Evaluation of LSTM for predicting grip strength using electromyography: a comparison of setups and methods

Despite decades of research in prosthetics and myocontrol, using electromyography (EMG) to accurately predict the force a user grasps an object with is still a subject of investigation. Although the problem seems trivial, the optimal EMG setup, able to deliver high prediction accuracy at a minimal e...

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Veröffentlicht in:	Neural computing & applications Jg. 37; H. 21; S. 16461 - 16485
Hauptverfasser:	Anam, Khairul, Sudrajat, Ahmad, Rizal, Naufal Ainur, Intyanto, Gramandha Wega, Muldayani, Wahyu, Negara, Mohamad Agung Prawira, Sumardi, Bukhori, Saiful, Gitakarma, Made Santo, Castellini, Claudio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.07.2025 Springer Nature B.V
Schlagworte:	Accuracy Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Electromyography Grip strength Image Processing and Computer Vision Methods Muscle function Neural networks Original Article Pattern recognition systems Probability and Statistics in Computer Science Prostheses Real time Regression analysis Sensors Support vector machines Time series Stacked autoencoder Attention mechanism LSTM Grip strength prediction
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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