SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
An exoskeleton robot is a kind of wearable mechanical instrument designed according to the shape and function of the human body. The main purpose of its design and manufacture is to enhance human strength, assist human walking and to help patients recover. The walking state of the exoskeleton robot...
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/12/5573 |
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| author | Zeyu Yin Jianbin Zheng Liping Huang Yifan Gao Huihui Peng Linghan Yin |
| author_facet | Zeyu Yin Jianbin Zheng Liping Huang Yifan Gao Huihui Peng Linghan Yin |
| author_sort | Zeyu Yin |
| collection | DOAJ |
| description | An exoskeleton robot is a kind of wearable mechanical instrument designed according to the shape and function of the human body. The main purpose of its design and manufacture is to enhance human strength, assist human walking and to help patients recover. The walking state of the exoskeleton robot should be highly consistent with the state of the human, so the accurate locomotion pattern recognition is the premise of the flexible control of the exoskeleton robot. In this paper, a simulated annealing (SA) algorithm-based support vector machine model is proposed for the recognition of different locomotion patterns. In order to improve the overall performance of the support vector machine (SVM), the simulated annealing algorithm is adopted to obtain the optimal parameters of support vector machine. The pressure signal measured by the force sensing resistors integrated on the sole of the shoe is fused with the position and pose information measured by the inertial measurement units attached to the thigh, shank and foot, which are used as the input information of the support vector machine. The max-relevance and min-redundancy algorithm was selected for feature extraction based on the window size of 300 ms and the sampling frequency of 100 Hz. Since the signals come from different types of sensors, normalization is required to scale the input signals to the interval (0,1). In order to prevent the classifier from overfitting, five layers of cross validation are used to train the support vector machine classifier. The support vector machine model was obtained offline in MATLAB. The finite state machine is used to limit the state transition and improve the recognition accuracy. Experiments on different locomotion patterns show that the accuracy of the algorithm is 97.47% ± 1.16%. The SA-SVM method can be extended to industrial robots and rehabilitation robots. |
| first_indexed | 2024-03-10T10:21:25Z |
| format | Article |
| id | doaj.art-5f7d326180634ad3bacefbaada4f8b31 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:21:25Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5f7d326180634ad3bacefbaada4f8b312023-11-22T00:22:58ZengMDPI AGApplied Sciences2076-34172021-06-011112557310.3390/app11125573SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton RobotZeyu Yin0Jianbin Zheng1Liping Huang2Yifan Gao3Huihui Peng4Linghan Yin5School of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaAn exoskeleton robot is a kind of wearable mechanical instrument designed according to the shape and function of the human body. The main purpose of its design and manufacture is to enhance human strength, assist human walking and to help patients recover. The walking state of the exoskeleton robot should be highly consistent with the state of the human, so the accurate locomotion pattern recognition is the premise of the flexible control of the exoskeleton robot. In this paper, a simulated annealing (SA) algorithm-based support vector machine model is proposed for the recognition of different locomotion patterns. In order to improve the overall performance of the support vector machine (SVM), the simulated annealing algorithm is adopted to obtain the optimal parameters of support vector machine. The pressure signal measured by the force sensing resistors integrated on the sole of the shoe is fused with the position and pose information measured by the inertial measurement units attached to the thigh, shank and foot, which are used as the input information of the support vector machine. The max-relevance and min-redundancy algorithm was selected for feature extraction based on the window size of 300 ms and the sampling frequency of 100 Hz. Since the signals come from different types of sensors, normalization is required to scale the input signals to the interval (0,1). In order to prevent the classifier from overfitting, five layers of cross validation are used to train the support vector machine classifier. The support vector machine model was obtained offline in MATLAB. The finite state machine is used to limit the state transition and improve the recognition accuracy. Experiments on different locomotion patterns show that the accuracy of the algorithm is 97.47% ± 1.16%. The SA-SVM method can be extended to industrial robots and rehabilitation robots.https://www.mdpi.com/2076-3417/11/12/5573exoskeleton robotlocomotion pattern recognitionfeature extractionSA-SVMfinite state machine |
| spellingShingle | Zeyu Yin Jianbin Zheng Liping Huang Yifan Gao Huihui Peng Linghan Yin SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot Applied Sciences exoskeleton robot locomotion pattern recognition feature extraction SA-SVM finite state machine |
| title | SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot |
| title_full | SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot |
| title_fullStr | SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot |
| title_full_unstemmed | SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot |
| title_short | SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot |
| title_sort | sa svm based locomotion pattern recognition for exoskeleton robot |
| topic | exoskeleton robot locomotion pattern recognition feature extraction SA-SVM finite state machine |
| url | https://www.mdpi.com/2076-3417/11/12/5573 |
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