Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle

• Published in: Scientific reports Vol. 15; no. 1; pp. 12575 - 15
• Main Authors: Liu, Shing-Hong, Sharma, Alok Kumar, Wu, Bo-Yan, Zhu, Xin, Chang, Chun-Ju, Wang, Jia-Jung
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.04.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 692/700/139; Adult; CatBoost; Correlation coefficient; Electromyography; Electromyography - methods; Fatigue; Female; Gait; Gait - physiology; Gait Analysis - methods; Gait parameters; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Male; multidisciplinary; Muscle fatigue; Muscle Fatigue - physiology; Muscle, Skeletal - physiology; Muscles; Neuromuscular diseases; Random forest; Science; Science (multidisciplinary); Sensors; Surface electromyogram; XGBoost; Young Adult; Surface electromyogram; Random forest; Gait parameters; XGBoost; CatBoost; Machine learning

The gait analysis has been applied in many fields, such as the assessment of falling, force evaluation in sports, and gait disorder detection for neuromuscular diseases. Its main recording techniques include video cameras and wearable sensors. However, the present methods involve measuring surface e...