TY - GEN
T1 - FEMO
T2 - 13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015
AU - Ding, Han
AU - Shangguan, Longfei
AU - Yang, Zheng
AU - Han, Jinsong
AU - Zhou, Zimu
AU - Yang, Panlong
AU - Xi, Wei
AU - Zhao, Jizhong
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - Regular free-weight exercise helps to strengthen the body's natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue-tooth) for activity sensing, recognition and counting etc. However, none of them have incorporate three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system responds to these demands, providing an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1): since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other and serves as a reliable signature for each activity. 2): the Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of each performed activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities and users, and provide valuable feedbacks for activity alignment.
AB - Regular free-weight exercise helps to strengthen the body's natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue-tooth) for activity sensing, recognition and counting etc. However, none of them have incorporate three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system responds to these demands, providing an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1): since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other and serves as a reliable signature for each activity. 2): the Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of each performed activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities and users, and provide valuable feedbacks for activity alignment.
KW - Activity recognition and assessment
KW - RFID
UR - https://www.scopus.com/pages/publications/84962834096
U2 - 10.1145/2809695.2809708
DO - 10.1145/2809695.2809708
M3 - 会议稿件
AN - SCOPUS:84962834096
T3 - SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
SP - 141
EP - 154
BT - SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
PB - Association for Computing Machinery, Inc
Y2 - 1 November 2015 through 4 November 2015
ER -