{"title":"表面肌电信号检测器采用离散小波变换","authors":"C. Toledo, R. Muñoz, L. Leija","doi":"10.1109/PAHCE.2012.6233441","DOIUrl":null,"url":null,"abstract":"This article presents a sEMG signal detector by means of different applications of the Discrete Wavelet Transform. A training system and protocol is used to acquire the sEMG signals. The last of four training stages is used. These signals are processed, segmented and labeled, automatically, as three different levels of contraction. The level's order (1-2-3) varies from one session to another. The automatic segmentation and label proved to work with the 20 sessions. Segmentation and labeling comparison of 4 training sessions' results is shown. The used algorithms provided good results with all 20 sessions.","PeriodicalId":255935,"journal":{"name":"2012 Pan American Health Care Exchanges","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"sEMG signal detector using discrete wavelet transform\",\"authors\":\"C. Toledo, R. Muñoz, L. Leija\",\"doi\":\"10.1109/PAHCE.2012.6233441\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents a sEMG signal detector by means of different applications of the Discrete Wavelet Transform. A training system and protocol is used to acquire the sEMG signals. The last of four training stages is used. These signals are processed, segmented and labeled, automatically, as three different levels of contraction. The level's order (1-2-3) varies from one session to another. The automatic segmentation and label proved to work with the 20 sessions. Segmentation and labeling comparison of 4 training sessions' results is shown. The used algorithms provided good results with all 20 sessions.\",\"PeriodicalId\":255935,\"journal\":{\"name\":\"2012 Pan American Health Care Exchanges\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Pan American Health Care Exchanges\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PAHCE.2012.6233441\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Pan American Health Care Exchanges","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PAHCE.2012.6233441","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
sEMG signal detector using discrete wavelet transform
This article presents a sEMG signal detector by means of different applications of the Discrete Wavelet Transform. A training system and protocol is used to acquire the sEMG signals. The last of four training stages is used. These signals are processed, segmented and labeled, automatically, as three different levels of contraction. The level's order (1-2-3) varies from one session to another. The automatic segmentation and label proved to work with the 20 sessions. Segmentation and labeling comparison of 4 training sessions' results is shown. The used algorithms provided good results with all 20 sessions.