Nikolaj Lars Holm Christiansen

Yde Frederiksen

Brit Susan Jensen
email: britjensen@gmail.com

Alex Ranch
email: alex_ranch@hotmail.com

Mikael B Skov

Nissanthen Thiruravichandran
email: nissanthen@gmail.com

Don't Look at Me, I'm Talking to You: Investigating Input and Output Modalities for In-Vehicle Systems

Keywords: In-vehicle systems, touch interaction, gesture interaction, eye glances, driving performance

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

Introduction

Driver attention is critical while drivers handle vehicles and interacts with emerging technologies in contemporary cars. Attention is the human's ability to concentrate on certain objects or situations and allocate processing resources accordingly [START_REF] Eysenck | Principles of Cognitive Psychology[END_REF]. But as technologies like GPS navigation systems or in-car media players are increasingly being used in cars, attention becomes an important aspect to consider when designing in-vehicle systems. With the driver's attention being divided between in-vehicle systems and the driving task, risks of accidents increase [START_REF] Brooks | In-vehicle Technologies, Advanced Driver Assistance Systems and Driver Distraction: Research Challenges[END_REF][START_REF] Geiger | Intermodal differences in distraction effects while controlling automotive user interfaces[END_REF]. A fundamental problem is that the driver has to remove attention from the primary task of driving to performing secondary tasks such as interacting with the car stereo. As the amount and complexity of in-vehicle systems increase, so does the demands on driver attention. This introduces challenges when trying to achieve safe and efficient interaction in order to minimize the amount of time the driver has to remove attention from the road in particular the driver's visual attention [START_REF] Bach | Interacting with In-Vehicle Information Systems: Understanding, Measuring, and Evaluating Attention[END_REF][START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF].

Technological progress and price reductions have pushed a growing use of touchbased interfaces to control various kinds of in-vehicle systems. The flexibility in its application capabilities, low price, and utilization of a more natural way of interaction, makes it an obvious choice for in-vehicle systems with its increasing presence in new cars and aftermarket GPS-units. But the inherent characteristics of touch screens imply high requirements for visual attention of the driver due to the lack of immediate tactile feedback or dynamics of screen layout [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF]. This leads to withdrawal of attention from the driver [START_REF] Tijerina | Issues in the Evaluation of Driver Distraction Associated with In-Vehicle Information and Telecommunications Systems[END_REF].

Brown [START_REF] Brown | Driver fatigue in Human Factors[END_REF] classifies two types of withdrawal of attention namely general and selective. The general withdrawal of attention refers to insufficient visual perception during the driving situation. This is also known as eyes-off-the-road [START_REF] Green | Customer Needs, New Technology, Human Factors, and Driver Science Research for Future Automobiles[END_REF]. Interacting with in-vehicle systems often requires selective withdrawal of attention as drivers read displays or push buttons. Selective withdrawal of attention is a more subtle distraction type as it deals with mental processing, e.g. memory processes or decision selection. It is also known as mind-off-the-road [START_REF] Green | Variations in Task Performance Between Younger and Older Drivers: UMTRI Research on Telematics in Association for the Advancement of Automotive Medicine Conference on Aging and Driving[END_REF] and takes place e.g. while talking to other passengers or while talking on the phone. Interacting with in-vehicle systems can lead to mind-off-the-road while interacting with e.g. a speech system or listening to audio instruction [START_REF] Bach | Interacting with In-Vehicle Information Systems: Understanding, Measuring, and Evaluating Attention[END_REF]. Thus, we can distinguish between input and output techniques that require high or low visual attention and perhaps lead to eyes-off-the-road or mind-off-the-road.

Inspired by previous research on touch-screen interaction technologies [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF][START_REF] Pirhonen | Gestural and Audio Metaphors as a Means of Control for Mobile Devices[END_REF], we compare different input and output techniques for in-vehicle systems to investigate and measure their effects on the driving activity and driver attention. The paper is structured as follows; initially we present previous research on in-vehicle systems, and secondly we introduce the interaction techniques. Then we describe the experiment and the results are presented and finally, we discuss and conclude the results.

Related Work

Driver attention and distraction are fundamental concepts when doing research or development within vehicle safety or in-vehicle systems design [START_REF] Bach | Interacting with In-Vehicle Information Systems: Understanding, Measuring, and Evaluating Attention[END_REF]. Attention can be defined as the ability to concentrate and selectively focus or shift focus between selected stimuli [START_REF] Eysenck | Principles of Cognitive Psychology[END_REF]18]. Within cars and other vehicles, driver attention is primarily focused on monitoring the environment and executing maneuvers also called the primary driving task [START_REF] Bach | Interacting with In-Vehicle Information Systems: Understanding, Measuring, and Evaluating Attention[END_REF][START_REF] Chewar | Secondary task display attributes: optimizing visualizations for cognitive task suitability and interference avoidance[END_REF][START_REF] Green | Driver Distraction, Telematics Design, and Workload Managers: Safety Issues and Solutions[END_REF][START_REF] Lansdown | Distraction from Multiple In-Vehicle Secondary Tasks: Vehicle Performance and Mental Workload Implications[END_REF]. Disruption of attention is defined as distraction and Green describes distraction as anything that grabs and retains the attention of the driver, shifting focus away from the primary driving task [START_REF] Bach | Interacting with In-Vehicle Information Systems: Understanding, Measuring, and Evaluating Attention[END_REF][START_REF] Brooks | In-vehicle Technologies, Advanced Driver Assistance Systems and Driver Distraction: Research Challenges[END_REF][START_REF] Green | Driver Distraction, Telematics Design, and Workload Managers: Safety Issues and Solutions[END_REF].

Within in-vehicle attention and distraction research, we observe a significant focus on the dynamics between the primary driving task and secondary driving tasks, e.g. operating various in-vehicle systems. This is significant since research identifies the use of in-vehicle systems as a cause of traffic accidents [START_REF] Brooks | In-vehicle Technologies, Advanced Driver Assistance Systems and Driver Distraction: Research Challenges[END_REF][START_REF] Green | Driver Distraction, Telematics Design, and Workload Managers: Safety Issues and Solutions[END_REF]. Green [START_REF] Green | Driver Distraction, Telematics Design, and Workload Managers: Safety Issues and Solutions[END_REF] stresses that most drivers will go to great lengths to complete a given secondary task and rarely abandon a task upon initiation. With a critical primary task, this seemingly irrational behavior and distribution of attention between the primary and secondary task can endanger the safety of the driver and the surroundings. Lansdown et al. [START_REF] Lansdown | Distraction from Multiple In-Vehicle Secondary Tasks: Vehicle Performance and Mental Workload Implications[END_REF] acknowledge this unsettling tendency concerning in-vehicle systems in a study focusing on driver distraction imposed by in-vehicle secondary systems.

A tendency within in-vehicle interaction research involves attempts to identify an interaction technique that surpasses the capabilities of the traditional tactile interface. In a comparative study, Geiger et al. [START_REF] Geiger | Intermodal differences in distraction effects while controlling automotive user interfaces[END_REF] set out to evaluate the use of dynamic hand movements (gestures) in order to operate a secondary in-car system and compare it to a traditional haptic (tactile) interface. The parameters used for comparison, were errors related to driving performance, tactile/gesture recognition performance and the amount of time drivers didn't have their hands on the steering wheel. The experiment showed that use of the tactile interface resulted in high task completion times and the system lacked in recognition performance when compared to the gesture interface. The gesture interface allowed users to perform the primary task appropriately, while the users also found the gesture interface more pleasant and less distracting. Alpern & Minardo [START_REF] Alpern | Developing a Car Gesture Interface for Use as a Secondary Task[END_REF] support these findings in a study where they evaluated gestures through an iterative development of an interface for performing secondary tasks. In the final iteration of their experiment, they noted that users made fewer errors compared to a traditional tactile radio interface. Findings from both studies indicate that gestures could be a viable alternative for secondary in-car systems.

