Mathieu Raynal
email: mathieu.raynal@irit.fr

Georges Badr
email: georges.badr@ua.edu.lb

I Scott Mackenzie

DESSK: description space for soft keyboards

Keywords: soft keyboards, description space, interaction models, descriptive model

We present dessk, a description space for soft keyboards. dessk provides a framework for any soft keyboard to be graphically described along four dimensions -context, representation, interaction, and linguistic system -each with multiple criteria. dessk begins with a blank working template which is populated with labels and symbols to characterize the dynamics and interactions of the keyboard. The goal is to provide a framework within which any soft keyboard can be placed and to serve as a theoretical basis to situate soft keyboards in relation to proposed or existing systems.

Introduction

Early soft keyboards were identical representations of physical keyboards: The character layout was similar and each soft button corresponded to a physical key. These keyboards were originally created to allow people with motor disabilities to enter text. The keyboards were "soft" or "virtual", meaning they were rendered in graphics on a display. Interaction proceeded either through single-switch scanning [START_REF] Damper | Text composition by the physically disabled: A rate prediction model for scanning input[END_REF] or using eye gaze [START_REF] Frey | Eye-gaze word processing[END_REF].

Soft keyboards are also standard on virtually all desktop systems. Interaction can use any pointing device, such as a mouse, trackball, joystick, or even an eye tracking system. With the emergence of pen-based computing, smartphones, and tablets, these text entry systems are more common as they replace physical keyboards. And new interaction devices, such as smartwatches, joysticks, or virtual reality headsets, also support text entry. Moreover, because these systems are created in software, they offer more possibilities for interaction than a physical keyboard. Thus, they are able to evolve dynamically, adjusting to the user's input, and they often interact with other software components or use several interaction modalities.

These possibilities result in a wide variety of text entry systems using soft or virtual keyboards. Although contexts of use are diverse, the goal is the same: supporting and improving text entry speed and accuracy. Nevertheless, there is no theoretical framework to situate this work in relation to proposed or existing systems. Several reviews and workshops reflect on text input [START_REF] Soukoreff | Recent developments in text-entry error rate measurement[END_REF][START_REF] Wigdor | A comparison of consecutive and concurrent input text entry techniques for mobile phones[END_REF][START_REF] Mackenzie | Text entry using a small number of buttons[END_REF][START_REF] Kristensson | Grand challenges in text entry[END_REF][START_REF] Majaranta | Twenty years of eye typing: Systems and design issues[END_REF]; however, given the diversity of systems, interaction contexts, or interaction modalities, these reviews focus on one or a few specific criteria -e.g., accessibility according to a motor impairment [START_REF] Sporka | Chanti: Predictive text entry using non-verbal vocal input[END_REF] or visual impairment [START_REF] Ye | QB-Gest: Qwerty bimanual gestural input for eyes-free smartphone text input[END_REF][START_REF] Tinwala | Eyes-free text entry with error correction on touchscreen mobile devices[END_REF], mobile use [START_REF] Mackenzie | Text entry systems: Mobility, accessibility, universality[END_REF], or 3D virtual environments [START_REF] Dube | Text entry in virtual reality: A comprehensive review of the literature[END_REF] -and compare the systems according to this prism.

The purpose of this paper is to describe soft keyboards generally, whatever the intended context and constraints. We present different types of soft keyboards according to the elements that characterize them, including their context of use, the interaction modalities, and the software components coupled to the keyboard. For this, we present a description space for soft keyboards, called dessk, which encompasses the criteria to describe any type of soft keyboard.

The goal of dessk is to describe each soft keyboard from a system point of view: that is, to represent the way the user interacts with the keyboard, to describe the internal functioning of the keyboard, and how it evolves as the user types. Moreover, beyond functional aspects, we contextualize soft keyboards by including the intended environment.

dessk only concerns soft keyboards. We define soft keyboards as any text entry system composed of several interactive zones to produce text strings. On a soft keyboard, the interactive zones appear on a display and are generally materialized by buttons or edges to which one or several characters (or codes) are linked. dessk does not take into account gesture recognition or handwriting recognition systems or voice dictation systems used for text entry.

DESSK: A descriptive model

In the space of modeling, dessk is an example of a descriptive model [START_REF] Mackenzie | Human-computer interaction: An empirical research perspective[END_REF]. Descriptive models are tools for thinking. They breakdown a problem space into constituent parts and offer a visual depiction of the problem space. With this, they empower researchers to understand and think in different ways about the problem space and, importantly, to develop a deeper understanding of current phenomenon and to inspire new possibilities. This is in contrast to predictive models or analytic models, which are tools for predicting or quantifying [START_REF] Mackenzie | Motor behaviour models for human computer interaction[END_REF].

Descriptive models are everywhere. Often, researchers describe the components of a problem space without framing their efforts as a model. That's what we do! The process is natural and unconscious, and usually just serves to present current practice as prelude to a new idea for empirical study. However, it is also the case that "describing the components of a problem space" is a contribution in itself, if done in a thorough, constructive, and illustrative manner. And the HCI literature is replete is with descriptive models presented in this way. Examples include Johansen's quadrant model for groupware [START_REF] Johansen | Groupware: Future directions and wild cards[END_REF], Buxton's three-state model for graphical input [START_REF] Buxton | A three-state model for graphic input[END_REF], MacKenzie and Castellucci's frame model for visual attention [START_REF] Mackenzie | Eye on the message: Reducing attention demand for touch-based text entry[END_REF], or Card et al.'s model human processor [START_REF] Card | The psychology of human-computer interaction[END_REF].

