  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.
