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FEMO: A Platform for Free-weight Exercise Monitoring with RFIDs

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Published:01 November 2015Publication History

ABSTRACT

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 countingetc.. 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.

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    • Published in

      cover image ACM Conferences
      SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
      November 2015
      526 pages
      ISBN:9781450336314
      DOI:10.1145/2809695

      Copyright © 2015 ACM

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      Publication History

      • Published: 1 November 2015

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      SenSys '15 Paper Acceptance Rate27of132submissions,20%Overall Acceptance Rate174of867submissions,20%

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