Technology and Health Care 22 (2014) 657–666 DOI 10.3233/THC-140839 IOS Press
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Design of smart home sensor visualizations for older adults Thai Lea,∗ , Blaine Reederb , Jane Chungc , Hilaire Thompsonc and George Demirisa,c a Department
of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA b College of Nursing, University of Colorado Anschutz Medical Campus, Denver, CO, USA c Biobehavioral Nursing and Health Systems, University of Washington, Seattle, WA, USA Received 23 May 2014 Accepted 12 June 2014 Abstract. BACKGROUND: Smart home sensor systems provide a valuable opportunity to continuously and unobtrusively monitor older adult wellness. However, the density of sensor data can be challenging to visualize, especially for an older adult consumer with distinct user needs. OBJECTIVE: We describe the design of sensor visualizations informed by interviews with older adults. The goal of the visualizations is to present sensor activity data to an older adult consumer audience that supports both longitudinal detection of trends and on-demand display of activity details for any chosen day. METHODS: The design process is grounded through participatory design with older adult interviews during a six-month pilot sensor study. Through a secondary analysis of interviews, we identified the visualization needs of older adults. We incorporated these needs with cognitive perceptual visualization guidelines and the emotional design principles of Norman to develop sensor visualizations. RESULTS: We present a design of sensor visualization that integrate both temporal and spatial components of information. The visualization supports longitudinal detection of trends while allowing the viewer to view activity within a specific date. CONCLUSIONS: Appropriately designed visualizations for older adults not only provide insight into health and wellness, but also are a valuable resource to promote engagement within care. Keywords: Information visualization, older adults, smart home, sensor
1. Introduction With a growing older adult population and limited healthcare resources available, technological solutions are vital towards supporting healthy aging and independence. One approach involves smart homes, defined by Demiris and Hensel as “a residence equipped with technology that facilitates monitoring of residents and /or promotes independence and increases residents’ quality of life (page 33) [1]” The goal of smart home health monitoring is to provide continuous unobtrusive data capable of assessing risks in an older adult’s health status that would otherwise be sporadic with clinical visits. ∗ Corresponding author: Thai Le, University of Washington, Box SLU-BIME 358047, 850 Republican St, Building C, Seattle, WA 98109, USA. E-mail: [email protected].
c 2014 – IOS Press and the authors. All rights reserved 0928-7329/14/$27.50
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Existing field research has found that older adults are receptive to smart home health monitoring [2– 4]. However, a caveat is that the health monitoring must provide personal utility and value. As such, older adults often cite perceived value of smart home technology, not necessarily for themselves, but for others who have restricted mobility or increased health risks. A limitation to the perceived value of smart homes is the lack of feedback provided to older adults regarding the health monitoring data. Having this feedback could better promote engagement of older adults in the care process while also encouraging health efficacy. Smart homes collect data from a broad range of sources and thus present a key challenge to information visualization. Visualizations serve to abstract data in representations that facilitate knowledge discovery and insight generation. The impact of health visualizations has been demonstrated in multiple studies where the format of visual displays influences decision making from both the older adult and health care provider perspectives [5–7]. However, poorly designed visualizations can cause confusion, obscure information, and contribute to misinterpretations. Appropriately designed visualizations require addressing the unique needs of the older adult user. Such information needs often differ from that of health care providers though existing sensor visualizations are often designed for academic or clinical settings. Older adults may lack the clinical expertise that facilitates understanding of the often dense nature of sensor data visualizations, thus limiting the utility of these visualizations for the older adult consumer. In this paper we describe the development of a novel visualization approach towards representing sensor activity within the home. Through a secondary analysis of interviews, we identified the visualization needs of older adults. We incorporated these needs with cognitive perceptual visualization guidelines and the emotional design principles of Norman. The result is a set of visualizations that represents both spatial and temporal components of sensor data, allowing for granular detail on activity at a daily level and coarse detail for longitudinal health monitoring. 1.1. Designing for older adults Czaja and Lee identify two factors contributing towards the growth of technology adoption by older adults: 1) the quickly aging population and 2) the rapid dissemination of technology [8]. The challenge for designers is understanding the needs and abilities of older adults in order to create systems that are not only usable but useful. Even within the older adult population, there is vast diversity due to the heterogeneity of aging. Czaja and Lee propose that designers focus not on the chronological age alone, but instead on the potential physical and behavioral changes that impact technology use as individuals age [8]. From a cognitive perspective, aging is associated with decreases in working memory, prospective memory, spatial cognition, and processing speed. These normal aging associated changes may manifest as older adults are presented with complex or novel cognitive situations, resulting in slower technology adoption. Recognizing these differences due to aging is not enough; designers are still faced with unfamiliarity in addressing these design needs. Chisnell et al. provide heuristics for designing web sites accessible to older adults through a review of existing literature [9]. Though not all heuristics apply to information visualization, Chisnell et al. highlight the importance of: 1) making overall visualization easy to skim or scan 2) making elements within the visualization easy to read, 3) visually grouping related elements, 4) making sure text and background colors contrast, and 5) using adequate white space [9]. Czaja et al. provide further guidelines for the interface design of computer systems. The guidelines are designed with the older adult in mind, however they focus primarily on interface level interaction between different elements of a system.
