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The Fatigue Assessment Model for Athlete Training by Integrating Inertial Sensors and Multimodal Physiological Signals

Wang Y, Wang’s Y. · Research Square · 2026

Researchers built a fatigue-tracking model that combined wrist-worn motion sensors (IMUs) with heart rate, heart rate variability, and athletes' own effort ratings (RPE) during training. Over a 30-minute session heart rate rose from about 75 to 141 bpm and RPE from 6.3 to 16.9, and combining the data streams identified exercises with 97.0% accuracy and graded fatigue with 95.1% accuracy.
Takeaway: Pair your wearable's heart-rate data with your own effort ratings to get a more reliable read on accumulating training fatigue.
Abstract (source)

Abstract This study constructs an athlete training fatigue assessment model that integrates inertial sensors and multimodal physiological signals. Through synchronous collection of dual wrist IMU, heart rate, HRV, and RPE scores, it achieves motion recognition, fatigue grading, and comprehensive evaluation of training efficiency. In the experiment, 18001 samples were collected from each channel of IMU, with a missing rate of 0%; After 30 minutes of training, the heart rate increased from approximately 75.00 bpm to 141.34 bpm, and the RPE increased from 6.32 to 16.90. The

Results: showed that the accuracy of multimodal fusion action recognition reached 97.0%, and the accuracy of fatigue assessment reached 95.1%, indicating that this

Method: can effectively reflect the accumulation of training load and fatigue changes.

Primary studyProgramming & Periodization
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