TY - GEN
T1 - MyMove
T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
AU - Kim, Young Ho
AU - Chou, Diana
AU - Lee, Bongshin
AU - Danilovich, Margaret
AU - Lazar, Amanda
AU - Conroy, David E.
AU - Kacorri, Hernisa
AU - Choe, Eun Kyoung
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/4/29
Y1 - 2022/4/29
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85130520365&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130520365&partnerID=8YFLogxK
U2 - 10.1145/3491102.3517457
DO - 10.1145/3491102.3517457
M3 - Conference contribution
AN - SCOPUS:85130520365
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 30 April 2022 through 5 May 2022
ER -