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1.2. Existing visualization approaches Smart home sensor data can be collected from a wide variety of sources include motion sensors, video, and pressure sensors. The goal of these sensors is to identify activity within the home in an unobtrusive manner. For this discussion, we focus on visualizing motion sensor data, which consists of a spatial component (the location where the sensor activated) and a temporal component (the time of activation). Thomas and Crandall describe the use of PyViz, a data visualization tool to represent sensor deployment within the home [10]. Using a 2D floor map, PyViz generates heat maps of sensor activity for live visualizations. Szewcyk et al. represented the spatial component of the sensors using a 3D simulation of the smart home environment through the Second Life protocol [11]. Sensor activity is highlighted as red dots throughout the home. The visualizations can be displayed in real time or played back from sensor event readings. Wang and Skubic apply a different approach towards visualizing sensor data through a density map [12]. The visualization consists of a grid, with the x-axis representing the 24 hours within the day while the y-axis displays each day of activity. Each grid element is colored with density representing amount of activity within the home. The visualization reduces complexity associated with location for sensor firing and instead takes an aggregate measure of sensor activity within an hour period. The authors applied these visualizations to sensor data within the TigerPlace smart home residence and demonstrated case studies of differences in activity over time [12]. We seek to extend these existing sensor visualization approaches focusing on the older adult’s perspective with the goal of creating designs that support comprehension of complex and dense sensor data. 1.3. Preliminary work As part of a pilot study described by Reeder et al., we implemented a sensor-based monitoring system within the apartments of eight community dwelling older adults for a six-month period [13]. All participants were at least 65 years old and spoke English. We used commercially available passive infrared sensors that detect motion through changes in ambient temperature. The sensors were installed throughout rooms within the apartment. We conducted visits at baseline, 3 months and 6 months. At the 3 month and 6 month visits we conducted interviews on perceived value of sensors and visualization needs [13]. We applied a participatory design approach to include older adults throughout the development of the visualization prototypes [14,15]. As part of the interviews, we presented participants with iterations of sensor visualizations based off of time series and bar plots [16]. We used the interviews to solicit feedback as part of the iteration process and to better understand the visualization needs of older adult consumers of the health information. The University Institutional Review Board approved all study procedures. 2. Methodology We developed innovative visualizations of health-related sensor data focused on the older adult consumer. First, we performed a content analysis of a sensor study pilot study with older adults to extract themes to guide our design efforts. Then, using cognitive principles of visualization and emotional design, we created streamgraphs and radial plot visualizations of simulated sensor data. 2.1. Content analysis of visualization needs The first author conducted a secondary analysis of interview data from the 3 month and 6 month interviews. Lab members transcribed the interviews verbatim. An open coding analysis was applied to
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identify the information visualization needs of older adults for sensor data. In particular, we wanted to understand at a fundamental level, what types of visualization information older adults would find useful. We extracted themes from the open coded transcripts and provide a qualitative descriptive analysis of the results per the methodology described by Corbin and Strauss [17,18]. This provides a framework for the redesign of sensor visualizations grounded on stakeholder needs. 2.2. Generating synthetic data Due to the confidential nature of sensor data, we chose to develop visualizations from a synthetic data set. Simulated data can be valuable towards imitating different behaviors within the real world [19,20]. The focus of our work is on visualization approaches; we generated the data as a resource to apply the visualizations. The synthetic data consists of two types. The first is a count of sensor activity firings in each of four locations (living room, bedroom, bathroom, and kitchen) aggregated over a one-day period across 60 days (from March 1, 2013 through April 29, 2013). Therefore each of the 240 data elements consisted of Date x Location x Number of Sensor Firings. The second data type was at higher granularity, representing sensor activity within a single day taken from two representative dates. Rather than counting the total number of sensor firings within the day, this data set consisted of time stamps of when each sensor fired along with the location of the sensors. The spatial locations were the same as the first data set. 2.3. Cognitive principles of visualization We base the functional component of the visualization designs on existing literature from the cognitive information visualization domain. This is a broad field, however we focus on theories and principles that directly relate human cognitive perception to visual design. As summarized by Shah and Hoeffner there are three primary steps involved in comprehending a visualization: 1) encoding the visual information 2) relating visual features to concepts and 3) associating concepts with existing knowledge [21]. Encoding visual information refers to identifying visual characteristics of a graph. The visual characteristics of the display can influence how effective the viewer can encode such information. Bertin provides an exhaustive overview of principles in graphical composition by identifying fundamental visual variables and defining rules to build graphics out of these variables [22]. Bertin characterizes visual variables into seven types: position, size, shape, value, color, orientation, and texture [22]. Cleveland and McGill identified a similar set of six elementary perceptual tasks of visualizations and ranked the tasks from most accurate to least accurate as: position along a common scale, position along a non-aligned scale, length and angle, area, volume, and shading [23]. The grouping of elements has also been found to impact how visual information is encoded as a gestalt whole [24–26]. After encoding visual information, a viewer has to map these features into conceptual relations such as differences in size, changes in trend, and differences in spatial location [26,27]. Pinker developed a model of cognitive processing for the translation of raw visual graphics into encoded visual descriptions. His work proposes a set of cognitive operations that are executed when processing a graph, creating a mapping (graph schema) that relates visual features to conceptual relations found in the display [28]. Errors in interpretation occur when visual characteristics do not effectively get translated into concepts or relationships [26,28,29]. Existing knowledge impacts the interpretation of visualizations by providing context for conceptual elements [30]. For example, Shah found that students interpreted graphs differently based on their existing expectations of content [25,31]. Sensor visualizations designed for older adults are challenging
