James A Colley

Benjamin Bedwell

Andy Crabtree

Tom Rodden

Exploring Reactions to Widespread Energy Monitoring

Keywords: Distributed energy monitoring, measurement, apportionment, representation, technology probe

This paper explores the measurement, apportionment and representation of widespread energy monitoring. We explicate the accountability to users of the data collected by this type of monitoring when it is presented to them as a single daylong picture. We developed a technology probe that combines energy measurement from the home, workplace and the journeys that connect these spaces. Through deployment of this probe with five users for one month we find that measurement need not be seamless for it to be accountable; that apportionment is key to making consumption for communal spaces accountable and that people can readily make useful inferences about their energy consumption from daylong pictures formed from widespread monitoring. Finally, we present four issues raised by the probe -the nature of real world monitoring, the dynamic and social nature of apportionment, disclosure of energy data and alignment of incentives with consumption -that need to be addressed in future research.

Introduction

Energy security and sustainability are established as global societal challenges and have prompted a variety of responses and initiatives. Technical and operational responses have turned to digital technologies, to capture the consumption of energy and to make this available to stakeholders to optimise energy use. The ability of the technology that captures and makes available information on energy use has been a major focus for researchers in HCI and ubiquitous computing. Researchers have sought to promote awareness of energy consumption as a way of encouraging changes in peoples' behaviour [START_REF] Disalvo | Mapping the landscape of sustainable HCI[END_REF]. A critical feature of much of this work has been the development and deployment of devices and interfaces that surface energy consumption in order to make consumption accountable to consumers and thus motivate them to reduce ener-gy use. Examples include technologies for the home [START_REF] Gustafsson | The Power-Aware Cord: Energy Awareness through Ambient Information Display[END_REF][START_REF] Gyllenswärd | Visualizing Energy Consumption of Radiators[END_REF], transport [START_REF] Froehlich | Investigating a Mobile Tool for Tracking and Supporting Green Transportation Habits[END_REF] and the workplace [START_REF] Schwartz | Sustainable energy practices at work[END_REF].

The framing of the energy challenge within HCI in terms of surfacing consumption in particular settings has not passed without criticism. Pierce and Paulos [START_REF] Pierce | Beyond energy monitors: interaction, energy, and emerging energy systems[END_REF], for example, have called for a shift beyond residential energy feedback systems, highlighting a disconnect between current HCI energy research with its emphasis on in home display; and energy research that adopts a much broader perspective. Others have questioned the emphasis on measurement and persuasion as a narrowing of the concerns involved in sustainability [START_REF] Brynjarsdottir | Sustainably unpersuaded: how persuasion narrows our vision of sustainability[END_REF]. However, considerable efforts persist in making energy measurement even more pervasive and fine-grained. For example, research is emerging within ubiquitous computing that focuses on capturing a rich picture of energy use across a broad range of settings [e.g, [START_REF] Froehlich | Investigating a Mobile Tool for Tracking and Supporting Green Transportation Habits[END_REF][START_REF] Patel | At the flick of a switch: detecting and classifying unique electrical events on the residential power line[END_REF]. As these technologies for energy data capture mature, a number of researchers have offered the vision of a personal energy monitor that captures peoples' energy footprint throughout the day [START_REF] Hay | A global personal energy meter[END_REF].

We are interested in understanding how people may react to a future world where information about their energy use throughout the day, across the multiple spaces they operate in, is captured and surfaced. What does it mean to surface energy consumption and make it accountable across and between the different places that people inhabit and how will people react to this? By way of an initial answer, this paper presents the development and study of a technology probe focusing on the measurement and surfacing of energy consumption across and between multiple spaces.

The probe combines a mobile device carried by users that works in tandem with energy monitoring systems in different spaces. The probe monitors consumption in users' homes and workspaces, and records the time spent in these places along with journeys made by users. Three key research issues drove the design of the probe:

1. Measurement. How widespread and how fine-grained does measurement need to be to surface energy consumption across and between multiple spaces? 2. Apportionment. How should measured energy consumption be apportioned across and between multiple spaces? 3. Representation. Will representations of apportionment make energy consumption across and between spaces accountable to consumers, and in what ways?

The probe was deployed in the wild with five participants over a period of one month. At the end of the month users were presented with a web-based representation of their data displaying consumption across their home and work lives as well as the journeys that are taken. The data was used to drive semi-structured interviews with each participant in order to understand the representation and its potential affordance to human reasoning about energy consumption.

The results from our probe suggest that while it is not possible to measure all of a person's daily consumption, partial measurement alongside user inference is sufficient to make the consumption accountable to users. However, that ability to make energy information available across different contexts raises a number of significant tensions that may cause widespread measurement and individual accountability to act as a significant source of demotivation for some. This is particularly true when we consider the differences between the roles of a user in domestic and workplace energy consumption.

EXISTING APPROACHES TOWARDS SURFACING CONSUMPTION

Recent research in HCI has focused on surfacing energy consumption to encourage consumers to change their behaviour. The basic premise is that surfacing energy use makes consumption visible and accountable to consumers and, in turn, enables them to take appropriate action. To date, there are two key approaches towards surfacing consumption: space-centric approaches and person-centric approaches.

Surfacing Energy Consumption within Spaces

A common approach has been to surface energy consumption within a space. This often relies on measuring the consumption of appliances, and results in the creation of artefacts or persuasive displays that surface appliance-based energy consumption data and imply accountability. Thus far, these feedback systems have focused mainly on domestic spaces. Examples include the power aware cord [START_REF] Gustafsson | The Power-Aware Cord: Energy Awareness through Ambient Information Display[END_REF], the power socket [START_REF] Heller | PowerSocket: towards on-outlet power consumption visualization[END_REF], the watt bot [START_REF] Jönsson | Watt-Lite: energy statistics made tangible[END_REF], light bulb radiators [START_REF] Gyllenswärd | Visualizing Energy Consumption of Radiators[END_REF] and the minimalist in-home energy consumption display [START_REF] Yun | Investigating the impact of a minimalist in-home energy consumption display[END_REF]. While novelty effects have been seen, significant long-term changes in behaviour have not yet been demonstrated by these HCI-led initiatives. However, long term studies of standard in home displays (IHDs) have demonstrated an impact. For example, in a review of 36 studies of feedback on behaviour between 1995 and 2010, Ehrhardt-Martinez et al. found an average of 9.2% reduction in electricity usage as a result of the use of an IHD [START_REF] Ehrhardt-Martinez | Advanced metering initiatives and residential feedback programs: A meta-review for household electricity-saving opportunities[END_REF].

Work within ubiquitous computing has focused on low-cost sensing technologies to capture more detailed information about domestic consumption. Patel et al. [START_REF] Patel | At the flick of a switch: detecting and classifying unique electrical events on the residential power line[END_REF] have developed a system that makes use of electrical noise signatures on residential power circuits to detect the switching of particular appliances. This demonstrates how very specific high-resolution sensing can allow micro-level events in the home to be surfaced. Abrahamse et al. [START_REF] Abrahamse | A review of intervention studies aimed at household energy conservation[END_REF] reviewed thirty-eight studies around technologies to capture and present energy consumption. It is notable that none surfaced consumption from more than one space and that the predominant focus was on surfacing energy use in the home. An additional review of studies of 'eco-feedback' by Froehlich et al. [START_REF] Froehlich | Investigating a Mobile Tool for Tracking and Supporting Green Transportation Habits[END_REF] echoes the concern that studies of such technologies seldom consider the connected nature of consumption in everyday life and that people are therefore made accountable for consumption in their homes in isolation from other spaces. As the majority of consumption still occurs outside the home1 , we are interested in exploring how measurement and feedback technologies might include, but also go beyond domestic infrastructures to consider energy use across multiple spaces.

2.2

Surfacing personal energy consumption

Efforts to surface consumption within a space have been complemented by other efforts to surface personal consumption. The UbiGreen application [START_REF] Froehlich | Investigating a Mobile Tool for Tracking and Supporting Green Transportation Habits[END_REF] makes use of mobile phones connected to Mobile Sensing Platform devices to semi-automatically infer consumers' transport modes, exploiting various visualisations to then make carbon consumption accountable to the user to promote greener travel choices. Related work by Mun et al. makes use of map matching and hidden Markov models to build a "Personal Environmental Impact Report", which visualizes the carbon intensity of trips. This work is technically impressive in the way that it combines multiple data sources however there is no data on its users' reactions [START_REF] Mun | the personal environmental impact report, as a platform for participatory sensing systems research[END_REF]. Surfacing personal energy use has also become a key feature in government initiatives. These have promoted concepts such as the carbon footprint and Personal Carbon Trading Schemes [e.g., 9]. The underpinning idea is that individuals are made accountable for their carbon footprint by having to deduct their personal portion of consumption away from an allocated quota based on an overall national cap.

An alternative person-oriented approach seeks to understand the human practices that shape energy consumption and sustainable behaviour [START_REF] Chetty | Getting to Green: Understanding Resource Consumption in the Home[END_REF]. This has highlighted the broad character of personal energy consumption.

