{"title":"基于Semg和CNN-GRU的机械手手势识别与主从控制","authors":"Zhaojie Ge, Zhile Wu, Xu Han, Ping Zhao","doi":"10.1115/1.4056325","DOIUrl":null,"url":null,"abstract":"\n Surface electromyography signal (sEMG) is the bioelectric signal accompanied by muscle contraction. For masterslave manipulation scenario such as patients with prosthetic hands, their upper limb sEMG signals can be collected and corresponded to the patient' s gesture intention. Therefore, using a slave manipulator that integrated with the sEMG signal recognition module, the master side could control it to make gestures and meet their needs of daily life. In this paper, gesture recognition is carried out based on sEMG and deep learning, and the master-slave control of manipulator is realized. According to the results of training, the network model with the highest accuracy of gesture classification and recognition can be obtained. Then, combined with the integrated manipulator, the control signal of the manipulator corresponding to the gesture is sent to the control module of the manipulator. In the end, a prototype system is built and the master-slave control of the manipulator using the sEMG signal is realized.","PeriodicalId":73734,"journal":{"name":"Journal of engineering and science in medical diagnostics and therapy","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Gesture Recognition and Master-slave Control of a Manipulator Based On Semg and CNN-GRU\",\"authors\":\"Zhaojie Ge, Zhile Wu, Xu Han, Ping Zhao\",\"doi\":\"10.1115/1.4056325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Surface electromyography signal (sEMG) is the bioelectric signal accompanied by muscle contraction. For masterslave manipulation scenario such as patients with prosthetic hands, their upper limb sEMG signals can be collected and corresponded to the patient' s gesture intention. Therefore, using a slave manipulator that integrated with the sEMG signal recognition module, the master side could control it to make gestures and meet their needs of daily life. In this paper, gesture recognition is carried out based on sEMG and deep learning, and the master-slave control of manipulator is realized. According to the results of training, the network model with the highest accuracy of gesture classification and recognition can be obtained. Then, combined with the integrated manipulator, the control signal of the manipulator corresponding to the gesture is sent to the control module of the manipulator. In the end, a prototype system is built and the master-slave control of the manipulator using the sEMG signal is realized.\",\"PeriodicalId\":73734,\"journal\":{\"name\":\"Journal of engineering and science in medical diagnostics and therapy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of engineering and science in medical diagnostics and therapy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4056325\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of engineering and science in medical diagnostics and therapy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/1.4056325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Gesture Recognition and Master-slave Control of a Manipulator Based On Semg and CNN-GRU
Surface electromyography signal (sEMG) is the bioelectric signal accompanied by muscle contraction. For masterslave manipulation scenario such as patients with prosthetic hands, their upper limb sEMG signals can be collected and corresponded to the patient' s gesture intention. Therefore, using a slave manipulator that integrated with the sEMG signal recognition module, the master side could control it to make gestures and meet their needs of daily life. In this paper, gesture recognition is carried out based on sEMG and deep learning, and the master-slave control of manipulator is realized. According to the results of training, the network model with the highest accuracy of gesture classification and recognition can be obtained. Then, combined with the integrated manipulator, the control signal of the manipulator corresponding to the gesture is sent to the control module of the manipulator. In the end, a prototype system is built and the master-slave control of the manipulator using the sEMG signal is realized.