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because there is often a gap in domain knowledge. Concepts of sensor activity and their association with health status may differ across participants. From a design perspective, this informs the need to articulate the underlying structure of the visualizations and to relate it to familiar existing experiences of participants. For example, explaining sensor activity within terms of interactions throughout the home allows viewers to relate concepts back to experiential knowledge. Providing this connection promotes the generation of insights derived from visualizations. We applied the concepts within Shah and Hoeffner’s model of information visualization processing throughout the development of the visualizations. This included selecting the appropriate graphical elements to construct the visualization, applying consistent mapping of data visual encodings, and incorporating prior knowledge within the context of the visual design. 2.4. Norman’s concept of emotional design Our approach to the development of sensor visualizations is based on the emotional design model. Norman conceptualizes the complex affective interactions between user and artifact through emotional design [32]. This model is based on three layers of sensory processing: the visceral, behavioral, and reflective levels. The visceral level is an initial reaction to the design, a rapid judgment of good or bad. This emotional response is an aesthetic first judgment based on how an artifact looks, feels, and sounds. The behavioral level involves interactions of the user with the artifact. The focus is on the functional component of design, whether or not the design sufficiently performs a desired task. The reflective layer is an introspective evaluation of how the artifact sends a message about the user. Designing at the reflective layer considers how the artifact evokes memory for the user and the self-image that it creates [32]. We recognize that cognitive principles play a valuable role during the behavioral level of the design process. However, we also take into account the visceral and reflective impact that visualizations may have for older adults. We apply the concepts proposed by Norman through explicitly identifying the visceral, behavioral, and reflective components of our visualization design. By accounting for design in a holistic manner across these three dimensions, we seek to develop visualizations that are appealing, understandable, and valuable for older adults. 3. Results Below we present 1) visualization needs of older adults identified through content analysis of interviews and 2) streamgraphs and radial plots as novel approaches towards visualizing sensor data. 3.1. Older adult sensor visualization needs We identified two themes related to sensor visualization needs through a content analysis of interview data: value of visualizations to identify behavior patterns and importance of longitudinal trends. In the first theme, participants cited the value of using visualizations to identify patterns of behavior that were not otherwise inherently recognized. As described by a participant, “. . . this incremental kind of stuff that goes very slowly everyday as I see it and that you are unaware of and then all at once it is all pulled together into a picture and it shows you that you’ve changed, you’ve changed, you hadn’t realized it but you changed.” Another participant repeated this concept in describing how the visualizations would be valuable for their spouse suffering from dementia. Participants commented on the value of having visualizations that are able to identify patterns that they were unaware of existing. This concept also
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applied in the opposite direction; participants did not find visualizations useful if they were only a strict recording of activity, “. . . now why would I want to look at this, because I know what’s happening and my wife knows what’s happening. We both, neither of us, have dementia so far.” In the second theme, older adults noted that daily variability is a secondary emphasis to longitudinal trends within the visualization. Older adults found that any given day may vary in terms of sensor activity; “So this is just a point in time? How would that be likely to help anybody?” In addition, there may be portions of the week where activity levels divulge from the norm due to special occasions such as a visitor from out of town. As a result, short-term visualizations of sensor data reflect these differences in variability; participants raised interest only after outliers in activity have become persistent over time, “I might initially look at it day by day, just for curiosity, and then I’m sure it would fade away, maybe to just looking every three months, or six months, or year. Its value would be more on a longer term basis, to show trends.” This is not to say short-term visualizations are irrelevant; participants noted the importance of having the ability to visualize activity at a daily level to compare patterns of behavior; “. . . if I were going to show this to someone I’d want to know what particular day did we break the pattern and if it was 12 days ago I’m dead because I can’t sort it out. I have to think for a second. . . but if it were coming daily or something like that then suddenly there was a break in the pattern and somebody called me the next day. . . ” As a participant pointed out, the increased granularity of visualizations provides insight into understanding differences in activity: “You’ve got to get enough data to have some meaning to it, breaking it down week by week by week.” Based on the results of the content analysis, our design objectives focused on providing longitudinal data visualizations. The visualizations should support the discovery of trends, in particular gradual changes in activity over time on the scale of months. We also wanted to create visualizations that presented information at higher granularity given that behavior can be cyclical about the day. Because the form of visualizations is heavily influenced by function, we chose to separate the design objectives into two visualizations incorporated into an interface. The interface provides users with a gross longitudinal view of the data using a streamgraph while allowing for the option to dwell into the data at a 24 hour daily cycle using a radial plot (Fig. 1). 3.2. Visualization designs – streamgraphs We visualized longitudinal sensor data using streamgraphs (shown on top of Fig. 1), a variant of stacked bar graphs [33]. In the streamgraph, each layer corresponds to a spatial location as identified by the sensor. For our data, this came from the set of (living room, bedroom, bathroom, and kitchen). The thickness of each layer corresponds to the amount of sensor activity while the x-axis represents time. The streamgraph contains an additional offset function that shifts the baseline of the stacked layers. In a stacked bar graph, this offset function is zero, resulting in layers on top of the x-axis. We used a symmetric offset function described by Havre et al., resulting in a layout that has horizontal symmetry between the top and bottom layers [34]. In considering cognitive design principles, we selected a stacked graph to display both the total activity on a given day and the location of that activity within the home. This satisfies the macro/micro composition guideline proposed by Tufte [35]. Because the layers are not aligned along a common axis, a tradeoff of streamgraphs is that it is difficult to compare thickness between different layers [23]. However, Byron and Watternberg show that using a symmetric offset baseline for the streamgraph layout results in a display that minimizes the sum of squares of the slopes at the topmost and bottommost silhouettes [33]. This creates visualizations that limit sharp spikes in the overall shape of the graphic [33].