The problem space of dessk is soft keyboards. What are the interaction and contextual parts of soft keyboards? Can these be organized in a comprehensive framework that encompasses all soft keyboards? Is there an appropriate visual organization for this framework? These are the sort of questions we considered in developing our description space for soft keyboards, or dessk for short. The components in dessk are presented in the following sections.

Description criteria

Our description space is defined along four dimensions. These are now described with reference to the blank working template shown in Figure 1. The template will be populated with additional details later using specific examples of soft keyboards.

Fig. 1: Working template for description space of soft keyboards (dessk) Briefly, the first dimension, Context, concerns the context of use of the soft keyboard; that is, characteristics of the user and environment for which the system has been designed. The second dimension, Representation, describes the visual appearance of the soft keyboard -the way the keyboard is structured and its graphic representation. The third dimension, Interaction, focuses on how the user interacts with the system, both to navigate on the keyboard, and to validate desired zones. Finally, the last dimension, Linguistic system, defines the language components used to facilitate the input with soft keyboards. Each of these dimensions is broken into criteria presented as follows.

Context of use

Soft keyboards were initially designed for people who cannot use a physical keyboard to enter text on their computer. However, since the emergence of smartphones and more generally touch screens, the soft keyboard has become an essential tool for most users. The design of a soft keyboard generally responds to a specific problem, often related either to the user's abilities or to the characteristics of the device on which the keyboard is used. Context of use therefore includes at least three criteria: User, Display, and Device.

User This indicates whether the keyboard is designed for a particular profile of people. This can include the abilities of the user (due to a disability or the context of interaction), but also the expertise of the user (novice vs. expert).

For users' abilities, we distinguish soft keyboards intended for people without major constraints, those designed for motor-disabled people [START_REF] Polacek | Text input for motor-impaired people[END_REF], and those studied for people with a visual impairment [START_REF] Banubakode | Survey of eye-free text entry techniques of touch screen mobile devices designed for visually impaired users[END_REF][START_REF] Siqueira | Braille text entry on smartphones: A systematic review of the literature[END_REF].

On the other hand, user performance evolves when using a soft keyboard [START_REF] Mackenzie | The design and evaluation of a high-performance soft keyboard[END_REF]. Users are initially "novices", but with time learn the new layout and gradually become "experts". Some keyboards have been designed more specifically to help users get started with this new keyboard [START_REF] Magnien | Mobile text input with soft keyboards: Optimization by means of visual clues[END_REF]. Conversely, other keyboards provide additional interactions or shortcuts to increase the text entry speed when users gain expertise with the keyboard [START_REF] Chen | Swipeboard: A text entry technique for ultra-small interfaces that supports novice to expert transitions[END_REF][START_REF] Isokoski | Performance of menu-augmented soft keyboards[END_REF].

Display

The first text input systems were mainly used on a traditional desktop computer where the soft keyboard was displayed on the screen. But the uses have greatly diversified over the last twenty years with the democratization of new display surfaces such as smartphones, smartwatches, interactive TVs, head-mounted displays, etc. The diversity of these display surfaces shows the importance of this criterion when designing a text entry system. For example, the layout may differ if displayed on a watch screen compared to a mixed-reality headset.

For the different possible values of this criterion, we have chosen to distinguish between soft keyboards displayed on traditional screens coupled to a computer, and those presented on touch screens. Usually the keyboards displayed on a computer screen are those used by motor-challenged people who use the keyboard to enter text on the computer.

With touch screens, we group together those on devices such as smartphones, tablets, or even interactive tables that are used with the finger or a stylus. On the other hand, we distinguish the very small touch screens used, for example, by smartwatches because the very small display surface brings about a specific problems of keyboard display and interaction.

Device Beyond the display surface, the other important criterion when designing the system is the interaction modality available to the user to interact with the soft keyboard. An interaction modality, as defined by Nigay and Coutaz [START_REF] Nigay | A design space for multimodal systems: concurrent processing and data fusion[END_REF], combines the physical device with the interaction language. This criterion determines which physical device (mouse, joystick, Wii Remote, touch screen, etc.) is used to interact with the soft keyboard. As we will see in the "interaction" dimension, input generally includes subtasks: navigation and validation. The interaction language used can be different for these two subtasks. This is why the interaction language is described in the "navigation" and "validation" criteria of the "interaction" dimension.

Many devices have been used to interact with a soft keyboard. The most widely used are certainly the touch screens that can be found in many situations: interactive terminals, interactive tables, or personal devices such as smartphones, tablets, or smartwatches. These screens have the particularity of being both the input device and the display surface. We distinguish two types of touch screens: those of smartwatches which are very small, and others with a larger footprint.

Some soft keyboards are also designed for entertainment systems. In this context, devices are varied: gamepad [START_REF] Isokoski | Quikwriting as a multi-device text entry method[END_REF], joystick [START_REF] Wobbrock | Writing with a joystick: a comparison of date stamp, selection keyboard, and edgewrite[END_REF], or remote controller (like Wii [START_REF] Jones | GesText: Accelerometer-based gestural text-entry systems[END_REF]).

