Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization
A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/22/6533 |
| _version_ | 1797547858002444288 |
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| author | Xinxin Li Zuojun Liu Xinzhi Gao Jie Zhang |
| author_facet | Xinxin Li Zuojun Liu Xinzhi Gao Jie Zhang |
| author_sort | Xinxin Li |
| collection | DOAJ |
| description | A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter was used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models. |
| first_indexed | 2024-03-10T14:50:13Z |
| format | Article |
| id | doaj.art-e490bb53f371456485eef21fe1e76725 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:50:13Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e490bb53f371456485eef21fe1e767252023-11-20T21:03:19ZengMDPI AGSensors1424-82202020-11-012022653310.3390/s20226533Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm OptimizationXinxin Li0Zuojun Liu1Xinzhi Gao2Jie Zhang3School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, ChinaSchool of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, ChinaSchool of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, ChinaSchool of Engineering, Merz Court, Newcastle University, Newcastle upon Tyne NE1 7RU, UKA novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter was used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models.https://www.mdpi.com/1424-8220/20/22/6533lower-limb prosthesisbicyclingphase recognitionparticle swarm optimization (PSO)support vector machine (SVM) |
| spellingShingle | Xinxin Li Zuojun Liu Xinzhi Gao Jie Zhang Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization Sensors lower-limb prosthesis bicycling phase recognition particle swarm optimization (PSO) support vector machine (SVM) |
| title | Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization |
| title_full | Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization |
| title_fullStr | Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization |
| title_full_unstemmed | Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization |
| title_short | Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization |
| title_sort | bicycling phase recognition for lower limb amputees using support vector machine optimized by particle swarm optimization |
| topic | lower-limb prosthesis bicycling phase recognition particle swarm optimization (PSO) support vector machine (SVM) |
| url | https://www.mdpi.com/1424-8220/20/22/6533 |
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