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Fig. 1. Visualization of smart home sensor data. At top is a streamgraph display over 60 days of total sensor activity within a day distributed by location within the home. Bottom radial plots show sensor activity within a single 24 hour day. Radial curves are colored by location of activity and emanate from center towards the time in the day when activity occurred on the outer circle. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/THC-140839)
At a visceral level, we chose streamgraphs as a visualization approach in order to create a natural flowing graphic consistent with the metaphor of data flowing over time. We selected a color palette with enough contrast to show differences amongst layers, however within the same family color of dark red to tan. We did not want a sharply contrasting color palette since this may be distracting and takes away from the part to whole composition of the sensor data. At a reflective level, we applied streamgraphs for the longitudinal visualizations out of concerns that other traditional techniques would be perceived of as too scientific or mathematical in nature. Given that these designs are for older adults, we focused on maintaining simplicity and satisfying the goal of visualizing longitudinal trends. The flow of the streamgraph from left to right in time creates an analogy of changing current flow [34]. It draws on existing knowledge and familiarity with bar charts to support comparisons of width within a layer. 3.3. Visualization designs – Radial plot We created a visualization of sensor activity for a single day (shown on bottom of Fig. 1) based on the radial plot [36]. The 24-hour cycle is arranged on the outer circle analogous to the face of a clock. The inner circle contains the legend, a set of four location labels for the sensor data, each in a separate color. There are four equally spaced nodes along the inner circle where curves emanate to the outer circle. The
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color of the curve corresponds to location of sensor activity and the point where the curve connects to the outer circle is the time during the day when the sensor activity occurred. The origin nodes divide the 24-hour cycle into 6 hour segments. For example, sensor activity occurring between 21:00–03:00 would originate from the northernmost node, while activity from 09:00–15:00 emanates from the southernmost node. The visualization applies gestalt principles of similarity to group together sensor activity by location. Frequent sensor activation within a room is mapped to spatially adjacent locations on the circle with the same color, creating a group of activity. The overlap of curves within the display creates complexity, however by the principles of good continuation, lines are visually followed along the smooth path. The visualization encodes two types of information consistent with the perceptual rankings of Mackinlay [37]. Time of sensor firing, a quantitative measure, is encoded through position and angle on the circle while location of activity, a nominal variable, is encoded through hue. At the visceral level of design, we selected the radial plot to represent connections between time and location of sensor activity. Though an initial attempt was made to use the same color scheme as the streamgraphs for consistency, we found it difficult to differentiate radial curves due to lack of contrast. As a result, we selected a more vibrant and strongly contrasting color scheme to represent location. The 24-hour day cycle lends itself to a circular representation and the clock-like face is a familiar analogy at the reflective level. 3.4. Visualization designs – Evaluation We evaluated the visualizations informally with gerontological experts through interviews, asking for heuristic-based feedback. Overall, participants understood the spatial and temporal component of the visualizations. In particular, the radial plot created an effective analogy to a 24-hour clock with participants commenting on the unusual late night pattern of activity on 4/21/13. In the streamgraph, participants focused on the aggregate pattern of activity, in particular the reduced sensor activity from 3/24/13–4/07/13. It was more difficult to identify what components contributed to the reduction in sensor activity. This is attributable to a limitation within the streamgraph. Each component of activity is shifted on the y-axis. As a result, comparisons between components involves a significant cognitive load to shift and re-align heights for comparison. In addition, though the streamgraph allows users to identify differences in overall activity quickly, it does not provide enough granular data to allow inferences as to why these changes occurred. This is something that the radial plot provides as added support. Participants also commented on the potential utility of having an interactive visualization allowing them to view activity in isolated locations or to change the date for the radial plot. 4. Discussion There exists value in visualizing sensor data to older adults. This is often lost within sensor research focusing heavily on visualizing from a clinical perspective; we found this sentiment clearly expressed in interviews with older adults. The visualizations provide feedback that allows consumers to engage with the information being collected about them. There are several key challenges in designing visualizations for older adult consumers related to both the type of data being visualized and the perceptual needs of the stakeholder population. We address this gap through the design of novel sensor visualizations informed by older adult needs. Using a participatory design approach, we solicited older adult feedback on early visualization prototypes through
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interviews [14,15]. We identified two specific visualization needs: 1) support the identification of longitudinal trends, 2) allow for detailed views of abnormal activity. We incorporated this along with cognitive design principles and a holistic emotional design framework into the development of the streamgraph and radial plot. The visualizations can be further applied to represent the theoretical concept of life space mobility. Life space refers to the locus of a person’s experiences and activities, in essence, a measure of their movements and interactions with the environment [38,39]. Adding concentric circles of increasing radius to the radial graph provides a mapping towards expanded life space. For older adults, reduced life space has been shown to correlate with depression, decreased functionality in activities of daily living, and increased risk of cognitive decline [38]. Integrating sensor data into an older adult consumer interface that quantifies life space can be a valuable resource to promote active engagement to maintain quality of life. Though the visualizations are informed by prior iterations, a next step in the research involves evaluation as part of an ongoing iterative design cycle. This evaluation can be summative in nature (asking general feedback on the design) and also task-based (assessing how the designs support the visualization needs identified from the interviews). Informal evaluations with gerontological experts, though valuable in identifying heuristic-based usability challenges with the design, are not a replacement for testing with older adult users. This is the next step within our research, a thorough evaluation with primary stakeholders. The long-term goal of the visualizations is to provide an integrated, continuous source of information for older adults to assess their health and wellness, supporting collaboration and engagement with family members and health care providers.
Acknowledgement This work was supported in part by National Library of Medicine (NLM) Training Grant T15LM0074 42 and National Institute of Nursing Research (NINR) Training Grant T32NR007106.