Bach et al. investigated how perceptual and task-specific resources are allocated while operating audio systems in a vehicle [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF]. Three system configurations -a conventional tactile car stereo, a touch interface and an interface that recognizes gestures as input -were evaluated in two complementary experiments. Bach et al. identified an overall preference for the gesture-based configuration as it enabled the drivers to reserve their visual attention for controlling the vehicle. The conventional car stereo on the other hand lacked an intuitive interface; consequently the system required high perceptual and task-specific resources to be operated affecting the primary task performance of the subjects. The touch interface introduced a reduction in overall task completion time and interaction errors when compared to both the conventional tactile and gesture interfaces. While the potentials of gestures as an input method for in-vehicle systems seems promising, little attention has been given to the possible influence of output methods. In order to address this it would be necessary to distinguish between input and output to clarify how combinations of different output and input methods might affect the interaction and primary task performance.

The need to separate output from input in relation to in-vehicle systems is acknowledged by Bach et al. [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF] as a limitation in their study and the need for further research on this topic is recognized. Their initial research focus was on system input as opposed to output, which meant the included output mechanisms differed for each of their configurations. The variation in output could have affected the findings -the results do not show which kind of output mechanism that is suitable for in-vehicle systems. This suggests an additional study on output methods in order to investigate how they influence primary task performance and secondary task performance in the vehicle domain.

The aim of our study is to compare different configurations of in-vehicle systems with an equal emphasis on both input and output mechanisms. We aim to confine system variables regarding input and intend to accomplish this through a study of visual and auditory output in combination with either touch or gesture input. The rationale behind this combination is the duality in the interaction possibilities of touch screens, which support both touch and gesture interaction and the polarity in the two different sensory channels of output.

In-Vehicle System: Input and Output

We distinguish between input and output in this experiment while using a touchscreen. We integrate two different kinds of input and two different kinds of output enabling four different in-vehicle configurations; touch input with visual output, touch input with audio output, gesture input with visual output and gesture input with audio output. These configurations will hereafter be referred to as <input>/<output>, e.g. touch/visual.

In order to evaluate these configurations with regards to their effect on attention, we chose a well-known in-vehicle system as case system namely a music player or car stereo. This decision was based on previous studies, e.g. [START_REF] Alpern | Developing a Car Gesture Interface for Use as a Secondary Task[END_REF][START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF][START_REF] Green | Driver Distraction, Telematics Design, and Workload Managers: Safety Issues and Solutions[END_REF], and the music player served as a somewhat simple yet sufficient platform for our experiment. The system was designed for an 8" touch sensitive screen and the graphical user interface in all configurations is divided into the same output and input areas, to keep the interaction areas the same for all conditions. Furthermore, the output area of the screen is covered by a transparent plastic shield to discourage deliberate input and prevent accidental input in this area.

Input: Touch-Based and Gesture-Based

We integrated two different input methods -conventional touch-based input with graphical buttons and gesture-based input using the touch-screen as a drawing canvas.

The layout of the two touch configurations was inspired by Bach et al. [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF] and our goal was to keep it as simple as possible, while still providing the necessary basic functionality for controlling the music player. The implemented icons on the interface resemble common music player icons to minimize interpretation. The buttons were furthermore grouped according to functionality. The layout includes a "'Song info" button which is only enabled in the touch/audio configuration, but is included in the touch/visual configuration to keep the design consistent. The size and spacing of the buttons was inspired by previous research on touch screen layout, e.g. [START_REF] Colle | Standing at a kiosk: Effects of key size and spacing on touch screen numeric keypad performance and user preference[END_REF][START_REF] Sears | Improving Touchscreen Keyboards: Design issues and comparison with other devices[END_REF][START_REF] Sears | Investigating Touchscreen Typing: The effect of keyboard size on typing speed[END_REF]. Input is only possible by pressing the buttons according to the click-on-release principle. This means that the buttons are activated only when the finger has left the button, which also means that nothing happens when a button is held.

The gesture-based systems have no buttons. Instead, the systems are controlled by gestures drawn directly on the screen using a finger. The gestures used are inspired by Pirhonen et al. [START_REF] Pirhonen | Gestural and Audio Metaphors as a Means of Control for Mobile Devices[END_REF] and Bach et al. [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF] and allow for the same functionality as the touch buttons. The only gesture that is different is the "Song info" gesture, which is performed by drawing a line straight down followed by a line straight up, without the finger leaving the canvas. This was chosen to resemble the "i" often used as an icon for "information". The player is controlled through gestures anywhere on the input area of the canvas. The gesture system features the same functionality as the the functionality on the touch-based interface, e.g. play, pause, skip forward or backward, volume up or down. 4) gesture/audio. The top part of the screen (white) is reserved for output, while the grey area is for input. On the visual (top row) configurations the buttons are, from left to right, "Next song", "Play/pause", "Previous song", "Volume up" and "Volume down", "Song info". In the figure for gesture/visual, the user has just performed the "Play" gesture causing the system to flash the "Play" icon.

Output: Visual-Based and Audio-Based

We integrated two different output methods namely visual using icons and text and audio using ear-cons and voice. Visual and audio output is not used simultaneously at any point. We further distinguish between two kinds of output feedback on input and information about the state of the system. The visual feedback was implemented using visual cues to inform the user of the result of his or her actions. For the touch/visual system (figure 1.a), this is done by changing the appearance of buttons to indicate they have been pressed. Furthermore, when the volume is all the way down, pressing the "Volume down" button will change its appearance to reflect a disabled state. The same principle applies to the "Volume up" button. For the gesture/visual system, the same icons are used to indicate a recognized gesture. The icon corresponding to the recognized gesture will be displayed in the middle of the input area for about one second (as shown on figure 1.b). Audio feedback is implemented using ear-cons. When the user either pushes a button or performs a gesture (for the touch/audio and gesture/audio configurations), the system will provide feedback in the form of a clearly audible "click" sound. Following the same principle, that applies to visual feedback, any attempt to adjust the volume either up or down when it is fully up or down results in a "dong" sound.

Output about the state of the system consists of information regarding the current song; the song's number in relation to the playlist, the artist and the title of the song. Visual output about the state of the system is provided by text in the output area of the screen and is available at all times. The equivalent audio output is implemented using playback of voice recordings containing the same information. Either pushing the "Song info" button or performing the "Song info" gesture plays these recordings.

Experiment

The purpose of the experiment was to compare the four different configurations of the system (visual, audio) x (touch, gesture) and consequently the different ways for invehicle interaction.

Experimental Design

We adapted a between-subject design with 32 participants with four groups of eight subjects corresponding to the four configurations. Each group consisted of four male and four female test subjects and was assigned to one of the four configurations of our music player.

Subjects

32 people (16 males and 16 females) in the age 21 to 56 years old (M=28.2, SD=9.2) participated in our experiment. They all stated that they were in good health condition. All of the test subjects carried valid driver's licenses and had done so for between 0.5 and 29 years (M=9.4, SD=8.7). Their driving experience was quite varied and ranged between 100 and 30.000 km/year (M=6114.7, SD=7989.9). Two test subjects stated that they had previous experience with the driving simulator used in our experiment. Subjects were balanced between the conditions according to gender, age, and driving experience.

Setting

We created a medium-fidelity driving simulator (similar to [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF][START_REF] Kern | Writing to your car: handwritten text input while driving[END_REF][START_REF] Lee | Studying the effectiveness of MOVE: a contextually optimized in-vehicle navigation system[END_REF]) at our HCI laboratory at Aalborg University. We adapted two car seats, steering wheels (with force feedback) and a brake and accelerator pedal using the application Test Drive Unlimited (TDU) running on a desktop computer (see figure 2). The driving application TDU was chosen as it provides a driving context that shares similar characteristics as real-traffic driving, e.g. driving in towns and rural areas introducing unexpected potential hazards as the application integrates computer-controlled drivers on the road (similar to [START_REF] Nass | Improving Automotive Safety by Pairing Driver Emotion and Car Voice Emotion[END_REF]). The setup also included two sets of speakers with one set of 4.1 surround sound speakers playing the sound of the game, and another set of 2.1 stereo speakers for music playback. The game was projected onto the wall in front of the subjects. The speedometer and tachometer of the car was visible to the subjects during the test as part of the projected image. The test subjects occupied the driver's seat while the test leader sat in the passenger seat during the test (figure 2.3).

