Fadhli Ismail Hatta, E. A. Suprayitno, Rachmad Setiawan
{"title":"腕部残肢肌电控制多致动器假肢的设计与工程","authors":"Fadhli Ismail Hatta, E. A. Suprayitno, Rachmad Setiawan","doi":"10.1109/CENIM56801.2022.10037349","DOIUrl":null,"url":null,"abstract":"This study will design a prosthesis with two actuators as a solution for disabled people with hand disabilities. The prosthesis control method used in this design is based on surface electromyography (sEMG) signals for each actuator. The designed system consists of several processes, namely the acquisition of sEMG signals, digitizing signals, as well as classification of sEMG signals with adaptive threshold algorithms and integration with actuator movements in the prosthesis. The classification of the two sEMG instrumentation signal will produce a command output for each servo activation or de-activation of the prosthesis. From the test of six normal subjects, using an adaptive thresholding algorithm which resulted in detecting 87 finger movements, 27 detections were found due to the cross-talk effect. Then the accuracy of the prosthesis with the adaptive thresholding algorithm is 68.97%.","PeriodicalId":118934,"journal":{"name":"2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design And Engineering Of sEMG-Controlled Multi-Actuator Prosthesis For Disabled Wrist-Amputee\",\"authors\":\"Fadhli Ismail Hatta, E. A. Suprayitno, Rachmad Setiawan\",\"doi\":\"10.1109/CENIM56801.2022.10037349\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study will design a prosthesis with two actuators as a solution for disabled people with hand disabilities. The prosthesis control method used in this design is based on surface electromyography (sEMG) signals for each actuator. The designed system consists of several processes, namely the acquisition of sEMG signals, digitizing signals, as well as classification of sEMG signals with adaptive threshold algorithms and integration with actuator movements in the prosthesis. The classification of the two sEMG instrumentation signal will produce a command output for each servo activation or de-activation of the prosthesis. From the test of six normal subjects, using an adaptive thresholding algorithm which resulted in detecting 87 finger movements, 27 detections were found due to the cross-talk effect. Then the accuracy of the prosthesis with the adaptive thresholding algorithm is 68.97%.\",\"PeriodicalId\":118934,\"journal\":{\"name\":\"2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CENIM56801.2022.10037349\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CENIM56801.2022.10037349","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design And Engineering Of sEMG-Controlled Multi-Actuator Prosthesis For Disabled Wrist-Amputee
This study will design a prosthesis with two actuators as a solution for disabled people with hand disabilities. The prosthesis control method used in this design is based on surface electromyography (sEMG) signals for each actuator. The designed system consists of several processes, namely the acquisition of sEMG signals, digitizing signals, as well as classification of sEMG signals with adaptive threshold algorithms and integration with actuator movements in the prosthesis. The classification of the two sEMG instrumentation signal will produce a command output for each servo activation or de-activation of the prosthesis. From the test of six normal subjects, using an adaptive thresholding algorithm which resulted in detecting 87 finger movements, 27 detections were found due to the cross-talk effect. Then the accuracy of the prosthesis with the adaptive thresholding algorithm is 68.97%.