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Design of smart home sensor visualizations for older adults Thai Lea,∗ , Blaine Reederb , Jane Chungc , Hilaire Thompsonc and George Demirisa,c a Department
of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA b College of Nursing, University of Colorado Anschutz Medical Campus, Denver, CO, USA c Biobehavioral Nursing and Health Systems, University of Washington, Seattle, WA, USA Received 23 May 2014 Accepted 12 June 2014 Abstract. BACKGROUND: Smart home sensor systems provide a valuable opportunity to continuously and unobtrusively monitor older adult wellness. However, the density of sensor data can be challenging to visualize, especially for an older adult consumer with distinct user needs. OBJECTIVE: We describe the design of sensor visualizations informed by interviews with older adults. The goal of the visualizations is to present sensor activity data to an older adult consumer audience that supports both longitudinal detection of trends and on-demand display of activity details for any chosen day. METHODS: The design process is grounded through participatory design with older adult interviews during a six-month pilot sensor study. Through a secondary analysis of interviews, we identified the visualization needs of older adults. We incorporated these needs with cognitive perceptual visualization guidelines and the emotional design principles of Norman to develop sensor visualizations. RESULTS: We present a design of sensor visualization that integrate both temporal and spatial components of information. The visualization supports longitudinal detection of trends while allowing the viewer to view activity within a specific date. CONCLUSIONS: Appropriately designed visualizations for older adults not only provide insight into health and wellness, but also are a valuable resource to promote engagement within care. Keywords: Information visualization, older adults, smart home, sensor
1. Introduction With a growing older adult population and limited healthcare resources available, technological solutions are vital towards supporting healthy aging and independence. One approach involves smart homes, defined by Demiris and Hensel as “a residence equipped with technology that facilitates monitoring of residents and /or promotes independence and increases residents’ quality of life (page 33) [1]” The goal of smart home health monitoring is to provide continuous unobtrusive data capable of assessing risks in an older adult’s health status that would otherwise be sporadic with clinical visits. ∗ Corresponding author: Thai Le, University of Washington, Box SLU-BIME 358047, 850 Republican St, Building C, Seattle, WA 98109, USA. E-mail: [email protected].
c 2014 – IOS Press and the authors. All rights reserved 0928-7329/14/$27.50
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Existing field research has found that older adults are receptive to smart home health monitoring [2– 4]. However, a caveat is that the health monitoring must provide personal utility and value. As such, older adults often cite perceived value of smart home technology, not necessarily for themselves, but for others who have restricted mobility or increased health risks. A limitation to the perceived value of smart homes is the lack of feedback provided to older adults regarding the health monitoring data. Having this feedback could better promote engagement of older adults in the care process while also encouraging health efficacy. Smart homes collect data from a broad range of sources and thus present a key challenge to information visualization. Visualizations serve to abstract data in representations that facilitate knowledge discovery and insight generation. The impact of health visualizations has been demonstrated in multiple studies where the format of visual displays influences decision making from both the older adult and health care provider perspectives [5–7]. However, poorly designed visualizations can cause confusion, obscure information, and contribute to misinterpretations. Appropriately designed visualizations require addressing the unique needs of the older adult user. Such information needs often differ from that of health care providers though existing sensor visualizations are often designed for academic or clinical settings. Older adults may lack the clinical expertise that facilitates understanding of the often dense nature of sensor data visualizations, thus limiting the utility of these visualizations for the older adult consumer. In this paper we describe the development of a novel visualization approach towards representing sensor activity within the home. Through a secondary analysis of interviews, we identified the visualization needs of older adults. We incorporated these needs with cognitive perceptual visualization guidelines and the emotional design principles of Norman. The result is a set of visualizations that represents both spatial and temporal components of sensor data, allowing for granular detail on activity at a daily level and coarse detail for longitudinal health monitoring. 1.1. Designing for older adults Czaja and Lee identify two factors contributing towards the growth of technology adoption by older adults: 1) the quickly aging population and 2) the rapid dissemination of technology [8]. The challenge for designers is understanding the needs and abilities of older adults in order to create systems that are not only usable but useful. Even within the older adult population, there is vast diversity due to the heterogeneity of aging. Czaja and Lee propose that designers focus not on the chronological age alone, but instead on the potential physical and behavioral changes that impact technology use as individuals age [8]. From a cognitive perspective, aging is associated with decreases in working memory, prospective memory, spatial cognition, and processing speed. These normal aging associated changes may manifest as older adults are presented with complex or novel cognitive situations, resulting in slower technology adoption. Recognizing these differences due to aging is not enough; designers are still faced with unfamiliarity in addressing these design needs. Chisnell et al. provide heuristics for designing web sites accessible to older adults through a review of existing literature [9]. Though not all heuristics apply to information visualization, Chisnell et al. highlight the importance of: 1) making overall visualization easy to skim or scan 2) making elements within the visualization easy to read, 3) visually grouping related elements, 4) making sure text and background colors contrast, and 5) using adequate white space [9]. Czaja et al. provide further guidelines for the interface design of computer systems. The guidelines are designed with the older adult in mind, however they focus primarily on interface level interaction between different elements of a system.
