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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Vydáno v:	Neural computing & applications Ročník 37; číslo 21; s. 16461 - 16485
Hlavní autoři:	Anam, Khairul, Sudrajat, Ahmad, Rizal, Naufal Ainur, Intyanto, Gramandha Wega, Muldayani, Wahyu, Negara, Mohamad Agung Prawira, Sumardi, Bukhori, Saiful, Gitakarma, Made Santo, Castellini, Claudio
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.07.2025 Springer Nature B.V
Témata:	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
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Abstract	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 economic and computational cost needs to be found. In this work, we compare several EMG setups consisting of one to eight sensors and deep learning methods to find out which combination is most convenient. In particular, we compare long short-term memory (LSTM), together with a stacked autoencoder (LSTM–SAE) and an attention mechanism (LSTMATT). Our experimental results reveal that, while the best performance is attained by LSTM–SAE (coefficient of correlation 0.9867 ± 0.0087 , coefficient of determination 0.9676 ± 0.0489 , normalized root mean square error 0.048 ± 0.0213 ), statistically significant differences can only be found when the number of sensors is drastically reduced, namely to 2 sensors, in which case, anyway, the performance is still close to optimal and even surpasses state-of-the-art methods. Further research will focus on testing the optimal approach and setup online on amputated users using prosthetic hardware in daily living activities.
AbstractList	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 economic and computational cost needs to be found. In this work, we compare several EMG setups consisting of one to eight sensors and deep learning methods to find out which combination is most convenient. In particular, we compare long short-term memory (LSTM), together with a stacked autoencoder (LSTM–SAE) and an attention mechanism (LSTMATT). Our experimental results reveal that, while the best performance is attained by LSTM–SAE (coefficient of correlation 0.9867 ± 0.0087 , coefficient of determination 0.9676 ± 0.0489 , normalized root mean square error 0.048 ± 0.0213 ), statistically significant differences can only be found when the number of sensors is drastically reduced, namely to 2 sensors, in which case, anyway, the performance is still close to optimal and even surpasses state-of-the-art methods. Further research will focus on testing the optimal approach and setup online on amputated users using prosthetic hardware in daily living activities. 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 economic and computational cost needs to be found. In this work, we compare several EMG setups consisting of one to eight sensors and deep learning methods to find out which combination is most convenient. In particular, we compare long short-term memory (LSTM), together with a stacked autoencoder (LSTM–SAE) and an attention mechanism (LSTMATT). Our experimental results reveal that, while the best performance is attained by LSTM–SAE (coefficient of correlation 0.9867±0.0087, coefficient of determination 0.9676±0.0489, normalized root mean square error 0.048±0.0213), statistically significant differences can only be found when the number of sensors is drastically reduced, namely to 2 sensors, in which case, anyway, the performance is still close to optimal and even surpasses state-of-the-art methods. Further research will focus on testing the optimal approach and setup online on amputated users using prosthetic hardware in daily living activities.
Author	Intyanto, Gramandha Wega Negara, Mohamad Agung Prawira Anam, Khairul Rizal, Naufal Ainur Muldayani, Wahyu Castellini, Claudio Sudrajat, Ahmad Gitakarma, Made Santo Sumardi Bukhori, Saiful
Author_xml	– sequence: 1 givenname: Khairul surname: Anam fullname: Anam, Khairul email: khairul@unej.ac.id organization: Department of Electrical Engineering, Universitas Jember, Intelligent Systems and Robotics Lab, CDAST, Universitas Jember – sequence: 2 givenname: Ahmad surname: Sudrajat fullname: Sudrajat, Ahmad organization: Department of Electrical Engineering, Universitas Jember, Intelligent Systems and Robotics Lab, CDAST, Universitas Jember – sequence: 3 givenname: Naufal Ainur surname: Rizal fullname: Rizal, Naufal Ainur organization: Department of Electrical Engineering, Universitas Jember, Intelligent Systems and Robotics Lab, CDAST, Universitas Jember – sequence: 4 givenname: Gramandha Wega surname: Intyanto fullname: Intyanto, Gramandha Wega organization: Department of Electrical Engineering, Universitas Jember, Intelligent Systems and Robotics Lab, CDAST, Universitas Jember – sequence: 5 givenname: Wahyu surname: Muldayani fullname: Muldayani, Wahyu organization: Department of Electrical Engineering, Universitas Jember, Intelligent Systems and Robotics Lab, CDAST, Universitas Jember – sequence: 6 givenname: Mohamad Agung Prawira surname: Negara fullname: Negara, Mohamad Agung Prawira organization: Department of Electrical Engineering, Universitas Jember, Intelligent Systems and Robotics Lab, CDAST, Universitas Jember – sequence: 7 surname: Sumardi fullname: Sumardi organization: Department of Electrical Engineering, Universitas Jember, Intelligent Systems and Robotics Lab, CDAST, Universitas Jember – sequence: 8 givenname: Saiful surname: Bukhori fullname: Bukhori, Saiful organization: Intelligent Systems and Robotics Lab, CDAST, Universitas Jember, Faculty of Computer Science, CDAST, Universitas Jember – sequence: 9 givenname: Made Santo surname: Gitakarma fullname: Gitakarma, Made Santo organization: Department of Electronic Systems Engineering Technology, Universitas Pendidikan Ganesha – sequence: 10 givenname: Claudio surname: Castellini fullname: Castellini, Claudio organization: Assistive Intelligent Robotics (AIROB) Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg
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SubjectTerms	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
Title	Evaluation of LSTM for predicting grip strength using electromyography: a comparison of setups and methods
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