Tasks

The subjects were asked to solve 32 tasks during the test. Half of the tasks focused on system input, while the other half on system output. Furthermore, we attempted to create the tasks in such a manner that they did not favor any of the four configurations. The tasks were chosen to reflect realistic interactions with an in-car music player, e.g. changing tracks on a CD. Instructions for each task were kept short and clear in order to minimize interruption. The tasks varied in complexity ranging from simple ones like "Stop the music" to more demanding ones like "Find and play the song by Coldplay called Viva la Vida". The tasks were all read aloud by the test leader.

Procedure

All sessions followed the same basic procedure. First, we collected demographical data for the test subjects. Then, the subjects were asked to take a seat in the simulator and make sure that the driving position was comfortable. The test leader then briefed the participants by reading a text aloud, which told them what they were about to do. They were also shown how to operate the music player in the particular configuration they were to use during the experiment. After each instruction was demonstrated, the subjects were asked to repeat it, in order to ensure they had understood how to operate the system. The subjects were instructed to drive the car between 40 and 60 km/h, except when performing maneuvers like turning and braking, to stay in the right lane, and otherwise observe normal traffic regulations and drive as they would in a real car. The subjects were then given a chance to familiarize themselves with the driving simulator and the steering wheel and pedals as they were allowed to try the game prior to the test itself.

After the practice run the test leader reset the simulator and the actual test session began. The driving itself was divided into two parts. In the first part the test leader would instruct the subjects where to turn, making sure they all followed the same predetermined route. In the second part, the subjects were told to drive freely in the environment. The length of each part was determined by the tasks, which they were asked to solve while driving. The tasks were divided evenly between the two parts, with 16 tasks to be solved in each. The subjects were instructed to start solving the tasks only when they felt ready to do so. The test sessions were recorded on four different video cameras for later analysis (as illustrated in figure 2).

Data Analysis

Inspired by [START_REF] Bach | Interacting with In-Vehicle Information Systems: Understanding, Measuring, and Evaluating Attention[END_REF][START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF][START_REF] Lansdown | Distraction from Multiple In-Vehicle Secondary Tasks: Vehicle Performance and Mental Workload Implications[END_REF]26] we integrated several dependent measures to assess driver attention and driving performance while participating in each of the configurations. We chose to apply the following:

1. Primary driving task performance 2. Secondary driving task performance 3. Eye glance behavior 1) Primary driving task performance was measured as the number of errors in lateral and longitudinal control (i.e. [START_REF] Alpern | Developing a Car Gesture Interface for Use as a Secondary Task[END_REF][START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF][START_REF] Lansdown | Distraction from Multiple In-Vehicle Secondary Tasks: Vehicle Performance and Mental Workload Implications[END_REF]). A lateral control error was defined as a lane excursion where the subject failed to stay within the two lines denoting the right hand side lane of the road. Longitudinal control errors were defined as failure to maintain a speed within the instructed range of 40-60 km/h. A longitudinal error was noted each time the subject went above or below the speed range. Thus, staying at a wrong speed for a period of time only counted as one driving error. We identified these driving errors through video examination (elaborated below).

2) Secondary driving task performance we defined as interaction errors and task completion time (commonly applied in in-vehicle research [START_REF] Alpern | Developing a Car Gesture Interface for Use as a Secondary Task[END_REF][START_REF] Lansdown | Distraction from Multiple In-Vehicle Secondary Tasks: Vehicle Performance and Mental Workload Implications[END_REF]). Interaction errors were defined as attempts to interact with the system that either had no effect or didn't have the effect towards completion of the task that the subjects expected. In order to identify these errors, one of the cameras recorded an up-close view of the interaction with the screen. Task completion time was measured from the time the subjects started solving the task, defined by either moving their hand from the steering wheel, or moving their head/eye gaze towards the system, until the task was completed.

3) Eye glance analysis is a highly used metric for analyzing driver attention within in-vehicle research [START_REF] Bach | Interacting with In-Vehicle Information Systems: Understanding, Measuring, and Evaluating Attention[END_REF][START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF][START_REF] Gellatly | The Use of Speech Recognition Technology in Automotive Applications[END_REF]. We divided into three categories according to duration as category 1 was an eye glance below 0.5 seconds, category 2 was a eye glance between 0.5 and 2.0 seconds, and category 3 was an eye glance above 2.0 seconds. The nature of particular the eye glance analysis meant that it was necessary to view the videos frame by frame. In determining the length of an eye glance for instance, we knew that the each second of video contained 25 frames and a glance of 0.5 seconds or less corresponded to 12.5 frames (in practice 13 frames).

We analyzed two randomly picked sessions in order to achieve agreement in the interpretations of the data. This gave us the opportunity to discuss the various types of incidents in the data and we compiled a list of guidelines for the individual analyses. Each of the 32 sessions was analyzed by three of the authors of this paper. Each reviewer analyzed the video individually while logging and categorizing instances of all the abovementioned incidents. The resulting three logs were then compared and compiled into one final list containing all the incidents for that session. This was done by way of majority vote; if for instance only one reviewer had recorded a specific incident, which neither of the two other reviewers had recorded, the incident would not make it to the final list, and so forth. The same principle applied to categorization of eye glances. In situations where no majority vote could be secured, the video recording was reviewed again in order to reach the final decision.

Results

The results of the data analysis are presented in three sections namely primary driving task performance (including lateral and longitudinal control), secondary driving task performance (interaction errors and time) and eye glance behavior.

In each section, we first compare the results for the two input methods (N=16), then the two output methods (N=16) and finally all four configurations (N=8). The results were subjected to either two-tailed unpaired Student's t-tests or one-way ANOVA tests as well as Tukey's HSD post hoc tests where applicable. The data is organized into two tables; one for N=16 (table 1) and one for N=8 (table 2). Any statistically significant differences are highlighted.

Primary Driving Task Performance

The metrics for measuring primary driving task performance included lateral control errors (lane excursions) and longitudinal control errors (deviations from accepted speed range). Across the 32 test sessions, we identified a total of 256 lateral control errors and 511 incidents of longitudinal control errors. When comparing the primary driving task performance across the two input methods, we see no significant difference between any of the metrics, although gesture input generally has a higher number of errors across all the metrics. Looking at the results of the primary task performance for the output methods, however, does reveal a significant difference in the number of speed increases, with visual having significantly fewer than audio, t=2,04, p<0.05. However, there are no significant differences in the number of total speed deviations, although it is worth noting that the number of speed decreases and total speed deviations is higher for audio output than for visual output. Considering the primary driving task performance results and compare the four configurations (see table 2), we see a significant difference in the number of speed increases, F(3, 28)=3.95, p<0.05. A Tukey's HSD post hoc test revealed that there are significantly fewer speed increases in the gesture/visual configuration vs. the gesture/audio configuration (p<0.05). The remaining measurements of primary driving task performance show no significant differences. But the results do show that the two audio configurations have the highest number of total speed deviations.

Secondary Driving Task Performance

For secondary driving task performance we measured the total task completion time and identified a total of 1018 interaction errors. Comparing just input methods, the results show only marginal differences in the number of interaction errors and the task completion time, although gesture does show a higher task completion time than touch, t=2.04, p<0.19. Whereas the input methods revealed no significant differences in secondary task performance, the results for output showed 77% more interaction errors for visual output compared to audio output. A t-test shows that this is a significant difference, t=2.04, p<0.05. However, the task completion times were significantly longer for audio output, t=2.04, p<0.05. Secondary driving task performance results reveal no significant differences in the number of interaction errors distributed among the four configurations, even though the average number of interaction errors for the touch/audio configuration is less than half that of the touch/visual and gesture/visual configurations, F(3, 28)=1.87, p<0.16. However, a significant difference does exist between the task completion times, F(3, 28)=3.06, p<0.05. A post hoc test showed that there is a significant difference between task completion times for the touch/visual and gesture/audio configurations (p<0.05).