Finally, as noted in the introduction, soft keyboards are also the main way to interact and communicate for people with a motor impairment. The interaction device is then adapted to the motor skills of the person. People who still have little motor skills use pointing devices such as a joystick [START_REF] Wobbrock | Text entry from power wheelchairs: Edgewrite for joysticks and touchpads[END_REF], trackball [START_REF] Wobbrock | Trackball text entry for people with motor impairments[END_REF], while the most paralyzed use specific devices such as an eye tracker [START_REF] Ward | Dasher: A data entry interface using continuous gestures and language models[END_REF], muscle contractions [START_REF] Felzer | Qanti: A software tool for quick ambiguous non-standard text input[END_REF], vocal input (non verbal) like humming [START_REF] Polacek | Humsher: a predictive keyboard operated by humming[END_REF] or hissing [START_REF] Poláček | Predictive scanning keyboard operated by hissing[END_REF].

In some scenarios, physical keys are used to move a cursor on the soft keyboard [START_REF] Bellman | A probabilistic character layout strategy for mobile text entry[END_REF][START_REF] Raynal | Slidekey: Impact of in-depth previews for a predictive text entry method[END_REF]. For soft keyboards with single-switch scanning, the cursor moves automatically from zone to zone and, when the cursor is on the right zone, the user validates it through some input mechanism [START_REF] Wandmacher | Sibylle, an assistive communication system adapting to the context and its user[END_REF].

Finally, we could also take physical keyboards into account in this criterion. Some of them use unconventional layouts or interactions (such as OrbiTouch (by Keybowl, Inc.) or DataHand [START_REF] Knight | Datahand: design, potential performance, and improvements in the computer keyboard and mouse[END_REF]).

Summary Table 1 summarizes the possible values for the three criteria of the "Context of use" dimension. It should be noted that the set of possible values is not restricted to those given, but is malleable as per new technologies and applications that arise.

Representation

The second dimension of our description space concerns the structure and the graphical representation of a soft keyboard. We use four criteria to describe the representation.

A soft keyboard can sometimes be decomposed into several text input systems. For example, a soft keyboard that includes word prediction or work completion is a combination of two input systems: the keyboard on the one hand and the word list on the other hand. Values are assigned to following criteria separately for each part of the keyboard.

Visibility Some parts of the keyboard are not permanently visible. For example, the POBox system [START_REF] Masui | POBox: An efficient text input method for handheld and ubiquitous computers[END_REF] displays a list of the most probable words but only after the first character of the word is entered. We therefore distinguish between those parts that are permanently visible and those that are visible only occasionally.

Language The language determines the information that appears in the interactive zones. This can be characters usable on the system, undefined words (for predicted words lists), or codes to represent or extend the character set. For example, EdgeWrite [START_REF] Wobbrock | EdgeWrite: A stylus-based text entry method designed for high accuracy and stability of motion[END_REF] uses codes representing the four corners of a square. All characters have a representation using a sequence of these codes.

Cardinality Cardinality corresponds to the number of interactive zones on the input system. We write "N" when there are as many interactive areas as there are characters. If there are less zones than elements, the number of zones is given. This can be the case of a soft keyboard using a code (such as EdgeWrite [START_REF] Wobbrock | EdgeWrite: A stylus-based text entry method designed for high accuracy and stability of motion[END_REF] or H4-Writer [START_REF] Mackenzie | 1 thumb, 4 buttons, 20 words per minute: Design and evaluation of H4-Writer[END_REF]), or when several characters are associated with the same zone. Examples of the latter are ambiguous keyboards, such as a phone keypad, where multiple letters are associated with each key.

Layout Here we describe how the different interactive zones are arranged in relation to each other. This can be any grid such as the Qwerty layout, a horizontal or vertical list, a circular layout [START_REF] Mankoff | Cirrin: A word-level unistroke keyboard for pen input[END_REF], or a square layout [START_REF] Perlin | Quikwriting: Continuous stylus-based text entry[END_REF]. Summary Table 2 summarizes the possible values for the four criteria of the "Representation" dimension. As with the Context-of-use dimension, the set of possible values may vary as per new technologies and applications that arise.

Interaction

Whatever the type of elements produced (codes, characters, or words), the production of these elements occurs in two stages: first, navigating to the desired interactive zone and then validating or confirming the zone. Navigation The navigation phase is only present for systems using a pointer or cursor to select the interactive zones. For soft keyboards on smartphones, connected watches, or other devices using a touch screen, there is no pointer:

The user directly accesses the zone, for example, using a finger or stylus. In this case, there is no navigation phase. If a pointer or cursor is present, its movement depends on the user's ability to manipulate a pointing device or its equivalence. For users with a motor impairment, navigation uses a cursor that is moved, typically without using a pointing device. One possibility is the use of non-verbal voice input: the user vocalizes sounds which map to virtual arrow and select keys [START_REF] Sporka | Chanti: Predictive text entry using non-verbal vocal input[END_REF]. More commonly, the cursor or hot spot is moved automatically from zone to zone on a soft "scanning keyboard" (aka "single-switch scanning"). When the cursor is on the right zone, the user validates it through some input mechanism. There are two types of movement: continuous movement of a pointer using a pointing device, and discrete movement of a cursor from zone to zone using directional keys or other discrete actions to realize and control the direction of movement. We therefore distinguish three types of navigation: automatic, direct, or indirect. Each type can be continuous or discrete.

