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Current activity tracking technologies are largely trained on younger adults'data, which can lead to solutions that are not well-suited for older adults. Tobuild activity trackers for older adults, it is crucial to collect trainingdata with them. To this end, we examine the feasibility and challenges witholder adults in collecting activity labels by leveraging speech. Specifically,we built MyMove, a speech-based smartwatch app to facilitate the in-situlabeling with a low capture burden. We conducted a 7-day deployment study,where 13 older adults collected their activity labels and smartwatch sensordata, while wearing a thigh-worn activity monitor. Participants were highlyengaged, capturing 1,224 verbal reports in total. We extracted 1,885 activitieswith corresponding effort level and timespan, and examined the usefulness ofthese reports as activity labels. We discuss the implications of our approachand the collected dataset in supporting older adults through personalizedactivity tracking technologies.

MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech