect identity: Player recognition in xbox -microsoft research[END_REF] shows an example of a truth table for users authentication. [START_REF] Zidek | Bellrock: Anonymous proximity beacons from personal devices[END_REF]. Mobile Thermal Imagine and a deep learning technique are used for sensing and recognizing the material [START_REF] Cho | Deep thermal imaging: Proximate material type recognition in the wild through deep learning of spatial surface temperature patterns[END_REF]. Currently, Mobile Computer Vision is a powerful technology which is based on advanced image processing that allows to detect the human face.

Location is defined in Chapter 2.3.4. In the work presented in [START_REF] Rector | Eyes-free art: Exploring proxemic audio interfaces for blind and low vision art engagement[END_REF], location is used to detect events related to hands' tracking. For example, when a blind user explores a painting with his hands, the application uses the 3D coordinate system of the Kinect depth camera [START_REF]Cameraspacepoint structure | microsoft docs[END_REF], to know the user's hand position in a specific region of the painting. In [START_REF] Grønbaek | Proxemics play: Exploring the interplay between mobile devices and interiors[END_REF][START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF], entities are associated with three-dimensional positions related to a fixed point that can be used for initial setting of smart environments. In such a way, it is possible to obtain the relative position among people and devices. Vicon motion capture system uses a reflective infrared markers to obtain the people's location and digital devices [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF]. Multi-Room Music System [START_REF] Sørensen | Proxemic interaction in a multi-room music system[END_REF] is an application that allows the user to hear the same songs playlist while changing his location. This application uses a comparison of received Wi-fi signal strength indicator (RSSI) measurements, handled by the smartphone application.

Movement is defined Chapter 2.3.5. It has been obtained using a Kinect depth camera and Leap Motion for capturing the movements of the hands with accuracy [START_REF] Dingler | Interaction proxemics: Combining physical spaces for seamless gesture interaction[END_REF]. Motion capture uses data sensing from images to triangulate the 3D positions of a person or an object between one or more cameras. The Vicon motion capturing system [START_REF] Vicon | About vicon motion systems[END_REF] tracks infrared reflective markers to retrieve fine-grained information of the person's movements. Figure 3.5 shows the architecture of Vicon motion capturing system. The movement also allows gesture recognition through smartphones or wearable technologies by employing Mobile Sensors [START_REF] Bâce | Collocated multi-user gestural interactions with unmodified wearable devices[END_REF][START_REF] Grønbaek | Proxemics play: Exploring the interplay between mobile devices and interiors[END_REF], such as accelerometer, gyroscope, and magnetometer which are generally incorporated in modern mobile devices . Orientation is defined in Chapter 2.3.6. Previous works have demonstrated how a person's orientation related to a display can be used for improving user interaction [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF][START_REF] Grønbaek | Proxemics play: Exploring the interplay between mobile devices and interiors[END_REF][START_REF] Marquardt | Cross-device interaction via micro-mobility and f-formations[END_REF][START_REF] Mostafa | Interacting with microseismic visualizations[END_REF] using Kinect depth camera sensor and the marker-based Vicon motion capturing system to obtain the orientation between two entities. In [START_REF] Grønbaek | Built-in device orientation sensors for ad-hoc pairing and spatial awareness[END_REF], has used a built-in compass orientation in mobile devices to support pairing between mobile devices equipped with Mobile sensors.

Related Work

Computer vision is frequently employed to obtain almost the whole DILMO proxemic dimensions using a Kinect depth camera. Kinect depth camera is powerful and low cost sensor that is frequently used in previous proposal for sensing proxemic interaction . Vicon motion capture technology is very accurate in seizing people's movement and objects in which users have to carry and wear Vicon marker sensors. Notwithstanding, theses sensor do not provide portability that allow developers to implement in mobile devices.

Conclusion

In this chapter, we have studied software engineering tools for the development of proxemic applications. We have described previous studies that have used DILMO dimensions or a subset of them. Previous solutions demonstrated the interest of researchers to develop tools that support the design and implementation of proxemic systems. However, our research found that there are needs from tools based on a symbology, in which designers without high-degree implementation skills can design proxemic systems.

From the analysis of previous proposals in Section 3.2.3, we note there exists a notable lack of general approaches able to support the design process to represent proxemics behaviours. Each work proposes and uses a particular notation and tool; there is not a standard process neither a standard model to design and implement proxemic applications. We overcome these limitations by proposing a development approach based on a formal and graphical modelling supported by a DSL, that allows the designer to represent different proxemic behaviors in smart environments, based on different combination of DILMO dimensions. Thus, the design phase is supported by formal notations and graphical objects, to facilitate the design process.

Furthermore, the vast majority of mobile devices are equipped with robust hardware capabilities. These capabilities allow devices to process and obtain proxemic information on mobile devices; for example, by using sensors and cameras embedded in a smartphone. In turn, it is possible to implement proxemic based mobile applications that facilitate users' contact and interaction with other people and devices in indoor and outdoor spaces, which we call smart proxemic environments. Therefore, we set ourselves the challenge of creating a functional proxemic mobile application. In the next chapter, we illustrate the implementation of a proxemic mobile application that allows us to identified if there are needs of tools for the development of proxemic environments based on mobile devices.

Chapter 4 First Aid Mobile Application (FAMA): A Proxemic Application in Mobile Devices

To demonstrate the feasibility of proxemic interactions with mobile devices, we implemented a mobile application to solve a daily life problem. In this chapter, we describe both the application and the development process. We developed a First Aid Mobile Application (FAMA) as proof-of-concept using a subset of proxemic dimensions (i.e., Distance, Identity, and Movement) to model proxemic HCI that allows flexible interaction between people and devices. We describe the technical aspects and challenges related with the development of FAMA. We show the benefit of using proxemic interaction to implement HCI, in the context of emergency and rescue services, in terms of better performance and accessibility, compared with an existing first aid mobile application on the market.

Motivating Scenario

Nowadays, it exists a wide variety of first aid applications for Android and iOS devices to help users in unexpected or dangerous situations. However, their massive use is limited due to several aspects, such as the complicated user interaction or the wearing of special devices for medical identification. In order to find solutions to these problems, proxemic interaction seems to be an appropriate solution. We proposed a mobile application based on proxemic interactions and developed with Bluetooth Low Energy (BLE) Beacon technology. Furthermore, FAMA's development allowed us to achieve the first approach to implement proxemic applications with mobile devices for sensing proxemic information. The prototype shows that the use of proxemics allows users to faster identification and more efficient interaction.

First Aid Mobile Application (FAMA): A Proxemic Application in Mobile Devices

First Aid Applications

In first aid situations, the unconscious persons commonly do not receive proper help until paramedics come on the scene. The medical identification is not available for people around the unconscious person, who could help meanwhile the paramedics arrive. In this regard, nowadays there exist a variety of first aid apps. Below we describe first mobile applications.

Today, we can find a variety of first aid apps for Android [START_REF] Appventive | Ice: In case of emergency -appventive[END_REF][START_REF] Pellegrino | Medical id -the android app that could save your life![END_REF]. Both applications [START_REF] Appventive | Ice: In case of emergency -appventive[END_REF] store emergency information for hospital staff, which can be accessed even when the is device locked. In such apps, the helper person can access emergency medical identification. However, these apps have the disadvantage that helper persons need to have a physical access to the injury person, in order to check the victim medical identification.

Another way to obtain the unconscious medical information is with the use of medical alert bracelets [START_REF] Beid | Medical bracelets[END_REF][START_REF]Medical id bracelets[END_REF] which are worn directly on the body by the user. These accessories allow sharing information directly or by interaction between the bracelet and the application on the smartphone. These solutions need additional accessories in which people must carry a bracelet. In contrast, in our proposal users only use the smartphone to provide aid and assistance. Below, we describe previous researches that allowed us to understand how to obtain the proximity between digital devices.

The work presented in [START_REF] Cheraghi | Guidebeacon: Beaconbased indoor wayfinding for the blind, visually impaired, and disoriented[END_REF], shows how blind people or visually impaired users equipped with smartphones can interact with beacons in order to receive assistance. In that work, the resolving of proximity detection (distance) takes great relevance. This study shows the advantage of implementing BLE beacon. The work described in [START_REF] Desjardins | Collaboration surrounding beacon use during companion avalanche rescue[END_REF], implements beacon to rescue persons caught in an avalanche (skiers). Authors propose the use of two physical beacons: transmitter and receiver, one for victim and the other for the rescuer. In a rescue situation, the victim and a rescue group need to work, both individually and collectively, the beacon transmits an electromagnetic signal, allowing achievement of the goal. This work has demonstrated how the beacon technology has contributed to save human lives. It shows the advantage of using proxemic interaction to implement HCI, in emergency and rescue services for better accessibility, compared with existing first aid mobile applications. However, the implementation of this solution requires sophisticated beacons based on electromagnetic signal which are not available on mobile devices. We proposed a FAMA based on proxemic interactions and developed using BLE Beacon technology to implement the application in mobile devices.

Problem

Problem

Previous proposals are focused on the identification of unconscious persons without taking into account the physical distance between people. In the context of first aid applications with unconscious persons, we have identified difficulties related to the interaction between unconscious person's app and helper persons. Generally, the helper person needs to access a physical device in order to see emergency identification on the injured person's smartphone. In other cases, the victim should wear special accessories (e.g., an alert id bracelet, smart watches, and NFC id card). With this kind of solutions, the intimate space of the unconscious person or victim can be affected by helper. Besides, the smartphone is a mobile device commonly used by people that allow the user to manage social distancing. Our aim was to identify unconscious people, facilitating natural interactions between human and mobile devices, without touching the unconscious person.

FAMA: Proxemic-based First Aid Mobile Application

This section describes our prototype based on the proxemics' theory to manage social proxemics distance among people. We illustrate the benefits of integrating this theory for first aid mobile application. Proxemic interaction provides foundational principles allowing users to have a seamless interaction with digital devices [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF][START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF]. These studies have demonstrated that DILMO proxemic dimensions help to create appropriate HCI for applications, while reducing physical contact with digital devices. The development of FAMA, our first aid application is a prototype that takes into consideration a subset of proxemic dimensions (distance, identity). The implementation of proxemic interaction allowed us to develop a novelty mobile application.

• Distance allows us to determine interaction zones. We evaluate the distances in order to fix general criteria of communication between both devices (unconscious person's device and helper's device).

• Identity allows us to identify a unconscious person. When a person changes his phone settings to a helper mode, he/she could identify people around him/her.

For our FAMA prototype, we define three interactive zones: Rescue Zone, Alert Zone, and Neutral Zone to offer better opportunities to unconscious or injury person to receive appropriate rescue aid. Each interaction zone was established in relation to distance between unconscious and helper person. Figure 4.1 shows the interaction zones for FAMA, these zones are inspired on previous researches [START_REF] Vogel | Interactive public ambient displays: transitioning from implicit to explicit, public to personal, interaction with multiple users[END_REF].

First Aid Mobile Application (FAMA): A Proxemic Application in Mobile Devices

• Rescue Zone (0-1m). Represents the intimate and personal space. In this zone, the helper can interact with mobile apps and reports events.

• Alert Zone (1m-4m). It represents the social space. People in this area, related to the unconscious person, can see the alert on their smartphones.

• Neutral Zone(4m). It represents the public space where nobody receives information.

FAMA explores the use of the proxemic concept, to manage effectively social proxemic zones that provides rapid identification of unconscious people, generates alerts, and shares medical identification in a secure way. In this context, FAMA was developed with BLE Beacon technology. The identification of the unconscious injured person can be accessed from other smartphones by BLE.

Scenario the exploration study

To illustrate our scenario, we consider a person in a public space, who feels weak and is about to faint. His/her smartphone will act as a virtual BLE beacon and will start to share emergency identification by establishing a Bluetooth connection. In the current version of FAMA, the user has to explicitly start the S.O.S mode by pressing a button (see Figure 4.4 This scenario allows us to explore the theory of proxemics and manage social proxemic distance by implementing a first aid mobile application that provides rapid identification of unconscious people, generates alerts, and shares people's identification securely. This emergency identification can be read by the smartphone of a helper person, exclusively when this is moving toward the Rescue Zone. People in the Alert Zone will then be able to see a notification; however, people in the Neutral Zone will not receive any notification. This scenario describes unexpected episodes in a public space, assuming that people carry their smartphone practically everywhere and at all times.

To evaluate the effectiveness of FAMA, we asked five PhD students to be in a university classroom (Neutral zone). We provided four smartphones running our prototype to theses students who play the role of helpers (H 1 ,H 2 , H 3 , H 4 ) and one for the unconscious or injured person whose device, in addition of our prototype, is running Medical ID (MID), an available first-aid mobile application. We choose MID [START_REF] Pellegrino | Medical id -the android app that could save your life![END_REF], because it has the same purpose of our prototype related to obtain emergency identification of unconscious or injured person. Also, Medical ID has received good comments from users on the google play platform, and the application provides a web site that describes the application to users.

We measured time from which the helper person tries to identify an unconscious person until the moment a helper H n obtains emergency identification. This time was measured, both for FAMA and MID. For MID the helper must search the smart cellphone inside the pants pocket or handbag of unconscious or injured person. In this case, the unconscious or injured person's intimate space could be affected by the helper when the helper was trying to find an unconscious person's smartphone. Also, it is very difficult to access an injured person's smartphone without moving it. While for FAMA the helpers person used the proxemic interaction application running on their smartphones in order to obtain the emergency identification of unconscious person without invading the intimate zone. In view of the foregoing, we have formulated two hypothesis as follows.