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1.2. Existing visualization approaches Smart home sensor data can be collected from a wide variety of sources include motion sensors, video, and pressure sensors. The goal of these sensors is to identify activity within the home in an unobtrusive manner. For this discussion, we focus on visualizing motion sensor data, which consists of a spatial component (the location where the sensor activated) and a temporal component (the time of activation). Thomas and Crandall describe the use of PyViz, a data visualization tool to represent sensor deployment within the home [10]. Using a 2D floor map, PyViz generates heat maps of sensor activity for live visualizations. Szewcyk et al. represented the spatial component of the sensors using a 3D simulation of the smart home environment through the Second Life protocol [11]. Sensor activity is highlighted as red dots throughout the home. The visualizations can be displayed in real time or played back from sensor event readings. Wang and Skubic apply a different approach towards visualizing sensor data through a density map [12]. The visualization consists of a grid, with the x-axis representing the 24 hours within the day while the y-axis displays each day of activity. Each grid element is colored with density representing amount of activity within the home. The visualization reduces complexity associated with location for sensor firing and instead takes an aggregate measure of sensor activity within an hour period. The authors applied these visualizations to sensor data within the TigerPlace smart home residence and demonstrated case studies of differences in activity over time [12]. We seek to extend these existing sensor visualization approaches focusing on the older adult’s perspective with the goal of creating designs that support comprehension of complex and dense sensor data. 1.3. Preliminary work As part of a pilot study described by Reeder et al., we implemented a sensor-based monitoring system within the apartments of eight community dwelling older adults for a six-month period [13]. All participants were at least 65 years old and spoke English. We used commercially available passive infrared sensors that detect motion through changes in ambient temperature. The sensors were installed throughout rooms within the apartment. We conducted visits at baseline, 3 months and 6 months. At the 3 month and 6 month visits we conducted interviews on perceived value of sensors and visualization needs [13]. We applied a participatory design approach to include older adults throughout the development of the visualization prototypes [14,15]. As part of the interviews, we presented participants with iterations of sensor visualizations based off of time series and bar plots [16]. We used the interviews to solicit feedback as part of the iteration process and to better understand the visualization needs of older adult consumers of the health information. The University Institutional Review Board approved all study procedures. 2. Methodology We developed innovative visualizations of health-related sensor data focused on the older adult consumer. First, we performed a content analysis of a sensor study pilot study with older adults to extract themes to guide our design efforts. Then, using cognitive principles of visualization and emotional design, we created streamgraphs and radial plot visualizations of simulated sensor data. 2.1. Content analysis of visualization needs The first author conducted a secondary analysis of interview data from the 3 month and 6 month interviews. Lab members transcribed the interviews verbatim. An open coding analysis was applied to
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identify the information visualization needs of older adults for sensor data. In particular, we wanted to understand at a fundamental level, what types of visualization information older adults would find useful. We extracted themes from the open coded transcripts and provide a qualitative descriptive analysis of the results per the methodology described by Corbin and Strauss [17,18]. This provides a framework for the redesign of sensor visualizations grounded on stakeholder needs. 2.2. Generating synthetic data Due to the confidential nature of sensor data, we chose to develop visualizations from a synthetic data set. Simulated data can be valuable towards imitating different behaviors within the real world [19,20]. The focus of our work is on visualization approaches; we generated the data as a resource to apply the visualizations. The synthetic data consists of two types. The first is a count of sensor activity firings in each of four locations (living room, bedroom, bathroom, and kitchen) aggregated over a one-day period across 60 days (from March 1, 2013 through April 29, 2013). Therefore each of the 240 data elements consisted of Date x Location x Number of Sensor Firings. The second data type was at higher granularity, representing sensor activity within a single day taken from two representative dates. Rather than counting the total number of sensor firings within the day, this data set consisted of time stamps of when each sensor fired along with the location of the sensors. The spatial locations were the same as the first data set. 2.3. Cognitive principles of visualization We base the functional component of the visualization designs on existing literature from the cognitive information visualization domain. This is a broad field, however we focus on theories and principles that directly relate human cognitive perception to visual design. As summarized by Shah and Hoeffner there are three primary steps involved in comprehending a visualization: 1) encoding the visual information 2) relating visual features to concepts and 3) associating concepts with existing knowledge [21]. Encoding visual information refers to identifying visual characteristics of a graph. The visual characteristics of the display can influence how effective the viewer can encode such information. Bertin provides an exhaustive overview of principles in graphical composition by identifying fundamental visual variables and defining rules to build graphics out of these variables [22]. Bertin characterizes visual variables into seven types: position, size, shape, value, color, orientation, and texture [22]. Cleveland and McGill identified a similar set of six elementary perceptual tasks of visualizations and ranked the tasks from most accurate to least accurate as: position along a common scale, position along a non-aligned scale, length and angle, area, volume, and shading [23]. The grouping of elements has also been found to impact how visual information is encoded as a gestalt whole [24–26]. After encoding visual information, a viewer has to map these features into conceptual relations such as differences in size, changes in trend, and differences in spatial location [26,27]. Pinker developed a model of cognitive processing for the translation of raw visual graphics into encoded visual descriptions. His work proposes a set of cognitive operations that are executed when processing a graph, creating a mapping (graph schema) that relates visual features to conceptual relations found in the display [28]. Errors in interpretation occur when visual characteristics do not effectively get translated into concepts or relationships [26,28,29]. Existing knowledge impacts the interpretation of visualizations by providing context for conceptual elements [30]. For example, Shah found that students interpreted graphs differently based on their existing expectations of content [25,31]. Sensor visualizations designed for older adults are challenging