Validation Finally, the validation of an interactive zone produces the element linked to this zone. For soft keyboards on smartphones or other touch screens, the most common validation consists in directly tapping the desired zone. Pressing a finger on the screen, the user validates the zone under the finger. During an interaction by a gesture stroke, the interactive zones are generally validated by crossing: Each zone crossed by the trace is considered validated. In this case, a language model makes it possible to determine the desired word from the sequence of zone crossings [START_REF] Zhai | Shorthand writing on stylus keyboard[END_REF]. It is also possible to perform a gesture on the desired zone to validate it. This technique of validation by gesture is sometimes used for zones where there are several characters. The use of gestures to validate the zone allows, in the same action, removing the ambiguity on the desired character [START_REF] Nesbat | A system for fast, full-text entry for small electronic devices[END_REF].

For soft keyboards used with a pointing device, validation is usually done with a button on the pointing device. Similarly, for keyboards where the selection of the zone is done by single-switch scanning, the user validates the selection using a switch or any other input mechanism that produces a discrete action to indicate validation of the selection [START_REF] Wandmacher | Sibylle, an assistive communication system adapting to the context and its user[END_REF]. Summary Table 3 summarizes the possible values for the three criteria of the "Interaction" dimension. As before, the set of possible values is not restricted to those given, but can expand as new interaction techniques emerge.

Linguistic system

Finally, the last dimension of our description space determines whether our soft keyboard uses algorithms based on linguistic knowledge. The process combines actions previously performed with linguistic knowledge (rules, statistics, etc.) to complete or modify a string of characters already entered. Alternatively, the process may dynamically modify part of the keyboard to help the user in his text input [START_REF] Merlin | SpreadKey: Increasing software keyboard key by recycling needless ones[END_REF]. These systems are of different types. The most well-known are word prediction algorithms that propose the most probable words according to the given prefix [START_REF] Badr | WordTree: Results of a word prediction system presented thanks to a tree[END_REF]. Prediction systems can also propose characters that are most likely to succeed the prefix already entered.

Soft keyboards can also use a deduction system, whereby information coming from the user's input is used to deduce the intended word. This information can include, for example, the ordered sequence of the zones previously hovered over or validated. From this information and linguistic knowledge, the system will then deduce the word best matching this information. Summary Table 4 summarizes the possible values for the three criteria of the "Context of use" dimension. Again, the set of possible values may expand as per new technologies and applications that arise.

Table 4: Possible values for each criteria of the "Context of use" dimension

Criteria

Possible values Linguistic system characters prediction, words prediction, words deduction 4 Putting DESSK into practice Our description space is presented in the form of a table with one criterion per line, as shown above in Figure 1. In populating the description space, the objective is to show the dynamics of the soft keyboard and possible interactions between the different parts of the keyboard. We use green circles to represent the possible productions, with C, W, or S displayed in these circles to infer that the system produces characters, words, or strings. Blue lines represent the interactions between the different criteria of the soft keyboard: The circle at one end indicates which criterion generates the interaction while the diamond at the other end indicates the criterion impacted by this interaction. Green and blue rectangles, respectively, define the information that is sent to the linguistic system, and the modification that is made on the soft keyboard in reaction to the information sent by the linguistic system.

The modifications can be of different types: The most common consist of modifying the set of words displayed in the list [START_REF] Badr | WordTree: Results of a word prediction system presented thanks to a tree[END_REF] or the set of characters displayed on additional keys [START_REF] Raynal | Keyglasses: Semi-transparent keys on soft keyboard[END_REF], or altering the character positions on a "scanning keyboard" [START_REF] Wandmacher | Sibylle, an assistive communication system adapting to the context and its user[END_REF]. It is also possible to modify the key sizes [START_REF] Al Faraj | Bigkey: A virtual keyboard for mobile devices[END_REF] or shapes [START_REF] Aulagner | Floodkey: Increasing software keyboard keys by reducing needless ones without occultation[END_REF] to facilitate access to the most probable characters. Other modifications have also been tried, for example, changing the font to highlight the most probable characters to assist searching for novice users [START_REF] Magnien | Mobile text input with soft keyboards: Optimization by means of visual clues[END_REF], or modifying the transfer function of the pointing device to improve navigating to keys [START_REF] Raynal | Semantic keyboard: Fast movements between keys of a soft keyboard[END_REF].

Modifications to the keyboard can also occur depending on user interactions. For example, the FishEye keyboard is a full keyboard designed to be displayed on a smartphone or PDA and used with the finger or a stylus [START_REF] Raynal | Fisheye keyboard: Whole keyboard displayed on pda[END_REF]. When the user (or stylus) touches the screen, the keys around this point magnify to make them easier to read (see Figure 2a). The user can then move his finger (or stylus) on the screen until hovering over the desired character. When he lifts his finger, the character is validated. Figure 2b is the description with dessk of the FishEye Keyboard.