Hypotheses 1: Is it possible that a helper person can interact with unconscious person's smartphone, in order to obtain his/her emergency identification, without physical contact and reduce the recognition time with respect to current applications ?.

Hypotheses 2: Can An unconscious person or victim share emergency identification without explicit interaction with a device ?.

Results

This section describes the result of our proposed FAMA in the scenario of exploration, compared with existing first aid mobile application (MID solution). The experiment allows us to verify each question that was previously formulated to evaluate the benefits of using proxemic interaction with mobile devices.

First Aid Mobile Application (FAMA): A Proxemic Application in Mobile Devices

Hypotheses 1: This first question is verified utilizing the FAMA application installed on mobile devices. Our application provides an unconscious person's identification faster than the MID mobile because the helper person does not need physical access to the injured person's mobile phone. The helper can obtain an unconscious identity base on social distancing. For MID, the recognition time can be long, depending on where the unconscious person carry his/her phone such as jean pocket,jacket pocket, and handbag. This could represent a huge waste of time to identify the unconscious persons by the helper. Figure 4.2 shows the recognition time in seconds of each helper H n person by using our proposal prototype FAMA and MID solution. In our prototype, the recognition time is faster because the helper person does not need to physically access the smartphone of the unconscious person and unblock the unconscious person's smartphone. This experiment demonstrates FAMA's functionality and the successful use of proxemic interaction to identify an unconscious person using our first aid mobile application. Hypotheses 2: The helper person accesses his own smartphone for recognizing unconscious person. Figure 4.3 shows results of a user survey related to the accessibility of the applications FAMA and MID, by participants who chose a scale from 1 to 10 (10 being the best). When we talk about accessibility, it holds features as the rapidness of obtaining medical identification and respecting the unconscious person's privacy. Our prototype scored better results than MID, according to the survey applied to each helper person.

These primary results confirm the potential benefit of using proxemic interaction to implement HCI, in the context of emergency and rescue services, in terms of better performance and accessibility, compared with existing first aid mobile applications.

First Aid Mobile Application (FAMA): A Proxemic Application in Mobile Devices

Bluetooth Low Energy (BLE)

BLE is used in order to obtain both the physical distance and identity between two mobile devices. In contrast to Classic Bluetooth, BLE is designed to provide significantly lower power consumption. This Technology allows Android apps to communicate with BLE devices with stricter power requirements, such as proximity sensors, heart rate monitors, and fitness devices. Android 4.3 (API level 18) introduces built-in platform support for BLE in the central role and provides native libraries that allowed us to implement and transmit proxemic information in small amounts of data between nearby devices using BLE Beacon technology. In the next, we describes how to create virtual beacon in mobile devices.

BLE Beacon virtualization

BLE is a digital radio protocol that allow us to create Beacon advertising base on proximity between BLE devices. To build virtual beacons for smartphones, it was required to use beacon protocols. In our prototype, we need to transmit a string data that allow user to identify unconscious person. This data is used to prove that a virtual beacon can diffuse different kinds of information by interaction zones. We use the Java library AlteBeacon [START_REF] Altbeacon | Altbeacon -the open proximity beacon[END_REF]. Figure 4.6 shows FAMA's code which is based on AlteBeacon protocol for both create alert in proxemic interaction zones and obtain emergency identification. • The distance dimension is characterized through the signal strength of the Bluetooth that allowed us to estimate the physical distance between two smartphone.

• The identity dimension is implemented with UUID that allow the application to identify unconscious person.

FAMA architecture is base on Android and BLE libraries that allows developers to detect proximity between two mobile devices. Nevertheless, FAMA was developed without tools that support us to implement proxemic interactions in mobile devices.

First Aid Mobile Application (FAMA): A Proxemic Application in Mobile Devices

Conclusion

We have developed FAMA, based on proxemic interaction that taking advantage of HCI. Through the first aid, the prototype illustrates that FAMA's interaction behaves better than the current first-aid applications. FAMA offers benefits for faster identification of unconscious or injured people, only using smartphones and avoiding physical contact between people. The scenario allows us to presents the results from the preliminary evaluation of our prototype by the users. Hypotheses showed the advantage of adopting proxemic interaction to implement HCI, in the context of emergency and rescue services, in terms of better performance and accessibility, related to existing first aid mobile applications. Implementing proxemic interaction with mobile devices can bring benefits to user experiences.

The current version of FAMA is focused on the improvement of interaction with first aid mobile applications. However, FAMA was implemented without tools that help us to design and implement proxemic mobile applications. The current proxemic tools do not offer feasible solutions for developing and implementing mobile applications. This proxemic mobile application development has motivated us to create solutions for supporting the design and implementation of proxemic mobile applications.

In the next chapter, we describe an approach for modelling proxemic environment based on a graphical DSL focusing on entities in the proxemic environments.

Chapter 5 A Graphical Domain-Specific Language for Modelling Mobile Proxemic Applications

As described in Chapter 3 there exists a notable lack of general approaches able to support design process to represent general proxemic behaviours. Thus, we propose a formal definitions and approach for modelling proxemic environments based on a graphical Domain-Specific Language (DSL). It allows non-specialist designers to express proxemic interactions for modeling proxemic environments and supports the development process. We have implemented a prototype to design proxemic behaviours in different proxemic environments.

Motivation to Develop a Graphical Domain-Specific Language

Intelligent environments are present in people's everyday life with the integration of digital devices. Proxemic interactions allow users to improve HCI experiences using these digital devices. As described in the Chapter 3, current studies in this area are focused on creating specific proxemic systems and few support is provided with DSL in order to better support the software development process. We aim to help design proxemic environments for nonspecialist designers in order to express proxemic interactions among digital devices. We have created a graphical DSL to design and describe proxemic behaviours based on the notation system proposed by Edward T. Hall in [START_REF] Hall | A system for the notation of proxemic behavior[END_REF]. He showed how people understand, interpret, and use space, especially related to the distance among people [START_REF] Evans | Cross-cultural differences in tolerance for crowding: Fact or fiction[END_REF]. We chose a graphical DSL because it allows us to represent proxemic environments and translate into it into an XML file that can be interpreted by the computer. Our work allows us to present a graphical notation that can help to design proxemic environment from contextual design. The formal definition provides the syntax for each symbol proposed that allows us to describe the proxemic environment.

The next Section 5.2 describes the formal definitions, the graphical, symbolic notations, and the XLM scheme description, including our proposed graphical DSL.

Formal Definitions for the graphical DSL

The formal definitions that govern our graphical DSL are mainly intended to define all components in a proxemic environment. A proxemic environment is described as a space containing a target entity that reacts according to DILMO dimensions of all other entities. The first terms that we need to formally state are related to the entities and the target entity that can interact in a proxemic environment. Def. 1 describes what we consider as entities and Def. 2 formally describes a target entity, that we call cyber physical system. With these formal definitions the proxemic environment (P_E) is defined. It means that proxemic zones (P_Z), the CPS, the entities E, the set of DILMO dimensions to be considered, and the initial conditions are established. From the formal definition, it is possible to define inference rules that can generate specific behaviors and actions of entities in the proxemic environment.

Definition 1 Entity (E). An entity, denoted as E, represents an interaction object (e.g., a person, an object, a device), that can be identified or not according to requirement of scenario in the physical space.

Definition 2 Cyber Physical System (CPS). A cyber physical system, denoted as CPS, represents the target entity, from which a proxemic environment (P_E) is defined. All proxemic zones (P_Z) and DILMO dimensions are measured for all other entities (E) with respect to the CPS, and the interaction among them will be defined accordingly.

We keep the same significance of DILMO dimensions from the original proxemic interaction proposal [START_REF] Ballendat | Proxemic interaction: designing for a proximity and orientation-aware environment[END_REF][START_REF] Greenberg | Proxemic interactions: the new ubicomp?[END_REF]. Def. 3, Def. 4, Def. 5, Def. 6, and Def. 7 show our formal definitions for Identity, Distance, Location, Movement, and Orientation, respectively. Definition 3 Identity (I). The Identity, denoted as I, represents an entity (E) that has a unique identification in the proxemic environment.

By analogy to Def. 1, an identity (I) is associated with a specific person or object. an entity E with respect to the CPS in each proxemic zone; it is denoted as a point in the plane: E.L = (x i , y i ).

Formal Definitions for the graphical DSL

Definition 6 Movement ( ⃗ M)

. The movement represents a vector that has magnitude (or length) and a direction of an entity E with respect to the CPS, denoted as E. ⃗ M = (l, d); where l is the length and d is the direction, such as d ∈ {-1, 0, +1}:

• if d = 1 represents that E is moving towards the CPS;

• if d = 0 represents that E is static;

• if d = -1 represents that E is moving in the opposite direction with respect to the CPS.

Definition 7 Orientation (O). The orientation represents the face angle of an entity E with respect

to the face of CPS, and it is denoted as E.O.

Entities delimit proxemic zones according to the distance D, with respect to the CPS. Def. 8 presents the formal meaning of a proxemic zone.

Definition 8 Proxemic Zone (P_Z).

A proxemic zone represents a circular zone delimited by a maximum distance (radio) with respect to the CPS. There are four proxemic zones defined according to such maximum distance: P_Z intimate , P_Z personal , P_Z social , and P_Z public . The proxemic zone in which an entity is located is denoted as: E.P_Z or I.P_Z, accordingly. Thus,

• E.P_Z = P_Z intimate i f 0 < E.D ≤ MAX_ID; • E.P_Z = P_Z personal i f MAX_ID < E.D ≤ MAX_PD; • E.P_Z = P_Z social i f MAX_PD < E.D ≤ MAX_SD; • E.P_Z = P_Z public i f MAX_SD < E.D ≤ MAX_PubD.
where MAX_ID, MAX_PD, MAX_SD, and MAX_PudD represent the maximum distances that define the intimate, private, social, and public zones, respectively. They are parameters that have to be provided by users or developers.

A proxemic environment, as Def. 9 describes, is then conformed by the set of entities that interact with the target entity (CPS), according to DILMO dimensions and proxemic zones (P_Z). A P_E represents the system based on proxemic behaviors. The change of DILMO measures over the time of E in a P_E, defines their behaviors (see Def. 10).

Definition 10 Behavior (B). A behavior represents the change of D, L, M, and O measures or

the P_Z of an entity E , starting from its initial behaviour (B 0 ). It is denoted as a tuple E.B i =< E.D i , E.L i , E.M i , E.O i , E.P_Z i > and is produced by a transition from the previous behaviour B i-1 , such that:

• E.B 0 =< E.D 0 , E.L 0 , E.M 0 , E.O 0 , E.P_Z 0 > (initial behaviour); • E.B i = ∇E.B i-1 =< E.D i , E.L i , E.M i , E.O i , E.P_Z i > (for i ≥ 1);
where ∇ is a user-defined function to detect the change of measures of DLMO dimensions.

Behaviors of entities might be defined by developers through user-defined ∇ functions. For example, a ∇ function to detect movement of an entity, can be implemented based on readings from an accelerometer sensor of a smartphone. Actually, when an entity's movement is detected, the rest of DLO measures can be affected. These behaviors should be specified by developers. Hence, according to behaviors of entities E in a P_E, they can react by performing actions to comply their functionalities. It is expected that the CPS in a P_E is the interaction object that should execute most actions in a P_E; however, it is also possible to define actions for other entities E in the P_E. Actions define the specific functionalities of proxemic applications and must be defined by developers which are not included in the DSL.

To define a P_E the following initial conditions are specified:

1. Distances that define the four P_Z are provided by designers/developers according to user requirements.

2. In a P_E exists one and only one target entity; thus, given a P_E =< CPS, entities, distances >, CPS is not null.

3. The CPS represents the target interaction object, with the following properties:

• The original location of CPS is denoted as CPS.L = (0, 0) based on Cartesian coordinate system, since proxemic zones and DILMO dimensions of all other interaction objects are determined with respect to the CPS. This DSL allows the designer to describes a two-dimensional space.

• It has a face from which its visual field is defined according to the following angles: ∡ MinAo fV and ∡ MaxAo fV . If ∡ MinAo fV =∡ MaxAo fV =NULL, The CPS can not sense the entities' orientation. The Field of view (FoV) can be between the 0 • and 360 • 4. For all entities (E) in the system, DILMO dimensions and proxemic zones can be assigned as their initial behavior. Formally:

∀E i ∈ P_E.entities E i .B 0 =< D 0 , L 0 , M 0 , O 0 > ;
and L 0 , M 0 , O 0 can be null.

It means that distance (D) is the only mandatory dimension for entities. Orientation is a dimension that only matters if the CPS has a view angle that shows the space where other entities can be detected (examples of such CPS are a screen, a smartphone using its camera. When the movement is equal to null the entity is static in the environment.

These definitions conform the formal basis of our DSL and are used to specify conditions in a P_E (behaviors -B) to which entities should react by executing Actions. Hence, from the initial conditions, proxemic Actions can be defined with respect to the P_Z and DILMO dimensions of entities in the P_E.

Next, we describe each graphic notation symbol that allows non-specialist designers to express proxemic interactions for modelling proxemic environments and supports the development process.

Graphical Notation of the graphical DSL

The graphical notation provided by our graphical DSL, represents in a visual way, all components in a proxemic environment; thus, it corresponds to the formal definitions presented in Section 5.2. Our graphical model is inspired on the notation system proposed by Edward T.

Hall in [START_REF] Hall | A system for the notation of proxemic behavior[END_REF], which provides an axis notation code that allows the representation of human relations. We proposed graphical symbols to represent a P_E conformed by the P_Z, entities, and their DILMO dimensions. Each symbol can be described as entity in the P_E, which has several behaviors. Our graphical model is a standard graphical language that allows non-specialist designers to describe the proxemic interaction among physical user and devices using DILMO. The graphical language has been proposed to help developers to understand and implement the P_E established by designer. Table 5.1 illustrates each symbols from the corresponding formal notation described in Section 5.2. Table 5.1 Graphical notation for proxemic environment.