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because there is often a gap in domain knowledge. Concepts of sensor activity and their association with health status may differ across participants. From a design perspective, this informs the need to articulate the underlying structure of the visualizations and to relate it to familiar existing experiences of participants. For example, explaining sensor activity within terms of interactions throughout the home allows viewers to relate concepts back to experiential knowledge. Providing this connection promotes the generation of insights derived from visualizations. We applied the concepts within Shah and Hoeffner’s model of information visualization processing throughout the development of the visualizations. This included selecting the appropriate graphical elements to construct the visualization, applying consistent mapping of data visual encodings, and incorporating prior knowledge within the context of the visual design. 2.4. Norman’s concept of emotional design Our approach to the development of sensor visualizations is based on the emotional design model. Norman conceptualizes the complex affective interactions between user and artifact through emotional design [32]. This model is based on three layers of sensory processing: the visceral, behavioral, and reflective levels. The visceral level is an initial reaction to the design, a rapid judgment of good or bad. This emotional response is an aesthetic first judgment based on how an artifact looks, feels, and sounds. The behavioral level involves interactions of the user with the artifact. The focus is on the functional component of design, whether or not the design sufficiently performs a desired task. The reflective layer is an introspective evaluation of how the artifact sends a message about the user. Designing at the reflective layer considers how the artifact evokes memory for the user and the self-image that it creates [32]. We recognize that cognitive principles play a valuable role during the behavioral level of the design process. However, we also take into account the visceral and reflective impact that visualizations may have for older adults. We apply the concepts proposed by Norman through explicitly identifying the visceral, behavioral, and reflective components of our visualization design. By accounting for design in a holistic manner across these three dimensions, we seek to develop visualizations that are appealing, understandable, and valuable for older adults. 3. Results Below we present 1) visualization needs of older adults identified through content analysis of interviews and 2) streamgraphs and radial plots as novel approaches towards visualizing sensor data. 3.1. Older adult sensor visualization needs We identified two themes related to sensor visualization needs through a content analysis of interview data: value of visualizations to identify behavior patterns and importance of longitudinal trends. In the first theme, participants cited the value of using visualizations to identify patterns of behavior that were not otherwise inherently recognized. As described by a participant, “. . . this incremental kind of stuff that goes very slowly everyday as I see it and that you are unaware of and then all at once it is all pulled together into a picture and it shows you that you’ve changed, you’ve changed, you hadn’t realized it but you changed.” Another participant repeated this concept in describing how the visualizations would be valuable for their spouse suffering from dementia. Participants commented on the value of having visualizations that are able to identify patterns that they were unaware of existing. This concept also
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applied in the opposite direction; participants did not find visualizations useful if they were only a strict recording of activity, “. . . now why would I want to look at this, because I know what’s happening and my wife knows what’s happening. We both, neither of us, have dementia so far.” In the second theme, older adults noted that daily variability is a secondary emphasis to longitudinal trends within the visualization. Older adults found that any given day may vary in terms of sensor activity; “So this is just a point in time? How would that be likely to help anybody?” In addition, there may be portions of the week where activity levels divulge from the norm due to special occasions such as a visitor from out of town. As a result, short-term visualizations of sensor data reflect these differences in variability; participants raised interest only after outliers in activity have become persistent over time, “I might initially look at it day by day, just for curiosity, and then I’m sure it would fade away, maybe to just looking every three months, or six months, or year. Its value would be more on a longer term basis, to show trends.” This is not to say short-term visualizations are irrelevant; participants noted the importance of having the ability to visualize activity at a daily level to compare patterns of behavior; “. . . if I were going to show this to someone I’d want to know what particular day did we break the pattern and if it was 12 days ago I’m dead because I can’t sort it out. I have to think for a second. . . but if it were coming daily or something like that then suddenly there was a break in the pattern and somebody called me the next day. . . ” As a participant pointed out, the increased granularity of visualizations provides insight into understanding differences in activity: “You’ve got to get enough data to have some meaning to it, breaking it down week by week by week.” Based on the results of the content analysis, our design objectives focused on providing longitudinal data visualizations. The visualizations should support the discovery of trends, in particular gradual changes in activity over time on the scale of months. We also wanted to create visualizations that presented information at higher granularity given that behavior can be cyclical about the day. Because the form of visualizations is heavily influenced by function, we chose to separate the design objectives into two visualizations incorporated into an interface. The interface provides users with a gross longitudinal view of the data using a streamgraph while allowing for the option to dwell into the data at a 24 hour daily cycle using a radial plot (Fig. 1). 3.2. Visualization designs – streamgraphs We visualized longitudinal sensor data using streamgraphs (shown on top of Fig. 1), a variant of stacked bar graphs [33]. In the streamgraph, each layer corresponds to a spatial location as identified by the sensor. For our data, this came from the set of (living room, bedroom, bathroom, and kitchen). The thickness of each layer corresponds to the amount of sensor activity while the x-axis represents time. The streamgraph contains an additional offset function that shifts the baseline of the stacked layers. In a stacked bar graph, this offset function is zero, resulting in layers on top of the x-axis. We used a symmetric offset function described by Havre et al., resulting in a layout that has horizontal symmetry between the top and bottom layers [34]. In considering cognitive design principles, we selected a stacked graph to display both the total activity on a given day and the location of that activity within the home. This satisfies the macro/micro composition guideline proposed by Tufte [35]. Because the layers are not aligned along a common axis, a tradeoff of streamgraphs is that it is difficult to compare thickness between different layers [23]. However, Byron and Watternberg show that using a symmetric offset baseline for the streamgraph layout results in a display that minimizes the sum of squares of the slopes at the topmost and bottommost silhouettes [33]. This creates visualizations that limit sharp spikes in the overall shape of the graphic [33].
