Hand-Grip Strength Estimation through Bioacoustic Sensing

Rakshita Ranganath, Zhi Zhang, Yuliang Fu, Jinyuan Jia, Ying Chen, Huining Li, Chenhan Xu

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Keywords: grip strength estimation, bioacoustic sensing

TL;DR: we propose a novel, cost-effective and low-power approach to estimate grip strength using bio-acoustic signals captured from the forearm via an armband equipped with MEMS microphones.

Abstract: In order to determine the overall health of an individual, particularly among the older population, hand grip strength has emerged as a reliable and widely used indicator of muscular and functional health. However, the conventional devices for measuring grip strength, such as dynamometers, require direct interaction with an external device. In this work, we propose a novel, cost-effective and low-power approach to estimate grip strength using bio-acoustic signals captured from the forearm via an armband equipped with MEMS microphones. Our method performs well on grip strength classification with an accuracy of 93.33\%, and as a proof of concept, demonstrates a promising direction for non-invasive grip strength estimation.

Track: 2. Sensors and systems for digital health, wellness, and athletics

NominateReviewer: Rakshita Ranganath ([e-mail])

Submission Number: 107