Another example is F.O.C.L., a soft keyboard designed in the late 90s to be implemented on a cell phone, pager, or other mobile device [START_REF] Bellman | A probabilistic character layout strategy for mobile text entry[END_REF]. The user moves a cursor from key to key using four directional keys. After each character is entered, the character layout changes so that the most likely characters are as close as possible to the cursor. Figure 3 shows the description of this keyboard in our space. We can observe that the characters entered are added to the text produced (green circle with a C) and sent, in parallel, to the character prediction system which returns the list of characters ordered according to the probability of each one to be entered. With this information, the system updates the character layout.

(a) Implementation [START_REF] Bellman | A probabilistic character layout strategy for mobile text entry[END_REF] (b) dessk description If the soft keyboard uses regions to enter elements, they will be presented in additional columns. See Figure 4. For example, a soft keyboard with a word list includes two regions: the soft keyboard and the list of predicted words. The border between the regions describes the relationship between these two parts. On the one hand, the two systems are used in parallel (dotted border) whereby the user switches between the keyboard and the prediction list during the input of a word. Or, the two systems are used separately and sequentially (solid border). For example, in the DUCK keyboard [START_REF] Raynal | DUCK: A DeDUCtive soft Keyboard for visually impaired users[END_REF], the user enters an approximate set of characters for a word and then selects the correct word from a set of words proposed by the deduction system.

The example in Figure 4 is sibylle [START_REF] Wandmacher | Sibylle, an assistive communication system adapting to the context and its user[END_REF] which is an assistive communication system for people with motor disabilities who cannot use a pointing device. Navigation on the soft keyboard is done by automatic switch scanning. The user validates the selection with a contact that is activated when the cursor is on the desired key. sibylle is a complete text entry system which proposes all the characters and functionalities proposed on a standard keyboard, but also complementary keys which propose additional functionalities. It includes regions for character input, a word list, a numeric keypad, and a menu bar. Two linguistic prediction systems are included, a character prediction system which rearranges characters after each character entered, and a word prediction system which proposes a list of the most probable words.

The representation of sibylle in dessk (Figure 4) presents only the part of sibylle that evolves dynamically during the input; that is, the character input block whose layout is rearranged after each character input, and the word list (on the left) that is updated after each new character entered. For many soft keyboards, zone validation produces a character or code. In the case of code-based keyboards, a sequence of codes is produced before being sent to a deduction algorithm that determines the character corresponding to the sequence. This sequence is produced by validating several interactive zones. To show this in our description space, we use a black rounded arrow between the selection and validation criteria. The number of repetitions necessary to achieve the sequence is given as an interval near the arrows. For example, in EdgeWrite [START_REF] Wobbrock | EdgeWrite: A stylus-based text entry method designed for high accuracy and stability of motion[END_REF], each character is coded by a sequence of corners the user has passed through (see Figure 5a). The right part of Figure 5 shows the description of EdgeWrite in dessk .

(a) Implementation [START_REF] Wobbrock | EdgeWrite: A stylus-based text entry method designed for high accuracy and stability of motion[END_REF] (b) dessk description Finally, the rounded arrow used with the validation criteria represents a repetition of the validation phase. This type of repetition is used in particular to remove an ambiguity when several characters are on the same interactive zone. For example, the selection of a character on a multi-tap soft keyboard is done by clicking once, twice, or three times on the button containing the desired character [START_REF] Vigouroux | Evaluation of aac for text input by two groups of subjects: Able-bodied subjects and disabled motor subjects[END_REF]. Figure 6 represents the operation of a multi-tap keyboard with our description space.

(a) Implementation [START_REF] Vigouroux | Evaluation of aac for text input by two groups of subjects: Able-bodied subjects and disabled motor subjects[END_REF] (b) dessk description Fig. 6: Multi-tap keyboard.

Conclusion

We present dessk, a description space for text entry with soft keyboards. The space is divided into four regions, Context, Representation, Interaction, and Linguistic system. The regions include sub-regions which are populated with labels and symbols to describe the operation of the keyboard, including dynamic and linguistics features and the context of use.

In order to serve the community working in text entry, we have designed and developed a website dedicated to text entry systems: http://text-entry.com/ This website is primarily a showcase of the various existing soft keyboard entry systems and aims to bring together as many research prototypes as possible. Beyond a simple catalog of existing solutions, the goal of our website is also to allow the exploration of this set of systems according to the desired characteristics. To do this, our website includes all the criteria from our description area to allow all solutions to be filtered according to these criteria. The description in our description space is available for each system presented on our page. All of these descriptions are available on our site http://text-entry.com/.

Our goal in proposing this descriptive model is to provoke thought about the problem space for the design of text input systems. We built our model from a large set of keyboards found in the literature. However, our descriptive model (certainly) has limits; some systems may be difficult to map into the DESSK model. Our description space will evolve over time depending on the feedback we get and the new systems we learn about.