Eye Glance Behavior

We identified a total of 2371 glances divided into 560 glances below 0.5 seconds, 1729 between 0.5 and 2.0 seconds and 52 above 2.0 seconds. Of the total glances, around 60% occurred with touch input, which amounts to a significant difference compared to gesture input, t=2.04, p<0.05. Looking at the individual eye glance categories, the results show a strong significant difference in the number of glances between 0.5 and 2.0 seconds, with gesture input having substantially fewer, t=2.04, p<0.01. But in the two remaining categories touch has the fewest, although the difference is only marginal. The number of glances for visual output account for 1523 (64%) of the total number of glances across output types, which amounts to an extreme significant difference, t=2.04, p<0.001. There is also an extreme significant difference in the number of glances between 0.5 seconds and 2.0 seconds with audio being significantly lower than visual, t=2.04, p<0.001. Finally, there also exists a strong significant difference in the number of glances above 2.0 seconds, with visual again having more (with 51 glances vs. just 1 glance), t=2.04, p<0.01. On the other hand, audio output has more glances below 0.5 seconds than visual output, albeit only marginally. Across the four configurations, the touch/visual configuration accounts for around 32% of the total amount of glances, 27% for touch/audio, 31% for gesture/visual and just 8% for gesture/audio. A one-way ANOVA showed this difference to be extreme significant, F(3, 28)=13.59, p<0.001. Looking at these percentages, it is perhaps not surprising that the post hoc test revealed that the number of glances for the gesture/audio configuration was significantly lower than for any of the other configurations, p<0.01.

Although touch/visual has substantially fewer glances below 0.5 seconds compared to e.g. touch/audio, this does not represent a significant difference, but a one-way repeated-measures ANOVA indicates that it is approaching significance, F(3, 28)=2.65, p<0.07. For glances between 0.5 and 2.0 seconds, however, an extreme significant difference exists, F(3, 28)=30.22, p<0.001. The results of the post hoc test showed that gesture/audio has significantly fewer glances in this category than any of the other configurations, p<0.01. This is perhaps not surprising, as gesture/audio accounts for just 8% of all the glances in this category. The post hoc test also revealed a significant difference between the number of glances between 0.5 and 2.0 seconds for touch/visual and touch/audio, p<0.05. In the last category, glances above 2.0 seconds, our results show an extreme significant difference in the number of glances, F(3, 28)=7.20, p<0.001. According to the post hoc test, gesture/visual has significantly more glances in this category than any of the other configurations, with p<0.01 compared to touch/audio (0 glances) and gesture/audio (1 glance), and p<0.05 compared to touch/visual.

Discussion

The overall problem we set out to investigate was how to design in-vehicle systems that require as little visual attention from the driver as possible in order to avoid a decrease in driving performance, as current conventional techniques tend to do [START_REF] Lansdown | Distraction from Multiple In-Vehicle Secondary Tasks: Vehicle Performance and Mental Workload Implications[END_REF].

In the following we discuss and reflect on our results. We were inspired by Bach et al. [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF] who raise a question on the effects of separating input and output. This is what we have pursued in our work, where the results show that a distinction between input and output is indeed an important one to make. Our results show that there is a significant difference in the number of eye glances when comparing across output technique. This seems to imply that when conducting experiments with in-vehicle systems it is important to isolate and focus on both the input and output methods of the system.

Input

We initially assumed that touch-based input would require more eye glances than gesture input, as drivers would need to visually obtain the position of the buttons before commencing interaction. This was supported by our findings where we found a strong significant difference in glances between 0.5 and 2.0 seconds, and a significant difference in the total number of glances, which is in line with the work by [START_REF] Alpern | Developing a Car Gesture Interface for Use as a Secondary Task[END_REF][START_REF] Pirhonen | Gestural and Audio Metaphors as a Means of Control for Mobile Devices[END_REF]. In fact, the touch technique accounted for 51% more glances than the gesture technique, with respect to the total amount of eye glances. This number is even greater when viewing the glances between 0.5 and 2.0 seconds isolated, where touch input accounts for almost twice as many glances (98%) as gesture input. This is in line with Alpern & Minardo's findings which show that gesture interfaces, although not attention free, help drivers solve their task while allowing them to keep their eyes on the road [START_REF] Alpern | Developing a Car Gesture Interface for Use as a Secondary Task[END_REF].

The difference in eye glance behavior can perhaps be explained by the fundamental design of the systems. When interaction fails with a touch button based interface, or if several interactions have to be performed in quick succession, users might have a tendency to use more glances in order to ensure/reassure that the correct button is being pressed. Similarly one might suspect that with gesture input, the user only has to visually confirm the position of the screen before being able to issue one or more commands without looking, as opposed to finding the correct button on the screen. This could be part of the explanation for the difference in the number of glances.

Before conducting the experiment we further assumed that gesture input would have relatively more glances below 0.5 seconds compared to touch, the rationale being that the aforementioned visual confirmation of the position of the screen should not take long. However, none of our findings corroborate this assumption. In terms of the number of interaction errors, the two input techniques show no significant difference to each other. In line with the findings of [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF] our results also show touch as the fastest of the two input forms, although not significantly.

Output

We found some differences between the audio and visual output configurations when comparing measurements of primary driving task performance. Only in the number of speed increases is this difference significant in favor of visual output. However, the total number of speed deviations is not significantly different, so what these results indicate, if anything, is unclear since the number of speed decreases is almost identical, and the total amount of speed deviations imply no significant difference.

When comparing task completion time for the two output techniques of our system, there is a significant difference between the two, with visual output being faster. We believe this is due to the nature of audio output. When solving tasks requiring audio output, the user first has to hear the audio message, which can be of arbitrary length, and then process the information they are presented with before being able to solve the task. With visual output the user only has to read the information before being able to answer, which presumably takes less time. Or perhaps the user has already seen the information while performing another task, which further decreases the time required solving certain tasks with the visual output technique.

Another interesting finding is that there is a strong to extreme significant difference in the number of eye glances between visual and audio. We believe that there are several reasons for this difference: first and foremost, the nature of audio output gives less incentive for looking at the screen, since it does not contain any visual information, nor does it give any kind of visual feedback. Obviously, users of touch/audio have more motivation for looking at the screen, compared to gesture/audio, since they still need to locate the buttons on the screen. However, for both configurations it applies that when issuing commands to the system, nothing is gained from looking at the screen, since no feedback is presented there. This is clearly different from the configurations with visual feedback, where there is no way of obtaining feedback other than looking at the screen, which would explain the difference in the number of glances. As a result, audio output leads to a higher task completion time, but fewer eye glances compared to visual output. And, aside from a significant difference in the number of increases in speed, there is no overall significant difference in the primary driving task performance.

In terms of road safety it can be argued that the increase in task completion time is a favorable tradeoff if it comes with fewer eye glances, which in turn leads to more attention on the road. Our results do not however show a link between the number of glances and primary driving task performance, similar to the findings in [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF]. However, other studies state that a relationship between eye glance behavior and driving performance do exist [START_REF] Gellatly | The Use of Speech Recognition Technology in Automotive Applications[END_REF][START_REF] Rockwell | Spare visual capacity in driving -revisited: New empirical results for an old idea[END_REF]. In line with Gellaty [START_REF] Gellatly | The Use of Speech Recognition Technology in Automotive Applications[END_REF], it is not difficult to imagine that more visual attention on the road is preferable, since the driver's primary method of assessing danger signs in traffic arguably is through the eyes. However, further studies are required in order to determine whether this is really the case. This is also indicated in a study on the effects of hands-free mobile phone conversations on driving performance [START_REF] Strayer | Cell-Phone Induced Driver Distraction[END_REF]. Strayer and Drews state that even if drivers conducting a hands-free mobile phone conversation direct their gaze at the road, they often fail to notice objects in the driving environment, since their attention is occupied with conducting the mobile phone conversation. However, their findings relate to mobile phone conversations, which they claim might differ qualitatively from other auditory tasks.