"Traditionally, we have taken a within household approach to infrastructure, emphasizing systems that will make our homes smart. But households are connected to a variety of infrastructures beyond the home … householders desire insight into … resource usage and more visible real-time information on within the home resource consumption. They also desire more information on between-homes consumption" (ibid.)

Chetty and Grinter's study draws attention to the distributed nature of energy consumption and the concomitant implication that personal energy consumption needs to bridge the home and other spaces that people inhabit if it is to be of broad utility. This view is supported by research that has explored the development of the 'personal energy meter' [START_REF] Hay | A global personal energy meter[END_REF]. This trades on a vision of a widespread energy-monitoring infrastructure to provide people with a personal account of energy use across their lives.

3

The Technology Probe

Existing approaches to surfacing energy consumption and making it accountable to users suggest the need to move beyond isolated contexts to surface energy use across multiple spaces. While there is a broad turn towards capturing detailed information about energy consumption in more locations through the widespread introduction of smart meters, the distributed character of energy consumption raises a host of challenges. How might we surface consumption across the multiple spaces we inhabit? How might we make consumption at work accountable to users? How might we understand energy use in public spaces that are seldom monitored?

Capturing information is one thing, making it into a useful and usable resource for action another. Making use of existing monitoring infrastructures, the aim of this paper is to contribute to an increased understanding of how people might respond when energy consumption from multiple spaces is surfaced and presented to them. We address the challenge through the development and deployment of a technology probe. Technology probes are an extension of the cultural probe methodology [START_REF] Gaver | Projected Realities Conceptual Design for Cultural Effect[END_REF], which seeks to "provoke inspirational responses" from potential users as a resource for design. Technology probes on the other hand are "thought-provoking technologies" -provoking in the sense that they enable people to reason about how future visions "do or don't fit into their lives" [START_REF] Hutchinson | Technology Probes: Inspiring Design for and with Families[END_REF]. They also have the added benefit that they enable data logging. While involving design and implementation, technology probes are not intended to be prototype solutions amenable to usability testing.

"They are not changed during the use period based on user feedback. In fact, a deliberate lack of certain functionality might be chosen in an effort to provoke the users" (ibid.)

Our interest is not in whether the technical intervention is usable, but rather in the principles underpinning future widespread monitoring technologies. The probe seeks to elicit views on capture and attribution and how we might design choices of energy apportionment. This is reflected in a number of explicit design choices. Most notably we have chosen a simplistic means of apportionment to provoke users to comment on their own personal accountability for energy use. We do not seek to record and represent the fact that different inhabitants will consume energy in a space in different ways. Likewise, we do not consider that individuals may stake claim to energy consumed in spaces that they do not inhabit (e.g., a dishwasher timed to commence while its owner is at work). We are interested in the strategies of apportionment that make sense to users and how these design choices might be reasoned about. Thus, rather than build a variety of apportionment models into the probe, we used a deliberately crude but consistent approach as starting point, which we could then open up to discussion during interview.

The probe itself combines three key elements designed through a series of iterative refinements: multi-site energy measurement, energy apportionment, and representation of personal energy consumption.

We describe each of these in turn below, before moving on to consider the results of deployment and users' reactions to the probe.

Measurement

Monitoring centred on a cloud-based logging service that received data from three distinct types of data source: home energy consumption collected via a deployed meter; workplace energy consumption collected by interfacing to a building management system, and the reported points of journeys taken, collected using a smartphone.

Monitoring the home

The home monitoring kit consisting of a current transformer monitoring clamp and a plug computer was used to record the rate of consumption in participant's homes. Data was sent to a secure cloud-based server every 6 seconds. On the server side, power readings were aggregated once every hour down to a resolution of 1 mean reading for every 5 minutes to reduce the computational expense of querying the data.

Monitoring the workspace

Energy consumption in the various workplace buildings in our study was logged by an estate management department using an existing building management system (BMS). Automated BMS export functions were used to deliver data to the probe's cloud-based logger. The BMS monitors are coarse at a temporal level, as they are designed to generate data for high-level monthly reports, providing readings per building every 30 minutes. Furthermore, each building's readings vary in resolution: for some, lighting and socket power readings per floor are recorded and for others only the consumption of the whole building is recorded. Newer buildings tend to have a higher resolution of monitoring compared to older ones, where retrofitting more monitors is deemed not cost-effective.

Monitoring the journey

Location information is collected through a dedicated mobile phone application, which reports the users' location based on Wi-Fi positioning via Google's location service on the Android platform. Wi-Fi was chosen both because it provides a level of accuracy suitable to determine which building a user currently inhabits (between 6 and 20 meters on average) and because it works inside and outside of buildings, while not draining the battery significantly. The service typically provides location updates (consisting of latitude-longitude coordinates) every 30 seconds. The location is sent securely along with a unique identifier for the user to the cloud-based logger.

Apportionment

The apportionment process collates the data relevant to a particular individual for a particular day based on a set of monitored spaces. Monitored spaces are defined using 'geo-fencing', i.e. the definition and storing of polygons that represent the boundaries of workplace buildings and homes inside which energy consumption is being monitored. These polygons are cross-referenced with users' locations as they are reported by the logging application on their mobile phones to determine whether an individual is present in any of the monitored areas. If entry into an area is detected (i.e., the user's mobile device automatically 'checks-in') this event is logged; no further latitudelongitude coordinates are logged until the user's mobile leaves ('checks-out' of) the area.

This record of automated check-ins and check-outs provides a simple means of apportioning energy consumption data across multiple spaces. It is based on the crude assumption that every individual inhabitant of a monitored space is responsible for an equal proportion of the energy used in that space while they are present. As all inhabitants of a space were not monitored, individuals were made accountable for personal energy consumption equal to the total energy consumption of the space divided by the maximum occupancy of that space. This is obviously a crude presumption -it will allow high consumers to "free-ride" -and as such we would not necessarily propose it for a fully deployed system.

Discovering an appropriate model of apportionment has already gathered some significant research interest. For example, Hay et al. have investigated different mod-els of apportionment for the workplace using access logs and energy monitoring to simulate policies for example individuals [START_REF] Hay | The case for apportionment[END_REF]. Our research interest focuses on the issues of intelligibility and accountability of apportionment. As such, a crude model that is simple to understand but potentially unfair allows us to takes a provocative stance to encourage reaction from users.

Representation

The way that data was represented to our participants was key in provoking them into thinking about the issues implicated in widespread energy monitoring, particularly the accountability of energy consumption when measured and apportioned across multiple spaces. We represented the data gathered during the deployment of the probe via a web-based 'personal energy dashboard', which was made available to participants during the interviews to surface and replay their energy usage and encourage them to reflect on their consumption patterns. It also demonstrates the apportionment done by the system so as to allow users to reflect upon and challenge it. 3. Two proportional pie charts (Figure 1, Section 3). One shows the proportion of consumption for the time spent in monitored space (Section 3, top). The other shows the segments of time spent in monitored buildings; this also includes a segment for time spent in unmonitored space (Section 3, bottom). This feature is intended to provoke views around apportionment and consumption across multiple infrastructures. 4. A date selector (Section 4, Figure 1), which allows the user to revisit previous days by navigating through the log on a day-by-day basis.

The Personal Energy Dashboard

The display is intended as a focal point for our semi-structured interviews rather than as an interface to promote energy reduction. The aim is thus reflection on the underlying approach and systems rather than actionable energy reduction suggestions. The personal energy dashboard allows users to both reflect upon the time they spend within different monitored and unmonitored spaces throughout their day, and to view the consumption data that pertains to them within those spaces.

THE PROBE STUDY

The probe deployment involved five participants recruited via email within one workplace that was chosen because researchers could readily access data from the workplace's BMS. We installed monitoring equipment in each participant's home and provided a mobile phone that reported his or her location to our system. The participants included:

• George, a 27 year-old professional who is recently married. He often spends weeks living alone as his wife travels internationally on business. He works in an office from 9 to 5 most days and occasionally from home. • Alice, a 31 year-old, married, middle-class office worker. She shares use of a car with her husband, but enjoys cycling to work when her husband needs the car. Alice works long hours in the office, but her work can occasionally spill into her home life. • Cecile, a 34 year-old single woman from Europe. She generally works a 9-to-5 day in the office and has an active but distinctly habitual social life. She commutes by bicycle and is proud to consider herself both energy conscious and a low consumer of energy. • Fred, a 32 year-old recently married professional who likes to keep his home and work life separate. He made the choice to live on the outskirts of the city where he works. He often drives to and from work but occasionally cycles when the weather is agreeable. • Bill, 26 years old and currently in the final stages of his formal education. He commutes 150 miles (twice a week) by either car or train between his place of study and his girlfriend's hometown, where he spends the weekend and works a part time job for 1 day a week. 2The probe deployment lasted one month, during which time a rich set of data about participants' energy consumption and their location was logged. Data from the probe was presented to users through the personal energy dashboard, allowing them to browse through their daily energy consumption over the period of the deployment. Semi-structured interviews were used to understand the accountable character of monitoring and apportionment across and between multiple spaces or, to put it another way, to explore how amenable this kind of information is to human reasoning.

