Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model
Continuous grasping force estimation based on electromyography (EMG) signals, is very useful in practical applications including prosthetic control and human force observation. However, implementing the practical grasping force estimation usually considers a trade-off between the computational preci...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9919873/ |
| _version_ | 1797805114367410176 |
|---|---|
| author | Xiaolei Xu Hua Deng Yi Zhang Jingwei Chen |
| author_facet | Xiaolei Xu Hua Deng Yi Zhang Jingwei Chen |
| author_sort | Xiaolei Xu |
| collection | DOAJ |
| description | Continuous grasping force estimation based on electromyography (EMG) signals, is very useful in practical applications including prosthetic control and human force observation. However, implementing the practical grasping force estimation usually considers a trade-off between the computational precision and resources. Specifically, the estimation based on the Huxley-type muscle model reaches detailed approximation of physiological process at a cost of larger computational resources for solving nonlinear partial differential equations while the counterpart with a traditional Hill-type muscle model. In this article, we achieve the grasping force estimation based on a reduced Huxley-type musculoskeletal model with high accuracy yet low time delay. Leveraging on a balanced truncation method, we further reduce the dimensionality of the spectral method solution in the Huxley-type musculoskeletal model for the model simplification. In addition, we introduce the Kalman filter method to process the EMG signals obtained by an armband, yielding better real-time performances and accuracy compared to the signal treatment using the traditional EMG filter method. Moreover, we also implement an effective identification of the model parameters using a particle swarm method. Finally, we trained the model on the first day and made grasping force estimation experiments involved with three participants over the course of a month. We envision that this effective and practical method would further improve the practical applications in the field of grasping force estimation. |
| first_indexed | 2024-03-13T05:46:35Z |
| format | Article |
| id | doaj.art-ca09fcf66d58414d870e339f1efadabd |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:35Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-ca09fcf66d58414d870e339f1efadabd2023-06-13T20:09:27ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-013134635510.1109/TNSRE.2022.32148669919873Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal ModelXiaolei Xu0https://orcid.org/0000-0002-6441-6940Hua Deng1https://orcid.org/0000-0003-2075-2990Yi Zhang2https://orcid.org/0000-0003-3398-992XJingwei Chen3https://orcid.org/0000-0003-0131-3044School of Mechanical and Electrical Engineering, Central South University, Changsha, ChinaState Key Laboratory of High Performance Complex Manufacturing, School of Mechanical Engineering, Central South University, Changsha, ChinaState Key Laboratory of High Performance Complex Manufacturing, School of Mechanical Engineering, Central South University, Changsha, ChinaSchool of Mechanical and Electrical Engineering, Central South University, Changsha, ChinaContinuous grasping force estimation based on electromyography (EMG) signals, is very useful in practical applications including prosthetic control and human force observation. However, implementing the practical grasping force estimation usually considers a trade-off between the computational precision and resources. Specifically, the estimation based on the Huxley-type muscle model reaches detailed approximation of physiological process at a cost of larger computational resources for solving nonlinear partial differential equations while the counterpart with a traditional Hill-type muscle model. In this article, we achieve the grasping force estimation based on a reduced Huxley-type musculoskeletal model with high accuracy yet low time delay. Leveraging on a balanced truncation method, we further reduce the dimensionality of the spectral method solution in the Huxley-type musculoskeletal model for the model simplification. In addition, we introduce the Kalman filter method to process the EMG signals obtained by an armband, yielding better real-time performances and accuracy compared to the signal treatment using the traditional EMG filter method. Moreover, we also implement an effective identification of the model parameters using a particle swarm method. Finally, we trained the model on the first day and made grasping force estimation experiments involved with three participants over the course of a month. We envision that this effective and practical method would further improve the practical applications in the field of grasping force estimation.https://ieeexplore.ieee.org/document/9919873/Force estimationHuxley modelsurface EMGspectral methodparameter identifyEMG filter |
| spellingShingle | Xiaolei Xu Hua Deng Yi Zhang Jingwei Chen Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model IEEE Transactions on Neural Systems and Rehabilitation Engineering Force estimation Huxley model surface EMG spectral method parameter identify EMG filter |
| title | Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model |
| title_full | Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model |
| title_fullStr | Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model |
| title_full_unstemmed | Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model |
| title_short | Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model |
| title_sort | continuous grasping force estimation with surface emg based on huxley type musculoskeletal model |
| topic | Force estimation Huxley model surface EMG spectral method parameter identify EMG filter |
| url | https://ieeexplore.ieee.org/document/9919873/ |
| work_keys_str_mv | AT xiaoleixu continuousgraspingforceestimationwithsurfaceemgbasedonhuxleytypemusculoskeletalmodel AT huadeng continuousgraspingforceestimationwithsurfaceemgbasedonhuxleytypemusculoskeletalmodel AT yizhang continuousgraspingforceestimationwithsurfaceemgbasedonhuxleytypemusculoskeletalmodel AT jingweichen continuousgraspingforceestimationwithsurfaceemgbasedonhuxleytypemusculoskeletalmodel |