Implementation of a Real-Time Force Estimation System Based on sEMG Signals and Gaussian Process Regression: Human–Robot Interaction in Rehabilitation
Human force estimation has numerous applications, including biomedical models, rehabilitation, biomechanical system control, and human-machine interfaces. To enable such applications, it is necessary and challenging to develop methods for efficiently estimating force. In this work, we propose a syst...
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IEEE
2025-01-01
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| author | Thantip Sittiruk Kiattisak Sengchuai Apidet Booranawong Pornchai Phukpattaranont |
| author_facet | Thantip Sittiruk Kiattisak Sengchuai Apidet Booranawong Pornchai Phukpattaranont |
| author_sort | Thantip Sittiruk |
| collection | DOAJ |
| description | Human force estimation has numerous applications, including biomedical models, rehabilitation, biomechanical system control, and human-machine interfaces. To enable such applications, it is necessary and challenging to develop methods for efficiently estimating force. In this work, we propose a system for muscle force estimation using eight-channel surface electromyography (sEMG) signals. The main contribution of this work is the implementation of a real-time force estimation system in MATLAB/Simulink to support real-time testing and usability. The regression models (i.e., Gaussian process regression (GPR), neural networks (NN), linear regression (LR), and support vector machines (SVM)) are applied to estimate the forearm muscle forces exerted by different elbow placement patterns for rehabilitation applications. The GPR model with exponential kernel function is chosen as the best model and is used to calculate forces at various elbow placement scenarios. Experimental results show that the GPR model can estimate forces on the X and Y axes concurrently in four movement directions: X+, X-, Y+, and Y-. The average root mean square errors (RMSEs) indicating estimation performance for offline and online testing of different elbow placement patterns are 1.09 to 1.99 N and 3.58 to 4.16 N, respectively. |
| format | Article |
| id | doaj-art-96c6ed0499824ebbb15e6769d4021cba |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-96c6ed0499824ebbb15e6769d4021cba2025-01-25T00:01:07ZengIEEEIEEE Access2169-35362025-01-0113137311374710.1109/ACCESS.2025.352998610843210Implementation of a Real-Time Force Estimation System Based on sEMG Signals and Gaussian Process Regression: Human–Robot Interaction in RehabilitationThantip Sittiruk0Kiattisak Sengchuai1Apidet Booranawong2https://orcid.org/0000-0002-5346-1594Pornchai Phukpattaranont3https://orcid.org/0000-0003-0885-0176Department of Electrical and Biomedical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, ThailandDepartment of Electrical and Biomedical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, ThailandDepartment of Electrical and Biomedical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, ThailandDepartment of Electrical and Biomedical Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla, ThailandHuman force estimation has numerous applications, including biomedical models, rehabilitation, biomechanical system control, and human-machine interfaces. To enable such applications, it is necessary and challenging to develop methods for efficiently estimating force. In this work, we propose a system for muscle force estimation using eight-channel surface electromyography (sEMG) signals. The main contribution of this work is the implementation of a real-time force estimation system in MATLAB/Simulink to support real-time testing and usability. The regression models (i.e., Gaussian process regression (GPR), neural networks (NN), linear regression (LR), and support vector machines (SVM)) are applied to estimate the forearm muscle forces exerted by different elbow placement patterns for rehabilitation applications. The GPR model with exponential kernel function is chosen as the best model and is used to calculate forces at various elbow placement scenarios. Experimental results show that the GPR model can estimate forces on the X and Y axes concurrently in four movement directions: X+, X-, Y+, and Y-. The average root mean square errors (RMSEs) indicating estimation performance for offline and online testing of different elbow placement patterns are 1.09 to 1.99 N and 3.58 to 4.16 N, respectively.https://ieeexplore.ieee.org/document/10843210/System implementationelectromyographyforce estimationregression analysispatient rehabilitation |
| spellingShingle | Thantip Sittiruk Kiattisak Sengchuai Apidet Booranawong Pornchai Phukpattaranont Implementation of a Real-Time Force Estimation System Based on sEMG Signals and Gaussian Process Regression: Human–Robot Interaction in Rehabilitation IEEE Access System implementation electromyography force estimation regression analysis patient rehabilitation |
| title | Implementation of a Real-Time Force Estimation System Based on sEMG Signals and Gaussian Process Regression: Human–Robot Interaction in Rehabilitation |
| title_full | Implementation of a Real-Time Force Estimation System Based on sEMG Signals and Gaussian Process Regression: Human–Robot Interaction in Rehabilitation |
| title_fullStr | Implementation of a Real-Time Force Estimation System Based on sEMG Signals and Gaussian Process Regression: Human–Robot Interaction in Rehabilitation |
| title_full_unstemmed | Implementation of a Real-Time Force Estimation System Based on sEMG Signals and Gaussian Process Regression: Human–Robot Interaction in Rehabilitation |
| title_short | Implementation of a Real-Time Force Estimation System Based on sEMG Signals and Gaussian Process Regression: Human–Robot Interaction in Rehabilitation |
| title_sort | implementation of a real time force estimation system based on semg signals and gaussian process regression human x2013 robot interaction in rehabilitation |
| topic | System implementation electromyography force estimation regression analysis patient rehabilitation |
| url | https://ieeexplore.ieee.org/document/10843210/ |
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