TY - GEN
T1 - Activity Recognition and Stress Detection via Wristband
AU - Wong, Johnny Chun Yiu
AU - Wang, Jun
AU - Fu, Eugene Yujun
AU - Leong, Hong Va
AU - Ngai, Grace
N1 - Funding Information:
This work is supported in part by the Collaborative Research Fund under grant number E-RB28 from the Hong Kong Research Grant Council.
Publisher Copyright:
© 2019 ACM.
PY - 2019/12/2
Y1 - 2019/12/2
N2 - Advancement of micro-electromechanical systems enables easy daily activity and physiological data collection with a smart wristband and smartphone. Making use of those signals in various intelligent algorithm can contribute much to trending m-health applications. The ability of continuously monitoring physical activities and stress level can help users to better track their health condition. In this study, we propose to recognize different physical activities and detect long lasting stress level based on the 3-axis acceleration signals and physiological signals. We are able to achieve accuracy of around 97% for physical activities recognition and more than 80% for stress detection. We also discover that physiological signals alone cannot distinguish well between the high intensity activities and the stress condition.
AB - Advancement of micro-electromechanical systems enables easy daily activity and physiological data collection with a smart wristband and smartphone. Making use of those signals in various intelligent algorithm can contribute much to trending m-health applications. The ability of continuously monitoring physical activities and stress level can help users to better track their health condition. In this study, we propose to recognize different physical activities and detect long lasting stress level based on the 3-axis acceleration signals and physiological signals. We are able to achieve accuracy of around 97% for physical activities recognition and more than 80% for stress detection. We also discover that physiological signals alone cannot distinguish well between the high intensity activities and the stress condition.
KW - electrodermal activity
KW - mHealth
KW - physical activity
KW - Stress
KW - wearable device
UR - http://www.scopus.com/inward/record.url?scp=85098261653&partnerID=8YFLogxK
U2 - 10.1145/3365921.3365950
DO - 10.1145/3365921.3365950
M3 - Conference article published in proceeding or book
AN - SCOPUS:85098261653
T3 - ACM International Conference Proceeding Series
SP - 102
EP - 106
BT - 17th International Conference on Advances in Mobile Computing and Multimedia, MoMM2019 - Proceedings
A2 - Haghighi, Pari Delir
A2 - Salvadori, Ivan Luiz
A2 - Steinbauer, Matthias
A2 - Khalil, Ismail
A2 - Anderst-Kotsis, Gabriele
PB - Association for Computing Machinery
T2 - 17th International Conference on Advances in Mobile Computing and Multimedia, MoMM2019
Y2 - 2 December 2019 through 4 December 2019
ER -