Paper in the CHI '22 proceedings on "MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech." Check out the great video describing the project at the bottom of the linked page.
Self-tracking of physical activities can support people of all ages in understanding their lifestyle behaviors and making healthy choices, reducing chronic disease risks. For older adults, movement behaviors are especially critical. They help people maintain functional abilities and live independently. Smart watches and other activity tracking technologies have become available, making self-tracking easier than before, but older adults have adopted them less. One barrier is that current physical activity trackers do not effectively identify and track older adults' activities. This project aims to understand (1) what kind of data are needed from older adults to make activity tracking work for them; and (2) how to engage older adults to collect the needed data. This project will develop a new approach to personalizing older adults' activity tracking. It will open up new research avenues on personalized and multimodal self-tracking that affect healthcare, quality of life, and privacy. This project is expected to make broader impacts for older adults in enhancing their motivation to engage in physical activities, as well as societal impacts in nurturing a culture of diversity and inclusion that benefits the lives of older adults and people with and without disabilities or health conditions.
This project uses 'teachable interfaces' to facilitate personalized, self-tracking for older adults' physical activities, while considering their changes in mobility and diverse physical characteristics. The teachable interfaces are intended to help people provide personalized activity labels, which will be used to recognize their unique movements. They will also enable self-tracking of meaningful and modifiable movement and non-movement activities, supporting older adults to displace inactivity with physical activity, which can provide significant health benefits. The research team will investigate: (1) older adults' movement and non-movement activities that they wish to change; (2) new personalized, multimodal activity trackers that provide opportunities for self-reflection through teachable interfaces; and (3) commonalities and differences in efficacies for subgroups of older adults (e.g., people with mild dementia) and what adjustments are needed to accommodate them. Combining expertise from human-computer interaction, interactive machine learning, accessibility, aging, and kinesiology, the project will employ a mixed-methods research approach: co-design with older adults, technology design and development, and evaluations both in the lab and in people's natural environments.