The Aware Home Research Initiative (AHRI) at Georgia Tech is devoted to the multidisciplinary exploration of emerging technologies and services based in the home. Starting in 1998, our collection of faculty and students has created a unique research facility that allows us to simulate and evaluate user experiences with off-theshelf and state-of-the-art technologies. With specific expertise in health, education, entertainment and usable security, we are able to apply our research to problems of significant social and economic impact.
Self-management of health is becoming increasingly important in today’s healthcare climate. Activity monitoring technologies have the potential to support health self-management by tracking, storing, compiling, and providing feedback about an individual’s engagement in movement activities. Older adults represent a fast growing segment of the population who may benefit from such technologies. To understand how to facilitate technology acceptance and adoption, more information is needed about older adults’ attitudes and usage of such technologies. Eight older adult participants (Mage = 65.0 years; SD = 3.2; range = 61–69) used one of four activity monitoring technologies in their own homes for two weeks. Attitudes and usability issues were assessed and evaluated within a technology acceptance framework. Participants’ initial attitudes were positive, but after using the technology for two weeks, attitudes were mixed. Three participants indicated they would continue using the technology, whereas five said they would abandon the technology. These data offer insight into older adults’ use of and attitudes toward activity monitoring technologies and provide improvement opportunities for designers. The results suggest that efforts should focus on conveying the usefulness and personal benefits of activity monitoring technologies specific to older adults.
People now have access to many sources of data about their health and wellbeing. Yet, most people cannot wade through all of this data to answer basic questions about their long-term wellbeing: Do I gain weight when I have busy days? Do I walk more when I work in the city? Do I sleep better on nights after I work out?We built the Health Mashups system to identify connections that are significant over time between weight, sleep, step count, calendar data, location, weather, pain, food intake, and mood. These significant observations are displayed in a mobile application using natural language, for example, "You are happier on days when you sleep more." We performed a pilot study, made improvements to the system, and then conducted a 90-day trial with 60 diverse participants, learning that interactions between wellbeing and context are highly individual and that our system supported an increased self-understanding that lead to focused behavior changes.
The proxemics of social interactions (e.g., body distance, relative orientation) influences many aspects of our everyday life: from patients' reactions to interaction with physicians, successes in job interviews, to effective teamwork. Traditionally, interaction proxemics has been studied via questionnaires and participant observations, imposing high burden on users, low scalability and precision, and often biases.In this paper we present Protractor, a novel wearable technology for measuring interaction proxemics as part of non-verbal behavior cues with fine granularity. Protractor employs near-infrared light to monitor both the distance and relative body orientation of interacting users. We leverage the characteristics of near-infrared light (i.e., line-of-sight propagation) to accurately and reliably identify interactions; a pair of collocated photodiodes aid the inference of relative interaction angle and distance. We achieve robustness against temporary blockage of the light channel (e.g., by the user's hand or clothes) by designing sensor fusion algorithms that exploit inertial sensors to obviate the absence of light tracking results.We fabricated Protractor tags and conducted real-world experiments. Results show its accuracy in tracking body distances and relative angles. The framework achieves less than 6 • error 95% of the time for measuring relative body orientation and 2.3-cm -4.9-cm mean error in estimating interaction distance. We deployed Protractor tags to track user's non-verbal behaviors when conducting collaborative group tasks. Results with 64 participants show that distance and angle data from Protractor tags can help assess individual's task role with 84.9% accuracy, and identify task timeline with 93.2% accuracy.
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