Hwang, Soree Kwon, Nayeon Lee, Dongwon Kim, Jongman Yang, Sumin Youn, Inchan Moon, Hyuk-June Sung, Joon-Kyung Han, Sungmin 2025-06-27T07:30:23Z 2025-06-27T07:30:23Z 2025-06-23 2025-05 https://pubs.kist.re.kr/handle/201004/152700 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. English Multidisciplinary Digital Publishing Institute (MDPI) A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model Article 10.3390/s25113309 1 Sensors, v.25, no.11 Sensors 25 11 Y scie scopus 001506193800001 2-s2.0-105007674319 Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering Instruments & Instrumentation Article EXERTION hybrid deep learning CNN-LSTM-attention gait kinematics physical fatigue detection inertial measurement unit (IMU) surface electromyography (sEMG)