A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model - Hwang, Soree; Kwon, Nayeon; Lee, Dongwon; Kim, Jongman; Yang, Sumin; Youn, Inchan; Moon, Hyuk-June; Sung, Joon-Kyung; Han, Sungmin (Sensors (Basel, Switzerland), 2025)
Keywords: Accuracy; Analysis; Classification; CNN-LSTM-attention; Deep learning; Detectors; Electrocardiography; Exercise; Fatigue; Fitness equipment; Gait; gait kinematics; hybrid deep learning; inertial measurement unit (IMU); Kalman filters; Kinematics; Machine learning; Muscle fatigue; Muscle function; Neural networks; physical fatigue detection; Physiology; Sensors; Software; surface electromyography (sEMG); Walking; South Korea; Massachusetts; United States--US