A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model
The loss of mobility function and sensory information from the arm, hand, and fingertips hampers the activities of daily living (ADL) of patients. A modern bionic prosthetic hand can compensate for the lost functions and realize multiple degree of freedom (DoF) movements. However, the commercially a...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/8/2576 |
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| author | Zhaolong Gao Rongyu Tang Qiang Huang Jiping He |
| author_facet | Zhaolong Gao Rongyu Tang Qiang Huang Jiping He |
| author_sort | Zhaolong Gao |
| collection | DOAJ |
| description | The loss of mobility function and sensory information from the arm, hand, and fingertips hampers the activities of daily living (ADL) of patients. A modern bionic prosthetic hand can compensate for the lost functions and realize multiple degree of freedom (DoF) movements. However, the commercially available prosthetic hands usually have limited DoFs due to limited sensors and lack of stable classification algorithms. This study aimed to propose a controller for finger joint angle estimation by surface electromyography (sEMG). The sEMG data used for training were gathered with the Myo armband, which is a commercial EMG sensor. Two features in the time domain were extracted and fed into a nonlinear autoregressive model with exogenous inputs (NARX). The NARX model was trained with pre-selected parameters using the Levenberg–Marquardt algorithm. Comparing with the targets, the regression correlation coefficient (R) of the model outputs was more than 0.982 over all test subjects, and the mean square error was less than 10.02 for a signal range in arbitrary units equal to [0, 255]. The study also demonstrated that the proposed model could be used in daily life movements with good accuracy and generalization abilities. |
| first_indexed | 2024-03-10T12:33:41Z |
| format | Article |
| id | doaj.art-3741c0521c504752b9abbd8added578c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:33:41Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3741c0521c504752b9abbd8added578c2023-11-21T14:27:15ZengMDPI AGSensors1424-82202021-04-01218257610.3390/s21082576A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous ModelZhaolong Gao0Rongyu Tang1Qiang Huang2Jiping He3Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaBeijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, ChinaBeijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, ChinaBeijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, ChinaThe loss of mobility function and sensory information from the arm, hand, and fingertips hampers the activities of daily living (ADL) of patients. A modern bionic prosthetic hand can compensate for the lost functions and realize multiple degree of freedom (DoF) movements. However, the commercially available prosthetic hands usually have limited DoFs due to limited sensors and lack of stable classification algorithms. This study aimed to propose a controller for finger joint angle estimation by surface electromyography (sEMG). The sEMG data used for training were gathered with the Myo armband, which is a commercial EMG sensor. Two features in the time domain were extracted and fed into a nonlinear autoregressive model with exogenous inputs (NARX). The NARX model was trained with pre-selected parameters using the Levenberg–Marquardt algorithm. Comparing with the targets, the regression correlation coefficient (R) of the model outputs was more than 0.982 over all test subjects, and the mean square error was less than 10.02 for a signal range in arbitrary units equal to [0, 255]. The study also demonstrated that the proposed model could be used in daily life movements with good accuracy and generalization abilities.https://www.mdpi.com/1424-8220/21/8/2576EMGNARXneural networkprosthesis |
| spellingShingle | Zhaolong Gao Rongyu Tang Qiang Huang Jiping He A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model Sensors EMG NARX neural network prosthesis |
| title | A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model |
| title_full | A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model |
| title_fullStr | A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model |
| title_full_unstemmed | A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model |
| title_short | A Multi-DoF Prosthetic Hand Finger Joint Controller for Wearable sEMG Sensors by Nonlinear Autoregressive Exogenous Model |
| title_sort | multi dof prosthetic hand finger joint controller for wearable semg sensors by nonlinear autoregressive exogenous model |
| topic | EMG NARX neural network prosthesis |
| url | https://www.mdpi.com/1424-8220/21/8/2576 |
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