Although our results show that systems with audio output lead to distinctly fewer eye glances than systems with visual output, the results also seem to indicate that audio output comes at a price -namely an apparent drop in primary driving task performance. For instance, the number of speed increases and total number of speed deviations are marginally higher for audio output than for visual output. This could indicate that listening to audio output while driving causes an increase in the cognitive load of the driver, thereby drawing mental resources away from the task of driving. This would be in line with a recent study in the field of brain research, which showed that driving while comprehending language, i.e. listening to voice messages from a hands-free mobile phone, results in a deterioration of driving performance [START_REF] Just | A decrease in brain activation associated with driving when listening to someone speak[END_REF]. Cognitive workload is also discussed in [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF] in relation to their gesture/audio system, but their setup does not allow them to see an explicit connection to the output method, which leads them to attribute it to memory load, e.g. the driver having to remember the gestures and the state of system. Another possible contributor to increased, or perhaps misaligned cognitive load, is the amount of the time the driver spends on solving a specific secondary driving task. As previously mentioned, our results show that the subjects receiving audio output spent significantly more time on completing the tasks. Hence, while audio output might result in fewer glances, the driver is occupied with the task for a longer time, if only mentally.

Limitations

Some of our participants found the limited level of realism in the simulator problematic. They pointed to the absence of tire noise, lack of opportunity to orientate themselves through the side and rear windows and sensation of movement, as some of the factors that they felt affected the realism and their driving performance. This was in part because these factors provide the drivers with a sensation of movement, which helps them estimate speed, without having to look at the road ahead. This could imply that particularly longitudinal control performance suffers from simulated driving [START_REF] Bach | You Can Touch, but You Can't Look: Interaction with In-vehicle Systems[END_REF].

Our choice of case system represents a possible source of inaccuracy. The nature of the music player means that it will always give a form of audio feedback, regardless of which output methods we choose. For instance, pushing the "Play" button will cause music to be played; turning up the volume will cause the music to become louder, etc. This means that test subjects given visual output would not necessarily need to look at the screen to receive feedback.

Conclusion

We currently witness a growing interest in research on in-vehicle systems and their effects on drivers and driving performance. Inspired by previous research on invehicle interaction with touch-screen technologies, we compared different input and output techniques for in-vehicle systems to investigate and measure their effects on the driving activity and driver attention. As we stated in the introduction, driver attention is critical while drivers handle vehicles and interacts with emerging technologies in contemporary cars. We conducted an experiment with 32 subjects in four configurations to investigate the effects of input and output.

Our findings showed that when addressing in-vehicle systems design separating input and output made a difference. Using gesture input resulted in significantly fewer eye glances compared to touch input, but also inferior primary driving task performance and longer task completion times. Audio output caused the test subjects to make more longitudinal control errors compared to visual output, and had a significantly longer task completion time. Visual output, on the other hand, accounted for significantly more interaction errors and a drastically higher number of eye glances. Looking at the individual input/output configurations, our results showed that gesture/audio had the lowest number of eye glances, but also a longer task completion time and more longitudinal control errors than any of the other configurations.

Our results did not, on the other hand, indicate that fewer eye glances necessarily entails better primary driving task performance. On the contrary, audio output, which had fewest eye glances, seemed to cause worse primary driving performance as well as longer total task completion times compared to visual output. This could imply that audio output has an effect on the mental load of the driver, distracting their cognitive attention from the primary task of driving the car. Further research might shed more light on this phenomenon.

Figure 1 .

 1 Figure 1. The graphical user interface for the four configurations (a) touch/visual, /B) gesture/visual, (c) touch/audio, and (4) gesture/audio. The top part of the screen (white) is reserved for output, while the grey area is for input. On the visual (top row) configurations the buttons are, from left to right, "Next song", "Play/pause", "Previous song", "Volume up" and "Volume down", "Song info". In the figure for gesture/visual, the user has just performed the "Play" gesture causing the system to flash the "Play" icon.

  (a) touch/visual configuration (b) gesture/visual configuration (c) touch/audio configuration (b) gesture/audio configuration

Figure 2 .

 2 Figure 2. Excerpt from the video recordings illustrating the driving simulator and the four different cameras. (1) Capturing eye glance for driver attention analysis, (2) input and output screen (here for gesture and audio), (3) the driver and experiment facilitator, and (4) (left) the driving simulator.

Table 1 .

 1 Primary driving task performance across the input and output configurations (N=16).

	A plus denotes a significant difference at a 5% significance level.
		Input	Output
		Touch	Gesture	Visual	Audio
		(N=16)	(N=16)	(N=16)	(N=16)
	Lane excursions	7.19 (4.79)	8.81 (7.13)	8.63 (6.64)	7.38 (5.5)
	Speed increases	6.31 (3.07)	6.69 (6.05)	4.56 (3.41) -	8.44 (5.15) +
	Speed decreases	8.31 (7.42)	10.63 (5.02)	9.38 (2.04)	9.56 (6.79)
	Total speed deviations	14.63 (8.50)	17.31 (7.67)	13.94 (5.78)	18.00 (9.63)

Table 2 .

 2 Primary driving task performance across the four configurations (N=8). A plus denotes a significant difference at a 5% significance level.

		Touch	Touch	Gesture	Gesture
		Visual	Audio	Visual	Audio
		(N=8)	(N=8)	(N=8)	(N=8)
	Lane excursions	7.63 (4.87)	6.75 (5.01)	9.63 (8.28)	8.00 (6.23)
	Speed increases	6.00 (3.66)	6.63 (2.56)	3.13 (2.59) -10.25 (6.54) +
	Speed decreases	6.38 (5.13)	10.25 (9.11)	12.38 (5.68)	8.88 (3.83)
	Total speed deviations	12.38 (8.79) 16.88 (11.67) 15.50 (8.27)	19.13 (10.37)

Table 3 .

 3 Secondary driving task performance across the input and output configurations (N=16). A plus denotes a significant difference at a 5% significance level.

		Input	Output
		Touch	Gesture	Visual	Audio
		(N=16)	(N=16)	(N=16)	(N=16)
	Interaction errors 29.38 (19.69)	34.25 (29.99)	40.69 (29.13) +	22.94 (16.82) -
	Task completion time	271.00 (62.13) 308.81 (95.20) 256.94 (67.66) -322.88 (82.40) +

Table 4 .

 4 Secondary driving task performance across the four configurations (N=8). A plus denotes a significant difference at a 5% significance level.

		Touch	Touch	Gesture	Gesture
		Visual	Audio	Visual	Audio
		(N=8)	(N=8)	(N=8)	(N=8)
	Interaction errors	42.38 (19.72)	16.38 (7.46)	39.00 (37.72)	29.50 (21.27)
	Task completion time	249.88 (24.28) -292.13 (81.62) 264.00 (95.42) 353.63 (75.66) +

Table 5 .

 5 Eye glance behavior across the input and output configurations (N=16). A plus denotes a significant difference at a 5% significance level.

		Input	Output
		Touch	Gesture	Visual	Audio
		(N=16)	(N=16)	(N=16)	(N=16)
	< 0.5 s.	16.44 (13.85)	20.44 (12.09)	15.94 (11.85)	20.94 (13.88)
	0.5 -2.0 s.	71.88 (19.35) + 36.19 (36.66) -76.06 (24.34) +	32.00 (13.88) -
	> 2.0 s.	0.88 (1.36)	2.38 (3.74)	3.19 (3.43) +	0.06 (0.25) -
	Total glances	89.19 (19.10) + 59.00 (46.83) -95.19 (30.14) +	53.00 (34.43) -

Table 6 .

 6 Eye glance behavior across the four configurations (N=8). A plus denotes a significant difference at a 5% significance level.

		Touch	Touch	Gesture	Gesture
		Visual	Audio	Visual	Audio
		(N=8)	(N=8)	(N=8)	(N=8)
	< 0.5 s.	8.88 (4.19)	24.00 (16.20)	23.00 (13.02)	17.88 (11.34)
	0.5 -2.0 s.	86.5 (12.40) + (+) 57.25 (12.62) + (-) 65.63 (29.44) + 6.75 (5.70) -
	> 2.0 s.	1.75 (1.49) -	0.00 (0.00) -	4.63 (4.27) +	0.13 (0.35) -
	Total	97.13 (18.08) +	81.25 (28.83) + 93.25 (46.73) + 24.75 (17.40) -

Acknowledgements

We would like to thank all the participating test subjects in our experiment. Also, we want to thank several anonymous reviewers for comments on earlier drafts of this paper.


