George Boateng, John A. Batsis, Patrick Proctor, Ryan Halter, and David Kotz. GeriActive: Wearable App for Monitoring and Encouraging Physical Activity among Older Adults. In Proceedings of the IEEE Conference on Body Sensor Networks (BSN), pages 46-49, March 2018. IEEE. DOI 10.1109/BSN.2018.8329655.

Abstract: The ability to monitor a person's level of daily activity can inform self-management of physical activity and assist in augmenting behavioral interventions. For older adults, the importance of regular physical activity is critical to reduce the risk of long-term disability. In this work, we present GeriActive, an application on the Amulet wrist-worn device that monitors in real time older adults' daily activity levels (low, moderate and vigorous), which we categorized using metabolic equivalents (METs). The app implements an activity-level detection model we developed using a linear Support Vector Machine (SVM). We trained our model using data from volunteer subjects (n=29) who performed common physical activities (sit, stand, lay down, walk and run) and obtained an accuracy of 94.3% with leave-one-subject-out (LOSO) cross-validation. We ran a week-long field study to evaluate the usability and battery life of the GeriActive system where 5 older adults wore the Amulet as it monitored their activity level. Their feedback showed that our system has the potential to be usable and useful. Our evaluation further revealed a battery life of at least 1 week. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for health delivery interventions that could improve the health of older adults.

Keywords: project-amulet,ctbh,ists,mhealth,sensors,wearable


PDF (302K)

Copyright © 2018 by IEEE.

The copy made available here is the authors' version; for a definitive copy see the publisher's version described above.