Daiki Suzuki, Yusuke Yamanoi, H. Yamada, Ko Wakita, R. Kato, H. Yokoi
{"title":"14. 基于手预成型的肌电信号抓取姿势分类,实现肌电手的自然控制","authors":"Daiki Suzuki, Yusuke Yamanoi, H. Yamada, Ko Wakita, R. Kato, H. Yokoi","doi":"10.1109/TePRA.2015.7219657","DOIUrl":null,"url":null,"abstract":"A stationary grasping posture is classified in the control method of an electromyogram prosthetic hand. This grasping posture is static, such as an open hand posture, and one in which the operator of an electromyogram prosthetic hand intentionally continues muscular contraction. In classifying the stationary grasping posture, a movement delay of the robot hand occurs, which feels unnaturally to the operator. To solve these problems, authors propose a method that predicts a grasping posture using the surface electromyogram (sEMG) of low muscle contraction power in hand pre-shaping. In this paper, our research on the performance of grasping posture classification using sEMG for naturally reaching for and grasping an object is presented. Experimental results demonstrate that when the sEMG amplitude peaks in hand pre-shaping, it is useful in classifying the grasping posture.","PeriodicalId":325788,"journal":{"name":"2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)","volume":"236 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"14. Grasping-posture classification using myoelectric signal on hand pre-shaping for natural control of myoelectric hand\",\"authors\":\"Daiki Suzuki, Yusuke Yamanoi, H. Yamada, Ko Wakita, R. Kato, H. Yokoi\",\"doi\":\"10.1109/TePRA.2015.7219657\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A stationary grasping posture is classified in the control method of an electromyogram prosthetic hand. This grasping posture is static, such as an open hand posture, and one in which the operator of an electromyogram prosthetic hand intentionally continues muscular contraction. In classifying the stationary grasping posture, a movement delay of the robot hand occurs, which feels unnaturally to the operator. To solve these problems, authors propose a method that predicts a grasping posture using the surface electromyogram (sEMG) of low muscle contraction power in hand pre-shaping. In this paper, our research on the performance of grasping posture classification using sEMG for naturally reaching for and grasping an object is presented. Experimental results demonstrate that when the sEMG amplitude peaks in hand pre-shaping, it is useful in classifying the grasping posture.\",\"PeriodicalId\":325788,\"journal\":{\"name\":\"2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)\",\"volume\":\"236 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TePRA.2015.7219657\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TePRA.2015.7219657","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
14. Grasping-posture classification using myoelectric signal on hand pre-shaping for natural control of myoelectric hand
A stationary grasping posture is classified in the control method of an electromyogram prosthetic hand. This grasping posture is static, such as an open hand posture, and one in which the operator of an electromyogram prosthetic hand intentionally continues muscular contraction. In classifying the stationary grasping posture, a movement delay of the robot hand occurs, which feels unnaturally to the operator. To solve these problems, authors propose a method that predicts a grasping posture using the surface electromyogram (sEMG) of low muscle contraction power in hand pre-shaping. In this paper, our research on the performance of grasping posture classification using sEMG for naturally reaching for and grasping an object is presented. Experimental results demonstrate that when the sEMG amplitude peaks in hand pre-shaping, it is useful in classifying the grasping posture.