RESULTS

Energy data was collected from all 5 participants' homes and 4 separate workplace buildings used by participants. Cumulatively, ~183000 data points for energy consumption were recorded across all monitored spaces, and ~4500 location reports outside of monitored space were recorded. This level of measurement allowed us to create energy dashboards that represented a month's worth of data for each participant.

The first and most obvious thing we noticed is that our participants spent a considerable proportion of their time in unmonitored space (11%). This included commutes to and from work, time out socializing, visits to the country and holidays. Their remaining time was split between personal space (62%) and the work place (27%). There were obviously individual differences in this data reflecting different lifestyles. For example, Fred who lives furthest from his place of work had a larger proportion of time in unmonitored space, reflecting his daily commute. Similarly, Cecile had a large proportion of unmonitored space reflecting her regular attendance at social events. There were also significant differences between the working week and weekends, where our participants often spent 50% of their time in unmonitored spaces.

While our participants spent the majority of their time in personal space, most energy consumption actually took place in communal spaces. Over 63% was consumed in communal spaces where our individuals had limited control of their consumption, with less than 37% being consumed in personal space where they had direct control of consumption (see Figure 2). The collected data provoked responses from our participants to the measurement, apportionment and representation of energy data and these three themes provided the focus for our interviews.

Measurement

The probe was designed to explore how widespread and how fine-grained measurement needs to be to surface energy consumption across and between multiple spaces. Deployment of the probe suggests that collecting sensor data in the wild, particularly from multiple sources, results in 'seams' [START_REF] Chalmers | Seamful Design and Ubicomp Infrastructure[END_REF] appearing within the data. These are a result of the environment (e.g., when users move outside of and between monitored spaces) and unexpected events (e.g., when users let their phones run out of battery or leave their phone at work overnight). When this seamful data was presented to participants through the dashboard interface they responded with discussions around what the seams meant to them and how they affected the legibility of the data.

Alice: I can see there's no energy data here, but I can see where I was travelling to and from … I did lots of travelling this day but by different means. I took the train to Liverpool then drove back. I can see that from the routes.

Rather than being seen and treated as unfathomable gaps in the data, participants were instead able to see their behaviour in the seams and infer usage. Discussions around the location trails that were recorded outside of monitored space revealed that participants were particularly good at recalling their travel habits and filling in the gaps when they existed. Sometimes this was done at a level of detail that would be difficult to achieve by sensing and measurement alone: Participants demonstrated an understanding around issues of connectivity, how this impacted their data, and accepted that this is part of life.

George: It didn't really get when I was out in the countryside, but there's no consumption there anyway. It's all fields and cows.

We designed the probe to allow participants to collect data from each other's houses when they visited them. Figure 1, Section 3 shows the spaces in which consumption occurred; when participants visited each other's houses they were able to see the visited house's consumption as part of their own chart. Our intent here was to surface and explore the issues of leakage that might possibly occur with monitoring of this form and to raise issues of privacy with the users. On measurement, we conclude that it is meaningful for participants to have consumption in the home and work place measured, a load reading every hour taken from these spaces allowed them to compare and contrast the varying consumption between the spaces that they passed through and reason about their accountability for consumption in these spaces. We also note that gaps in data are acceptable as long as seams are meaningful enough to be repaired by user inference.

Apportionment

The probe was also designed to enable us to address the key question of apportionment and how it should be calculated across and between multiple spaces. Although we adopted a crude means of calculation, participants nevertheless expressed concerns over the level of control available over consumption in the home versus the workplace.

Alice: I can't turn off my computer at work, because I need it to work. I can't turn the heating down even though its too warm here, at home I can just turn the heating off! Participants were able to read from the dashboard that the energy monitored for their time at work far outweighed the energy consumption for their time at home.

Bill: The work consumption makes my home look like a piss in the ocean.

However, people also elaborate rationales for these high loads in communal buildings, examples of which include recognizing that the energy consumption was necessary for health or a sense of shared responsibility for the provision of services to multiple users of buildings.

Fred: I think I do feel some responsibility for all the servers on at work all the time but the servers at work mostly serve my building so they're related to our work.

When discussing apportionment of energy with participants, a desire for a more detailed breakdown of energy consumption in communal spaces was sought to assist in their reasoning about the high levels of consumption.

George: I get that the consumption is high here, I mean the building is huge so it must cost a lot to run, but I'd feel more comfortable with this if I knew how much was used for heating, lighting, and other stuff. I can work that out in my home from my bill but I've got no idea here.

People were also sensitive to how the apportionment might affect the way they are viewed in comparison to others, particularly when a partial representation of carbon consumption was exposed.

Fred: I've got so many friends who think because they recycle paper at home they're being green but then they drive 50 miles a day and they're probably in the top 5% of consumers in the world of energy.

On apportionment, we conclude that the way in which energy is apportioned to consumers, particularly when measured from a communal space is key in attempting to make it accountable. Our simplistic method of apportionment was sufficient for participants to understand their effect on widespread energy monitoring and afforded them sufficient reasoning to be able to make choices about how and where they work.

5.3

Representation

Our third key research question concerns the adequacy and appropriateness of representations of apportionment across and between multiple spaces -do they suffice to make the distributed character of energy consumption accountable to consumers, and in what ways? Our elaboration of the previous issues concerning the measurement and apportionment of widespread energy measurement for accountability already provide some insight into this; here we go into more detail.

Given that some of the recruits were friends and were likely to visit one another's homes, coupled with the sensitizing effect of coverage of location-based services in the media, it was anticipated that discussions surrounding data ownership and privacy would arise from use of the application. While not frequent, there were cases during the study where users visited one another, and in the interviews participants spoke about whom they would like to share data with and whom they would not:

Cecile: I really don't like the idea of people I don't know being able to see my energy consumption. It's fair if I've invited someone to my house for them to see and share part of my energy consumption but I don't want random robbers seeing my consumption.

The issue here seemed to focus on control of disclosure and an associated view that friends had access to energy consumption as part of visiting the home.

Reflecting general concerns about the security of the Internet, participants raised issues around the sensitive nature of the data collected by the system. The concerns related to what might happen if people were to access the collected energy data and use it for ill gain. Sharing of location was viewed as less concerning than the sharing of data from the home. For the majority of participants in the study, there was an acceptance of location sharing as being part of modern every day life, with understanding around mobile applications that make use of locational data.

Alice: I can tell you what I share my location with at the moment, [points to apps on phone]

Movies, Facebook, Latitude, Foursquare, Voucher Cloud.

To this we would add that the data also reflected routine patterns of consumption and activity. The dashboard enabled participants to reason about routines and how these impacted their consumption across different spaces. Unsurprisingly, a regular pattern of consumption and space occupation was common in the data. People could readily see their activities in the data and provide accounts and rationales for the routines. Consider, for example, a week's of George's time charts that he explored in the interview using the lower chart in section 3 of the dashboard (Figure 1). In this week George was at work each weekday (yellow), he also had some time in unmonitored space (red). On two days the time in unmonitored space increased as he was engaged in social activities. This was revealed during his interview:

George: Ah, I left work early on that day, Monday. I was driving the wife to her piano lesson, the traffic was crap, was on the road for ages. I can see that 'cause that bit's bigger [points to unmonitored space section of chart].

On Saturday, George spent no time in communal or unmonitored space as a result of him staying at home to attend to household chores. George also paid particular attention to section 2 of the dashboard for weekend days. He noted that for the Saturday he spent at home the amount of energy consumed was higher than normal and he was able to attribute his actions to this high consumption George: Ha, that'll be me doing all the washing and drying then. Plenty of work done there.

The representations used made it possible for participants to identify their routines and associated consumption across multiple spaces they could easily spot significant changes to those routines and how this altered their consumption profiles.

When making the workplace consumption visible to participants through the dashboard, the accountability of that consumption to them as workers within the space was drawn into question.

Bill: I can't be held accountable for the consumption here, even if it is high 'cause I'm only the doer of the work not the one ordering it to be done, or the one who controls how these buildings are run. I mean if it were up to me I'd just work at home and then none of it could be blamed on me Here, Bill demonstrates how he feels that he should not be held to account for consumption in the workplace, as it is someone else's job to manage it.

The dashboard thus provided an interesting means of showing the impact of their everyday activities on their daily routines and the possibilities offered by widespread energy monitoring. Our results show that presenting consumption information for different buildings occupied by a user over a day affords strong accountability for the consumption that takes place inside these buildings, provided that some apportionment of communal consumption is applied.

As access to energy data grows, new opportunities for systems that surface consumption for people across multiple spaces arise. However, the reactions to our probe from individuals who were subject to the data that this kind of monitoring can provide suggest that there are a number of issues that the community must consider.