Fig. 2 :

 2 Fig. 2: FishEye Keyboard

Fig. 3 :

 3 Fig. 3: FOCL system.

Fig. 4 :

 4 Fig. 4: sibylle system.

Fig. 5 :

 5 Fig. 5: EdgeWrite system.

Table 1 :

 1 Possible values for each criteria of the "Context of use" dimension

	Criteria	Possible values
	User	able-bodied, motor impairment, visual impairment, novice, expert
	Display	all, screen, projection, touch screen (smartphones, tablets), small touch
		screen (smartwatches), interactive TV, head-mounted display
	Device	mouse, joystick, touch screen (smartphones, tablets), small touch
		screen (smartwatches), remote control (Wii), eye tracker, muscle con-
		tractions, vocal input (non verbal), hissing, tongue

Table 2 :

 2 Possible values for each criteria of the "Representation" dimension

	Criteria	Possible values
	Visibility	none, occasionally, permanent
	Language	characters, words, codes
	Cardinality	full, number
	Layout	none, grid, list, square, circle

Table 3 :

 3 Possible values for each criteria of the "Interaction" dimension

	Criteria	Possible values
	Navigation	couple (automatic, direct, indirect) × (discrete, continuous)
	Validation	discrete, continuous


Pan Liu

Armelle Lokossou

Sidy Fall

Malek Makki

Olivier Baledent

Post Processing Software for Echo Planar Imaging Phase Contrast Sequence

The new sequence Echo Planar Imaging Phase Contrast (EPI-PC) allows real-time imaging of blood flow and can be used to study the effect of breathing unlike to the normal Phase Contrast Magnetic Resonance Imaging sequence (Nor-PC). However, there is no software for the processing of EPI-PC data. We developed new software to visualize, segment and analyze EPI-PC data. We implemented in the software functions as filtering, denoising, segmentation, reconstruction, and extraction that can be applied on EPI-PC signal. This software was easy to use and gave promising results for the quantification of blood flow and the study of breathing effect.

2018/11/8 https://

INTRODUCTION

Phase Contrast Magnetic Resonance Imaging (Nor-PC) is a main non-invasive technique for quantifying blood and cerebrospinal fluid flows during the Cardiac Cycle (CC). However, Nor-PC is limited by its temporal resolution due to the combination of signal from many CC to create cine-phase sampling of one time-averaged CC. Nevertheless, the relative effects of CC variation and also the impact of breathing on cerebral flows cannot be studied with Nor-PC [START_REF] Balédent | Cerebrospinal fluid dynamics and relation with blood flow: a magnetic resonance study with semiautomated cerebrospinal fluid segmentation[END_REF][START_REF] Alperin | PUBS: pulsatility-based segmentation of lumens conducting non-steady flow[END_REF] since it has been demonstrated that breathing impacts cerebral flows [3,[START_REF] Schroth | Cerebrospinal fluid flow. II. Physiology of respiration-related pulsations[END_REF]. Echo Planar Imaging PC-MRI (EPI-PC) is a recent technique that has an ultra-fast acquisition (Fe ≈10Hz) and is challenging Nor-PC [3]. Since there is no software dedicated to the processing of this new sequence, the purpose of this study was to develop a new software to visualize, segment and analyze the EPI-PC images.

METHODS

Algorithms:

The software is devolved by Interactive Data Language (IDL). The main objective of this software is to reconstruct a mean curve flow during a cardiac cycle from a continuous flow signal acquired by EPI-PC. In order to obtain the reconstructed curve of CC, the principal steps are:

1. Import the DICOM files to put the images in the matrix memory with the main parameters (spatial and temporal resolution) 2. Segment region of interest (ROI) then the software can extract the parameter of the ROI such as areas, flows, velocity.

RESULTS

Figure 3 shows the different signals acquired after using of the different functions on the EPI-PC images.

Figures 4c and4d show a similar shape between Nor-PC and EPI-PC flow curves for the two analyzed tubes.

3 .

 3 Visualize the flow signal of the ROI. The signals can be scaled, moved, stored and contrasted 4. Physiological signal comparison. Respiratory and cardiac signals recorded can be added in the window (figure.3 (a, b)). 5. Filtering function is based on Fast Fourier Transform (FFT). Frequencies spectrum can be cleaned to keep only cardiac and respiratory frequencies 6. Cut every cardiac cycle. The software can automatically analyze the parameters of the signals of the ROI to find the split point (red points in figure.3 (c, d)). After that, we can obtain every single CC and identify them whether they belong to the inspiration or expiration period. 7. Reconstruction. The software can reconstruct an average blood flow in each CC (figure.3 (e)) by using all CC or by using the CC only in inspiration or expiration periods (the red and blue line segment in figure.3). 8. Save the 3 reconstructed flow curves in each CC (average flow curve, inspiration flow curve, expiration flow curve), and export them to excel or txt file. Applications: An in vitro and in vivo example of our post processing is presented. In vitro experiment consists in the generation of pulsatile flow in the different tubes of the phantom. For in vivo experiment performed in one volunteer, flows in right internal carotid artery (RICA) and sagittal sinus (SS) were assessed. To obtain Nor-PC (figure.1(a1) & (b1)) and EPI-PC (figure.1 (a2) & (b2)) images, with a 32 channel coil at 3T. The parameters of sequences were summarized in figure 2. Flows in tube 2 and 3 of the phantom and also in RICA and SS Right were analyzed with the new software.

Figures 4a and 4bFigures

  Figures4a and 4bshow average, expiration and inspiration of EPI-PC flow curves plotted against Nor-PC flow. We found that inspiration increases flow and expiration decreases flow both in right internal carotid artery and sagittal sinus. In the volunteer, we found fewer details in the shape of EPI-PC flow curve compared to Nor-PC flow curve both in Right Internal Carotid Artery

Acknowledgements

No acknowledgement found.

submissions.mirasmart.com/ISMRM2019/ViewSubmission.aspx?sbmID=4991 https://submissions.mirasmart.


