“Optimizing sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand” Article Swipe

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Mahendra Pratap Yadav , Sandip Raosaheb Patil ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/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.

Related Topics

Autoencoder

Artificial Intelligence

Computer Science

Gesture

Engineering

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Autoencoder Artificial neural network Artificial intelligence Computer science Pattern recognition (psychology) Gesture Speech recognition Engineering

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.mex.2025.103207
OA Status: gold
Cited By: 1
References: 12
Related Works: 10
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DOI: https://doi.org/10.1016/j.mex.2025.103207

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Title: “Optimizing sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand”

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Publication year: 2025

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Publication date: 2025-02-15

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Authors: Mahendra Pratap Yadav, Sandip Raosaheb Patil

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Landing page: https://doi.org/10.1016/j.mex.2025.103207

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Concepts: Autoencoder, Artificial neural network, Artificial intelligence, Computer science, Pattern recognition (psychology), Gesture, Speech recognition, Engineering

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Cited by: 1

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abstract_inverted_index.Matlab-ADAMS	193
abstract_inverted_index.architecture	141
abstract_inverted_index.classifiers,	95
abstract_inverted_index.hierarchical	23
abstract_inverted_index.1.09.Advanced	112
abstract_inverted_index.cosimulation.	194
abstract_inverted_index.demonstrating	149
abstract_inverted_index.effectiveness	151
abstract_inverted_index.outperforming	107
abstract_inverted_index.technologies.	174
abstract_inverted_index.Classification	44
abstract_inverted_index.Decomposition:	46
abstract_inverted_index.classification	143
abstract_inverted_index.distinguishing	124
abstract_inverted_index.rehabilitation	173
abstract_inverted_index.representation	24
abstract_inverted_index.electromyography	10
abstract_inverted_index.components.•Time	64
abstract_inverted_index.DECOMPOSITION(Maximal	55
abstract_inverted_index.features.•Classifier	84
abstract_inverted_index.classification.•Feature	41
cited_by_percentile_year.max	95
cited_by_percentile_year.min	91
countries_distinct_count	1
institutions_distinct_count	2
citation_normalized_percentile.value	0.72331308
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True