Physical System CPS: represents the target entity, from which a proxemic environment is defined in Definition 2. The CPS has a field of view angle that shows the space where other entities can be detected by the camera (examples of such CPS are a screen, a smartphone using its camera). If the angle between MaxFofV and MinFofV is equal to zero, the CPS cannot obtain entities in front to it or it can detect entities all around it (an example of such CPS is a smartphone detecting beacons near to it). The CPS is an entity which has relationship cardinality 1:N between the CPS and E or I that are involved in a P_E. 1:N refers to a one-to-many relationship one CPS the other side related to E.

The entity E is denoted as white and black symbol, that represents a role in the P_E without a unique identification, for example a user, a person, a visitor or client. It is defined in Definition 1. The front face of E can be recognized and the orientation angle can be obtained from the CPS. The Entity E face is represented as white circle in one side of vertical line.

The identity I is denoted as black, that represents a unique identification (e.g., "John", id number). It is defined in Definition 3. The front face of I can be recognized and the orientation angle can be obtained from the CPS. The identity I face is represented as semi-circle in one side of vertical line.

The entity E is denoted as white and black symbol without face which is represented as a white circle in the middle of a vertical line. This symbol can be used to describe the proximity between entities without considering the identity and orientation.

Continuation of Table 5.1

The identity I is denoted as black without face which is represented as circle in the middle of a vertical line. This symbol can be used to represent proximity between identified entities.

The physical distance between E and CPS is represented as E.D according to Definition 4. This behaviour can be illustrated using a combination of symbols, such as CPS and E, in which is not necessary the face orientation. The P_E can has multiple entities with different distances from the CPS.

The location of E with respect to the CPS is represented as E.L = (x i , y i ) according to Definition 5. The location in a P_E can be illustrated describing below of the interaction object symbol E the cartesian notation E.L = (x i , y i ) to each entity in the P_E. Only one CPS must be presented in a P_E for designing a P_E. The designer must provide the circle's sizes according to the requirement of the scenario.

Tangible interface

We made a tangible interface based on the symbols proposed in order to experiment with our graphical DSL. This interface allowed us to represent different proxemic behaviours with a standard interface using the symbology above mentioned in Section 5.3 . The tangible interface was a first experience to prove the feasibility of graphical DSL. We built the interface using office supplies such as paper click, adhesive tape, and white paper. Our aim was to use a low-tech that allow us to describe proxemic behavior before writing code. We made each entity that was described in Table 5.1. The use of paper prototypes that allow users to express their understanding of a software application before implementation [START_REF] Scialdone | How to teach information systems students to design better user interfaces through paper prototyping[END_REF]. Next, we show some examples of proxemic environments that were simulated with our tangible interface (paper prototype). Figure 5.1 illustrates four proxemics scenarios that were modeled by our research team using the tangible interface: example shows a subset of proxemic dimensions such as distance, movement, and orientation -i.e., a DMO proxemic environment. This case can represent an interactive display that works as CPS and any user who acts as an entity. Figure 5.1 (a) shows the symbol that was chosen for representing the entity's front. When the user (E) is walking straight towards CPS, the CPS can trigger an event according to proxemic DMO dimensions' values.

Scenario (b). This case allowed us to represent a specific person or object that is stopped within the proxemic zone of the CPS. An identity (I) without face is static in the CPS's P_Z social . The symbol illustrates a subset of proxemic dimensions, such as DI. This case represents a smart environment in which a person carries a mobile device such as a mobile phone, smartwatch, smart card and, electronic bracelet, which can be identified. The mobile devices can be detected by the CPS using radio frequency signals to estimate the physical distance between a device and the CPS. shows the symbol that was chosen for representing the entity without face.

Scenario (c). Scenario (a)

. textcolorblackThe third case shows three entities the are moving toward to CPS's P_Z intimate and the entities are oriented to the CPS. The CPS detects entities according to the CPS field of view. The symbols illustrate entities that implement a subset of proxemic dimensions, such as distance, movement and orientation -i.e., a DMO proxemic environment. The CPS can detected the entities in front it. With this case, the designer described graphically multiple entities that can interact with the CPS. The CPS can generate a specific event according to each entity detected in the simulated environment.

Scenario (d).

The last case describes an entity (E) without face that is static in the CPS's P_Z social . The example allows designer to illustrate the interaction between the CPS and an entity based in physical distance between the CPS and E. This case represents a proximity marketing scenario based on beacon BLE technology that sends signals to any smart device nearby. The proximity targeting supports restaurants with interactions with their customers from a distance, including order-ahead and informing customers about the variety of dishes on the menu.

The symbols provides arrows that allow designers to describe the direction of movement of an entity with front or without front. When an entity without a face moving toward or moving away from the CPS's P_Z personal . This first experience of modelling proxemic environments with tangible interaction allowed us to refine the symbology to simulate proxemic behaviour based on DILMO dimensions. For example, the movement sense of entities was made by adding arrows to the entities. The fill color was used to identification of identity. The position of a semi-circle on the entity for describing the entity's orientation. This experience also permitted us to realize that we need to find a data structure or a markup language for gathering proxemic information in a file from the initial design.

Modelling Proxemic Environments: A Systematic Process

This section presents a general modelling process based on our graphical DSL. The DSL allows designers to represent proxemic environments and proxemic behaviours from initial conditions based on DILMO dimensions. The DSL is intended to provide standardization and a common language to model proxemic behaviours of each entity in a proxemic environment. The developers can generate an XML file from the blueprint previously created in the design phase.

We describe a systematic process to design proxemic environments based on our DSL. Our graphical DSL provides a symbology based on formal notations and graphical objects representing all components in a proxemic environment, i.e., entities, DILMO dimensions, proxemic behaviors, etc. During the design phase, the proxemic environment and the respective proxemic behaviors are graphically modelled. We described all symbols in Table 5.1. Our graphical DSL constitutes a tool that supports design phase of proxemic applications. Besides that, it establishes a systematic developing process consisting of the following stages: (i) Graphical modelling; (ii) XML generation based on XML Schema; and (iii) proxemic behaviour simulation. We designed XSD that allowed us to offer the developers data structure for supporting the XML generation. The XSD was created by the World Wide Web Consortium (W3C) to describe XML documents' content and structure in an XSD 1 . Below, we describe each stage that compose the modelling of proxemic environments. Figure 5.2 illustrates the systematic process for modelling proxemic environments.

Graphical modelling

At this stage, the designer creates a P_E using the symbols that were described in table 5.1. Our notation provides a set of symbols that enables designers to represent P_E in a graphical model. The designer must select the symbols to use for designing the P_E. To design a graphical proxemic environment the designers must be kept in mind that only one CPS must be created for a proxemic environment. The designer can add multiple Fig. 5.2 Systematic process entities to the proxemic environment; however, the maximum limit of entities that a CPS can detect is based on hardware capability. For example, if the CPS use a camera for sensing entities the maximum number of faces that can be detected is based on the camera's field of view. The CPS may use different sensors to detect entities according to the hardware available for using the systems. The designer can use any graphic design software to create a blueprint for the P_E and represent the entity's behaviour using the graphical notation aforementioned. The graphical notation language is focused on the design of proxemic applications by using a common language for non-specialist developers. The user has a variety of symbols that represent the entities as shown in Table 5.1. In Section 5.5, we will present a scenario, which describes one example showing the sequence of steps for simulation proxemic behaviours in P_E. This stage described the process that allow designers and end-users without programming knowledge to design proxemic applications for smart environments. In the next Section 5.4.2, we describe how the developer can convert the designer's blueprint to an XML file from the designer's initial design.

XML generation

The XML file is manually generated by the developer, who converts the blueprint according to its design. From the formal specification and the graphical design of a proxemic environment, it is possible to generate an XML file to keep the P_E: initial conditions. The proxemic information related to the initial conditions of the P_E, as well as the proxemic behaviour and actions are stored in an XML file based on the XML schema.

With the aim to guide the developer for converting the graphical modelling into an XML file, we designed XSD to describe multiples entities that interact with only one CPS in the P_E. This XSD enables to present the graphical modelling as a list of elements and attributes (data structure) which the computer systems can interpret. We describe each element of P_E with graphical view XSD that allowed us to present an abstract representation of the P_E, showing the XSD structure without syntactic confusion. Figure 5.3 shows a P_E that has been structured with graphical view XSD. The XSD graphical view structure provided us a logical view of P_E using XML Scheme. The XSD defines the shape or structure of an XML file that contains each component of P_E. The XSD file contains a syntax that allows the description of each element of the P_E. We implemented XSD Editor2 eclipse IDE plug-in to create XSD graph view and source code. Next, we show an example of equivalent XML code that describe a graphic modelling with one entity identified in the P_E (see Figure 5.5).

(a) This element allows developers to register the physical distance between an entity identified and CPS, defined by the designer.

(b) This element allows developers to register an entity identified (I) that is recognized by the CPS, (for example using face recognition). (e) The element allows the developers to describe if an entity is oriented towards the CPS. This example illustrates an identity that is oriented towards the CPS (true).

All values are obtained from initial conditions established by the designer. However, these vales can be updated according to the technologies used to sensing the proxemic information. Figure 5.6 shows the P_E scheme source code proposed that guide developer to manually generate the XML file. This structure is equivalent to graphical model in a way that computer can process the proxemic information. In Section 5.5, we show the equivalent XML file that is generated from graphical modelling an formal notation.

This process can be improved in the future in order to create a file automatically using a software application. One of our aims in this phase of research was to find a data structure that would enable developers to register proxemic information in the an XML file.

With the purpose of minimize the complexity of steps that developers need to read XML file we implemented JAXB class generator. The JAXB [START_REF]Using the jaxb class generator and jaxb users guide[END_REF] technology simplifies access to XML documents that describes the P_E. This tool establishes mappings between Java classes and the XML schema. We implemented the JAXB Builder eclipse plugin. This plugin allows the developer to process proxemic information from an XML file, including a JAXB project wizard for generating the classes.

Proxemic behaviour simulation

Once the P_E is defined and modeled, it is possible to simulate initial proxemic behavior according to the considered DILMO dimensions to simulate the proxemic actions of the CPS and entities. In the implementation phase, developers decide how DILMO dimensions are captured according to the available sensor technology (e.g., BLE, depth camera, kinect sensor, smart mobile capabilities). Also, the specific actions of the CPS are implemented according to user requirements (e.g., play a song, display a video, show publicity, transmit information, face recognition).

The interfaces and classes generated in the previous stage, can be integrated, included, and used in the developing of a specific proxemic application. We developed a prototype of our DSL able to support the specification of proxemic environments: entities, proxemic zones, DILMO combinations, and conditions to react; from which the corresponding XML file. The XML file represents the P_E that is manually generated from the initial conditions by the developer. This file contains proxemic information of each entity which is uploaded using the prototype's graphical user interface (GUI). The prototype's GUI elements are explained on Figure 5.8. The GUI allows user to update the entities' proxemic behaviour such as: (i) update the distances; (ii) adjust the DILMO dimensions in the frame that represents each entity condition (in the grey zone) ; (iii) specify that an action is performed by the CPS ( by clicking on the Action button ). Also other actions can be generated from different behaviors; thus, it is possible to simulate multiple proxemic interactions in the same P_E. The initial conditions that can be updated using an input range slider to simulate the different distance between an entity and the CPS. The prototype allows user to view the corresponding interaction code based on the entity's behavior and a proxemics zone in which an entity is encased (by clicking on the Action button). The developer can identify any proxemic behavior of entities that were established by the designer in a specific proxemic zone. In this prototype, orientation of Furthermore, the graphical notation presented in table 5.1 could be implemented as a graphical interface for a drag and drop facility to use the tool. Developers will be able to assign their user-defined functions to actions; and technology to gather DILMO dimensions could be specified.

To demonstrate the suitability and functionality of our proposed graphical DSL, we have implemented a scenario to show the design of proxemic behaviors in a proxemic environment, with their respective generated XML file. In the following, we describe a systematic process that allow designer modelling a proxemic environment.

Graphical modelling of Proxemic scenario: an illustrative example

To illustrate a simulation of proxemic behaviours using our DSL we describe an P_E scenario that simulates activities of daily living, in which people interact with smart environments where I 1 and I 2 , move towards the CPS into the personal zone (P_Z personal ) and face the CPS; and the entity E 1 , who stands in front the CPS in the social zone (P_Z social ), is detected; thus different actions of the CPS can be accordingly defined. With the purpose of modelling of proxemic scenario, we follow the systematic process: (i) Graphical modelling, (ii) XML generation and, (iii) proxemic behaviour simulation.

• Graphical Modelling: we designed graphical representation of this scenario and the initial conditions. Figure 5.9 shows the design that emerged from symbology proposed.

Representing entities (E) or identities (I) in a P_E, depends on the nature of actions: if they are generic (i.e., same action for a specific behaviour of any entity) or if they should be performed as a response to of a specific behaviour of a specific identified entity (i.e., an identity). For example, in a P_E where a digital screen (CPS) shows advertisement messages (Action), when people is approaching it (Behavior), people can be represented as entities (E). Meanwhile, in a P_E where an automatic door (CPS) allows entering the garage (Action), when a specific car plate arrives (Behavior), the car should be modelled as an identity (I).

