Optimizing sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand

• Published in: MethodsX Vol. 14; p. 103207
• Main Authors: Yadav, Mr. Amol Pandurang, Patil, Dr. Sandip.R.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2025; Elsevier
• Subjects: computer software; Deep learning, stacked neural networks; discriminant analysis; domain; electromyography; Engineering; Feature Extraction; Gesture recognition, prosthetic control; Hierarchical representation; Machine learning; MATLAB; Ninapro Database; Performance Evaluation; prostheses; Real-world Applications, ADAMS; Rehabilitation technology; Robustness; Signal Processing; Stacked autoencoder neural network (SAE); Surface electromyography (sEMG); Temporal Dependencies; wavelet; Deep learning, stacked neural networks; Ninapro Database; Feature Extraction; Real-world Applications, ADAMS; Rehabilitation technology; Hierarchical representation; Temporal Dependencies; MATLAB; Signal Processing; Surface electromyography (sEMG); Performance Evaluation; Stacked autoencoder neural network (SAE); Machine learning; Robustness; Gesture recognition, prosthetic control

This study presents a novel deep learning approach for surface electromyography (sEMG) gesture recognition using stacked autoencoder neural network (SAE)s. The method leverages hierarchical representation learning to extract meaningful features from raw sEMG signals, enhancing the precision and robu...