MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech
This addresses the need for personalized activity tracking for older adults by enabling data collection with them, though it is incremental as it focuses on a specific labeling method.
The study tackled the problem of activity tracking technologies being trained on younger adults' data by developing MyMove, a speech-based smartwatch app for older adults to collect in-situ activity labels, resulting in 1,224 verbal reports and 1,885 extracted activities from a 7-day deployment with 13 participants.
Current activity tracking technologies are largely trained on younger adults' data, which can lead to solutions that are not well-suited for older adults. To build activity trackers for older adults, it is crucial to collect training data with them. To this end, we examine the feasibility and challenges with older adults in collecting activity labels by leveraging speech. Specifically, we built MyMove, a speech-based smartwatch app to facilitate the in-situ labeling with a low capture burden. We conducted a 7-day deployment study, where 13 older adults collected their activity labels and smartwatch sensor data, while wearing a thigh-worn activity monitor. Participants were highly engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities with corresponding effort level and timespan, and examined the usefulness of these reports as activity labels. We discuss the implications of our approach and the collected dataset in supporting older adults through personalized activity tracking technologies.