6.1

The nature of real world monitoring

Our probe highlighted the messy nature of real world data collection. Ubiquitous computing often looks to the future outlining new services or presuming the existence of future capabilities. For example, the vision of a global personal energy meter described by Hay et al. [START_REF] Hay | A global personal energy meter[END_REF] depicts fine grain modelling and the ability to show how much energy has been used as a result of our daily activities at the level of the appliances used and activities conducted.

Our probe sought to explore the extent to which this vision may currently be realised in the real world. Our probe does not offer fine-grained measurement. The deployed infrastructure did not exploit appliance-level monitoring in the home or finegrained monitoring in communal spaces. Rather we sought to exploit systems in widespread use or in the process of widespread deployment. Working with existing sensing systems is a key feature of real world monitoring and is likely to be a perennial feature for any energy monitoring system given the nature of the built environment where legacy plays a critical role.

Energy consumption data is often monitored for reasons other than apportionment to individual people (e.g. to support the maintenance of buildings). This means that unambiguous attribution of measurement is likely to remain technically very demanding. Gaps and seams in data were ever-present in this data collection of the probe and we would suggest this a perennial feature of this class of system.

Rather than seeing these gaps as problematic we would suggest an approach drawing upon 'seamful design' [START_REF] Chalmers | Seamful Design and Ubicomp Infrastructure[END_REF] to exploit unmeasured space and limited accuracy. Letting people see the gaps in data allows them to make inferences about their activities, provoking thoughtful interactions with energy. In fact, the ease with which participants could infer or recall the activities that generated their data suggests that there may not be much need to process the data further to reveal patterns: self-reporting may instead be a useful (and enjoyable) means of a user improving the accuracy of their own data.

6.2

The dynamic and social nature of apportionment

The probe made use of a very simple apportionment model to elicit reactions from people to the notion of apportionment for energy, particularly in communal spaces. Building management systems in communal buildings often represent consumption on a much greater scale and with less granularity than the home. This can make it difficult for individuals to relate to data from communal spaces. However, this is not the only reason for the lack of a personal view of accountability. The complex nature of our interactions with energy throughout our lives means that the principles of apportionment are highly contextualized in practice. Consequently, negotiation regarding the cause of energy use is integral to understanding energy consumption and attributing responsibility. In the home, apportionment is negotiated amongst a small, intimate social network. In larger communal spaces this network of accountability can be very broad, including people and practices unfamiliar to the individual, dissuading the individual from taking responsibility.

Ubiquitous computing systems that seek to encourage some level of personal accountability within communal spaces should consider how to leverage the broader social networks in communal spaces. Who is responsible for consumption and how much of the consumption for a space they are responsible for will change constantly depending upon the members of the space and roles that they take. This is not a simple issue. We suggest that the development of this kind of system should consider the need for a human interface to the apportionment models applied, allowing all actors in a system to have influence over the way in which they are made accountable for consumption.

Disclosing energy data

Participants' responses to the probe highlight the sensitive nature of energy data and awareness that energy use could tell others a great deal about an individual's everyday activity and routine. The idea of sharing energy data with other people for various reasons was accepted by our participants: the participants appeared to have an understanding that they were often donating personal data as they used their phones, computers, and other technologies, but that they tended to receive some kind of reward for doing so (improved services, financial offers, etc.). Participants' reactions to the probe indicated that it was not the inference that someone might draw from their data that was of concern, but that there appeared to be no clear mechanisms to prevent others from accessing their data without permission.

Participants drew on a range of analogies such as inviting people into the privacy of their home to suggest suitable mechanisms that they were familiar with. This highlighted how the nature of relationship greatly affects the control a user might expect over the disclosure of their data. For example, when considering disclosing energy data to a commercial partner, participants spoke of formal contracts, laying out specific terms and returns. When considering disclosure to friends and family, participants related to practices of visiting suggesting with respect to the data from their home that if they were happy to allow someone through their front door then they were happy to share their energy data with them.

This leads us to suggest that personal energy monitoring systems should provide mechanisms for controlling disclosure of personal energy data and that these mechanisms should be modelled on existing metaphors for, or be integrated with existing practices of, sharing and disclosure. These mechanisms should allow for renegotiation as and when necessary, and explicitly involve a discussion of the value returned to the individual in disclosing the data.

6.4

The alignment of incentives with consumption People are currently exposed to multiple incentives to reduce energy consumption. In the workplace, the motivation is often provided in terms of carbon reduction as part of initiatives that aim to improve the public image of a company or organization and reduce running costs. In the home people are exposed to raw data in the form of meters and tariffs, as this is a space in which people directly incur cost for their consumption. This direct relationship suggests the use of tariffs and individual monetary reward as an incentive for change.

Our probe also surfaced energy consumption from different spaces that users inhabit as they go about their everyday lives. This invited cross-comparison between these spaces and promoted user reflection on the amount of energy used in each context. Understanding and managing the transitions between these spaces and the possibility of linking and comparing energy use across them will become a critical issue to consider as energy monitoring becomes increasingly widespread.

Offering individuals greater access to their energy consumption opens up the possibility of foregrounding the different incentives and drivers towards energy reduction. In doing so, this raises issues of the extent to which these are complementary or in conflict with each other. There is an as yet unexploited opportunity to transfer good practice through common mechanisms for surfacing energy use and unifying incentives, making people accountable for their consumption across all aspects of their lives in a consistent manner, and rewarding them for managing their throughout the day.

Conclusion

We have presented the deployment of a technology probe used to elicit user reactions to the measurement, apportionment and representation of energy consumption across multiple spaces. Our probe combined energy monitoring systems from the home and the workplace allowing us to study reactions by users towards the measurement and apportion of energy use. This has involved exploiting representations of personal energy consumption to investigate accountability of consumption measured across the home and the workspace.

A striking feature of our probe was the extent to which the measurement of energy consumption intertwines with a broad range of other social, political and economic drivers. The measurement and display of energy consumption surface these in terms of issues around apportionment and accountability. The future design of any widespread energy monitoring system will, by the very nature of the energy data collected, be embedded within a range of concerns about the nature of sustainability [START_REF] Disalvo | Mapping the landscape of sustainable HCI[END_REF].

Our probe suggests that these issues will play out in the ways in which the technology seeks to attribute energy use and the ways in which the presentation of consumption provides information about the different contexts of energy use. Most striking for us was the ways in which energy consumption at work dominated total energy use but was not matched by the ability for people to exercise control over this use.

The probe highlights the importance of understanding and aligning with the broad social, political and economic drivers at play. Measurement and presentation of energy use is unlikely to change users' behaviour without some form of initiative to encourage and reward change. It is important that we understand the initiatives and that our measurement aligns with them rather than undermines them.

Our final observation is the way in which any ubiquitous computing system designed to measure energy embodies a sense of accountability. The challenge here is the way in which accountability and attribution of use are encoded in the system. Energy apportionment and accountability is a dynamically negotiated process. For example, the apportionment of energy use from a utility can often be a source of major discussion and debate involving consideration, compromise and trading. People need to understand the rationale for apportionment at play and may often wish to negotiate the principles by which this apportionment is carried out.

Fig 1 .

 1 Fig 1. The personal energy dashboard

Fig 2 .

 2 Fig 2. Proportion of time (a) and energy consumed (b) in different spaces

Fred: I took

  the train from there to there, then I cycled down [road x].Bill: That day I had to go a different way because of road works, it took me longer.

Fig 3 .

 3 Fig 3. A week's time apportionment charts for George

e.g., 71% of energy is consumed outside of the home in the UK[START_REF]UK Climate Change Sustainable Development Indicator: 2010 Greenhouse Gas Emissions, Provisional Figures[END_REF].

These are, of course, not the participants' real names.


Mohit Jain
email: mohitjain@in.ibm.com

Khushboo Tekchandani

Khai N Truong

Evaluating the Effect of Phrase Set in Hindi Text Entry

Keywords: Hindi, Text Input, Phrase Set

Recently, many different Indic text entry mechanisms have been proposed and evaluated. Whereas the use of a common phrase set across text-entry research may help to produce generalizable results across studies, previous Indic Text entry evaluations have used a variety of different text entry phrases. In this paper, we develop and evaluate three different types of Hindi phrase sets that have been previously used in the literature -Hindi films, a grade VII textbook and a translated version of MacKenzie and Soukoreff's phrasesto study effects of their characteristics on performance. No statistical difference was found in novice user performance due to the different phrase sets. However, based on participant feedback, we report that consideration should be taken with regards to phrase length, frequency, understandability, and memorability in the design and selection of text-entry phrases.

