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
• ISSN: 2215-0161; 2215-0161
• DOI: 10.1016/j.mex.2025.103207

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 robustness of gesture classification.•Feature Extraction and Classification MODWT Decomposition: The sEMG signals were decomposed using the MODWT DECOMPOSITION(Maximal Overlap Discrete Wavelet Transform) to capture various frequency components.•Time Domain Parameters: A total of 28 features per subject were extracted from the time domain, including statistical and spectral features.•Classifier Evaluation: Initial evaluations involved Autoencoder and LDA (Linear Discriminant Analysis) classifiers, with Autoencoder achieving an average accuracy of 77.96 % ± 1.24, outperforming LDA's 65.36 % ± 1.09.Advanced Neural Network Approach: Stacked Autoencoder Neural Network: To address challenges in distinguishing similar gestures within grasp groups, a Stacked Autoencoder Neural Network was employed. This advanced neural network architecture improved classification accuracy to over 100 %, demonstrating its effectiveness in handling complex gesture recognition tasks. These findings emphasize the significant potential of deep learning models in enhancing prosthetic control and rehabilitation technologies. . To verify these findings, we developed a 3d hand module in ADAMS software that is simulated using Matlab-ADAMS cosimulation. [Display omitted]