I 2 . ⃗ M = (l 2 , 1); Movement of E 1 (static):E 1 . ⃗ M = (l 3 , 0);
Orientation of entities (faces of entities are in the area of vision of the CPS):

I 1 .O = 20 • , I 2 .O = 35 • , E 1 .O = 75 • , thus ∡ MinAo fV ≤ I 1 .O, E 1 .O ≤ ∡ MaxAo fV ;
-Proxemic zones of entities:

I 1 .P_Z, I 2 .P_Z=P_Z personal , E 1 .P_Z=P_Z social ;
-Thus, initial behaviour of entities are:

I 1 .B 0 =< 0.85, (0.8, 0.3), (l 1 , 1), 20 • , P_Z personal > I 2 .B 0 =< 0.8, NULL, (l 2 , 1), 35 • , P_Z personal > E 1 .B 0 =< 3, NULL, (l 3 , 0), 75 • , P_Z social >
From these initial conditions, the CPS can react with Action 1 and Action 2 , that can be denoted in several ways, for example: i f ∃ E in P_Z social , then CPS.Action 1 .

∀ I j ∈ P_E.identities ∧ I j in P_Z personal , then CPS.Action 2 .

• XML generation: we converted the design that was shown in Figure 5.9 to an XML file. The XML scheme allows developers implementing the right structure for the scenario described. Figure 5.10 describes the XML equivalent to the design that was modeled in design phase. The entities' behavior are formally modelled during graphical modelling. This file has been composed of the CPS and the set of entities according to the designer's graphical modeling. According to the graphical modelling, the CPS proxemic zones and initial conditions of entities have been recorded in the XML file. The prototype reads the XML based on the initial conditions that were fixed in the design phase. In the development phase, the XML file will be updated according to the system's events. This XML file presents an approach for modelling proxemic environment based on a graphical DSL focusing on entities in the P_E. • Proxemic behaviour simulation: we implemented the scenario previously described in Section 5.5. We show how the prototype can support the developer's implementation process from the design phase. Figure 5.11 shows the representation of the initial conditions of this Scenario with our prototype. The scenario shows how the prototype can support the developer's implementation process from design. When the XML files are uploaded, and the user makes a click on Create button the prototype will show the respective P_E and entities in the grey zone of the prototype. Once the P_E has been created through prototype the user can see the initial conditions which can be updated using the interface graphic. For this scenario, the user has selected a personal zone using a radio button. When the user click on the "Action button," the black panel shows the interaction code of each entity in the P_E.

Limitations of the Proposed Graphical DSL 63

We have implemented a prototype that helps end-user to describe proxemic behaviours in different proxemic environments based on our DSL proposed. Nevertheless, new challenges arise when modelling proxemic environments that we describe in the next Section 5.6 .

Limitations of the Proposed Graphical DSL

We proposed an approach for modelling proxemic environments based on a DSL. Our proposed graphical DSL provides an symbology based on formal notations and graphic objects representing components in a proxemic environment. Nevertheless, the current symbology does not include a fine-grained notation for representing gesture interaction.

Adding gesture interaction to proxemic interaction can improve proxemic interactions in a specific context. A future version of our DSL could consist of an element that describes the kind of behaviour. We showed that our proposition allows a transition from the design phase to the implementation phase through our prototype. Notwithstanding, the graphical interface of the prototype presented in Section 5.4.3 could be implemented in a visual programming language, allowing designers to obtain a better perspective from proxemic environments.

64 A Graphical Domain-Specific Language for Modelling Mobile Proxemic Applications

Conclusion

In this chapter, we presented an approach for modelling a proxemic environment based on a graphical DSL. Our proposition is focused on entities in the proxemic environments, helping developers identify the trigger conditions from the design proposed. The DSL allows nonspecialist designers to describe proxemic interactions for modelling proxemic environments and supports the development process. In order to prove its suitability of the proposition, we have implemented a prototype to simulate proxemic behaviours for proxemic environments.

We built XML scheme that allows the developer to convert a graphical modelled to a data structure. The XSD scheme file generated from the DSL allows developers to create classes in order to support development from initial design. We have implemented a prototype with the purpose to show the design of proxemic behaviors in proxemic environments, with their respective XML file. We plan to develop an integral framework for designing and implementing proxemic mobile applications, supported by this DSL. In the next chapter, we illustrate a framework that supports the implementation of functions to process the proxemic information in mobile devices.

Chapter 6 Proxemic Environments: A Framework for Developing Mobile Applications based on Proxemic Interactions

This chapter describes a framework that supports the development of applications based on proxemic interactions with mobile devices. Our proposal is a development approach to support the construction of mobile proxemic applications for smart proxemic environments P_E from DSL design, based on mobile technology and smart wearable technology. This framework proposes a concrete solution for developing mobile proxemic applications comprised of entities whose interactions are defined according to DILMO combinations. In the next Section 6.1, we describe the technology based on Android Operating Systems used for sensing proxemic information.

Sensing Proxemic Information with Mobile Devices

Progress in mobile technology has led to smartphone evolution with advanced connectivity features that have quickly made it a constant presence in our daily lives [START_REF] Del Rosario | Tracking the evolution of smartphone sensing for monitoring human movement[END_REF]. Most mobile devices are currently equipped with a wide range of hardware such as camera, motion sensor, GPS, wireless, and Bluetooth Low Energy (BLE) technology. Android Operations Systems provides native libraries (APKs) that allow the developer to obtain environmental conditions from the mobile devices associated with DILMO proxemic dimensions. Next, we describe technologies that are used for sensing proxemic information from mobile devices.

Mobile Computer Vision

Mobile computer vision is a powerful technology based on advanced image processing. The Android operating system provides libraries that allow developers to detect human face and some objects, such as bar-codes and QR-codes. Face detection is the process of automatically locating human faces in a image. A face that is detected is reported at a position with an associated size and orientation. When the face is detected, it can be searched for landmarks such as the eyes and nose.

Face Orientation

Android vision libraries provides methods that allow developers to detect human faces. Once a list of faces is detected the developer can gather information about each face and specific landmarks on these face. Android library provides the measurement of Euler Y and Euler Z (but not Euler X) for detected faces [START_REF] Developers | Android developers[END_REF]. All coordinate values are absolute where image position (0, 0) represents the upper-left corner of the image. Figure 6.1 shows face's orientation to obtain Euler X, Euler Y, and Euler Z angles.

Motion and position Sensor

Motion sensors allow developers to measure physical movements of mobile devices, such as orientation, rotation, and acceleration. Android provides methods that let developers monitor the motion of a mobile device. At present, most Android-powered devices have an accelerometer, a gyroscope, and include magnetometer.

Sensing Proxemic Information with Mobile Devices

67

• Accelerometer determines the acceleration applied to the device, including the force of gravity in three-dimensional space. This sensor returns forces applied on the phone for x, y and z directions for the three coordinate axes in meters per second squared, (m/s 2 ) in the X, Y, and Z. Each axis is given separately. The force of gravity gives a value of 9.81 divided on all three axis, depending on how the phone is oriented. For example, if the phone lies on a table with the display pointing upwards, the force in z-axis is 9.81 while y and z is 0. Figure 6.2 shows the X, Y, and Z orientation axes relative to a typical Android mobile phone. Fig. 6.2 Android accelerometer coordinate system

• Gyroscope measures the movement of the device, returning the rate of rotation in degrees per second (rad/s) around each device axis around (change of angles over the timestep). This sensor is usually used for games that explicit response to device movements. The coordinate system is equal to the one used for the acceleration sensor.

• Magnetometer utilizes the modern solid state technology that detects the Earth's magnetic field along three perpendicular axes X, Y, and Z. The sensor produces voltage which is proportional to the strength and polarity of the magnetic field along the axis each sensor is directed. The Magnetometer sensor reads the strength of the magnetic field around an Android device.

Bluetooth Low Energy

Bluetooth is a standard wireless technology for sending and receiving data. Bluetooth is used for building Beacon protocol. Beacon can be implemented in mobile devices to uses as a radio transmitter. The receiver measures the signal's strength when the transmitter is close; the signal is strong, and when the transmitter is far, the signal is weak.

Framework Architecture

A framework is an abstraction in which software provides generic functionalities and guidance that allows programmers to develop systems based on the basic building blocks [START_REF] Chan | Acceptance of agile methodologies: A critical review and conceptual framework[END_REF]. The architecture of our framework is designed to allow the developer to focus on how to obtain the data collected from the smart environment (i.e., smartphone, wearable device sensors). These sensors are used to trigger events based on user conditions. Our proposition takes advantage of the current capabilities of mobile technology trends related to gathering and processing different types of data that can be used to create proxemic applications. Our approach helps the developer with the development process and the management of proxemic information to establish the appropriate combination of DILMO dimensions and, implement the mobile application in the P_E. To support this process, the framework based on android platform is composed by mainly three components aligned with each step (see Figure 6.3): (i) Proxemic Zones module; (ii) DILMO module; and (iii) an API that supports the instantiation of the two previous mentioned components. In the following, we describe each module in detail.

Fig. 6.3 Framework architecture.

Framework Architecture

69

The Proxemic Zones module allows the definition of proxemic zones sizes (P_Z), according to user needs. The interaction between two entities changes in accordance of the P_Z in which they are located. The values of distances that delimit the proxemic zones are configured through the API. Figure 6.4 is a guide that allows the developer to know which methods must be implemented on the API or which objects must be created from the API according to DILMO dimensions for processing proxemic information. For example, a DIL proxemic environment (row 9 in Figure 6.4) means that Distance (D) and Location (L) are considered for Identities (I). Combination of proxemic dimensions are also valid, although the entities have not unique identification; e.g., a person, a device beacon, instead of the smartphone's owner, my device beacon. Hence, proxemic environments denoted in Figure 6.4. Fig. 6.4 DILMO proxemic dimensions and nomenclature to describe each combination that is available through the API methods for processing proxemic information.

API Implementation

In this section, we describe the API structure and some of the most important available methods. The API lets developers build P_Z and process proxemic information from Android sensors, that are required for implementing a P_E. It was developed in Java, hence the jar files are provided to be added to the Android Studio platform. 1. The ProxZone class allows to define proxemic zones (P_Z) according to user requirements (i.e., user/developer decides the measures that delimit each P_Z), when this class is instantiated (i.e., by its constructor method). The constructor method of this class receives as parameters the respective maximum measures of distance D, which define each P_Z. A P_Z can be associated to one or more entities. Inappropriate arguments are validated in order to have valid measurements (e.g., not overlapped zones, right order of measures). (e) The setProxemicDistance(double D) method allows assigning the P_Z to an entity, according to the distance (D).

(f) The getProxemicZoneByDistance() method returns the P_Z of an entity, based on the distance.

3. The Distance class allows the developer to estimate the distance among identities (I). Distance (D) can be calculated by using any available method. In the current version of our API, we have integrated some methods to calculate D, based on the Android platform, such as:

(a) The setBluetoothDistance(double rssi, double txtPower) that allows estimating distance based on BLE.

(b) The setFaceHeight(float faceHeight), which allows estimating the distance from camera using visual computing; distance is proportional to the height of the detected face.

(c) The getDistance() method, that allows obtaining D in meters.

4. The Entity class represents the interaction objects in a P_E, whose behavior is determined or will be determined according to its DILMO proxemic dimensions. This class allows the discovering of the entities on a P_E.

5.

The Location class provides methods to manage location (L) of interaction objects (E and I). As in the Distance class, location (L) of entities can be calculated by any available method. Currently, we have integrated in our API some methods to calculate L, such as:

(a) The setRelativeLocationCPS(float L) method, which sets the relative position of an entity E to CPS, based on the coordinates of E on the CPS.

(b) The getRelativeLocationOnCPS() method, which returns the relative position of an entity E.

6. The Movement class has methods that allow motion processing from the coordinate system of smart-mobile sensors (e.g., Azimuth, Pitch, Roll), such as:

Proof-Of-Concept of our Framework

Our goal is to demonstrate that our framework allows developers to build proxemic mobile applications effectively. For this purpose, we have tested the implementation of two mobile applications, called IntelliPlayer and Tonic, based on proxemic interactions and developed by undergraduate students. These apps were implemented using Android Studio platform version 3.3; however a higher Android studio version can be used. The two apps1 are available for free downloading.

Both apps have been developed by undergraduate students, as part of their final project in computer science. The developing team was formed by four students whose average age was 21 years-old, who have developer experience using Java object-oriented programming. They are enrolled in the computer science programs level 2 (IUT des Pays de l'Adour Bachelors Universitaires de Technologie DUT Informatique, BAC +2). The experiment was the first challenge for them implementing Android applications and MobileHCI based on proxemic interactions.

Two training sessions of two hours each were organised for the students. The training process allows students to understand the systematic process for building proxemic mobile applications with our framework. They learned: (i) how to define each P_Z; (ii) how to select each combination of proxemic dimension for recreating a P_E; and (iii) how to use methods and classes of the API. The development time of both applications was 64 hours done by two developers over a period of 4 weeks. IntelliPlayer took 44 hours of work, while Tonic was finished in 20 hours, in the same four weeks. Development time was obtained from student interviews and observation of code developing in classrooms. The students developed Tonic mobile application in the first place to understand how the API works and to determine their respective functions for processing proxemic information from mobile sensors. IntelliPlayer was developed in the second plate because this mobile application includes more functionalities than the previous one. Below, we describe both applications that were develop by students as an proof-of-concept.

Tonic is an educational mobile app for learning musical notes, developed for illustrative purposes. In the first step of the approach, the students have defined four proxemic zones: P_Z intime (0 mts to 0.5mts), P_Z personal (0.51 mts to 1 mts), P_Z social (1.1 mts to 2 mts), and P_Z public (2.1 mts to 4 mts) that were defined in the DSL (see Figure 6.8). In the second step, a DIMO combination was stated for the proxemic environment, P_E.