Introduction

Researchers are increasingly investigating how to best support text-entry for non-English languages, such as Indic languages [START_REF] Dey | Voice Key Board: Multimodal Indic Text Input[END_REF], [START_REF] Joshi | Design and Evaluation of Devanagari Virtual Keyboards for Touch Screen Mobile Phones[END_REF], [START_REF] Joshi | Keylekh: A Keyboard for Text Entry in Indic Scripts[END_REF], [START_REF] Jung | Solving the Great Indian Text Input Puzzle: Touch Screen-based Mobile Text Input Design[END_REF], [START_REF] Krishna | Gesture Keyboard -User centered Design of a Unique Input Device for Indic Scripts[END_REF], [START_REF] Kumar | Saral: Devanagari Text Input System in Mobile Phones[END_REF]. Indic languages such as Hindi, Marathi, Nepali, use Devanagari script. Hindi has 53 base letters -34 consonants, 11 vowels and 8 diacritic marks. Unlike English, Hindi is not disconnected and mostly requires two or more letters to be combined together to form a character [START_REF] Joshi | Keylekh: A Keyboard for Text Entry in Indic Scripts[END_REF]. The combination of letters changes a character's visual appearance (e.g., typing क(k) and ई(i) produces की(ki)).

The many challenges involved with entering text in Indic languages, including Hindi, have been discussed at length by others; for more detail, consult [START_REF] Joshi | Design and Evaluation of Devanagari Virtual Keyboards for Touch Screen Mobile Phones[END_REF], [START_REF] Joshi | Keylekh: A Keyboard for Text Entry in Indic Scripts[END_REF], [START_REF] Jung | Solving the Great Indian Text Input Puzzle: Touch Screen-based Mobile Text Input Design[END_REF]. One of the key challenges is that for some characters, there are differences between the phonetic sequence of letters and the visual sequence of writing the letters (e.g., character पि(pi) in the word पिता(pitaa) requires writing the vowel इ(i) before the consonant ि(p), but pronounce the इ(i) after the ि(p)) [START_REF] Joshi | Keylekh: A Keyboard for Text Entry in Indic Scripts[END_REF]. Furthermore, multiple base letters share a key button, often resulting in the use of multiple keystrokes to enter a letter (e.g., typing Shift + L for थ). Many novel hardware and alphabetical layouts have been proposed to support effective Indic text entry on computers [START_REF] Dey | Voice Key Board: Multimodal Indic Text Input[END_REF], [START_REF] Joshi | Keylekh: A Keyboard for Text Entry in Indic Scripts[END_REF], [START_REF] Krishna | Gesture Keyboard -User centered Design of a Unique Input Device for Indic Scripts[END_REF], [START_REF] Rathod | A Dynamic Text Input scheme for phonetic scripts like Devanagari[END_REF] and mobile devices [START_REF] Joshi | Design and Evaluation of Devanagari Virtual Keyboards for Touch Screen Mobile Phones[END_REF], [START_REF] Joshi | Keylekh: A Keyboard for Text Entry in Indic Scripts[END_REF], [START_REF] Kumar | Saral: Devanagari Text Input System in Mobile Phones[END_REF], [START_REF]Panini Keypad[END_REF], [START_REF] Shanbhag | An Intelligent Multi-layered Input Scheme for Phonetic Scripts[END_REF]. For evaluation, participants enter phrases of text using a technique while data is logged to calculate text entry performance. Due to the unavailability of a standard Hindi phrase set, researchers have used a variety of alternatives, including random words [START_REF] Kumar | Saral: Devanagari Text Input System in Mobile Phones[END_REF], random phrases [START_REF] Dey | Voice Key Board: Multimodal Indic Text Input[END_REF], [START_REF] Joshi | Keylekh: A Keyboard for Text Entry in Indic Scripts[END_REF], paragraphs from a news portal [START_REF] Krishna | Gesture Keyboard -User centered Design of a Unique Input Device for Indic Scripts[END_REF], phrases from textbooks [START_REF] Joshi | Design and Evaluation of Devanagari Virtual Keyboards for Touch Screen Mobile Phones[END_REF], and Hindi films dialogues [START_REF] Jung | Solving the Great Indian Text Input Puzzle: Touch Screen-based Mobile Text Input Design[END_REF]. Different phrase sets may have different properties, which can affect the textentry task [START_REF] Vertanen | A Versatile Dataset for Text Entry Evaluations Based on Genuine Mobile Emails[END_REF] and can potentially bias performance. Read [START_REF] Read | Deceit, Distortion and Decision -Choosing Phrase Sets for Text Entry Research[END_REF] argues that when constructing text phrases, language and context must be taken into consideration.

In this research, we test three different Hindi phrase sets representing those that have been commonly used in previous studies. The phrases were taken from Hindi films (similar to [START_REF] Jung | Solving the Great Indian Text Input Puzzle: Touch Screen-based Mobile Text Input Design[END_REF]), grade VII Hindi textbook (similar to [START_REF] Joshi | Design and Evaluation of Devanagari Virtual Keyboards for Touch Screen Mobile Phones[END_REF]), and a Hindi translated version of the MacKenzie and Soukoreff's phrase set [START_REF] Mackenzie | Phrase Sets for Evaluating Text Entry Techniques[END_REF] (state-of-the-art for English text entry) [http://www.dgp.toronto.edu/~mjain/HindiTextEntry.zip]. We show that the phrase sets differed in a variety of ways, such as the frequency of the words used, the length of the phrases, their correlation with a Hindi corpus, and the readability of the phrases. We conducted a study to examine whether these properties can bias performance of novice Hindi text entry users. We did not find any statistical difference exists in user performance, in terms of input speed and error rate, due to the different phrase sets. However, Mackenzie and Soukoreff [START_REF] Mackenzie | Phrase Sets for Evaluating Text Entry Techniques[END_REF] previously argue that use of a standard phrase set for English will help in attaining more generalizable results across studies. The same argument should hold true for a standard Hindi phrase set, and hence using these standard phrase sets for future Hindi text entry research will help in generalizing results. Additionally, based on participant feedback, consideration should be taken with regards to phrase length, frequency, understandability and memorability in the design of the phrases. In particular, our post-study questionnaire shows that ~8 words per phrase is an effective phrase length and familiar phrases minimize learnability and help motivate the participants.

Phrase Sets

We developed three different types of phrase sets previously used in the literature. Similar to [START_REF] Mackenzie | Phrase Sets for Evaluating Text Entry Techniques[END_REF], none of the phrase sets include any punctuation.

Phrases from Hindi Films (FP).

Previous Hindi text entry research has used phrases from Hindi films [START_REF] Jung | Solving the Great Indian Text Input Puzzle: Touch Screen-based Mobile Text Input Design[END_REF], as they are very memorable and capture the attention of the audience. We generated a phrase set comprising of 60 famous phrases from Hindi films. The phrases were randomly selected from online forums and blogs, citing famous Hindi film dialogues. Example phrases include: हम जहााँ खड़े हो जाते हैं लाइन वह ीं से शु रू होती है (where I stand, the queue begins from there), डान को िकड़ िाना मु श्ककल ह नह ीं नामु मककन है (to catch Don is not only hard but impossible).

Phrases from Textbooks (TP).

Grade VII textbook phrases have been used previously for evaluating Hindi text entry [START_REF] Joshi | Design and Evaluation of Devanagari Virtual Keyboards for Touch Screen Mobile Phones[END_REF]. We generated a phrase set consisting of 50 phrases from a grade VII standard Hindi textbook [12]. We extracted phrases from a paragraph from 6 different stories in the book, thus maintaining some relationship between consecutive phrases. Example phrases include: दिव्या अननल कक छोट बहन है (Divya is Anil's younger sister), यों तो वह शु रू से ह कमजोर है ले ककन इधर कु छ दिनों से उसे हर समय थकान महसू स होती रहती है (we know she is weak from the start but the past few days she seems tired all the time).

Translated MacKenzie and Soukoreff's Phrase Set (MSP).

The phrase set provided by MacKenzie and Soukoreff [START_REF] Mackenzie | Phrase Sets for Evaluating Text Entry Techniques[END_REF] is considered as standard and been used extensively for the evaluation of English-based text entry techniques. One of the authors, who is a native Hindi speaker, translated the phrase set into Hindi.

In some instances, more context-appropriate words were used in place of English proper nouns; for example, spaghetti, racketball, and Sam were replaced with words with Indian context such as खखचड़ी (khichdian Indian food dish), किके ट (cricket), and राम (Raman Indian name) respectively. Additionally, some English adjectives also do not have a direct translation in Hindi. We used the closest matching adjective with a high frequency in the Hindi language. Example phrases include: हाथी चू हों से डरते हैं (elephants are afraid of mice), प्यार के कई मतलब है (love means many things).

To verify the validity of the translated phrase set, two native Hindi speakers who are fluent in English were hired to rate the translation in three different parameters. The raters were provided with the English phrase set [START_REF] Mackenzie | Phrase Sets for Evaluating Text Entry Techniques[END_REF] of 500 phrases along with their Hindi translations. First, the raters were asked to rate the Hindi translated phrase according to this rating scheme -1: Directly Translatable such that the English and the Hindi phrase mean exactly the same. 2: Indirectly Translatable such that synonyms must be used to keep the intended meaning (for example, "ginormous" to "very large"). 3: Indirectly & Partially Translatable such that words required changes to gain similar meaning (for example, "Sam" to "Ram", "spaghetti" to "khichdi"). 4: Not Translatable such that meaning is lost because the phrase contains idioms, colloquial expressions, etc. Second, these raters were asked to rate a Hindi phrase as "Y" if it is a valid Hindi phrase, "N" otherwise. A high-correlation was found between the two raters (95.45%). The rater rated 255 phrases as Directly Translatable, 173 as Indirectly Translatable, 12 as Indirectly Partial Translatable, and 60 as Not Translatable. Raters agreed that 444 phrases were valid Hindi phrases. For the study, we chose the first 150 valid phrases, which were rated as 1 by both the raters.

