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

• Published in: Neural computing & applications Vol. 37; no. 21; pp. 16461 - 16485
• Main Authors: 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
• Language: English
• Published: London Springer London 01.07.2025; Springer Nature B.V
• Subjects: 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

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...