Tonic allows a user to play a note and modify it from her/his smartphome on another smart mobile. The user's smartphone is identified (i.e., it is an I) by the mobile device which plays the sound (i.e., it is an CPS). Thus, CPS plays and modifies a sound, by using proxemic interactions based on the P_Z, and on D, I, M, and O dimensions (i.e., a DIMO proxemic environment). In Tonic, the distance (D) between the two devices is obtained by using BLE technology. I broadcasts its identifier to nearby portable electronic devices, thus it is caught by CPS. The volume of the sound is adjusted according to the P_Z in which CPS is, with respect to I (see Figure 6.8). IntelliPlayer is a mobile application that plays a video in a smartphone and reacts according to four proxemic zones and DILO proxemic dimensions. In the first step of the approach, the four P_Z were created: P_Z intime (0 mts to 0.25mts), P_Z personal (0.26 mts to 0.45 mts), P_Z social (0.46mts to 1 mts), and P_Z public (1.1 mts to 2 mts). Then, in the second step, the MobileHCI was designed according to D, I, L, and O, thus a DILO P_E was defined. Figure 6.9 shows the proxemic zones that have been defined by the developer through the API and DSL. With this application, The students illustrate a proxemic environment using a mobile player app that reacts to the distance (D) and location (L) of a person (E 1 ) and his face orientation (O), with respect to the smartphone (CPS) displaying a video. The computer vision technique has been used for this purpose, based on the properties of an Android camera and through the API methods setFaceHeight(float faceHeight) and getDistance() described respectively in items 3.(b) and 3.(c) in Section 6.2.1. Figure 6.10 shows a block code of this case. With the distance (D) between the user (E 1 ) and the smartphone (CPS), IntelliPlayer determines the proxemic zone (P_Z) of E 1 (a user), with respect to the smartphone (CPS). To do so, it invokes the method getProxemicZoneByDistance() described in item 2.(f) in Section 6.2.1. IntelliPlayer automatically adjusts the volume of the video according to the P_Z in which E 1 (the user) is with respect to the smartphone (CPS): when E 1 is in P_Z intime , it decreases to 25% volume of speaker; for P_Z personal , it increases to 50% volume; for P_Z social , it increases to 75% volume; and for P_Z public , it increases to 100% volume). When a second person (E 2 ) is in front of the smartphone camera (CPS), the application verifies if both users are looking at the screen at the same time, as shown in Figure 6.11(a) (method setDetectedFaces (String[][]detectedFaces, ProxZone p), item 7.(a) in Section 6.2.1). In the case one user (E 1 or E 2 ) turns his face, the video will be paused automatically by the mobile application (see Figure 6.11(b)).

API Validation

We applied two of more used strategies to validate API and tool-kit according to Lendo [START_REF] Ledo | Evaluation strategies for hci toolkit research[END_REF]. This work analyzed evaluation techniques applied to 68 toolkit papers in the HCI field that allowed them to propose the most relevant criteria from the aforementioned strategies (demonstration, usability study, technical benchmarks, and heuristic).

Demonstration

Our goal was to demonstrate that our API permits students to build efficiently proxemic mobile applications. Through our API, the students developed novelty mobile apps based on proxemics using only mobile device capabilities that offer alternative forms of interaction with mobile devices. For this matter, API process proxemic information from different sensing methods such as mobile computer vision and BLE Technology

Usability Study

It describes the facility of use of the proposed API for the developing of proxemic mobile apps. To this extent, the aforementioned API has an architecture that allows developers to set the initial conditions of a proxemic environment with two lines of code such as ProxZone p = new ProxZone(0.5,1,4,50) and Dilmo dilmo= new Dilmo(p); while adding new identities require only one line of code which facilitates the relationships between object and target for example: dilmo.setProxemicDI("Peter", 0.5). The API is supported by documentation that provides information about classes and methods and a table of proxemics nomenclature that provides a guide for selecting methods according to a combination of proxemics dimensions DILMO. In order to evaluate the usability of the API, we applied a survey composed of nine questions to the undergraduate students who developed the applications described. The score of each question was taken from the average of responses. This average is the sum of each student's response divided by the total number of students in the survey. We obtain the answer from the aforementioned survey. Results of the survey are shown in Figure 6.13.

These results indicate that 100% of surveyed students have strongly agreed with Q1: "It was easy to implement the API with Android Studio" and Q9: "The API is useful for the development of proxemic applications with Android?". While 95 % of students endorsed Q5: "The API allowed you to process information of a combination of DILMO dimensions", Q7: "The API's method for estimating distance based on face detection was accurate", and Q8: "The API allows you to obtain the proxemic zone when it is using Bluetooth proximity sensing". For Q4: "The API allows you to improve your productivity for developing proxemic applications by hiding complexity" and Q6 "The API allows you to create the proxemic zones quickly", 78% of students expressed acceptation. Finally, Q2: "The documentation provides enough information to interact with the API" and Q3: "The API provides enough examples for creating proxemic applications", were accepted only by 53% and 61% of students, respectively. The proof-of-concept and survey allow corroborating that the API supports development of proxemic applications and creation of proxemic environments, based on the exclusive use of mobile devices. These applications offer portable implementations that facilitate using the proxemic interaction in comparison to previous works that have used fixed platforms for similar purposes such as ProximiThings [START_REF] Cardenas | Proximithings: Implementing proxemic interactions in the internet of things[END_REF] and Toolkit [START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF]. Moreover, the survey results allow us to know aspects to be developed, such as the quality of documentation and the examples presented in the API.

Conclusion

Mobile technologies are frequently used in daily life for different activities, and their use keeps increasing. This chapter introduced a framework that includes an API for developing proxemic applications for smart environments comprised of entities whose interactions are supported by proxemic dimensions DILMO. We demonstrated the framework's effectiveness through the proof-of-concept, which details the implementation of two proxemic mobile Touchless interaction has been used in operating rooms (ORs) during surgical procedures, notably to provide control of medical image analysis, such as the work proposed in [START_REF] Mentis | Interaction proxemics and image use in neurosurgery[END_REF], which uses the notion of proxemic and touchless interaction systems in surgical contexts. However, previous studies reveal that nosocomial transmissions is also a real problem for HCWs in other areas of the hospital, even beyond the OR [START_REF] Cronin | Touchless computer interfaces in hospitals: A review[END_REF][START_REF] Thomas-Rüddel | Coronavirus disease 2019 (covid-19): update for anesthesiologists and intensivists march 2020[END_REF].

Mobile technologies in hospital are increasing in a way without precedent in different areas of the hospital. HCWs can interact in different contexts through electronic devices, such as personal mobile phones, tablets, and wearable technologies, to accomplish their daily tasks, based on specific Human-Computer Interaction (HCI). Some studies have emphasized that mobile devices are reservoirs for pathogens with potential to cause nosocomial infections [START_REF] Cobb | Mobile device usage contributes to nosocomial infections[END_REF][START_REF] Ibrahim | Mobile phones and nosocomial infections[END_REF][START_REF] Debnath | Presence of multidrug-resistant bacteria on mobile phones of healthcare workers accelerates the spread of nosocomial infection and regarded as a threat to public health in bangladesh[END_REF].

In order to reduce the HCWs contamination through mobile devices, we propose the use interpersonal distances to develop mobile applications based on proxemic HCI, using combinations of proxemic dimensions -i.e., Distance, Identity, Location, Movement, and Orientation (DILMO dimensions). We are interested in handling interpersonal distances for interacting with mobile devices, according to different combination of DILMO dimensions. We are therefore seeking to develop new useful user interfaces, that allow HCWs to reduce the touchability of mobile devices and limit the spread of nosocomial infection of COVID-19. We also propose a development architecture based on mobile technology to support the easy construction of proxemic HCI for mobile apps. To show the usability and suitability of our proposal, we present two prototypes of apps for mobile devices as proof-of-concept, using several combination of DILMO dimensions to model proxemic HCI that allow flexibility in interpersonal and devices-people interactions.

HCI to reduce the risks of nosocomial infection for HCWs

In this section, we present a review of studies that describe the difficulties and risks of HCWs using computer equipment in the workplaces and how HCI can be used to reduce such as risks. We also survey applications that show how proxemic DILMO dimensions have been used to implement interactions among digital devices and devices-people.

Today, HCWs are facing a difficult situation in the workplace because many of them have acquired COVID-19 disease during medical service [START_REF] Ling | Infection control in non-clinical areas during the covid-19 pandemic[END_REF]. The HCWs are exposed to a nosocomial infection that exists in a specific location, such as a hospital and more specifically from health care equipment. In fact, the use of technology is very common in health care settings however it is a difficulty for fully secure access to computer controls (sterilization for example) which increases opportunities to spread pathogens [START_REF] Cronin | Touchless computer interfaces in hospitals: A review[END_REF]. Previous studies have shown microbial contamination of computer peripheral like keyboard and mouse used to enter information in a care center during the workday [START_REF] Engelhart | Microbial contamination of computer user interfaces (keyboard, mouse) in a tertiary care centre under conditions of practice[END_REF]. Moreover, recent studies show that the use of mobile phones inside hospitals might serve as repositories of microorganisms that could be rapidly transmitted from the mobile phones to the HCWs' hands and therefore can help the transmission of bacterial from one patient to another person [START_REF] Badr | Mobile phones and nosocomial infections[END_REF][START_REF] Karabay | The role of mobile phones in the spread of bacteria associated with nosocomial infections[END_REF].

Besides, Harvard University researchers published results of the simulation of a mathematical COVID-19 model [START_REF] Kissler | Projecting the transmission dynamics of sars-cov-2 through the postpandemic period[END_REF][START_REF] Kissler | Social distancing strategies for curbing the covid-19 epidemic[END_REF], which predicts that recurrent winter outbreaks will probably happen after the first, most severe pandemic wave; prolonged or intermittent physical distancing may be necessary until 2024. Therefore, it is vitally important to reduce the spread of COVID-19. Previous studies demonstrated drastic reductions in infections and deaths thanks, in large part, to the physical distancing among individuals [START_REF] Kissler | Social distancing strategies for curbing the covid-19 epidemic[END_REF][START_REF] Soucy | Estimating the effect of physical distancing on the covid-19 pandemic using an urban mobility index[END_REF][START_REF] Brzezinski | The covid-19 pandemic: government vs. community action across the united states[END_REF]. Physical distancing in the health centers is important for HCWs. Several studies suggest that implementing physical distancing in the hospital helps to prevent nosocomial transmission of COVID-19 and ensures the health of HCWs to meet the hard challenge. For example, the study presented in [START_REF] Arora | Implementing physical distancing in the hospital: A key strategy to prevent nosocomial transmission of covid-19[END_REF], suggests the reallocation of mobile workstations, laptops, or desktops to private rooms that avoid all sources of contamination and clean surfaces such as keyboards and desktops frequently because COVID-19 can survive on surfaces for several hours. Some studies have proposed the use of computer equipment in hospital environments based on touchless interaction to help decrease the risk of nosocomial contamination and prevent nosocomial transmission of COVID-19 [START_REF] Cronin | Touchless computer interfaces in hospitals: A review[END_REF][START_REF] Juhnke | Evaluating the microsoft kinect compared to the mouse as an effective interaction device for medical imaging manipulations, iowa state university digital[END_REF]. In particular, the work proposed by O'Hara in [START_REF] Mentis | Interaction proxemics and image use in neurosurgery[END_REF] uses touchless interaction and voice commands to control and manage imaging technologies within a surgical setting where touchless inputs might permit new kinds of interaction during surgery.

Touchless computer interfaces are widely used in ORs for decreasing opportunities for the spread of pathogens on computer controls and facilitating HCI [START_REF] Mentis | Interaction proxemics and image use in neurosurgery[END_REF][START_REF] Jalaliniya | Touch-less interaction with medical images using hand & foot gestures[END_REF][START_REF] Cronin | Touchless computer interfaces in hospitals: A review[END_REF]. However, the Health Informatics Journal [START_REF] Cronin | Touchless computer interfaces in hospitals: A review[END_REF] has recommended to explore the use of touchless systems in other areas of the hospital environment. Besides, touchless systems [START_REF] Gallo | Controller-free exploration of medical image data: Experiencing the kinect[END_REF][START_REF] Mentis | Interaction proxemics and image use in neurosurgery[END_REF][START_REF] Kim | Kinect technology for hand tracking control of surgical robots: technical and surgical skill comparison to current robotic masters[END_REF][START_REF] Ruppert | Touchless gesture user interface for interactive image visualization in urological surgery[END_REF] have utilized Microsoft Kinect sensor, which produces motion tracking with far high accuracy. Notwithstanding, the studio presented in [START_REF] Chao | Comparative efficacy of new interfaces for intra-procedural imaging review: the microsoft kinect, hillcrest labs loop pointer, and the apple ipad[END_REF] demonstrates that mobile technologies could offer radiology residents the highest usability when employing touchless interaction.

All those previous mentioned works demonstrate the current interest for researchers and medical professionals to decrease physical interactions with computers to avoid spreading of infections. In this context, proxemic interactions can play an important role to implement HCI that reduce touchability of devices. Therefore, it is necessary to provide software architectures on mobile devices that incorporate DILMO dimensions for reducing the risk of cross-contamination with mobile devices.

DILMO in Medical environments

In this section, we demonstrate how the combination of DILMO proxemic dimensions can be used to create appropriate proxemic HCI for applications in medical environments, to improve seamless interactions between digital devices and users, while reducing the risks of noscomial transmissions. Proxemic dimensions are captured from sensors, which means that a variety of hardware technologies can be substituted or combined for sensing proxemic information [START_REF] Marquardt | The proximity toolkit: prototyping proxemic interactions in ubiquitous computing ecologies[END_REF]. Thus, measures of DILMO dimensions should be gathered from the available technology (i.e., sensors) in mobile devices.