Linguistic Analysis of the Phrase Sets.

An important characteristic that MacKenzie and Soukoreff [START_REF] Mackenzie | Phrase Sets for Evaluating Text Entry Techniques[END_REF] have established about their phrase set is its high frequency-based correlation with the English corpus. MacKenzie and Soukoreff's phrases vary from 16 to 43 characters (m=28.61). There are 2712 words (1163 unique) varying from 1 to 13 characters (m=4.46). It has a 0.945 single-letter correlation with the English letter frequencies reported by Mayz-ner and Tresselt [START_REF] Mayzner | Table of single-letter and digram frequency counts for various word-length and letter-position combinations[END_REF]. Word-based correlation shows that 85.08% of the words in MacKenzie and Soukoreff's phrase set are from 1% of the total words from the COCA corpus (an English corpus that is recent to 2012: http://corpus.byu.edu/coca/).

The EMILLE/CIIL corpus [START_REF]The EMILLE/CIIL Corpus[END_REF] consists of 2387 Hindi files, totaling 737528 sentences and 12295677 words (202042 unique). Overall, all three phrase sets have a high word-based correlation with the EMILLE/CIIL corpus (0.70, 0.68 and 0.75 of the words in FP, TP and MSP, respectively, are from 1% of the total words from the corpus). This shows that the words used to build the phrase set are very frequently used words. The three phrase sets were similar in terms of single-letter correlation with the EMILLE/CIIL corpus (FP: 0.97, TP: 0.98, MSP: 0.98). Even the number of letters forming a word in each phrase set was not significantly different: FP: 3.46, TP: 3.63, MSP: 3.73. However, the phrase sets differed in terms of the number of words forming a phrase: FP: 8.16, TP: 13.46, MSP: 5.87 (Table 1).

An important characteristic of written text is readability, the ease with which text can be read. The lower the readability score of a phrase, the easier it is to read that phrase. The US Government Department of Defense uses Flesch-Kincaid Grade Level formula, which relies on the fact that the difficulty of a word is directly proportional to the number of syllables. Past Hindi research [START_REF] Agnihotri | Evaluating the Readability of School Textbooks: An Indian Study[END_REF] has used this measure to evaluate readability. The readability attribute takes the effect of conjuncts into account. To identify the number of syllables in a Hindi word, we used the syllabification algorithm proposed by [START_REF] Choudhury | A Rule Based Schwa Deletion Algorithm for Hindi[END_REF] which has been reported to be more than 96% accurate. Our phrase sets readability level varies: FP: 5.36, TP: 8.0, MSP: 5.68. Phrases from grade VII textbook are found to be of grade 8 (sd=3.82), showing that the readability measure being used is efficient (Table 1).

Hypothesis.

We hypothesize that (H1) use of MSP and FP will result in faster text entry and a lower error rate than TP. This is based on the fact that MSP and FP have lower readability, higher word correlation, and lower words per phrase (Table 1) compared to TP. Additionally, MSP's high word-based correlation to the EMILLE/CIIL corpus shows that it is very similar to what people read and write on a day-to-day basis; hence, we hypothesize that (H2) participants would prefer MSP over FP and TP.

Evaluation Method: Participants, Apparatus and Procedure

We recruited 18 individuals (mean age=21.8, 12 males, 4 females) to participate in a user study comparing the phrase sets. All were undergraduate Computer Science students, with an average 10.16 years of QWERTY keyboard usage. The criteria for selecting participants for the study was that they must know how to read, write, and speak in Hindi, but have never used an Inscript (Indian Script) keyboard [START_REF]TDIL Inscript Layout[END_REF] before.

The mother tongue of all the participants, except two (who also know Gujarati), was Hindi. All participants, except one, received their primary education in English. Each participant was paid Rs 100 (~$2) per session. To motivate the participants, prize money of Rs 1000 and Rs 500 was given to the fastest and second fastest participants respectively. We used the standard Inscript keyboard layout [START_REF]TDIL Inscript Layout[END_REF], attached to a 15.4 inches laptop screen. We developed custom software in C# which presented a test phrase at the top of the screen and asked the user to type the same phrase into a text box below it. The software logs all keystrokes for later analysis.

A within-subject multiple session study was conducted. Each participant took part in three 45-minute sessions. In each session, the participant was presented with phrases from one of the phrase set, and was asked to enter the visible text as quickly and as accurately as possible. A session consisted of two 20-minute typing blocks with a break of 3-5 minutes between the blocks. A minimum gap of 2 hours and a maximum gap of 2 days was maintained between two consecutive sessions. We counter-balanced the ordering of the phrase sets among the 18 participants, to allow learning to equally affect all conditions. Phrases within a set were always in the same order. After each session, participants were required to complete a questionnaire, rating the phrase set in terms of memorability, understandability, phrase length, and frequency of usage (phrase set measures were taken from previous research [START_REF] Mackenzie | Phrase Sets for Evaluating Text Entry Techniques[END_REF], [START_REF] Vertanen | A Versatile Dataset for Text Entry Evaluations Based on Genuine Mobile Emails[END_REF]), on a 5-point Likert scale. For instance, for understandability 1 was very difficult to understand and 5 was very easy to understand.

Study Results

All participants completed the 3 session study. In total, together they entered 1340 phrases (388 FP, 310 TP and 642 MSP).

Speed and Accuracy

Text entry speed is measured in terms of words per minute (wpm), calculated using (letters per second)*60/5, with the definition that a word consists of 5 letters [START_REF] Soukoreff | Metrics for Text Entry Research: An Evaluation of MSD and KSPC, and a New Unified Error Metric[END_REF]. This definition was used to make the results more generalizable with the previous studies, and also to provide context for a larger audience to be able to relate the obtained results with English text entry speed; this has been done for Korean text input as well [START_REF] Ilinkin | Evaluation of Text Entry Methods for Korean Mobile Phones, a User Study[END_REF]. The average text entry rate over all the sessions across all the participants was 6.91 wpm (sd=2.43) (FP: m=6.22, sd=2.16; TP: m=7.28, sd=2.62; MSP: m=7.22, sd=2.48). Contrary to our hypothesis H1, no significant difference (F 2,34 =2.5, p=0.1) was found between the three phrase sets. Two metrics were used to measure text entry accuracy: (a) Keystrokes per Letter (KSPL) [START_REF] Soukoreff | Metrics for Text Entry Research: An Evaluation of MSD and KSPC, and a New Unified Error Metric[END_REF] the number of keystrokes required to input a letter in Hindi, (b) Minimum String Distance (MSD) [START_REF] Soukoreff | Metrics for Text Entry Research: An Evaluation of MSD and KSPC, and a New Unified Error Metric[END_REF] between the presented and transcribed phrase. MSD accounts for the uncorrected errors in the final transcribed text; while KSPL measures the corrected errors as every correction adds multiple keystrokes, i.e., delete letter, reenter letter. Note that for English, ideal KSPL value is 1 [START_REF] Soukoreff | Metrics for Text Entry Research: An Evaluation of MSD and KSPC, and a New Unified Error Metric[END_REF]. For Hindi, several letters require multiple keystrokes; we calculated and found the ideal KSPL for Inscript keyboard to be 1.12. For the entire study, a low average KSPL of 1.41 (sd=0.16) was found (FP: m=1.41, sd=0.13; TP: m=1.40, sd=0.1; MSP: m=1.43, sd=0.22). The average MSD value was 0.035 (sd=0.02) (FP: m=0.028, sd=0.01; TP: m=0.046, sd=0.03; MSP: m=0.03, sd=0.01). The higher MSD value for TP could be because the textbook phrases are not only longer than FP and MSP, but also have a higher readability value. However, no significant accuracy difference (KSPL: F 2,34 =1.3, p=0.3, MSD: F 2,34 =2.4, p=0.1) was found between the phrase sets, disproving our hypothesis H1.

Post-Session Questionnaire

Because MSP preserves many of the positive qualities found in MacKenzie and Soukoreff's English phrase set (i.e., short, easy to understand and memorable phrases), participants preferred it the most (Friedman χ 2 (2)=14.7, p<0.01) (H2). The preference is derived cumulatively from post-questionnaire rating of phrases in terms of memorability, understandability, phrase length, and frequency of usage (FP: m=3.87, sd=0.1; TP: m=2.78, sd=0.1; MSP: m=4.41, sd=0.08). However, we did not find any significant difference between FP and MSP in terms of participants rating for understandability and memorability.

