{"title":"基于表面肌电图的独立于用户的实时手势识别","authors":"Frederic Kerber, M. Puhl, A. Krüger","doi":"10.1145/3098279.3098553","DOIUrl":null,"url":null,"abstract":"In this paper, we present a novel real-time hand gesture recognition system based on surface electromyography. We employ a user-independent approach based on a support vector machine utilizing ten features extracted from the raw electromyographic data obtained from the Myo armband by Thalmic Labs. Through an improved synchronization approach, we simplified the application process of the sensing armband. We report the results of a user study with 14 participants using an extended set consisting of 40 gestures. Considering the set of five hand gestures currently supported off-the-shelf by the Myo armband, we outperform their approach with an overall accuracy of 95% compared to 68% with the original algorithm on the same dataset.","PeriodicalId":120153,"journal":{"name":"Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"63","resultStr":"{\"title\":\"User-independent real-time hand gesture recognition based on surface electromyography\",\"authors\":\"Frederic Kerber, M. Puhl, A. Krüger\",\"doi\":\"10.1145/3098279.3098553\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a novel real-time hand gesture recognition system based on surface electromyography. We employ a user-independent approach based on a support vector machine utilizing ten features extracted from the raw electromyographic data obtained from the Myo armband by Thalmic Labs. Through an improved synchronization approach, we simplified the application process of the sensing armband. We report the results of a user study with 14 participants using an extended set consisting of 40 gestures. Considering the set of five hand gestures currently supported off-the-shelf by the Myo armband, we outperform their approach with an overall accuracy of 95% compared to 68% with the original algorithm on the same dataset.\",\"PeriodicalId\":120153,\"journal\":{\"name\":\"Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"63\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3098279.3098553\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3098279.3098553","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
User-independent real-time hand gesture recognition based on surface electromyography
In this paper, we present a novel real-time hand gesture recognition system based on surface electromyography. We employ a user-independent approach based on a support vector machine utilizing ten features extracted from the raw electromyographic data obtained from the Myo armband by Thalmic Labs. Through an improved synchronization approach, we simplified the application process of the sensing armband. We report the results of a user study with 14 participants using an extended set consisting of 40 gestures. Considering the set of five hand gestures currently supported off-the-shelf by the Myo armband, we outperform their approach with an overall accuracy of 95% compared to 68% with the original algorithm on the same dataset.
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