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Fig. 1. Visualization of smart home sensor data. At top is a streamgraph display over 60 days of total sensor activity within a day distributed by location within the home. Bottom radial plots show sensor activity within a single 24 hour day. Radial curves are colored by location of activity and emanate from center towards the time in the day when activity occurred on the outer circle. (Colours are visible in the online version of the article; http://dx.doi.org/10.3233/THC-140839)
At a visceral level, we chose streamgraphs as a visualization approach in order to create a natural flowing graphic consistent with the metaphor of data flowing over time. We selected a color palette with enough contrast to show differences amongst layers, however within the same family color of dark red to tan. We did not want a sharply contrasting color palette since this may be distracting and takes away from the part to whole composition of the sensor data. At a reflective level, we applied streamgraphs for the longitudinal visualizations out of concerns that other traditional techniques would be perceived of as too scientific or mathematical in nature. Given that these designs are for older adults, we focused on maintaining simplicity and satisfying the goal of visualizing longitudinal trends. The flow of the streamgraph from left to right in time creates an analogy of changing current flow [34]. It draws on existing knowledge and familiarity with bar charts to support comparisons of width within a layer. 3.3. Visualization designs – Radial plot We created a visualization of sensor activity for a single day (shown on bottom of Fig. 1) based on the radial plot [36]. The 24-hour cycle is arranged on the outer circle analogous to the face of a clock. The inner circle contains the legend, a set of four location labels for the sensor data, each in a separate color. There are four equally spaced nodes along the inner circle where curves emanate to the outer circle. The
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color of the curve corresponds to location of sensor activity and the point where the curve connects to the outer circle is the time during the day when the sensor activity occurred. The origin nodes divide the 24-hour cycle into 6 hour segments. For example, sensor activity occurring between 21:00–03:00 would originate from the northernmost node, while activity from 09:00–15:00 emanates from the southernmost node. The visualization applies gestalt principles of similarity to group together sensor activity by location. Frequent sensor activation within a room is mapped to spatially adjacent locations on the circle with the same color, creating a group of activity. The overlap of curves within the display creates complexity, however by the principles of good continuation, lines are visually followed along the smooth path. The visualization encodes two types of information consistent with the perceptual rankings of Mackinlay [37]. Time of sensor firing, a quantitative measure, is encoded through position and angle on the circle while location of activity, a nominal variable, is encoded through hue. At the visceral level of design, we selected the radial plot to represent connections between time and location of sensor activity. Though an initial attempt was made to use the same color scheme as the streamgraphs for consistency, we found it difficult to differentiate radial curves due to lack of contrast. As a result, we selected a more vibrant and strongly contrasting color scheme to represent location. The 24-hour day cycle lends itself to a circular representation and the clock-like face is a familiar analogy at the reflective level. 3.4. Visualization designs – Evaluation We evaluated the visualizations informally with gerontological experts through interviews, asking for heuristic-based feedback. Overall, participants understood the spatial and temporal component of the visualizations. In particular, the radial plot created an effective analogy to a 24-hour clock with participants commenting on the unusual late night pattern of activity on 4/21/13. In the streamgraph, participants focused on the aggregate pattern of activity, in particular the reduced sensor activity from 3/24/13–4/07/13. It was more difficult to identify what components contributed to the reduction in sensor activity. This is attributable to a limitation within the streamgraph. Each component of activity is shifted on the y-axis. As a result, comparisons between components involves a significant cognitive load to shift and re-align heights for comparison. In addition, though the streamgraph allows users to identify differences in overall activity quickly, it does not provide enough granular data to allow inferences as to why these changes occurred. This is something that the radial plot provides as added support. Participants also commented on the potential utility of having an interactive visualization allowing them to view activity in isolated locations or to change the date for the radial plot. 4. Discussion There exists value in visualizing sensor data to older adults. This is often lost within sensor research focusing heavily on visualizing from a clinical perspective; we found this sentiment clearly expressed in interviews with older adults. The visualizations provide feedback that allows consumers to engage with the information being collected about them. There are several key challenges in designing visualizations for older adult consumers related to both the type of data being visualized and the perceptual needs of the stakeholder population. We address this gap through the design of novel sensor visualizations informed by older adult needs. Using a participatory design approach, we solicited older adult feedback on early visualization prototypes through
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interviews [14,15]. We identified two specific visualization needs: 1) support the identification of longitudinal trends, 2) allow for detailed views of abnormal activity. We incorporated this along with cognitive design principles and a holistic emotional design framework into the development of the streamgraph and radial plot. The visualizations can be further applied to represent the theoretical concept of life space mobility. Life space refers to the locus of a person’s experiences and activities, in essence, a measure of their movements and interactions with the environment [38,39]. Adding concentric circles of increasing radius to the radial graph provides a mapping towards expanded life space. For older adults, reduced life space has been shown to correlate with depression, decreased functionality in activities of daily living, and increased risk of cognitive decline [38]. Integrating sensor data into an older adult consumer interface that quantifies life space can be a valuable resource to promote active engagement to maintain quality of life. Though the visualizations are informed by prior iterations, a next step in the research involves evaluation as part of an ongoing iterative design cycle. This evaluation can be summative in nature (asking general feedback on the design) and also task-based (assessing how the designs support the visualization needs identified from the interviews). Informal evaluations with gerontological experts, though valuable in identifying heuristic-based usability challenges with the design, are not a replacement for testing with older adult users. This is the next step within our research, a thorough evaluation with primary stakeholders. The long-term goal of the visualizations is to provide an integrated, continuous source of information for older adults to assess their health and wellness, supporting collaboration and engagement with family members and health care providers.
Acknowledgement This work was supported in part by National Library of Medicine (NLM) Training Grant T15LM0074 42 and National Institute of Nursing Research (NINR) Training Grant T32NR007106.
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