Understandability. Although participants perceived that the words used in the phrases were easy to understand across all the phrase sets, participants strongly agreed that FP and MSP contained phrases that are very easy to understand, while being neu-tral towards TP. Fisher's Exact test showed significant differences for understandability between FP (m=4.6, sd=0.8) and TP (m=3.1, sd=0.9) with p<0.0001, and between TP and MSP (m=4.17, sd=1) with p=0.01. This could be because TP has the smallest word-based correlation of the three phrase sets, but the highest readability level (where a high readability score means that the text is hard to read).

Length. Whereas participants agreed that phrases in TP were too long to type, they were neutral towards FP and somewhat disagreed that phrases from MSP were too long. Thus, around 8 words per phrase seems an acceptable length for phrases, but this might be influenced due to high understandability and memorability of FP. We found a strong correlation between frequency of phrases and phrase length, with Pearson's r(52) = 0.57, p<0.0001. Participants thought that MSP contained very frequently used phrases but somewhat disagreed that FP and TP contained very frequently used phrases. Thus, seemingly longer phrases may not represent what participants would associate as frequently used phrases. [START_REF] Vertanen | A Versatile Dataset for Text Entry Evaluations Based on Genuine Mobile Emails[END_REF] argue that phrases should be memorable to help participants type them after only reading once. Participants strongly agreed that FP and MSP contained phrases that are very easy to memorize, while being neutral towards TP. We found that participants were able to quickly recognize the movies the phrases from FP were from and enjoyed typing them: "Phrases should be interesting, so that we enjoy typing." Many would often read the phrases aloud and laugh while typing. Fisher's Exact test showed significant differences for memorability between FP (m=4.2, sd=0.2) and TP (m=2.5, sd=0.1), and between TP and MSP (m=3.9, sd=0.2) with p<0.0001. Also a strong correlation was found between memorability and understandability, with Pearson's r(52)=0.64, p<0.0001.

Memorability. Recently, Vertanen and Kristensson

Conclusion

In this paper, we develop and evaluate three types of phrase sets for Hindi text entry: phrases taken from Hindi films, a grade VII Hindi textbook, and a Hindi translated version of the MacKenzie and Soukoreff's phrase set. Although these phrase sets have different characteristics (Table 1), we did not find any text entry performance difference resulting amongst them. Our results suggest the use of different types of phrases in previous Hindi text entry studies potentially did not significantly affect performance results. However, it has yet to be shown that there is perhaps no significant difference between any sets of phrases. Thus, use of these common phrase sets can help in attaining more generalizable results across future research papers and hence facilitate comparison of different text entry techniques. Evaluating additional types of phrase sets (e.g., [START_REF] Paek | Sampling representative phrase sets for text entry experiments: A procedure and public resource[END_REF]) and different characteristics of the phrase sets is important future work. Additionally, we learned from participant feedback that MSP was preferred, and the acceptable Hindi phrase length should be ~8 words/phrase, but ~13 is too long. Readability contributed to the acceptability of FP, as does memorability. Familiar phrases help motivate the participants and make the whole typing task fun; this can be important in studies which ask participants to complete a large number of typing sessions. Thus, factors like readability, memorability, and phrase length, should all be taken into consideration together while choosing a phrase set.

Table 1 .

 1 Linguistic Analysis of the Phrase Sets and Corpus

	Metrics	EMILLE/C	FP	TP	MSP	MS Eng-
		IIL Corpus				lish Set
	Number of phrases/sentences	737528	60	50	150	500
	Number of words	12295677	490	673	881	2712
	Number of unique words	202042	267	382	464	1163
	Minimum word length	2	2	2	2	1
	Maximum word length	33	10	13	14	13
	Min. phrase length (# words)	1	4	3	3	3
	Max. phrase length (# words)	888	14	39	11	9
	Min. phrase length (# letters)	1	16	10	12	16
	Max. phrase length (# letters)	4752	58	167	49	43
	Single-letter correlation	-	0.97	0.98	0.98	0.95
	Word-based correlation	-	0.70	0.68	0.75	0.85
	Readability	m=10.34	m=5.36	m=8.0	m=5.68	m=4.17
		sd=6.76	sd=2.4	sd=3.82	sd=2.46	sd=3.88
	Words per phrase	m=16.67	m=8.16	m=13.46	m=5.87	m=5.4
		sd=13.27	sd=2.4	sd=7.45	sd=1.6	sd=1.1
	Letters per phrase	m=83.34	m=35.45	m=61.44	m=26.82	m=28.61
		sd=67.4	sd=10.15	sd=34.63	sd=7.08	sd=5.02
	Letters per word	m=4.06	m=3.46	m=3.63	m=3.73	m=4.46
		sd=2.16	sd=1.44	sd=1.65	sd=1.72	sd=2.4


Monchu Chen
email: monchu@m-iti.org

Veraneka Lim
email: v.lim@tue.nl

Tracking Eyes in Service Prototyping

Keywords: Eye-Tracking, Service Design, Experience Prototyping, Heat Map

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Eye-tracking technique has been applied to various areas such as psychology, human factors, marketing research, etc. It has been particularly successful in the usability evaluation of human-computer interface design [START_REF] Jacob | Eye tracking in Human-Computer Interaction and Usability Research: Ready to deliver the Promises[END_REF]. An eye-tracker allows researchers and designers to follow the eye gaze of a user, providing insights to how a user perceive and interact with an interactive system. Knowledge about when and where customers target their visual attention and how this is affected by certain aspects of the service or environment is expected to aid service designers in understanding customers' mental processes under circumstances like information processing and its influence on decision making. Several eye movement analysis metrics have been proved to be useful for desktop scenarios (e.g. a web site design), however, little have been applied to ubiquitous computing environments (e.g. a service design). Therefore, this paper proposes to depict customer experiences while using a service through the study of eye gaze.

In the last decades, most research on visual attention was primarily based on twodimensional (2D) stimuli and static scenarios, regarding both observer and content (e.g. websites and interfaces). With the increased interest in interactions of people in natural settings and mobile uses of interaction technology, current advancements in mobile eye tracking systems now allows the observer to freely interact physically in a three-dimensional (3D) scenario, providing the means to assess eye gaze and visual attention and understand mental processes in arbitrary 3D environments (e.g. driving simulator and virtual reality). This approach answers questions on how to improve the guidance of visual attention afforded by 3D environments with multiple levels of depth. Research on visual attention, however, has still mainly targeted 3D objects and 3D virtual reality [START_REF] Pfeiffer | Measuring and Visualizing Attention in Space with 3D Attention Volumes[END_REF][START_REF] Stellmach | 3D attentional maps: Aggregated gaze visualizations in three-dimensional virtual environments[END_REF][START_REF] Stellmach | Trends and Techniques in Visual Gaze Analysis[END_REF]. Limited to none research has focused on visual attention in Service Design, which is a new, holistic, multi-disciplinary and integrative field [START_REF] Moritz | Service Design, practical access to an evolving field[END_REF]. One reason why eye tracking hasn't been applied to Service Design might be related with the issue of eye gaze data collection and representation in 3D environments for an effective interpretation of eye tracking results.

A common technique for investigating visual attention is the aggregation and representation of gaze target positions in a scan path or a superimposed attention map, also commonly referred to as heat map [START_REF] Pfeiffer | Tracking and Visualizing Visual Attention in Real 3D Space[END_REF][START_REF] Spakov | Visualization of Eye Gaze Data using Heat Maps[END_REF][START_REF] Velichkovsky | New technological windows into mind: There is more in eyes and brains for human-computer interaction[END_REF]. Scan paths are drawn as linearly connected dot sequences, depicting raw gaze points (x, y, t) or a subset of those points that satisfy criteria for labeling as fixations, the relatively stationary component of the eye movement. Heat maps provide a much cleaner depiction of aggregate gaze by combining gaze fixations from multiple viewers. These two representations of gaze target positions are suitable for 2D stimuli, but as eye tracking studies typically comprise a time consuming frame by frame analysis of captured screen recordings with superimposed attention maps, the reduction of 3D scenarios to 2D is likely to lead to data loss without assessing the hypotheses on 3D stimuli too [START_REF] Pfeiffer | Measuring and Visualizing Attention in Space with 3D Attention Volumes[END_REF]. Although, several approaches has been done recently to transfer the notion of attention maps from 2D content to 3D content [START_REF] Stellmach | 3D attentional maps: Aggregated gaze visualizations in three-dimensional virtual environments[END_REF][START_REF] Pfeiffer | Tracking and Visualizing Visual Attention in Real 3D Space[END_REF][START_REF] Spakov | Visualization of Eye Gaze Data using Heat Maps[END_REF][START_REF] Pfeiffer | Understanding Multimodal Deixis with Gaze and Gesture in Conversational Interfaces[END_REF]] they all have targeted only 3D objects and 3D virtual environments. Beside these, heat maps have received relatively little attention and have remained unchanged since their introduction. Hence, visual gaze analysis in real 3D environments lacks methods and techniques for aggregating attentional representations.