Table 7.1 shows examples of combination of DILMO dimensions in medical environments and the hardware of mobile devices required for sensing proxemic information from the environment. Currently, most mobile devices are equipped with a camera, GPS facilities, wireless connections as Bluetooth, and Bluetooth Low Energy Beacon (BLE) technology. At least one sensor must be implemented for sensing proxemic information. It's possible to use a combination of mobile sensors to obtain multiples proxemic dimensions that privilege the touch-less interaction in medical environments. We characterize some DILMO combinations and how proxemic information can be obtained from mobile devices. Furthermore, we describe semantic information of such as combinations that can be used to create mobile applications for medical environments.

In the following, we describe some examples of DILMO combinations in medical service applications that allow limiting touch interactions. It is not described as an exhaustive list, some other DILMO combinations can be defined, according to the available technology and requirements of applications. Actually, the inverse condition can also be exploited: DILMO combinations can characterize the hardware necessary to implement medical applications on mobile devices, in which the user interaction with contaminated devices is avoided. Thus, it 7.4 Architecture to support the development of DILMO-based Mobile applications 85 is possible to identify DILMO combinations according the available hardware or to decide which mobile hardware is needed to implement specific DILMO combinations.

• DI combination allows identifying specific HCWs (Identity) and their proxemic zones (according to Distance among them). This combination enables HCWs to keep physical distance with others using mobile devices. Moreover, the use of DI allows the development of applications that interact using the proximity between HCWs or between HCWs and medical device.

• IO combination allows facial recognition (Orientation) of authorized users (Identity) for access control using mobile computer vision, for areas such as laboratory, pharmacy, emergency rooms, and isolation rooms, for example, to access sensible patient information or private services.

• DIO combination offers HCWs the possibility to interact with information systems avoiding physical contact with computer peripheral. With BLE technology it is possible to estimate Distance between mobile devices and to get their Identities; while mobile computer vision allows sensing HCW's orientation and identification. This combination can avoid the usage of peripherals, which are difficult to sterilise and a potential source of contamination, such as mouses, keyboards, and smartphones.

• DIMO combination allows building mobile apps for gesture interaction (Distance, Movement, and Orientation) that help the HCWs (Identity) to manipulate information (such as images) displayed on the screen, using mobile computer vision and body tracking. DIMO applications provide more direct control of medical procedures based on gesture interaction captured with mobile computer vision.

These DILMO combinations are the base to develop some applications intended to reduce the spread of nosocomial infection of COVID-19 in hospital environments. In the next Section 7.4, we propose an architecture aimed to support the development of such as applications, based on the basic technology of mobile devices.

Architecture to support the development of DILMObased Mobile applications

Our proposed development architecture, shown in Figure 7.1, is supported from our API and BLE libraries that allows developers to manage different social distances according to requirements of the specif application. Through the mobile devices sensors, the proxemic information is gathered (i.e., DILMO measures), according to the desired combination (see Table 7.1). We describe each component of our proposed architecture as follows.

Proximity: Virtual Bluetooth Low Energy Beacon (BLE) on smartphone

To create a virtual beacons BLE in a mobile device, it is required to use Beacon protocols. We employ the Java android library AlteBeacon [START_REF] Altbeacon | Altbeacon -the open proximity beacon[END_REF], which allows to estimate the physical distance between two devices and catch their corresponding UUID (identity). In our architecture, mobile devices transmit string data to another mobile using BLE, which is processed in real-time through the API.

API for proxemic social interaction management

We implemented an API that lets developers define the proxemic zones required for implementing mobile application based on social distancing. Depending on the application to be developed, it will be necessary to define several proxemic zones (as the four proxemic zones proposed by T. Hall [START_REF] Hall | The Hidden Dimension: An anthropologist examines man's use of space in private and public[END_REF]). Through the API, developers are able to define and customize the size of the proxemic zones. It also offers methods that allow developers to obtain the proxemic zone of entities, based on the distance between mobile devices. The by using the Bluetooth technology and the camera that the majority of smart devices have in their hardware configuration. • Proxemic zones of entities:

I.P_Z=P_Z personal

From these conditions, the CPS can react with Action 1 , that can be denoted in several ways, for example:

i f ∃ I in P_Z personal then CPS.Action 1 .

To illustrate this scenario, we explain the use case shown in Figure 7.3: there are three HCWs in the hospital hall, which carry their smartphones in their pocket, running the InZone DI app that retrieves UUIDs around it. When a HCW invades the personal zone of another HCW, their smartphones trigger an alarm, which allows users to know that they are infringing the social distancing. In this way, the app creates a virtual security bubble which keeps physical distancing between two or more individuals only using smartphones. In a health care scenario, where touchable interaction must be limited to the minimum necessary between HCWs and mobile devices, we use an appropriate subset of proxemic dimensions (DIO, as shown in Table 7.1). DIO combination allows developing a mobile application which can help HCWs to obtain electronic patient record using a mobile phone without touching the mobile device. Design of proxemic environment for scenario 1 formal definition and graphical design (see Figure 7 When an HWC is in the intimate zone or personal zone of the patient device (Distance dimension), and her/his face is oriented towards its camera (Orientation dimension), the identification of the patient (Identity dimension) is sent to the HWC device, which is in the pocket of the HWC. Figure 7.6 shows an HWC situation in which the HWC is carrying a wrapping smartphone in plastic in the pocket to avoid physical contact with the smartphone during a medical check-up. An application in the HWC device sends a request to the server, along with the identification obtained from the patient device by Bluetooth, asking to the web server for the whole information of the patient. Then, the HCW can read the patient's electronic records on the screen in the wall of the room, exclusively using proxemic interaction.

Conclusion

In this chapter, we explore how proxemic interactions can exploit the capabilities of mobile devices to the needs of the healthcare sector improving HWCs interaction on mobile devices. Based on the proxemic dimensions (i.e., Distance, Identity Orientation -DIO), it is possible to implement proxemic HCI that reduces the touchability of mobile devices. To support the development of mobile applications based on proxemic HCI, we also proposed a development architecture, lying in mobile devices technology and an API such as that shown in Figure 7.1. We presented two scenarios based on combinations of DIO dimensions, which help to limit physical contact and touch interaction with mobile devices using interpersonal distances. Both applications were developed with the proposed architecture, which allows developers making suitable several combinations of subset of DILMO to each scenario. We described the development of two mobile applications base on our DSL and framework as a proof-ofconcept using proxemic interaction in the current pandemic scenario to avoid virus spread in line with people's real-life needs.

Chapter 8 Conclusion and Future Works

In this research, we proposed an approach with the aim to develop proxemic mobile interaction which can be implemented in mobile devices. Moreover, this dissertation investigated the social theory of proxemics and how it is used to improve the HCI in an ubiquitous environment. We analyzed and investigated the evolution of HCI based on proxemic interaction.Additionally, we described previous proposals that implemented all DILMO proxemic dimensions or a subset of them. Besides, we summarized sensing technologies that were implemented with the purpose to obtain proxemic information by previous proposal. We examined the current tools aiming to design and implement proxemic systems in order to identify proxemic mobile development weaknesses. The main contributions of this study can be summarized as follows.

Contributions

• First Aid Mobile Application (FAMA), we have developed a proxemic mobile application that identifies an unconscious or an injured person using smart mobile devices. We focused on studies and proposals using proxemic interaction that support HCI. Furthermore, we also evaluated several first aid mobile applications that currently exist in the marketplace. Our first proxemic mobile application lets us know and determine relations between entities and people, such as digital devices, non-digital devices, and persons using DILMO proxemic dimensions. This study showed the advantage of implementing BLE Beacon for obtaining the proximity between two entities. Moreover, this first work demonstrated that proxemic interaction in mobile devices allows users more seamless and natural interactions between human and mobile devices. This experience enabled us to highlight that current proxemic tools did not offer achievable solutions to design and implement proxemic mobile applications. Published in Conference on l'Interaction Homme-Machine ( L'IHM 2018).

• Proxemic Environments Modelling Based on a Graphical Domain-Specific Language. In this case, the purpose was to present a common language that supports the design of proxemic applications in the design phase towards the implementation process's uniformity, which inspired on the notation system proposed by Edward T.

Hall. Additionally, we proposed an intuitive symbology based on graphical objects that allowed designers representing all components in a proxemic environment such as entities, DILMO dimensions, and proxemic behaviours. We described proxemic environments using a formal notation. Besides, we have created a prototype in order to support the specification of proxemic environments from design phase. This language is the foundation of the design of proxemic representation environment, which can continue to evolve for supporting tools that help end-users without programming knowledge to implement their design. Published in International Conference on Computer Systems and Applications (AICCSA 2020).

• Framework for Developing Proxemic Mobile Applications. We proposed a framework that helps developers to build proxemic mobile applications based on DILMO proxemic dimensions governing the HCI field. The framework was based on a sequential process in order to enable developers to build proxemic mobile applications supported by an API, in which the end-users only need to use mobile devices or wearable devices in order to run the applications. The prof-of-concept and survey allowed us to validate the API. The aforementioned API allowed developers to create applications, by reducing the difficulty level of implementation. Moreover, the API's methods can be updated, and it is possible to add new methods with the purpose to improve the processing of proxemic information from mobile device sensors. Published in International Conference on Advances in Mobile Computing & Multimedia (MoMM 2020).

• Proxemic Interactions in Mobile Devices to Avoid the Spreading of Infections.

Our work previously brought us the technical and theoretical background necessary for developing proxemic mobile applications that respond to society's real-life needs. This work aimed to facilitate the mobile application development in the healthcare sector, improving HCWs' interaction on mobile devices. We developed two prototypes of the mobile applications, which involved a combination of proxemic dimensions in order to stop spreading nosocomial infection of COVID-19 and others. Solutions were proposed with the aim to avoid physical contact among computer peripheral, mobile devices, and 8.2 Future Work

95

HCWs. Our solutions allow HCWs to maintain social distancing between HCWs using mobile devices. Additionally, we reduced touchability between the user and mobile devices and maintained semantic interaction. Our proposal was based on low-cost architecture web and current mobile devices capabilities that allow healthcare sector to implement proxemic applications in different hospital environments. Published in International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2020).

Future Work

In this thesis, we have proposed an approach that allows the development and implementation of proxemic applications in mobile devices using mobile sensors. We describe possible directions for future works that would advance our study and improve the tools proposed and experimental experiences.

DSL notation and prototype user interface

We developed a prototype that supports the specification of proxemic environments from our graphical DSL. Notwithstanding, the graphical user interface can be improved by adding a drag and drop facility. It could also be implemented with augmented reality or tangible interface in order to improve the user experience when designing proxemic environments. The current Domain-Specific Language (DSL) is based on a symbology which does not include notation for representing gesture control. Our research has addressed the modeling of proxemic environments aiming to represent entities' proxemic behavior. With this knowledge, it is possible to implement a new DSL version including both the validation and approach to reporting results from the experiment.

API methods

The current version of the API considers the extraction of DILMO values from smartphones or mobile devices based on Android native libraries (APKs). The new smartphone mobile computer vision technologies have included stereo depth cameras that improve the detection of human motion tracking. In this respect, we can add more methods that allow developers to quickly implement the motion tracking of objects or people using libraries like TensorFlow and OpenCV for android studio. Combining proxemic interaction and depth mobile computer vision can offer a wide range of applications such as gesture interaction, gesture control, face recognition, and object with more accurate detection that allows end-users to improve their mobile HCI.

New scenarios for evaluating DSL and Framework

Proxemics described how individuals perceive their personal space relative to the distance between themselves and others. This theory allows us to create mobile applications reducing physical contact with hardware keeping up interactions with devices. Nonetheless, we can continue to find more meaningful scenarios in hospital environments to avoid additional complexity used healthcare equipment applying proxemic interactions. Through this research, we show that proxemic interactions can be implemented with mobile devices in different contexts, which may be adapted according to the use of people's physical space such as interactive public display, multimedia controller, information Kiosks, and medical area, etc. Extend the validation of the framework' API with more experimentation and with more developers' users.

