A CW-CNN regression model-based real-time system for virtual hand control
For upper limb amputees, wearing a myoelectric prosthetic hand is the only way for them to continue normal life. Even until now, the proposal of a high-precision and natural performance real-time control system based on surface electromyography (sEMG) signals is still challenging. Researchers have p...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-12-01
|
| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2022.1072365/full |
| _version_ | 1811195256838815744 |
|---|---|
| author | Zixuan Qin Zixun He Yuanhao Li Supat Saetia Yasuharu Koike |
| author_facet | Zixuan Qin Zixun He Yuanhao Li Supat Saetia Yasuharu Koike |
| author_sort | Zixuan Qin |
| collection | DOAJ |
| description | For upper limb amputees, wearing a myoelectric prosthetic hand is the only way for them to continue normal life. Even until now, the proposal of a high-precision and natural performance real-time control system based on surface electromyography (sEMG) signals is still challenging. Researchers have proposed many strategies for motion classification or regression prediction tasks based on sEMG signals. However, most of them have been limited to offline analysis only. There are even few papers on real-time control based on deep learning models, almost all of which are about motion classification. Rare studies tried to use deep learning-based regression models in real-time control systems for multi-joint angle estimation via sEMG signals. This paper proposed a CW-CNN regression model-based real-time control system for virtual hand control. We designed an Adaptive Kalman Filter to smooth the joint angles output before sending them as control commands to control a virtual hand. Eight healthy participants were invited, and three sessions experiments were conducted on two different days for all of them. During the real-time experiment, we analyzed the joint angles estimation accuracy and computational latency. Moreover, target achievement control (TAC) test was applied to emphasize motion regression in real-time. The experimental results show that the proposed control system has high precision for 3-DOFs motion regression in simultaneously, and the system remains stable and low computational latency. In the future, the proposed real-time control system can be applied to actual prosthetic hand. |
| first_indexed | 2024-04-12T00:40:19Z |
| format | Article |
| id | doaj.art-26d8c665539f4a3c8810ea6e9bbb0e35 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-04-12T00:40:19Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-26d8c665539f4a3c8810ea6e9bbb0e352022-12-22T03:55:01ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182022-12-011610.3389/fnbot.2022.10723651072365A CW-CNN regression model-based real-time system for virtual hand controlZixuan Qin0Zixun He1Yuanhao Li2Supat Saetia3Yasuharu Koike4Department of Information and Communications Engineering, Tokyo Institute of Technology, Yokohama, JapanDepartment of Information and Communications Engineering, Tokyo Institute of Technology, Yokohama, JapanDepartment of Information and Communications Engineering, Tokyo Institute of Technology, Yokohama, JapanInstitute of Innovative Research, Tokyo Institute of Technology, Yokohama, JapanInstitute of Innovative Research, Tokyo Institute of Technology, Yokohama, JapanFor upper limb amputees, wearing a myoelectric prosthetic hand is the only way for them to continue normal life. Even until now, the proposal of a high-precision and natural performance real-time control system based on surface electromyography (sEMG) signals is still challenging. Researchers have proposed many strategies for motion classification or regression prediction tasks based on sEMG signals. However, most of them have been limited to offline analysis only. There are even few papers on real-time control based on deep learning models, almost all of which are about motion classification. Rare studies tried to use deep learning-based regression models in real-time control systems for multi-joint angle estimation via sEMG signals. This paper proposed a CW-CNN regression model-based real-time control system for virtual hand control. We designed an Adaptive Kalman Filter to smooth the joint angles output before sending them as control commands to control a virtual hand. Eight healthy participants were invited, and three sessions experiments were conducted on two different days for all of them. During the real-time experiment, we analyzed the joint angles estimation accuracy and computational latency. Moreover, target achievement control (TAC) test was applied to emphasize motion regression in real-time. The experimental results show that the proposed control system has high precision for 3-DOFs motion regression in simultaneously, and the system remains stable and low computational latency. In the future, the proposed real-time control system can be applied to actual prosthetic hand.https://www.frontiersin.org/articles/10.3389/fnbot.2022.1072365/fullchannel-wise CNN (CW-CNN)real-time control systemregression modelsurface electromyography (SEMG)target achievement control (TAC)virtual hand control |
| spellingShingle | Zixuan Qin Zixun He Yuanhao Li Supat Saetia Yasuharu Koike A CW-CNN regression model-based real-time system for virtual hand control Frontiers in Neurorobotics channel-wise CNN (CW-CNN) real-time control system regression model surface electromyography (SEMG) target achievement control (TAC) virtual hand control |
| title | A CW-CNN regression model-based real-time system for virtual hand control |
| title_full | A CW-CNN regression model-based real-time system for virtual hand control |
| title_fullStr | A CW-CNN regression model-based real-time system for virtual hand control |
| title_full_unstemmed | A CW-CNN regression model-based real-time system for virtual hand control |
| title_short | A CW-CNN regression model-based real-time system for virtual hand control |
| title_sort | cw cnn regression model based real time system for virtual hand control |
| topic | channel-wise CNN (CW-CNN) real-time control system regression model surface electromyography (SEMG) target achievement control (TAC) virtual hand control |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2022.1072365/full |
| work_keys_str_mv | AT zixuanqin acwcnnregressionmodelbasedrealtimesystemforvirtualhandcontrol AT zixunhe acwcnnregressionmodelbasedrealtimesystemforvirtualhandcontrol AT yuanhaoli acwcnnregressionmodelbasedrealtimesystemforvirtualhandcontrol AT supatsaetia acwcnnregressionmodelbasedrealtimesystemforvirtualhandcontrol AT yasuharukoike acwcnnregressionmodelbasedrealtimesystemforvirtualhandcontrol AT zixuanqin cwcnnregressionmodelbasedrealtimesystemforvirtualhandcontrol AT zixunhe cwcnnregressionmodelbasedrealtimesystemforvirtualhandcontrol AT yuanhaoli cwcnnregressionmodelbasedrealtimesystemforvirtualhandcontrol AT supatsaetia cwcnnregressionmodelbasedrealtimesystemforvirtualhandcontrol AT yasuharukoike cwcnnregressionmodelbasedrealtimesystemforvirtualhandcontrol |