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

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...

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Bibliographic Details
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
Online Access:	Get full text
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