6 . 9

 69 IntelliPlayer proxemic zones. . . . . . . . . . . . . . . . . . . . . . . . . . 6.10 Block code of IntelliPlayer. . . . . . . . . . . . . . . . . . . . . . . . . . . 6.11 Play pause video using users faces orientation. . . . . . . . . . . . . . . . . 6.12 The split view provides video description. . . . . . . . . . . . . . . . . . . 6.13 Results of students feedback. . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Architecture to support development DILMO Mobile applications . . . . . 7.2 Scenario 1: Design of proxemic environment graphical representation for each HWC who needs to keep physical distancing. . . . . . . . . . . . . . 7.3 Scenario1:Physical distancing between HCWs exclusively using Bluetooth BLE mobile devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Scenario 2: Design of proxemic environment graphical representation. . . . 7.5 Scenario 2: HWC reads a patient's electronic record from the screen using proxemic interaction in which the HCW avoid physical contact with his mobile phone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 HWC carries her smartphone in her pocket. . . . . . . . . . . . . . . . . . 7.7 Hospital environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 . 3 Structure of the Dissertation 5 1. 3

 1353 Dalmau, M., Masson, D., Couture, N. (2020) Mobile Proxemic Application Development for Smart Environments. In Proceedings of the 18th ACM International Conference on Advances in Mobile Computing & Multimedia( MoMM 2020), Chiang Mai, Thailand, November 3-December 2, pp. 94 -103.• Pérez, P., Roose, P., Cardinale, Y., Dalmau, M., Masson, D., Couture, N. (2020) Proxemic Environments Modelling Based on a Graphical Domain-Specific Language. In Proceedings of the 17th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2020), Antalya, Turkey, November 2-5, pp. 1-8. • Pérez, P., Roose, P., Cardinale, Y., Dalmau, M., Masson. (2020) Proxemic Interactions in Mobile Devices to Avoid the Spreading of Infections. In Proceedings of the 16th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2020), Thessaloniki, Greece, October 12-14. pp. 48-55. • Pérez, P., Roose, P., Cardinale, Y., Dalmau, M., Masson, D, Couture, N. (2020) Proxemic Environments: A Framework for Developing Mobile Applications based on Proxemic Interaction. In Proceedings of the 15th IEEE Conference on Computer Science and Information Systems (FedCSIS 2020), Sofia, Bulgaria, September 6-9, pp. 653-656. • Pérez, P., Roose, P., Dalmau, M., Cardinale, Y., Masson, D, Couture, N. (2020) Modélisation graphique des environnements proxémiques basée sur un DSL. In Proceedings of the INFormatique des ORganisations et Systèmes d'Information et de Décision (INFORSID 2020), Dijon, France, Jun 2-4, pp. 99-114. • Pérez, P., Roose, P., Dalmau, M.,Couture, N. Cardinale, Y., Masson, D. (2018) Proxemics for first aid to unconscious injured person. In Proceedings of the 30th ACM Conference on l'Interaction Homme-Machine ( L'IHM 2018) Brest, France, October 23-26, pp, 156-162. space Structure of the Dissertation

Fig. 2 . 2

 22 Fig. 2.2 Proxemic zones: (a) intimate 1 , (b) personal 2 , (c) social 3 , and (d) public 4 .

Fig. 2 . 3

 23 Fig. 2.3 Seating arrangements affecting people interaction: (a) Chairs arranged in a circle encourage communication 1 ; (b) Chairs arranged in a row limit interactions among people 2 ;

11

 11

Figure 2 .

 2 Fig. 2.4 Ubiquitous Computing environment Xerox Parc: (a) The standing man in the room uses Liveboard Yard-sized [93]; (b) It is a note-book device ParPad [93]; (c) Personal digital assistant ParcTab Inch-sized [93].

Fig. 2 .

 2 Fig. 2.6 Distance: (a) distance between devices [64]; (b) distance between device and human [88].

Fig. 2 . 8

 28 Fig. 2.8 Fixed and semi-fixed feature space.

Fig. 2 .

 2 Fig. 2.10 User is facing to the screen

23

 23

Fig. 3 . 1

 31 Fig. 3.1 Proxemic interaction to support collaborative scenarios 3.1.

  Fig. 3.2 MiniStudio Components: (a) a photoshop plug-in application; (b) tangible support materials including hidden AR stickers (c) a camera-projector system [54].

  .

  3): (a) a Proximity Toolkit server (b) a Tracking pug-in (c) a Visual Monitoring tool, and (d) an API. The Proximity Toolkit server is the central component in the distributed client-server architecture which allows multiple client devices to access the captured proxemic information.

Fig. 3 . 3

 33 Fig. 3.3 Proximity Toolkit Distributed Architecture: (a) Proximity Toolkit server; (b) Tracking plug-in; (c) Visual Monitoring tool; and (d) Application Programming Interface (API) [64].

Fig. 3 .

 3 Fig. 3.4 BLE Beacons 1

Fig. 3 . 5

 35 Fig. 3.5 Vicon motion capture systems 1 : (a) The Vicon 3D motion capture system; (b) Motion Capture Camera; (c) Vicon markers

35

 35

Fig. 4 .

 4 Fig. 4.1 FAMA's zones and interactions between users in the public space

Fig. 4 . 2

 42 Fig. 4.2 Recognition time for Medical Id (MID) vs FAMA Prototype.

Fig. 4 . 3

 43 Fig. 4.3 Opinion score (on a scale from 1 to 10, 10 being the best) relate to accessibility.

  4.5.2 FAMA ImplementationFAMA was developed for Android BLE mobile devices. It consists of two modules: Register and Helper modules.

Figure 4 .

 4 [START_REF] Appventive | Ice: In case of emergency -appventive[END_REF] shows a print screen for the Register module. This is dedicated to create an alert when the unconscious or injured person demands help. The initial conditions must be filled out by users in order to share his/her emergency contact number. The Helper module is able to see emergency contact number identification of unconscious or injured person, depending on the helper person's location in the interaction zones.

  Figure 4.5 shows a print screen for the Helper module.

Fig. 4 . 4

 44 Fig. 4.4 Graphical user interface for creating an alert.

Fig. 4 . 5

 45 Fig. 4.5 Graphical user interface for helper module.

Fig. 4 .

 4 Fig. 4.6 FAMA's Architecture code: (a) Code for creating a virtual Beacon with Mobile devices; (b) Code for detecting unconscious person's smartphone.

Figure 4 .

 4 7 below shows the global architecture that describes how proxemic dimensions (distance and identity) were considered in the application.

Fig. 4 .

 4 Fig. 4.7 FAMA's Global Architecture.

45 Definition 4

 454 Distance (D). The distance is the physical measure of proximity between an entity (E) and the CPS; it is denoted as E.D. Definition 5 Location (L). The location represents a cartesian notation indicating the location of

Definition 9

 9 Proxemic Environment (P_E). A proxemic environment, denoted as P_E, represents a set of sensors and devices attached to entities (E) that can in turn interact according to D, I, L, M, and O. It is denoted as a four-tuple containing a target entity (CPS), a set of interaction objects, general or identifiable, and the distances that define the proxemic zones, such asP_E= < CPS, entities, distances > P_E= < CPS, entities = {E 1 , ...,E n |E i is an Entity (Def. 1)}, identities = {I 1 , I 2 , ..., I m |I j is an Identity (Def. 3)}, distances={MAX_ID, MAX_PD, MAX_SD, MAX_PubD}(De f . 8) >

  The movement of an E identified with respect to the CPS, is denoted as E. ⃗ M = (l, d) according to Definition 6. The arrow orientation on the symbols describes the direction of entities in a P_E. The symbology illustrates the two-movement of E with respect to the CPS and static case of E. The orientation of an E identified with respect to the CPS, is denoted as E.O according to Definition 7. The projection of vectors on the symbol illustrates the CPS field of view to detect an E in a P_E. The symbology allows the designer to represent more than one E in the field of view. CPS Proxemic zones P_Z are denoted as P_Z intimate , P_Z personal , P_Z social , and P_Z public according to Definition 8. The circle around the CPS describes each (P_Z) in a P_E.

Fig. 5 . 1

 51 Fig. 5.1 Proxemics scenarios modelled with tangible interface

Figure 5 .

 5 1 (b)

Fig. 5 .

 5 Fig. 5.3 Graphical XML Schema (XSD) Below, we show XML Schema source code describing each component P_E (see Figure 5.4 ).Figure 5.4 (a) shows XSD complexType element that allows developer to describe the attributes of a CPS such as P_Z: P_Z intimate , P_Z personal , P_Z social , and P_Z public .Figure 5.4 (b) shows XSD complexType elements that allow developer to describe the attribute type of one entity (E) or a list of entities such as DILMO proxemic dimensions.Next, we show an example of equivalent XML code that describe a graphic modelling with one entity identified in the P_E (see Figure5.5).

Figure 5 .

 5 Fig. 5.3 Graphical XML Schema (XSD) Below, we show XML Schema source code describing each component P_E (see Figure 5.4 ).Figure 5.4 (a) shows XSD complexType element that allows developer to describe the attributes of a CPS such as P_Z: P_Z intimate , P_Z personal , P_Z social , and P_Z public .Figure 5.4 (b) shows XSD complexType elements that allow developer to describe the attribute type of one entity (E) or a list of entities such as DILMO proxemic dimensions.Next, we show an example of equivalent XML code that describe a graphic modelling with one entity identified in the P_E (see Figure5.5).

Figure 5 .

 5 Fig. 5.3 Graphical XML Schema (XSD) Below, we show XML Schema source code describing each component P_E (see Figure 5.4 ).Figure 5.4 (a) shows XSD complexType element that allows developer to describe the attributes of a CPS such as P_Z: P_Z intimate , P_Z personal , P_Z social , and P_Z public .Figure 5.4 (b) shows XSD complexType elements that allow developer to describe the attribute type of one entity (E) or a list of entities such as DILMO proxemic dimensions.Next, we show an example of equivalent XML code that describe a graphic modelling with one entity identified in the P_E (see Figure5.5).

Fig. 5 .

 5 Fig. 5.4 XSD complexType element source code; (a) CPS XSD source code; (b) List of XSD source code.

Figure 5 .

 5 7 illustrates a UML class diagram that describes the structure and relationship among entities and proxemic zones, represented in

Fig. 5 .

 5 Fig. 5.5 XML Code from graphical view

Fig. 5 . 6

 56 Fig. 5.6 P_E XML Schema (XDS)

Fig. 5 .

 5 Fig. 5.7 UML class diagram derived from the XSD

Fig. 5 . 8

 58 Fig. 5.8 Description of GUI Prototype

Fig. 5 . 9 9 :--

 599 Fig. 5.9 Graphical model for scenario

Fig. 5 .

 5 Fig. 5.10 XML file for scenario.

Fig. 5 .

 5 Fig. 5.11 Scenario modelled with our DSL

Fig. 6 . 1

 61 Fig. 6.1 Pose angle estimation: (a) The coordinate system with the image in the XY plane and the Z axis coming out of the figure. (b) Pose angle examples where yEuler Y, rEuler Z.

Figure 6 .

 6 6 describes the structure of the API, represented by a UML Class diagram. The main classes are described as follows.

Fig. 6 .

 6 Fig. 6.6 UML Class Diagram of the API (7 classes).

Figure 6 .

 6 Figure 6.7 shows an example of the ProxZone constructor method, in which the maximum distance, in meters, for each P_Z are specified (see Def. 8): P_Z intimate is

Fig. 6 . 7

 67 Fig. 6.7 Example of ProxZone Class Constructor invocation.

( a )

 a The setAzimuthWithRange(float MAX, float MIN, float value) method which allows processing the azimuth angle (rad/s) of an entity E, that has been established by the developer. (b) The isAzimuthInRange() method returns true if the azimuth angle of an interaction object (E) is within a range of reference. 7. The Orientation class provides methods to validate the face orientation (O) of interaction objects on a P_E. Some of them are: (a) The setDetectedFaces(String[][] detectedFa ces,ProxZone p) method, that receives a collection of faces to be defined as interaction objects (E or I) in the P_E. (b) The isFaceDetected(String I,String P_Z) method, which returns true if a specific face (I) is detected in the P_Z.

Fig. 6 . 8

 68 Fig. 6.8 Tonic Proxemic Zones based on Bluetooth Low Energy.

Fig. 6 . 9

 69 Fig. 6.9 IntelliPlayer proxemic zones.

Fig. 6 .

 6 Fig. 6.10 Block code of IntelliPlayer.

Fig. 6 .

 6 Fig. 6.11 Play pause video using users faces orientation.

Fig. 6 .

 6 Fig. 6.12 The split view provides video description.

Fig. 6 .

 6 Fig. 6.13 Results of students feedback.

Fig. 7 . 1

 71 Fig. 7.1 Architecture to support development DILMO Mobile applications

7. 5 . 1

 51 InZone-19 DI (scenario 1): Mobile app reacting to distance and identityAn important strategy to limit nosocomial transmission is to implement physical distancing or avoiding close contact with others[START_REF] Arora | Implementing physical distancing in the hospital: A key strategy to prevent nosocomial transmission of covid-19[END_REF]. This physical distancing should be kept during the whole work day and in every where in the hospital. For example, during meal time, HCWs are encouraged to maintain physical distancing. To help maintain this requirement, we implement InZone-19 DI app, a DI proxemic application. Design of proxemic environment for scenario 1 formal definition and graphical design (see Figure7.2).

Fig. 7 . 2 89 •∡

 7289 Fig. 7.2 Scenario 1: Design of proxemic environment graphical representation for each HWC who needs to keep physical distancing.

Fig. 7 . 3

 73 Fig. 7.3 Scenario1: (a) Physical distancing between HCWs exclusively using Bluetooth BLE mobile devices. Each HCW also acts as an CPS there are multiples P_E; (b) Graphical user interface (GUI) that allows the HCW to start virtual security bubble based on his/her id phone number.

  .4). • The four tuple of P_E: P_E =< CPS, entities = identities = {I}, distances = {0.5, 1, 4, 50} >; • CPS.L = (0, 0); ∡ MinAo fV = 0 • and ∡ MaxAo fV = 60 • ; • Distance of entities: I.D = 1; Orientation of entities (faces of entities are in the area of vision of the CPS): I.O = 30 • , thus ∡ MinAo fV ≤ I.O, ≤ ∡ MaxAo fV ; • Proxemic zones of entities: I.P_Z=P_Z personal From these conditions, the CPS can react with Action 1 , that can be denoted in several ways, for example: i f ∃ I in P_Z personal ∧ ∡ MinAo fV ≤ I.O ≤ ∡ MaxAo fV , then CPS.Action 1 .

Fig. 7 . 4

 74 Fig. 7.4 Scenario 2: Design of proxemic environment graphical representation.

Fig. 7 . 5

 75 Fig. 7.5 Scenario 2: HWC reads a patient's electronic record from the screen using proxemic interaction in which the HCW avoid physical contact with his mobile phone.

Fig. 7 .

