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A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model.

Soree Hwang^1,2, Nayeon Kwon¹, Dongwon Lee¹

¹Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.

Sensors (Basel, Switzerland)

|September 19, 2025

Summary

This study presents a new system using surface electromyography (sEMG) and inertial measurement unit (IMU) signals to detect physical fatigue. The multimodal approach achieves high accuracy in identifying fatigue states, offering personalized monitoring solutions.

Keywords:

CNN-LSTM-attention gait kinematics hybrid deep learning inertial measurement unit (IMU)physical fatigue detection surface electromyography (sEMG)

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Area of Science:

Biomedical Engineering
Wearable Technology
Human Performance Monitoring

Background:

Physical fatigue poses significant risks to safety and performance in various sectors.
Current fatigue detection methods struggle with individual variability and limited generalizability.
Objective and reliable fatigue assessment is crucial for preventing accidents and optimizing performance.

Purpose of the Study:

To develop and validate a multimodal fatigue detection system using sEMG and IMU signals.
To investigate the efficacy of a hybrid CNN-LSTM-Attention model for fatigue classification.
To address inter-individual differences in fatigue detection for personalized monitoring.

Main Methods:

A multimodal system integrating sEMG and IMU signals was developed.
A hybrid CNN-LSTM-Attention model processed the sensor data for fatigue classification.
Fatigue was induced in 35 healthy participants through step-up-and-down exercises.
sEMG from gastrocnemius lateralis and IMU jerk signals from tibialis anterior and rectus femoris were utilized.
Leave-one-subject-out cross-validation (LOSOCV) was employed for evaluation.

Main Results:

The system achieved 87.94% accuracy using bilateral sEMG signals.
A balanced recall of 87.94% for fatigued states was obtained with a combined IMU-EMG approach.
The model demonstrated robustness in classifying fatigue states across participants.

Conclusions:

The developed multimodal fatigue detection system shows high accuracy and robustness.
This system offers a promising solution for personalized fatigue monitoring.
The approach effectively addresses inter-individual differences, outperforming traditional subject-dependent methods.