2

The Study

In this study, a 3D cave automatic virtual environment (CAVE) has been set up for prototyping the service design of a tourist information office space (see figure 1). A CAVE is an immersive virtual reality environment where projectors are directed to three, four, five or six of the walls of a room-sized cube, allowing the exploration of users' experiences in a more realistic but controlled space. Users' viewing behaviour will be collected with the Tobii [START_REF]Tobii Glasses Eye tracker[END_REF] mobile eye tracker while experiencing the simulated service. Eye gaze patterns will then be presented on a 3D heat map for understanding customers' experiences while in a service. The purpose of this study is in twofold. First, as Experiential Prototyping lacks quantitative methods for gaining insights in users' experiences, the present study, introduces the use of eye gaze as a direct measurement into users' mental processes. Second, an implementation using Augmented Reality (AR) markers has been proposed for representing customers' visual attention with actual distances in a CAVE, while experiencing the service. The result, a 3D attention heat map, is expected to be a valuable tool for Service Prototyping.

Apparatus and Participants

A semi-virtual CAVE of a tourist information office was simulated with 3 large projection screens and real physical objects (an information kiosk, a table with brochures and a TV screen for showing advertisements) (see figure 1). The side screens were projected with a video (including audio) and static images with text variating in information content. The center screen was projected with a dynamic synthetic customer representative showing closed non-verbal behavior (e.g. on the phone or working on the computer).

The CAVE was used as an experience prototyping platform. 5 participants, composed of students and researchers from the University, were recruited for this study. The mobile eye-tracker, developed by Tobii [START_REF]Tobii Glasses Eye tracker[END_REF], provided the technical basis for an analysis of visual attention during the mobile exploration in the CAVE (see figure 1).

Augmented Reality (AR) markers

Augmented Reality markers are small printable black and white shapes that interact with software downloaded to a computer equipped with a webcam or a smart phone with camera to create 3D images overlaid on the real world image seen on screen. A total of 16 AR markers were located strategically within the CAVE near visual tar-gets. The set-up was designed so that at least 1 tag was always visible when participants looked through the Tobii Glasses.

Procedures

All participants were asked to enact a tourist information office visit by going through two tasks, an open task condition and a goal directed task condition. Two different tasks were chosen in order to create the possibility of getting two different types of behavioral approaches and gaze patterns. In the open task condition participants were given an absence of specific plans. In the second task condition they were given a presence of specific plans; they were asked to imagine the following "You heard a tourist talking about their great time during their hike from x to y. You are interested in this hike as well as other popular hikes and would like to know how to get there, if there is a possibility to camp and any other information about the trail. You also want to know about the possibilities to do whale watching. You have to select one for tomorrow's activity".

Each task, which lasted no longer than 5 minutes, was explained verbally to participants before entering the CAVE. After the task was explained they were asked to enter the CAVE and scan through the environment in order to see what they want to do, see or know more about. All participants were asked to think aloud as much as possible and say out loud as soon as they have decided what they want to approach. This was also an indication of the end of the task. For technical limitations regarding the mobile glasses and AR tags, participants were assigned to first walk to a starting point in the CAVE, indicated with a round symbol on the floor. They were also asked to stay within a particular area when in the CAVE, which was also indicated on the floor.

Implementation

After data collection, the first step was to re-create the tourist information office in virtual space using Quartz Composer Development Environment available in Mac OSX, where interactive 3D navigation and real-time visualization is possible. Figure 2 shows the 3D virtual space of the tourist information office tagged with AR markers, which were used as anchor points for projecting gaze points. Gaze positions were converted from 2D into 3D space for each AR marker individually (see figure 3). A video layer was first captured by the scene camera on Tobii Glasses and a gaze layer with eye gaze position was acquired by Tobii Studio Gaze positions in scene camera coordinate system. Since Tobii Glasses is an off-line eye tracker, post-processing is required after experiments. Gaze data and video streams from scene camera were analyzed and exported from Tobii Studio before sending to Quartz Composer for further analysis. Then, the AR markers are detected and recognized in the Quartz Composer. The marker position and rotation in scene camera coordinate system were extracted. The origin was then shifted to the center of the AR marker and the video stream from the scene camera as well as the gaze points are translated to the AR marker's coordinate system. A 3D rotation transformation was performed to adjust the perspective so that the norm vector of the maker is perpendicular to the display. Finally, the eye gaze position was transformed to the appropriate position related to the marker in the virtual tourist office. Since the positions of all 16 AR markers in the tourist information office coordinate system were pre-measured in the coordinate system of the tourist office, refer-ences were known for calculating gaze points in 3D space. Gaze points are further converted from the gaze position in the marker coordinate system into the coordinate system of the tourist information office. Gaze points were aggregated over the course of the experiment, and across all subjects. The intensity of the luminance represents the frequency of the visual attention. Hence, the "hot" spots in the virtual tourist office indicate areas that attract customers more attention.

Results

Participants' verbal comments during the think aloud, on the spot observations of participants behaviour and video material were analyzed qualitatively. Overall, in the open task condition, participants seem to divide their attention to different targets within the CAVE in a more balanced manner spending the same amount of time on fixating towards a target as well as moving to other targets. They made more movements and put more effort in trying to cover all information available in the tourist information office.

In the goal directed task condition, participants seem to scan quickly through all targets until they find what they were searching for. They spend more time on fixating toward a target of interest and less time for moving to other targets.

Figure 4 shows the 3D attention heat map of the aggregated eye gaze for all participants separately for both conditions. It seems that in the goal directed task condition, the heat map is more focused and concentrated supporting the qualitative findings. The 3D heat map, however, provides specific details on what has been targeted. The intensity of the heat map reflects the quantity of visual attention of a given position. The by-product of the process provides the access to the raw data of gaze positions in 3D space. With further defining Area of Interests, other quantitative analysis such as fixation count, fixation duration, percentage fixed, etc. could be easily obtained.

Discussion and Conclusion

The interest in exploring and developing experiences in HCI is growing, while often designers of interactive systems go beyond their limits when using tools to explore and communicate what it will be like to interact with the things they are designing. A popular tool to understand, explore or communicate what it might be like to engage with the product, system or space that is being designed is Experience Prototyping. This tool has been of importance in the application of Service Design. Hence, Experience Prototyping is qualitative in nature and characterized with an indirect approach into customers' experience or view towards products or services. Therefore, one of the purposes of this study is to introduce a quantitative method for gaining insights in users' experiences, by looking at eye gaze as a direct measurement into users' mental processes.

Although current advancements in mobile eye tracking systems allows the observer to freely interact physically in a 3D scenarios, providing the means to assess eye gaze and visual attention and understand mental processes in arbitrary 3D environments, eye tracking hasn't been applied to Service Design yet. Visual gaze analysis in real 3D environments also seem to lacks methods and techniques for aggregating attentional representations, which directs us to a second purpose of this study; an implementation of representing customers' visual attention with actual distances, while experiencing the service using AR Markers.

A mixed reality CAVE was set up for prototyping the service design of a tourist information office space and users' viewing behaviour was collected with a mobile eye tracker while in a service. Eye movements were recorded and visualized virtually on a 3D attention heat map. The different viewing patterns for different conditions, concludes the 3D heat map to be a valuable tool for prototyping service design. The 3D heat map provides specific details on what has been targeted compared with qualitative methods. The proposed paradigm is independent to the brand, type and model of mobile eye-trackers. Additional efforts can be made to port the software to support other mobile eye-trackers in order to benefit service design community. Some limitations were found during this study. First, the slow shuttle speed of the scene camera in the eye tracker resulted in blurry images when head movements occurred, which makes it more difficult for AR marker detection. Second, there were problems with the marker distribution within the CAVE. When more markers are used, indicating higher density, the performance improves but obscures the scene. The use of IR markers could be a solution for future studies. In additional to invisible markers, object and scene recognition should be considered as well when tracking in a more realistic scenario where planting markers to cover whole physical space is not feasible. Also, the implementation requires manual operation. Therefore, an eye gaze proxy could be useful once online access to gaze data on the mobile tracker is possible. Future work should focus on more empirical studies to validate the usefulness of the 3D heat maps as well as other new ways of quantitative analysis of visual attention that can be applied to Service Design.

Fig 1 .

 1 Fig 1. The simulated tourist information office.

Fig 2 .

 2 Fig 2. A visualization of the AR markers in the virtual space.

Fig 3 .

 3 Fig 3. A video stream captured by the scene camera on Tobii Glasses (top left); A gaze layer with eye gaze position acquired by Tobii Studio Gaze positions in scene camera coordinate system (top middle); AR markers (marker id# 10) detection and recognition of marker position and rotation in scene camera coordinate system (top right); 3D rotation transformation (bottom left); Eye gaze position transformation to the appropriate position (bottom middle); gaze position converted from the marker coordinate system into the coordinate system of the tourist office (bottom right).

Fig 4 .

 4 Fig 4. A visualization of the 3D heat map in the virtual space for the open task condition (left) and goal directed task condition (right).

Acknowledgments

The study was done in Logica Service Design Lab with the supports from Knowledge+ incentive system in Madeira, Portugal. We would also like to thank all the volunteers who participated in the study during data collection.