Lyn Bartram

Johnny Rodgers

Rob Woodbury

Smart Homes or Smart Occupants? Supporting Aware Living in the Home

Keywords: Residential resource use, interaction design, ubiquitous computing, information visualization, sustainability, domestic design

Awareness of resource consumption in the home is a key part of reducing our ecological footprint yet lack of appropriate understanding and motivation often deters residents from behaviour change. The coming deployment of smart metering technologies, the increasing practicality of embedded devices, and the widespread use of Internet and mobile tools offer new opportunities for "greener" residents. We report on the design and implementation of a holistic interactive system that supports residents in awareness of resource use and facilitates efficient control of house systems to encourage conservation in daily activities. Initial response from two highprofile deployments in unique homes indicates this approach has great potential in engaging residents in sustainable living, but presents many challenges in how technology is integrated into the home environment.

Introduction

A sustainable home is more than an efficient building: it is also a living experience that encourages occupants to use fewer resources more effectively. Research has shown that small changes in behaviour, such as turning off lights, reducing heat and uncovering or covering windows, can result in energy savings between 10% and 20% [START_REF] Woodbury | Buildings and Climate Solutions[END_REF]. Water conservation is seen as a major strategy for present and future residential needs. But changing the way we use resources is proving challenging. We envision the combination of pervasive, intelligent and interactive technologies as a powerful vehicle for reducing demand and building a conservation ethic. These technologies and patterns of use are the focus of recent research but knowledge of how they should be most effectively integrated into sustainable home design is in its infancy. We seek to enable occupants in making appropriate resource use decisions without imposing undue technological complexity or effort. In this paper we describe the design and implementation of the Aware Living Interface System (ALIS) to support and encourage sustainable living practices. We discuss the issues identified, design criteria and future work resulting from implementing the system in two unique houses.

A Tale of Two Houses

Our insights and experience in building information and control systems for the aware resident arise from our involvement in the design and implementation of two sustainable homes: North House and West House.

North House is a small solar-powered home that recently placed 4th overall in the 2009 Solar Decathlon. North House incorporates sophisticated custom energy systems, adaptive intelligent building envelope technologies, specialized lighting and climate systems, and automated optimization behaviour. No conventional control systems were integrated into the home: a digital control panel and an iPhone™ provided the only means for the resident to control, track and manage energy performance in the house.

West House, our second and current project, addresses a different set of goals. It is a small, passively efficient house that uses electricity and natural gas from the public utilities and solar energy to augment heating, hot water and electricity production. It is presented as a conventional home, and typical controls (light switches, thermostats and security systems) are included throughout the house, so that digital and physical controls and feedback are intermingled. We built West House as part of our ongoing collaboration with the City of Vancouver, whose policy makers are keenly interested in how information technology, social media, alternative energies and building design can be combined to foster more sustainable living practices in "typical" houses. During the 10 days of the Solar Decathlon, North House saw more than 60,000 visitors. 65,000 people visited West House during its public display at the Vancouver 2010 Winter Olympic Games. We used these opportunities to engage in conversation and informal interviews with many different people interested in the systems.

These design cases provided us with pragmatic insights into where tradeoffs are likely to occur when deploying these systems into realworld environments. We are faced with a multitude of design constraints not typically associated with traditional approaches. The physical layout of a home introduces numerous constraints on placement, visibility, aesthetic choices, and interactive affordances. Similarly, designing for the idiosyncratic habits and expectations of users in home environments requires sensitivity to how such tools are likely to be used in everyday activities, and to how they cohere with other elements in the home. For example, residents have competing ideas about where visible technology should be located and who controls it. Simply installing software on the home PC or hanging a monitor on the wall is only going to help residents make some kinds of decisions -and only as long as it integrates coherently with their daily activities.

Related Work

The general promise of the smart home -that intelligent operation could be off-loaded to a computational component -has been confounded by the human factor. The single most daunting factors in smart home automation have not been technological capability but complexity and poor usability [START_REF] Eckl | Smart Home Challenges and Approaches to Solve Them: A Practical Industrial Perspective[END_REF]. However, significant research has focused on the power of pervasive and networked computing to automate and enable supportive and adaptive services within smart homes. This work has largely been targeted at enabling assistive environments for in-home care such as the Aware home [START_REF] Kientz | The Georgia Tech aware home[END_REF]. A recent Apple patent [START_REF] Fadelli | Apple Inc. Intelligent Power Monitoring[END_REF] extends home automation to support variable control of how devices are powered and provide feedback on consumption at the device level. More recently, sensing networks have been proposed to analyse and react to user behaviour in the environment to optimize power use [START_REF] Harle | The potential for location-aware power management[END_REF] and enable load shifting. The focus in these technologies is measurement, analysis and control of power in the home, with automation as an underlying principle. In a different approach, Weiss et al. developed a web-based application for monitoring home energy use that allows the resident to monitor consumption on a smart phone and turn individual appliances on or off [START_REF] Weiss | Handy Feedback: Connecting Smart Meters with Mobile Phones[END_REF].

Psychological research [START_REF] Abrahamse | A review of intervention studies aimed at household energy conservation[END_REF] shows that feedback is a central aspect of motivating resource conservation in the home. Recent web-based services partnered with power utilities approach this goal. Google PowerMeter™ and Microsoft Hohm™ allow residents to monitor and analyse aspects of their energy consumption using common "energy dashboard" displays and some description of energy use impacts. Inhome displays such as Blueline's Power Cost Monitor™ show total electricity consumption in terms of kWh hours and money spent. Point of consumption tools such as the Kill A Watt™ are dedicated energy monitoring units attached to a particular appliance or outlet that provide numerical electrical and financial expenditure. All of these tools present aggregate data related to overall use. In contrast with these traditional computing displays, recent work has brought considerable attention to the design of "eco-feedback" technologies [START_REF] Froehlich | The Design of Eco-Feedback Technology[END_REF][START_REF] Riche | Studying Always-On Electricity Feedback in the Home[END_REF] and visualization techniques to promote resource use awareness [START_REF] Holmes | Eco-Visualization: Combining Art and Technology to Reduce Energy Consumption[END_REF]. While others have described the design of eco-feedback technology [START_REF] Froehlich | The Design of Eco-Feedback Technology[END_REF][START_REF] Riche | Studying Always-On Electricity Feedback in the Home[END_REF] and eco-visualization at a high level [START_REF] Holmes | Eco-Visualization: Combining Art and Technology to Reduce Energy Consumption[END_REF], our work builds on design-driven exploration of working prototype solutions in this space, similar to [START_REF] Gustafsson | The Power-Aware Cord: Energy Awareness Through Ambient Information Display[END_REF][START_REF] Kim | ViridiScope: Design and Implementation of a Fine-Grained Power Monitoring System for Homes[END_REF] and fits into the Persuasive Technology and Pervasive Sensing genres that diSalvo distinguishes [START_REF] Disalvo | Mapping the Landscape of Sustainable HCI[END_REF]. We depart beyond much of the HCI research, however, in that we are exploring an entire system for human-home interaction, considering both feedback and control.

How these technologies may aid or hinder residents in developing more sustainable behaviours within the home is an open question. As several researchers have found, simple data feedback is not enough. Awareness does not equate to behaviour change, and a diversity of motivations exists for conservation. Key issues [START_REF] Chetty | Getting to Green: Understanding Resource Consumption in the Home[END_REF] for residents are the lack of real time information around consumption; comprehension of what the energy use units actually mean in terms of behaviour [START_REF] Wood | Energy-use information transfer for intelligent homes: Enabling energy conservation with central and local displays[END_REF]; the complexity of energy-management devices such as programmable thermostats; poor location of feedback away from locations where resource use decisions are made [START_REF] Wood | Energy-use information transfer for intelligent homes: Enabling energy conservation with central and local displays[END_REF]; and the need for motivational tools such as goal-setting abilities and social networking features [START_REF] Mankoff | Leveraging Social Networks to Motivate Individuals to Reduce their Ecological Footprints HICSS[END_REF][START_REF] Petersen | Dormitory residents reduce electricity consumption when exposed to real-time visual feedback and incentives[END_REF]. Perhaps the most widespread example of a poorly implemented human-house interactive tool is the programmable thermostat. It fails for two primary reasons. First, the interface is non-standard and invariably complex across products, so only a minority of users actually configures it successfully. More important, however, is its functional design. Most peoples' lives are more complex and variable than the simple schedules the thermostat accepts. As a result, people tend to program it for the minimum case, and end up heating their homes unnecessarily in periods when they are absent. This example is representative of the need for a more flexible and responsive design approach.

