{"title":"基于前臂肌电图的手指符号识别遗传推理","authors":"T. Tsujimura, Takahiro Hashimoto, K. Izumi","doi":"10.1109/AE.2014.7011724","DOIUrl":null,"url":null,"abstract":"This paper proposes a meta-heuristic data-clustering application to identify finger signs only by measuring surface electromyogram (EMG) of a forearm. It classifies EMG signal patterns peculiar to finger signs. Genetic programming learns intensity characteristics of EMG signals, and creates classification algorithm. Three typical finger signs are evaluated in terms of generated EMG. Experiments are conducted to reveal the successful identification of finger signs in real time.","PeriodicalId":149779,"journal":{"name":"2014 International Conference on Applied Electronics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Genetic reasoning for finger sign identification based on forearm electromyogram\",\"authors\":\"T. Tsujimura, Takahiro Hashimoto, K. Izumi\",\"doi\":\"10.1109/AE.2014.7011724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a meta-heuristic data-clustering application to identify finger signs only by measuring surface electromyogram (EMG) of a forearm. It classifies EMG signal patterns peculiar to finger signs. Genetic programming learns intensity characteristics of EMG signals, and creates classification algorithm. Three typical finger signs are evaluated in terms of generated EMG. Experiments are conducted to reveal the successful identification of finger signs in real time.\",\"PeriodicalId\":149779,\"journal\":{\"name\":\"2014 International Conference on Applied Electronics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Applied Electronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AE.2014.7011724\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Applied Electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AE.2014.7011724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic reasoning for finger sign identification based on forearm electromyogram
This paper proposes a meta-heuristic data-clustering application to identify finger signs only by measuring surface electromyogram (EMG) of a forearm. It classifies EMG signal patterns peculiar to finger signs. Genetic programming learns intensity characteristics of EMG signals, and creates classification algorithm. Three typical finger signs are evaluated in terms of generated EMG. Experiments are conducted to reveal the successful identification of finger signs in real time.
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