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Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test

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Machine Learning-Driven Muscle Fatigue Estimation in Resistance Training with Assistive Robotics.

Jun-Young Baek^1,2, Jun-Hyeong Kwon², Hamza Khan²

¹Department of Mechanical Engineering, Pusan National University, 2, Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, Republic of Korea.

Sensors (Basel, Switzerland)

|November 13, 2025

Summary

This study predicts muscle fatigue using force data, showing fatigue progression is key. Machine learning accurately estimates perceived exertion for smarter resistance training and rehabilitation.

Keywords:

assistive robotics isokinetic exercise machine learning muscle fatigue estimation resistance training

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Area of Science:

Biomechanics
Exercise Physiology
Machine Learning

Background:

Monitoring muscle fatigue is crucial for safe and effective resistance training.
Current methods like EMG, IMU, and RPE have limitations, especially in automated or unsupervised settings.

Purpose of the Study:

To develop a machine learning model for predicting ratings of perceived exertion (RPE) directly from force-time data during resistance exercise.
To assess the relationship between biomechanical features and fatigue progression.

Main Methods:

Thirty-two male participants performed isokinetic bench press sets at a 7RM load.
Force-time data and RPE were recorded; biomechanical and engineered features were extracted.
A Random Forest model was trained to predict RPE from these features.

Main Results:

Muscle fatigue progression, not absolute force, strongly correlates with RPE.
Engineered features significantly enhanced predictive accuracy.
The Random Forest model achieved >93% accuracy within ±1 RPE unit.

Conclusions:

A machine learning approach using force-time data can accurately predict RPE during resistance training.
This method offers potential for integration into intelligent exercise machines for automated load adjustment.
Applications include enhancing athletic training and rehabilitation programs.