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Sensors
Volume 25
Issue 11
10.3390/s25113309
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Soree Hwang
1,2,
Nayeon Kwon
1,
Dongwon Lee
1,
Jongman Kim
1,
Sumin Yang
1,
Inchan Youn
1,
Hyuk-June Moon
1,
Joon-Kyung Sung
2,* and
Sungmin Han
1,3,4,*
1
Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
2
School of Biomedical Engineering, Korea University, Seoul 02841, Republic of Korea
3
Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Republic of Korea
4
KHU-KIST Department of Converging Science and Technology, Kyung Hee University, Seoul 02447, Republic of Korea
*
Authors to whom correspondence should be addressed.
Sensors 2025, 25(11), 3309; https://doi.org/10.3390/s25113309 (registering DOI)
Submission received: 17 March 2025 / Revised: 22 May 2025 / Accepted: 22 May 2025 / Published: 24 May 2025
(This article belongs to the Special Issue Wearable Sensing of Medical Condition at Home Environment)
Versions Notes

Abstract

Physical fatigue significantly impacts safety and performance across industrial, athletic, and medical domains, yet its detection remains challenging due to individual variability and limited generalizability of existing methods. This study introduces a multimodal fatigue detection system integrating surface electromyography (sEMG) and inertial measurement unit (IMU) signals, processed through a hybrid convolutional neural network–long short-term memory–attention (CNN-LSTM-Attention) model. Fatigue was induced in 35 healthy participants via step-up-and-down exercises, with gait data collected during natural walking before and after fatigue. The model leverages sEMG from the gastrocnemius lateralis and IMU-derived jerk signals from the tibialis anterior and rectus femoris to classify fatigue states. Evaluated using leave-one-subject-out cross-validation (LOSOCV), the system achieved an accuracy of 87.94% with bilateral EMG signals and a balanced recall of 87.94% for fatigued states using a combined IMU-EMG approach. These results highlight the system’s robustness for personalized fatigue monitoring, surpassing traditional subject-dependent methods by addressing inter-individual differences.
Keywords: physical fatigue detection; surface electromyography (sEMG); inertial measurement unit (IMU); hybrid deep learning; CNN-LSTM-attention; gait kinematics physical fatigue detection; surface electromyography (sEMG); inertial measurement unit (IMU); hybrid deep learning; CNN-LSTM-attention; gait kinematics

Share and Cite

MDPI and ACS Style

Hwang, S.; Kwon, N.; Lee, D.; Kim, J.; Yang, S.; Youn, I.; Moon, H.-J.; Sung, J.-K.; Han, S. A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model. Sensors 2025, 25, 3309. https://doi.org/10.3390/s25113309

AMA Style

Hwang S, Kwon N, Lee D, Kim J, Yang S, Youn I, Moon H-J, Sung J-K, Han S. A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model. Sensors. 2025; 25(11):3309. https://doi.org/10.3390/s25113309

Chicago/Turabian Style

Hwang, Soree, Nayeon Kwon, Dongwon Lee, Jongman Kim, Sumin Yang, Inchan Youn, Hyuk-June Moon, Joon-Kyung Sung, and Sungmin Han. 2025. "A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model" Sensors 25, no. 11: 3309. https://doi.org/10.3390/s25113309

APA Style

Hwang, S., Kwon, N., Lee, D., Kim, J., Yang, S., Youn, I., Moon, H.-J., Sung, J.-K., & Han, S. (2025). A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model. Sensors, 25(11), 3309. https://doi.org/10.3390/s25113309

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