Prediction and Fitting of Nonlinear Dynamic Grip Force of the Human Upper Limb Based on Surface Electromyographic Signals
This study aimed to predict and fit the nonlinear dynamic grip force of the human upper limb using surface electromyographic (sEMG) signals. The research employed a time-series-based neural network, NARX, to establish a mapping relationship between the electromyographic signals of the forearm muscle...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/1/13 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841548980896399360 |
|---|---|
| author | Zixiang Cai Mengyao Qu Mingyang Han Zhijing Wu Tong Wu Mengtong Liu Hailong Yu |
| author_facet | Zixiang Cai Mengyao Qu Mingyang Han Zhijing Wu Tong Wu Mengtong Liu Hailong Yu |
| author_sort | Zixiang Cai |
| collection | DOAJ |
| description | This study aimed to predict and fit the nonlinear dynamic grip force of the human upper limb using surface electromyographic (sEMG) signals. The research employed a time-series-based neural network, NARX, to establish a mapping relationship between the electromyographic signals of the forearm muscle groups and dynamic grip force. Three-channel electromyographic signal acquisition equipment and a grip force sensor were used to record muscle signals and grip force data of the subjects under specific dynamic force conditions. After preprocessing the data, including outlier removal, wavelet denoising, and baseline drift correction, the NARX model was used for fitting analysis. The model compares two different training strategies: regularized stochastic gradient descent (BRSGD) and conjugate gradient (CG). The results show that the CG greatly shortened the training time, and performance did not decline. NARX demonstrated good accuracy and stability in dynamic grip force prediction, with the model with 10 layers and 20 time delays performing the best. The results demonstrate that the proposed method has potential practical significance for force control applications in smart prosthetics and virtual reality. |
| format | Article |
| id | doaj-art-301ec749d33b4f27b92302ec0b98f954 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-301ec749d33b4f27b92302ec0b98f9542025-01-10T13:20:33ZengMDPI AGSensors1424-82202024-12-012511310.3390/s25010013Prediction and Fitting of Nonlinear Dynamic Grip Force of the Human Upper Limb Based on Surface Electromyographic SignalsZixiang Cai0Mengyao Qu1Mingyang Han2Zhijing Wu3Tong Wu4Mengtong Liu5Hailong Yu6School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaThis study aimed to predict and fit the nonlinear dynamic grip force of the human upper limb using surface electromyographic (sEMG) signals. The research employed a time-series-based neural network, NARX, to establish a mapping relationship between the electromyographic signals of the forearm muscle groups and dynamic grip force. Three-channel electromyographic signal acquisition equipment and a grip force sensor were used to record muscle signals and grip force data of the subjects under specific dynamic force conditions. After preprocessing the data, including outlier removal, wavelet denoising, and baseline drift correction, the NARX model was used for fitting analysis. The model compares two different training strategies: regularized stochastic gradient descent (BRSGD) and conjugate gradient (CG). The results show that the CG greatly shortened the training time, and performance did not decline. NARX demonstrated good accuracy and stability in dynamic grip force prediction, with the model with 10 layers and 20 time delays performing the best. The results demonstrate that the proposed method has potential practical significance for force control applications in smart prosthetics and virtual reality.https://www.mdpi.com/1424-8220/25/1/13surface electromyographic signalsnonlinear dynamic grip forceNARX neural networkgrip force prediction |
| spellingShingle | Zixiang Cai Mengyao Qu Mingyang Han Zhijing Wu Tong Wu Mengtong Liu Hailong Yu Prediction and Fitting of Nonlinear Dynamic Grip Force of the Human Upper Limb Based on Surface Electromyographic Signals Sensors surface electromyographic signals nonlinear dynamic grip force NARX neural network grip force prediction |
| title | Prediction and Fitting of Nonlinear Dynamic Grip Force of the Human Upper Limb Based on Surface Electromyographic Signals |
| title_full | Prediction and Fitting of Nonlinear Dynamic Grip Force of the Human Upper Limb Based on Surface Electromyographic Signals |
| title_fullStr | Prediction and Fitting of Nonlinear Dynamic Grip Force of the Human Upper Limb Based on Surface Electromyographic Signals |
| title_full_unstemmed | Prediction and Fitting of Nonlinear Dynamic Grip Force of the Human Upper Limb Based on Surface Electromyographic Signals |
| title_short | Prediction and Fitting of Nonlinear Dynamic Grip Force of the Human Upper Limb Based on Surface Electromyographic Signals |
| title_sort | prediction and fitting of nonlinear dynamic grip force of the human upper limb based on surface electromyographic signals |
| topic | surface electromyographic signals nonlinear dynamic grip force NARX neural network grip force prediction |
| url | https://www.mdpi.com/1424-8220/25/1/13 |
| work_keys_str_mv | AT zixiangcai predictionandfittingofnonlineardynamicgripforceofthehumanupperlimbbasedonsurfaceelectromyographicsignals AT mengyaoqu predictionandfittingofnonlineardynamicgripforceofthehumanupperlimbbasedonsurfaceelectromyographicsignals AT mingyanghan predictionandfittingofnonlineardynamicgripforceofthehumanupperlimbbasedonsurfaceelectromyographicsignals AT zhijingwu predictionandfittingofnonlineardynamicgripforceofthehumanupperlimbbasedonsurfaceelectromyographicsignals AT tongwu predictionandfittingofnonlineardynamicgripforceofthehumanupperlimbbasedonsurfaceelectromyographicsignals AT mengtongliu predictionandfittingofnonlineardynamicgripforceofthehumanupperlimbbasedonsurfaceelectromyographicsignals AT hailongyu predictionandfittingofnonlineardynamicgripforceofthehumanupperlimbbasedonsurfaceelectromyographicsignals |