Chetty advocates several design principles: make real-time information visible and comprehensible; design for individual and collective agency for motivation and reward; ensure technologies are attainable; and seek new ways of stimulating discussion and engagement [START_REF] Chetty | Getting to Green: Understanding Resource Consumption in the Home[END_REF]. Fitting these technologies into the home poses additional challenges. Residents have competing ideas about where visible technology should be located and who controls it. They also feel overburdened by the complexity and inflexibility of home technologies they already use [START_REF] Stringer | Lessons for the Future: Experiences with the Installation and Use of Today's Domestic Sensors and Technologies[END_REF]. This can be seriously aggravated by automation: humans have an uneasy relationship with automated control [START_REF] Woods | Automation: Apparent simplicity, real complexity[END_REF], as we discovered in North House.

Lifestyle and human factors

Social scientists provide some insight into what kinds of lifestyle factors contribute to residential energy use. Research indicates four variables influence behaviour: norms and beliefs; external persuasive forces (community pressure, advertising); personal knowledge and skills; and habit and routine [START_REF] Stern | The value basis of environmental concern[END_REF]. Energy use affected by the last is driven by comfort and effort (time and actions required to execute): conservation activities depend on lowering both cost and effort [START_REF] Schuitema | Percepties van energieverbruik van huishoudelijke apparaten (Perception of energy use of domestic appliances)[END_REF][START_REF] Steg | Promoting Household Energy Conservation[END_REF]. In addition, this research pinpoints the role of knowledge; many people have incorrect estimations of how much energy is used in hot water and appliances, influencing their use. So while demographic factors such as gender and income play major roles, the more malleable psychological aspects of habit, effort and knowledge have a strong influence.

Design Rationale

These themes were repeated in our preliminary user workshops with people who described themselves as "interested" in sustainable living environments but who had no experience with them. Participants included students, professionals and blue and white-collar workers. While the motivating models differed (some were more interested in positive financial outcomes where others were more interested in their energy footprint and ecological impact), we found several common threads. The first was time: all of our "users" identified themselves as very busy people and were concerned about having to spend too much time and effort in managing the house. A related thread was place: people are very mobile, wanted appropriate information and controls accessible from wherever they were, and wanted localized and contextually appropriate access in the house itself. For example, none found the notion of a central control panel and dashboard for lights, shutters, etc. very useful, but liked the idea of information and controls in place. A third was related to complexity: whereas several indicated they would be interested in learning more about how the house actually worked, all wanted a simple interface with a low learning curve that would provide quick access to reasonable house configuration while allowing the more expert user to fine tune settings. Finally, participants really wanted to know "how they were doing" in the context of their particular goals (financial, energy use) and how this changed over time and events.

The design dialogue in the development of efficient buildings has largely focused on smart automation of the building systems and components for optimal performance rather than on effectively supporting how people use them [START_REF] Leaman | Assessing building performance in use 4: the Probe occupant surveys and their implication[END_REF]. But the single most daunting factors in smart home automation have not been technological capability but complexity and poor usability [START_REF] Eckl | Smart Home Challenges and Approaches to Solve Them: A Practical Industrial Perspective[END_REF]: residents are not professional facilities managers. In contrast to the automated smart home populated with intelligent devices, we focus on the aware home with support for the smart occupant, and we focus on an integrated, extensible system as opposed to a loose collection of different tools. We aim to reduce the technological and cognitive effort required to make decisions about resource use and understand its impacts. Our rough "grounding equation" can be expressed as Cost > Benefit ≠ Change. That is, if the perceived cost (effort, time) of doing something outweighs the perceived benefits, people are unlikely to change their behaviour [START_REF] Steg | Promoting Household Energy Conservation[END_REF]. We propose we need to reduce the overhead of performing conservation actions and increase the motivating benefits, including non-financial incentives. Additionally, we posit that a piecemeal approach introduces complexity, and that residents will benefit from a coherent ecosystem of information feedback and control tools that are integrated into systems they already use [START_REF] Bartram | Chasing the negawatt: visualization for Sustainable Living[END_REF]. We based our system design on the following criteria.

1. Rich, real-time feedback. Make real-time and cumulative resource use information available to support decision-making and information access at a variety of levels: in-the-moment awareness; lightweight monitoring; analysis and reasoning; consequential judgment and prediction.

2. Context. Present information in contextually appropriate ways: for example, energy use expressed in both financial terms and common usage ("enough to power a washing machine"). Embed information where decisions are made.

Individual and Social Motivation:

Provide goal-setting capabilities and integration with social and community networks.

4. Control: Enable efficient resource use decisions by building a control hierarchy for optimizing resource conservation. Design and distribute controls appropriately: embedded in the home, remote or mobile.

Aesthetics:

Respect the design sensibility of a home. Explore subtle ways to provide feedback.

6. Familiarity: Reduce complexity by leveraging tools people already use in their information landscape, such as calendars, browsers and clocks.

This combination of design goals is substantially more sophisticated than what available home automation systems or monitoring applications offer. In fact, it moves the design brief from that of smart home automation or energy use monitoring to a more comprehensive resource management system that is integrated with the home environment and residents' activities while respecting the dimensions of aesthetic and emotional comfort.

The Aware Living Interface System

Our system comprises three main components: a control backbone that provides fine-grained measurement, device control and automation logic that is based on a heavily customized commercial application; a Web services layer that manages data and commands between components; and the Aware Living Interactive System (ALIS) that embodies the resident's interaction with the home (Figure 1). ALIS is built on a comprehensive information model incorporating control and device details, resource-specific production and consumption data in terms of standard units, pricing levels, and standard usage equivalencies, personal and shared goals, and a hierarchical model of energy-control settings to enable "one-step" optimization. It currently comprises different forms: a set of variably configured client interfaces run on web browsers in several platforms; a mobile application; and ambient, "informative art" displays embedded in the home.

ALIS supports a variety of feedback displays and analytical tools. These displays are presented as web pages running in a browser in different platforms: while they can be served to any web-enabled device, they are optimized for the viewing experience of a "typical" computer screen. We currently link to and embed information related to conservation from a number of sources, including the public utilities, directly into the ALIS information framework.

At the top level is the Resource Dashboard that expresses resource use in variable terms: as standard units, financial figures, by usage ("Today's water use is equivalent to two baths") and in relative terms ("25% less electricity than yesterday") (Figure 2). We are exploring appropriate contextual ways to present the information, as these vary not only by individual preference, but also by location and task: for example, in the garage, the resident may wish to see the power consumed by his/her electric car overlaid on the top-level Dashboard view, and to see it in terms of "kilometers earned." Detailed information on resource production and use is available in real-time and historical views, categorized in different ways (by type of device, by location in house, by time of use). We also make use of professional building energy analysis tools from the industry leader, Pulse™ Energy, for detailed performance analysis and prediction. The application integrates visualization components in the display of energy use data in multiple formats: as numerical financial data and graphs in Residents can set personal milestones and challenges that can be measured by the system -for example, "use 10% less energy than last month." Figure 2 shows the progress towards one such goal on the Dashboard.

Social Interaction: The Neighbourhood Network

The community interface, which we call the Neighbourhood Network, encourages competition, comparison, and collaboration between community members. It is designed to take advantage of the widespread use of social networking software to enable individuals to connect with a wider community of people pursuing similar goals around sustainability, enabling them to share strategies, incentives and successes. It is available on both PCs and mobile devices as part of the web application. Occupants can see a historical view of their energy consumption compared to a community average and set conservation goals, allowing them to compete with others in achieving those goals while sharing tips and comments.