Pan Liu

Ariston Reis

Paulo J S Gonçalves
email: paulo.goncalves@ipcb.pt

A Tool Towards Eeg Semi-Autonomous Electrode Placement

Keywords: Medical Devices, Sensors, Visualization, Signal Processing

The paper proposes a novel medical device based on a 9 dof IMU to help health professionals performing more precisely the electrode placement task in EEG exams. The tool precisely tells the operator if the 10-20 electrode placement system is being correctly followed. The manual task is of major importance and time consuming, because all the electrodes must be correctly and very precisely placed in the head of the patient. The gold standard process is manual, and although several medical devices (developed to other types of medical procedures) can be applied to increase the precision of the electrode placement, they are still very expensive. The proposed medical device, based only on the sensors of a 9 dof IMU, and the processing capabilities of a microprocessor, diminish the price of the device. Moreover, the size of the apparatus is also diminished, when compared with infrared vision based systems. The developed system includes a visualization sub-system that visualises the position of the electrodes in a virtual head of the patient, using a specific tool, 3DSlicer, to receive and visualise the 3D pose of the medical device when point to the patient head. Preliminary results showed the validity of the proposed device.

Introduction

Obtaining the 3D pose of an object in real environments is complex, and requires sophisticated tools if high accuracy is required, especially in medical applications [START_REF] Gonçalves | A Vision System for Robotic Ultrasound Guided Orthopaedic Surgery[END_REF]. The problem becomes even bigger if the object is moving in the environment, or if it can change its position a few millimetres [START_REF] Torres | Robotic motion compensation for bone movement, using ultrasound images[END_REF]. Classic vision systems, even with the use of fiducial markers [START_REF] Gonçalves | A Vision System for Robotic Ultrasound Guided Orthopaedic Surgery[END_REF] have some drawbacks when tracking objects [START_REF] Torres | Robotic motion compensation for bone movement, using ultrasound images[END_REF]. Taking this into account the paper proposes to study a 9-dof Inertial Measuring Unit (IMU) to add further sensor data, and add value to the previous work framework and enhance the previous obtained results. As such the paper proposes, a medical device to output the 3D pose an object, where the device is moving in.

As depicted in figure 1, if the medical device proposed is attached to a pointer, that points to the head it outputs the 3D pose of all the points that are present, only based in the IMU data. As depicted, the application in this paper is EEG electrode placement.

The paper presents the state-of-the-art on manual electrode placement for EEG in section 2. Section 3, presents the developed medical, both the electronics components, sensors and microprocessor, along with the mechanical drawings. The signal processing needed to obtain the pose of the medical device, to define the electrode placement, is presented in Section 4. In section 5 are presented results with the tool developed along with the visualization results in 3DSlicer. Finally, some conclusions are drawn in section 6.

Electrodes position in EEG exam

The 10-20 system [START_REF] Sharbrough | American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature[END_REF] or International 10-20 system is an internationally recognized method to describe and apply the location of scalp electrodes in the context of an EEG test or experiment. This method was developed to ensure standardized reproducibility so that a subject's studies could be compared over time and subjects could be compared to each other. This system is based on the relationship between the location of an electrode and the underlying area of cerebral cortex. The "10" and "20" refer to the fact that the actual distances between adjacent electrodes are either 10% or 20% of the total front-back or right-left distance of the skull.

Each site has a letter to identify the lobe and a number to identify the hemisphere location. The letters F, T, C, P and O stand for frontal, temporal, central, parietal, and occipital lobes, respectively. (Note that there exists no central lobe; the "C" letter is used only for identification purposes.) Even numbers [START_REF] Torres | Robotic motion compensation for bone movement, using ultrasound images[END_REF][START_REF] Gebre-Egziabher | A low-cost GPS/inertial attitude heading reference system (AHRS) for general aviation applications[END_REF]6,8) refer to electrode positions on the right hemisphere, whereas odd numbers (1, 3, 5,7) refer to those on the left hemisphere. A "z" (zero) refers to an electrode placed on the midline. In addition to these combinations, the letter codes A, Pg and Fp identify the earlobes, nasopharyngeal and frontal polar sites respectively.

Two anatomical landmarks are used for the essential positioning of the EEG electrodes: first, the nasion which is the distinctly depressed area between the eyes, just above the bridge of the nose; second, the inion, which is the lowest point of the skull from the back of the head and is normally indicated by a prominent bump.

In the semi-autonomous procedure proposed in the paper the operator must identify the nasion and the inion, precisely. Additionally, points A1 and A2, in the ear of the patient are also fundamental to define the four lower points of the semi-sphere of the approximate skull of the patient. These four points are the first land marks from where the remain points will be obtained, using the 10-20 system.

Medical Device

The medical device is comprised by electronic components and mechanical components to encapsulate the prior. This section is divided in two sub-sections: first the electronic components are depicted, after the designed mechanical components are presented.