 7 Fig. 7.6 HWC carries her smartphone in her pocket.

  Figure 7.7 shows an example of hospital environment using the mobile application inZone-19 where the HWC is carrying her smartphone in a safe place out of the nosocomial infection.

Fig. 7 . 7

 77 Fig. 7.7 Hospital environment: (a) HCW is patient's personal zone and is oriented to the phone's camera; (b) The HCW's smartphone transmits the information from server to chromecast TV.

  Proxemic Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.5 Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.6 Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Systems based on DILMO Proxemic Dimensions . . . . . . . . . . . . . . 3.2 Software Engineering for the Development of Proxemic Interactions . . . . 3.2.1 Domain-Specific Languages . . . . . . . . . . . . . . . . . . . . . Technologies for sensing DILMO Proxemic Dimentions . . . . . . . . . . 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Motivating Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 First Aid Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 FAMA: Proxemic-based First Aid Mobile Application . . . . . . . . . . . 4.5 Scenario the exploration study . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 FAMA Implementation . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XML Code from graphical view . . . . . . . . . . . . . . . . . . . . . . . 5.6 Proxemic Environment XML Schema . . . . . . . . . . . . . . . . . . . . 5.7 UML class diagram derived from the XSD . . . . . . . . . . . . . . . . . . 5.8 Description of Prototype GUI . . . . . . . . . . . . . . . . . . . . . . . . 5.9 Graphical model for scenario . . . . . . . . . . . . . . . . . . . . . . . . . 5.10 XML file for scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.11 Scenario modelled with our DSL . . . . . . . . . . . . . . . . . . . . . . . 6.1 Pose angle estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Android accelerometer coordinate system . . . . . . . . . . . . . . . . . . 6.3 Framework architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 DILMO proxemic dimensions and nomenclature to describe each combination that is available through the API methods for processing proxemic information. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 The Screenshot API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 UML Class Diagram of the API (7 classes). . . . . . . . . . . . . . . . . . 6.7 Example of ProxZone Class Constructor invocation. . . . . . . . . . . . . . 6.8 Tonic Proxemic Zones based on Bluetooth Low Energy. . . . . . . . . . . .

	Table of contents List of figures List of tables 1 Introduction 1.1 Contributions . 3 Related Work 3.1 viii 3.3 4 First Aid Mobile Application (FAMA): A Proxemic Application in Mobile De-Table of contents xiii xi vices List of figures 4.1 xii 5.5

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Structure of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . 2 Proxemic Interactions in Human-computer interaction: preliminaries 2.1 Theory of Proxemics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Human-Computer Interactions . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Movement Recognition . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Ubiquitous Computing . . . . . . . . . . . . . . . . . . . . . . . . 2.3 2.3.1 Proxemic DILMO Dimensions . . . . . . . . . . . . . . . . . . . . 2.3.2 Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Identity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4 3.2.2 Tools for Designing Proxemic Applications . . . . . . . . . . . . . 3.2.3 Frameworks to build Proxemic Systems . . . . . . . . . . . . . . . 5 A Graphical Domain-Specific Language for Modelling Mobile Proxemic Applications 5.1 Motivation to Develop a Graphical Domain-Specific Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Table 3 .

 3 1 Systems based on DILMO proxemic dimensions

	Reference	D I L M O
	Ballendat et al.[9]	■	■	■	■	■
	Bhagya et al.[10]	■	■	■	■	■
	Brock et al.[12]	■	■		■	■
	Brudy et al.[14]	■	■	■	■	■
	Cardenas et al.[16]	■	■	■	■	■
	Cho et al.[21]	■	■	■
	Dingler et al.[31]	■	■	■	■
	Dostal et al.(a)[32]	■	■	■	■	■
	Dostal et al.(b)[33]	■	■			■
	Garcia-Macias et al.[39]	■	■	■	■	■
	Grønbaek et al.(b)[42]	■		■	■	■
	Grønbaek et al.(a)[43]	■		■	■	■
	Grønbaek et al.(c)[44]	■	■			■
	Kim et al.[54]	■	■	■	■	■
	Ledo et al.[59]	■	■	■	■	■
	Marquardt et al. [64]	■	■	■	■	■
	Mentis et al.[67]	■	■	■	■	■
	Mojgan et al.[70]	■	■	■	■	■
	Rector et al.[76]	■	■	■	■	■
	Sørensen et al.[81]	■	■	■	■
	Vermeu et al.[85]	■		■
	Wang et al.[90]	■	■		■	■
	Wolf et al.[94]	■	■			■

Table 3 .

 3 2 Tools for design proxemic enviroments

	Design tool	Graphical notation Proxemic modelling Hardware requirement
	SpiderEyes [32]	no	yes	yes
	Ministudio [54]	yes	yes	yes

Table 3 .

 3

		3 Proxemic Frameworks Technology
	Framework	Technologies	Portability Offline service Mobile API
	Toolkit [64]	Kinect Depth Camera Vicon motion capture	Low	yes	No
	ProximiThings [16]	Infrared Sensors IR Accelerometer Gyroscope	High	No	No
		Magnetometer

Table 3 .

 3 Bluetooth BLE is a radio transmitter that broadcast BLE signals and Universally Unique Identifier (UUID) allowing identification of digital devices in the environment

		5 A truth table for identifying a user
	Characteristic	user 1	user 2	user 3	user 4
	Face detection Positive	Negative	Positive Unknown
	Clothing color Unknown Unknown Negative Negative
	Height	Positive	Positive Unknown Unknown
	Identification	✔	✖	✖	✖

Table 7 .

 7

		1 DILMO Medical enviroments
	DILMO	Description	Camera	BLE	GPS
	D	Distance between HCWs and devices	✓	✓ .
	I	HCWs' identity or device identifier	✓	✓ .
	L	Medical equipment position	.	. ✓
	M	HCWs Gesture	✓	.	.
	O	HCWs' gaze or face orientation	✓	.	.
	DI	Physical distancing between entities	.	✓ .
	IO	HCW's orientation and identity	✓	.	.
	DIO IO in a proxemic zone	✓	✓ .
	DIMO IO in a proxemic zone and body tracking ✓	.	.

Table 7

 7

			.2 Methods Implemented
	API/APKs/AlteB	Class	Constructor and Method	InZone-19 DI	InZone-19 DIO
	API	ProxZone	ProxZone(0.5, 1.0, 4.0, 50.0)	✓	✓
	API	Dilmo	Dilmo(proxzone)	✓	✓
	API	Dilmo	setProxemicDistance (distance)	✓	✓
	API	Dilmo	getProxemicZoneByDistance()	✓	✓
	API	Dilmo	setProxemicDI(uuid,D)	.	✓
	API	Dilmo	getProxemicDI(uuid)	.	✓
	APKs	Frame	Builder()	.	✓
	APks	Face	detect(frame)	.	✓
	AlteB	Beacon	getId1()	✓	✓
	AlteB	Beacon	getDistance()	✓	✓

Royalty-free stock photo ID: 506837092

Royalty-free stock photo ID: 479653195

Royalty-free stock photo ID: 66163672

Royalty-free stock photo ID: 1517239640

Royalty-free stock photo ID: 1023809758

Royalty-free stock photo ID: 1302594496

https://developer.estimote.com/lte-beacon/using-bluetooth/

https://www.vicon.com/hardware/

https://www.w3.org/TR/xmlschema11-1/

https://wiki.eclipse.org/Introduction_to_the_XSD_Editor

https://github.com/paulocpd76/ProxemicEnt15-01-20.git/

The APPS are available in https://github.com/llagar910e/

Acknowledgements

heartfelt thanks to the Informatics Department for acceptance during my work in the IUT Bayonne.

I give many thanks to my wife and son for their love, understanding, prayers, and continuing support to complete this research work. Also, I express my thanks to my sister, brother, sisters-in-law, brother-in-law, lovely mother-in-law, dear nephews, and niece for their support and valuable prayers.

Finally, my thanks go to all the people who have supported me in completing

Proxemic Environments: A Framework for Developing Mobile Applications based on Proxemic Interactions

The API facilitates developers processing proxemic information and values. The API is available for free download (see Figure ). The API provides 7 classes called (Dilmo, ProxZone, Distance, Entity, Movement, Location, and Orientation) to define the P_Z, as well as to manage the different combinations of DILMO dimensions. For example, for a DIL proxemic environment, methods to identify entities (I) and to process D and L are available in DILMO class. Thus, the API behaves as a bridge between the Proxemic Zones and DILMO modules. The API provides methods that the developers can implement for processing proxemic information using motion sensors and mobile computer vision cameras. The majority of current smartphones have a wide range of sensors in their hardware configuration [START_REF] Developers | Sensors overview[END_REF], which allow the application to run proxemic apps. For example, through the BLE beacons mechanisms [START_REF] Altbeacon | Altbeacon -the open proximity beacon[END_REF], it is possible to know the distance (D) between two mobile devices. The distance is estimated using the signal strength of the mobile device's Bluetooth signal. Another way to estimate the distance between two entities is to use computer vision (face detection). Android provide methods for detecting faces as image frame. This image frame size is used to estimate the distance between the user and the mobile phone camera through the API methods.

Proxemic Environments: A Framework for Developing Mobile Applications based on

Proxemic Interactions applications developed by undergraduate students in computer science. With this framework, we provide a tool that can help developers to build novelty proxemic mobile applications using social distancing.

The following chapter describes two mobile applications implemented using our framework and DSL in the current pandemic scenario to avoid virus spread. We present two prototypes of two mobile applications as a proof-of-concept, using several combinations of proxemic DILMO dimensions in accordance with real-life needs.

Chapter 7 Proxemic Mobile App Design and Development to Avoid the Spreading of Infections

In this chapter, we illustrate how proxemic interaction in mobile devices that can avoid the spreading of nosocomial infection. We use our DSL to design the proxemic environment and the framework to develop two mobile applications as proof-of-concept and validation of our approach. The pandemic produced by the COVID-19 has affected people's daily lives. One of the main problem is the quickly and easy spreading of the virus, that causes a huge quantity of infected people assisting to healthcare centers. Thus, nosocomial infections (also called as hospital-acquired infections) of COVID-19 affect Health Care Workers (HCWs) in the workplace according to recent studies [START_REF] Ling | Infection control in non-clinical areas during the covid-19 pandemic[END_REF][START_REF] Vukkadala | Covid-19 and the otolaryngologist: preliminary evidence-based review. The Laryngoscope[END_REF]. This infection is defined as contamination during the process of care in a hospital and also it is considered as occupational infections among HCWs. HCWs use technologies in hospital, laboratories, intensive care units, and operating rooms; thus during any interaction with computers and mobile devices, they come into close contact with strongly contaminated devices.

Avoid the Spreading of Infections: motivation research

Previous studies demonstrated drastic reductions of nosocomial transmission of COVID-19 thanks, in large part, to physical distancing among people in hospitals [START_REF] Kissler | Social distancing strategies for curbing the covid-19 epidemic[END_REF][START_REF] Soucy | Estimating the effect of physical distancing on the covid-19 pandemic using an urban mobility index[END_REF][START_REF] Brzezinski | The covid-19 pandemic: government vs. community action across the united states[END_REF]. However, contamination of computer user interfaces and mobile devices, due to their touchability, are frequent in the clinical workrooms contributing to spread of COVID-19 [START_REF] Wang | Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (covid-19) implicate special control measures[END_REF]. Several studies suggest touchless interaction to avoid the contact of contaminated materials [START_REF] Gallo | Controller-free exploration of medical image data: Experiencing the kinect[END_REF][START_REF] Mentis | Interaction proxemics and image use in neurosurgery[END_REF][START_REF] Kim | Kinect technology for hand tracking control of surgical robots: technical and surgical skill comparison to current robotic masters[END_REF][START_REF] Ruppert | Touchless gesture user interface for interactive image visualization in urological surgery[END_REF]. API offers methods to process DILMO information locally, without a connection to a server. However, connection to the server can be established according to DILMO conditions. The ProxZone class allows to define proxemic zones according to user requirements (i.e., the user/developer is able to decide the measures that delimit each proxemic zone when this class is instantiated by its constructor method). The DILMO class offers the possibility of identifying the proxemic zones of all entities which will interact in the environment; and processing the DILMO information from mobile device sensors.

Web Service for storing patient information

The proposed architecture uses a web service, which has been created for experimental purposes. The patient information is provided according to client needs (health centres). We implement a post method that sends a patient key to a server from HCW's smartphone. The patient information is then returned by the server to the mobile phone, which in turns transfer that information by cast receivers to a screen in the room. The access to the web server and the screen should be configured in HCWs' mobile devices and it will be allowed according to the implemented DILMO combination. The interpersonal distances are locally managed by the mobile devices using Bluetooth and the API.

Prototype: Proxemic Mobile Applications based social distancing POC

To demonstrate the applicability and suitability, in the medical area, of our proposal, we built two mobile applications with proxemic HCI, as proof-of-concept, which are based on some DILMO combinations. These applications illustrate how the need of reducing the spread of bacteria associated with nosocomial infections, can be approached, by avoiding computer's peripheral and mobile devices touching and maintaining physical distance among HCWs. The scenarios described with both applications give a better understanding of proxemic interactions in medical environments.

The first application, called InZone-19 DIO, allows careful social interactions in hospital environments, reducing physical contact with hardware and limits the spread of nosocomial infection of COVID-19. The second one, called InZone-19 DI, helps to keep physical distancing between two HCWs. Table 7.2 summarizes API's methods, AlteBeacon (AlteB) libraries and mobile vision methods from Android native libraries (APKs) that were implemented to build both InZone-19 DIO and InZone-19 DI applications. Our API considers the extraction of DIO values from smartphones or mobile devices based on the Android operating system