Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning
Deep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. Herein, we investigated cross-joint TL between two up...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/22/7301 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850227786320445440 |
|---|---|
| author | Haopeng Wang He Wang Chenyun Dai Xinming Huang Edward A. Clancy |
| author_facet | Haopeng Wang He Wang Chenyun Dai Xinming Huang Edward A. Clancy |
| author_sort | Haopeng Wang |
| collection | DOAJ |
| description | Deep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. Herein, we investigated cross-joint TL between two upper-limb joints with four DNN architectures using sliding windows. We used two feedforward and two recurrent DNN models with feature engineering and feature learning, respectively. We found that the dependencies between sEMG and force are short-term (<400 ms) and that sliding windows are sufficient to capture them, suggesting that more complicated recurrent structures may not be necessary. Also, using DNN architectures reduced the required sliding window length. A model pre-trained on elbow data was fine-tuned on hand–wrist data, improving force estimation accuracy and reducing the required training data amount. A convolutional neural network with a 391 ms sliding window fine-tuned using 20 s of training data had an error of 6.03 ± 0.49% maximum voluntary torque, which is statistically lower than both our multilayer perceptron model with TL and a linear regression model using 40 s of training data. The success of TL between two distinct joints could help enrich the data available for future deep learning-related studies. |
| format | Article |
| id | doaj-art-8553c584bac749189a624ee5e38982a3 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-8553c584bac749189a624ee5e38982a32025-08-20T02:04:44ZengMDPI AGSensors1424-82202024-11-012422730110.3390/s24227301Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer LearningHaopeng Wang0He Wang1Chenyun Dai2Xinming Huang3Edward A. Clancy4Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USADepartment of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USASchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200241, ChinaDepartment of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USADepartment of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USADeep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. Herein, we investigated cross-joint TL between two upper-limb joints with four DNN architectures using sliding windows. We used two feedforward and two recurrent DNN models with feature engineering and feature learning, respectively. We found that the dependencies between sEMG and force are short-term (<400 ms) and that sliding windows are sufficient to capture them, suggesting that more complicated recurrent structures may not be necessary. Also, using DNN architectures reduced the required sliding window length. A model pre-trained on elbow data was fine-tuned on hand–wrist data, improving force estimation accuracy and reducing the required training data amount. A convolutional neural network with a 391 ms sliding window fine-tuned using 20 s of training data had an error of 6.03 ± 0.49% maximum voluntary torque, which is statistically lower than both our multilayer perceptron model with TL and a linear regression model using 40 s of training data. The success of TL between two distinct joints could help enrich the data available for future deep learning-related studies.https://www.mdpi.com/1424-8220/24/22/7301electromyogramtransfer learningdeep neural networksforce estimationCNNLSTM |
| spellingShingle | Haopeng Wang He Wang Chenyun Dai Xinming Huang Edward A. Clancy Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning Sensors electromyogram transfer learning deep neural networks force estimation CNN LSTM |
| title | Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning |
| title_full | Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning |
| title_fullStr | Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning |
| title_full_unstemmed | Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning |
| title_short | Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning |
| title_sort | improved surface electromyogram based hand wrist force estimation using deep neural networks and cross joint transfer learning |
| topic | electromyogram transfer learning deep neural networks force estimation CNN LSTM |
| url | https://www.mdpi.com/1424-8220/24/22/7301 |
| work_keys_str_mv | AT haopengwang improvedsurfaceelectromyogrambasedhandwristforceestimationusingdeepneuralnetworksandcrossjointtransferlearning AT hewang improvedsurfaceelectromyogrambasedhandwristforceestimationusingdeepneuralnetworksandcrossjointtransferlearning AT chenyundai improvedsurfaceelectromyogrambasedhandwristforceestimationusingdeepneuralnetworksandcrossjointtransferlearning AT xinminghuang improvedsurfaceelectromyogrambasedhandwristforceestimationusingdeepneuralnetworksandcrossjointtransferlearning AT edwardaclancy improvedsurfaceelectromyogrambasedhandwristforceestimationusingdeepneuralnetworksandcrossjointtransferlearning |