Electronic Components

The electronic components are depicted in figure 2: -a 9DOF Razor IMU that incorporates three sensors: an ITG-3200 (MEMS triple-axis gyro), an ADXL345 (triple-axis accelerometer), and an HMC5883L (tripleaxis magnetometer) (horizontal board); -an on-board ATmega328 (horizontal board); -a basic breakout board for the FTDI FT232RL USB to serial IC (vertical board, to interface with the signal processing unit defined in the next section); The on-board ATmega328 have the following capabilities that were programmed:

-to output the orientation angles of the device, according to the AHRS algorithm proposed in [START_REF] Gebre-Egziabher | A low-cost GPS/inertial attitude heading reference system (AHRS) for general aviation applications[END_REF];

-the acceleration raw values from the accelerometer; -the angular velocity raw values from gyroscope;

Design

In figures 3 and 4 are depicted the enclosures of the electronic components present in figure 2, along with the pointer to the points of the EEG landmarks in figure 1.

Signal Processing

In this section are presented the signal processing algorithms used to process the signal captured from the sensors. To a better understanding of the process, the output signals will be presented.

Figure 5, presents the raw data captured from the gyroscope and the accelerometer, i.e., the angular velocity of the device and its acceleration. It can be seen that the capture starts at second 4 and lasts 2 seconds. The device first moves, stops and moves again, as depicted in the acceleration and velocities graphs. All the presented signals are captured with a sample time of 0.004[seconds] and all filtered first with a high and after with a low pass Butterworth filter, with DC gain G 0 =1 and n=2, with appropriate cut-off frequencies. If the acceleration is below a given threshold the medical device is not moving, i.e., is stationary. The rotation of the medical device, is obtained according with the AHRS algorithm proposed in [START_REF] Gebre-Egziabher | A low-cost GPS/inertial attitude heading reference system (AHRS) for general aviation applications[END_REF]. The outputs quaternions are used directly to obtain the Euler angles of the pose of the device. The next step of the processing part is to obtain the linear velocity from the linear acceleration measured, depicted in figure 6. This is performed by first order numerical integration of the acceleration measured, starting from a stop position. The result, depicted in figure 6, is calculated taking into account a numerical compensation for the velocity drift due to the numerical calculus. When the sensor is not stationary the velocity drift is incremented to compensate the numerical error. When the medical device is steady the velocity drift is set to zero and the process starts again. Finally, to obtain the pose of the device a final integration is performed, using the same technique from the prious integration, starting from a stop position.

The results is depicted in figure 8, where is presented the X, Y, Z path of the medical device coordinates.

The Romanian Review Precision Mechanics, Optics & Mechatronics, 2016, Issue 49 150

Results and Discussion

In this section are presented the final results of the application of the medical device to obtain the 3D pose and to follow a path in a real world scenario.

In figure 9 is presented a Real Scenario for a 3D path capture along a Bone Phantom. It is depicted the sensor and the processor along with the cable to connect to the signal processing unit, i.e., a PC running Matlab. The final result, depicted in figure 10, of the experiment shows the 3D path captured along the bone, i.e., at each capture point a reference frame is depicted, clearly showing the rotation angles differences between captures. It is also verified that the trajectory is smooth. At the initial point of the path , left side of figure 10, a large concentration of frames are observed, due to noise.

Visualization

For Visualization of the results obtained from the signal processing unit, running in Matlab, 3D Slicer [5] was used to receive data and to visualize it in the head of an human. 3D Slicer (Slicer) is a free and open source software package for image analysis and scientific visualization.

In figure 11 is depicted the visualization of the head of a human with the pointer receiving input directly from matlab from the MatlabOpenIGTLinkInterface of 3D Slicer.

Conclusions

The paper proposed a medical mevice capable to deliver the 3D pose of a pointer. The medical device helps health professionals performing more precisely the electrode placement task in EEG exams. The tool precisely tells the operator if the 10-20 electrode placement system is being correctly followed.

The proposed medical device, based only on the sensors of a 9 dof IMU, and the processing capabilities of a microprocessor, diminish the price of state-of-theart devices. Moreover, the size of the apparatus is also diminished, when compared with infrared vision based systems.

The developed system includes a visualization subsystem that visualises the position of the electrodes in a virtual head of the patient, using a specific tool, 3DSlicer, to receive and visualise the 3D pose of the medical device when point to the patient head. Preliminary results showed the validity of the proposed device to track trajectories in 3D space.

Figure 1 :

 1 Figure 1: Electrodes Position in the Scalp[START_REF] Sharbrough | American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature[END_REF]

Figure 2 :

 2 Figure 2: Electronic components of the medical device

Figure 3 :

 3 Figure 3: Medical Device Pointer with enclosure.

Figure 4 :

 4 Figure 4: Medical Device Pointer without enclosure.

Figure 5 :

 5 Figure 5: Raw Sensor Data from the Sensor. Angular Velocity and Acceleration.

  Figure 6: Linear Acceleration

  Figure 7: Linear Velocity

Figure 8 :

 8 Figure 8: Position of the Medical Device.

Figure 9 :

 9 Figure 9: Real Scenario for Capture in Bone Phantom

The Romanian Review Precision Mechanics, Optics & Mechatronics, 2016, Issue 49

Acknowledgements

This work was partly supported by Instituto Politécnico de Castelo Branco and by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2013.

The Romanian Review Precision Mechanics, Optics & Mechatronics, 2016, Issue 49 151