Those who follow through with their commitments receive awards (in the form of digital trophies) that are displayed to others within the community. These eco-trophies are not only applied to challenges, but also recognize the degree to which one engaged with the community by posting conservation advice and replying to other members' feedback. We note that we are not developing a social network itself; rather we integrate existing forum tools. A Facebook™ plug-in connects to the ALIS server to automatically collect data to publish (Figure 3): other non-ALIS data we intend to collect using voluntary self-entered reporting. Note that an issue in sharing energy use information relates to normalization: simple numbers between

Informative Art

We are exploring the use of informative art visualizations in two contexts. The Ambient Canvas is an informative art piece embedded in the kitchen backsplash that provides feedback on the use of electricity, water, and natural gas. As opposed to typical graphical displays that may use numbers or charts to convey information, the Ambient Canvas combines LED lights and filters of various materials to produce light effects on the kitchen backsplash. This subtle feedback on performance and energy efficiency does not require active attention on the part of the resident, and integrates into the home cohesively. In addition, we are currently studying the use of landscape photographs that subtly change according to levels of water consumption. These are meant to serve as both aesthetic elements and informative views. In both these contexts, informative art is intended to promote awareness of resource use to assist and influence sustainable in-the-moment decision-making while respecting aesthetic constraints. We emphasize that the attraction of such imagery is idiosyncratic to each home and are actively exploring the utility and acceptance of different representations.

Control

As in standard home automation, ALIS enables the resident to control and monitor lights, shades, and climate settings. In addition, the for example, turning off most lights and lowering the thermostat in a "Sleep mode", or tuning settings and shutting down standby power in "Away" mode. These presets can be activated via one button from any ALIS control interface (Figure 4). Modes can also be scheduled. Modes are entirely user configurable, and coexist with individual control settings for fine-grained control when desired. Finally, we implemented a control hierarchy so that residents could easily access master controls and the most commonly used switches, or selectively drill down to fine-grained controls on demand.

Platforms

ALIS is currently implemented largely as a Web services application and delivered on four hardware platforms: embedded touch panel computers, personal computers, mobile phones, and the integrated informative art. The system can also accommodate expansion across further platforms. As implemented, the user interfaces provide control, feedback, and community features across the various devices, with each set of features contextualized for the location, application and form factor of the delivery hardware.

The ALIS PC interface offers the most detailed access to feedback about resource consumption in the home. It provides the resident with standard full access to the entire ALIS system. Residents use the PC interface from any computer on the Internet to configure modes, set goals, and schedule house operation. Three touch panels are embedded into the structure of each home. Each display runs a browser in full screen kiosk mode. A large touch panel is placed in a central location: in the kitchen backsplash in North House, and on the wall in the central hallway in West House. Additionally, two smaller panels provide localized control and feedback throughout the home. In North House these are placed at the two entryways. In West House, one is installed in the garage and the other is placed beside the upstairs desk. Access to the full ALIS PC interface is possible at each access point, but complicated by the affordances and form factor of a touch screen. To address this, we have configured the interfaces on the panels to default to information and control views appropriate to their location. For example, the main control panel allows easy access to all house controls and the Resource Dashboard, while the garage panel provides lighting and climate controls for the garage area, one-touch home control presets, and community and transportation information. We anticipate that at this location the resident may be particularly interested in public transit schedules, carpooling options, and data on the performance of their electric car.

The mobile device provides feedback and control to the residents of the home from their pocket -a simplified remote house control.

Mobile applications were developed as an extension of the web application (Figure 5). They offer a subset of the features available in the PC user interface, with each feature redesigned for use on a mobile device. For example, the controls available from the mobile emphasize ease of use through logical groupings. These "master" controls allow the resident to adjust the lights for a whole room, or shades for a whole house façade, with a single control. More fine-grained control of individual fixtures is still available, but a hierarchy of control makes the most used items easily accessible. Graphing displays are simplified; a playful 'Spinner' interface allows residents to select pairs of performance variables through a slot-machine style interaction to compare and graph on demand. The mobile application provides alerts about house status and energy consumption and production thresholds and allows residents to keep in touch with their neighbourhood network, offering challenge monitoring and community chat.

Discussion

There are many issues and challenges with meeting the design criteria we have established, and numerous open questions and investigations to pursue. Recently, West House has become tenanted as a living lab, and we will explore the system through longitudinal studies in a real-world context over the coming years. This design exercise of building the house system has given us the initial chance to assess the challenges and utility of the initial criteria.

ALIS provides a variety of views that support all levels from in-themoment awareness to detailed analytical visualizations. These are available both via the Internet and distributed throughout the house. However, while there has been enormous interest and keen appreciation of the informative art approach, we have as yet not established what are the best mappings to communicate resource use, nor even if the simple resource use data is most appropriate to show. In North House we displayed bands of light for progress towards the resident's current goals as well as electricity production and use. In West House, the backsplash illuminates successively more as the day's usage of resources increases. We discovered immediately (and unanimously) that our visitors loved the Ambient Canvas fully lit -exactly the wrong reinforcement message to send! Similarly, we discovered in our water photos study that even some depictive images are ambiguous. While the informative art approach seems very attractive and participants and visitors very positive about it, careful attention will need to paid to the representational strategies.

We have only begun to explore contextual presentation and delivery. We do use common usage where possible to "demystify" energy use, but this is only the tip of the iceberg. Location affects meaning: water use in the laundry might be expressed in terms of loads of laundry and in the bathroom as bathtub equivalents.

We have embedded information in some places where decisions are made -notably the kitchen and the living room. We are beginning to prototype small, lowdata displays for appliances to simulate different time-of-day and grid/water-load conditions, anticipating the introduction of sophisticated smart metering.

ALIS' controls seamlessly integrate with the standard house devices of switches and thermostats and are ubiquitously available. Simple onebutton presents allow house energy use to be finely tuned to userconfigured modes, avoiding the programmable thermostat paradigm, as the resident can now simply control the house according to his or her current activity as opposed to a schedule. We are developing computational models of "effort" to evaluate how this might reduce and/or aggravate the overhead of using the house.

We have not yet fully implemented social norming tools, although currently we support a simplistic personal challenge that allows the user to set simple quantifiable goals. ALIS data can also be published to a Facebook™ plug-in that enables a community to share and compare challenges, as well as allowing the resident to compare their performance against regional performance if that data is provided by the public utilities.

Finally, we opted to build the ALIS as a web application because it allowed us to easily connect to external services and tools already in use by residents, such as Google Calendar, localized weather APIs, social networks, and energy management software tools, and to provide a familiar interface and interaction paradigm to residents through the browser. Currently we are prototyping integrating ALIS data with common information management tools such as calendars. With this, the resident can see his or her energy use in the context of daily, weekly or monthly activities. This same philosophy guided the decision to set simple one-step controls to turn the house "on" or "off"; into active or automated states. People are already used to using light switches when they leave the house or go to sleep: we simply extend the reach of that simple habit to whole-home configurations.

Conclusion and Future Work

The Aware Living Interface System is an integrated monitoring and interactive control system to encourage more efficient resource use in the home. ALIS is based on an extensible control and feedback architecture that can accommodate a diversity of energy sources, hardware devices, and home control contexts. It provides a range of user interfaces on different platforms, from smart phones and web browsers to embedded and ambient displays, each designed to support sustainable decision-making in the home. We have operationally tested the system in two very challenging public showcases, where more than 120,000 people have visited and interacted with the live systems over a combined period of 4 weeks. Our design experience with these two unique homes has helped us refine a set of criteria for building more effective information systems for residential conservation. We offer these criteria as a contribution.

Our future work will build on these lessons, and emphasize thorough evaluation of the system in a real-world context. Studying residents in this fully functional home in a vibrant neighbourhood will enable us to evaluate a variety of approaches, and engage with occupants and community members to further develop our understanding of social motivations. We also intend to expand the range of visualization and control devices, and to explore adaptive display and lighting control that responds to viewing requirements and resident activity. We hope this research will help to improve the design and deployment of interactive interfaces for in-home technological systems that support sustainability,

Figure 1 .

 1 Figure 1. The ALIS software architecture.

Figure 2 .

 2 Figure 2. The Dashboard

Figure 3 .

 3 Figure 3. Facebook App

Figure 4

 4 Figure 4. The Ambient Canvas

Figure 4 :

 4 Figure 4: One view of the ALIS controls interface.

Figure 5 :

 5 Figure 5: Mobile

ACKNOWLEDGMENTS

MITACS Accelerate BC, Western Economic Diversification Canada, BC Hydro, and the City of Vancouver funded this research. We sincerely thank our many industrial and government partners for their extensive support.