Qingsong Ai, Lei Wang, Kun Chen, Anqi Chen, Jiwei Hu, Yilin Fang, Quan Liu, Zude Zhou
{"title":"基于人意向的踝关节康复机器人协同控制","authors":"Qingsong Ai, Lei Wang, Kun Chen, Anqi Chen, Jiwei Hu, Yilin Fang, Quan Liu, Zude Zhou","doi":"10.1109/DEVLRN.2018.8761006","DOIUrl":null,"url":null,"abstract":"Motor imagery electroencephalogram (EEG) is a kind of brain signal induced by subjective consciousness. Relevant studies in the field of sports rehabilitation show that motor imagery training can promote the recovery of damaged nerves and the reconstruction of motor nerve pathways. This paper proposes a human-brain cooperative control strategy of a pneumatic muscle-driven ankle rehabilitation robot based on motor imagery EEG. Robots provide assisted rehabilitation training for patients with impaired neural transmission but with movement intentions. The brain network algorithm is used to select the optimal channels for the motor imagery signal, and the common spatial pattern (CSP) method is combined with the time-frequency analysis method local characteristic-scale decomposition (LCD) to extract the time-frequency information. Finally, the classification is processed by the spectral regression discriminant analysis (SRDA) classifier. In addition, two rehabilitation training modes are designed, namely, synchronous rehabilitation training and asynchronous rehabilitation training. The experimental results prove that a brain intention driven human robot cooperative control method is realized to complete an ankle rehabilitation training task effectively.","PeriodicalId":236346,"journal":{"name":"2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Cooperative Control of An Ankle Rehabilitation Robot Based on Human Intention\",\"authors\":\"Qingsong Ai, Lei Wang, Kun Chen, Anqi Chen, Jiwei Hu, Yilin Fang, Quan Liu, Zude Zhou\",\"doi\":\"10.1109/DEVLRN.2018.8761006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Motor imagery electroencephalogram (EEG) is a kind of brain signal induced by subjective consciousness. Relevant studies in the field of sports rehabilitation show that motor imagery training can promote the recovery of damaged nerves and the reconstruction of motor nerve pathways. This paper proposes a human-brain cooperative control strategy of a pneumatic muscle-driven ankle rehabilitation robot based on motor imagery EEG. Robots provide assisted rehabilitation training for patients with impaired neural transmission but with movement intentions. The brain network algorithm is used to select the optimal channels for the motor imagery signal, and the common spatial pattern (CSP) method is combined with the time-frequency analysis method local characteristic-scale decomposition (LCD) to extract the time-frequency information. Finally, the classification is processed by the spectral regression discriminant analysis (SRDA) classifier. In addition, two rehabilitation training modes are designed, namely, synchronous rehabilitation training and asynchronous rehabilitation training. The experimental results prove that a brain intention driven human robot cooperative control method is realized to complete an ankle rehabilitation training task effectively.\",\"PeriodicalId\":236346,\"journal\":{\"name\":\"2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DEVLRN.2018.8761006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DEVLRN.2018.8761006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cooperative Control of An Ankle Rehabilitation Robot Based on Human Intention
Motor imagery electroencephalogram (EEG) is a kind of brain signal induced by subjective consciousness. Relevant studies in the field of sports rehabilitation show that motor imagery training can promote the recovery of damaged nerves and the reconstruction of motor nerve pathways. This paper proposes a human-brain cooperative control strategy of a pneumatic muscle-driven ankle rehabilitation robot based on motor imagery EEG. Robots provide assisted rehabilitation training for patients with impaired neural transmission but with movement intentions. The brain network algorithm is used to select the optimal channels for the motor imagery signal, and the common spatial pattern (CSP) method is combined with the time-frequency analysis method local characteristic-scale decomposition (LCD) to extract the time-frequency information. Finally, the classification is processed by the spectral regression discriminant analysis (SRDA) classifier. In addition, two rehabilitation training modes are designed, namely, synchronous rehabilitation training and asynchronous rehabilitation training. The experimental results prove that a brain intention driven human robot cooperative control method is realized to complete an ankle rehabilitation training task effectively.