レーダーによる筋肉疲労の解析（Decoding Muscle Fatigue With Radar）

2026-05-11 カーネギーメロン大学

＜関連情報＞

• https://www.ece.cmu.edu/news-and-events/story/2026/05/decoding-muscle-fatigue-with-radar.html
• https://dl.acm.org/doi/10.1145/3774906.3802776

GigaFlex：カオス理論に着想を得たレーダーによる運動中の筋肉振動の非接触モニタリング GigaFlex: Contactless Monitoring of Muscle Vibrations During Exercise with a Chaos-Inspired Radar

Jiangyfei Zhu、Yuzhe Wang、Tao Qiang、Vu Phan、Zhixiong Li、Evy Meinders、+3
SenSys ’26: Proceedings of the 2026 ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems  Published: 10 May 2026
DOI:https://doi.org/10.1145/3774906.3802776

Abstract

In this paper, our goal is to enable quantitative feedback on muscle fatigue during exercise to optimize exercise effectiveness while minimizing injury risk. We seek to capture fatigue by monitoring surface vibrations that muscle exertion induces. Muscle vibrations are unique as they arise from the asynchronous firing of motor units, producing surface micro-displacements that are broadband, nonlinear, and seemingly stochastic. Accurately sensing these noise-like signals requires new algorithmic strategies that can uncover their underlying structure. We present GigaFlex the first contactless system that measures muscle vibrations using mmWave radar to infer muscle force and detect fatigue. GigaFlex draws on algorithmic foundations from Chaos theory to model the deterministic patterns of muscle vibrations and extend them to the radar domain. Specifically, we design a radar processing architecture that systematically infuses principles from Chaos theory and nonlinear dynamics throughout the sensing pipeline, spanning localization, segmentation, and learning, to estimate muscle forces during static and dynamic weight-bearing exercises. In a 23-participant study within a controlled environment, GigaFlex estimates maximum voluntary isometric contraction (MVIC) with 5.9% RMSE and detects 1–3 Repetitions in Reserve (RIR) with an AUC of 0.83–0.86, performing comparably to a contact-based IMU baseline. Our proof-of-concept system opens new opportunities for physiological sensing of complex, non-periodic biosignals.