{"title":"一种可穿戴式FMG传感系统的设计,用于软性机器人手套手部康复过程中的用户意图检测","authors":"H. Yap, Andrew Mao, J. Goh, C. Yeow","doi":"10.1109/BIOROB.2016.7523722","DOIUrl":null,"url":null,"abstract":"This paper presents the design of a wearable feedback system based on force myography (FMG) for user intent detection during hand rehabilitation with a soft robotic glove. We present the development of a form-fitting FMG sensor band using force sensitive resistor (FSR). A supervised learning classifier, Artificial Neural Network (ANN), was implemented to classify four different hand motions with nearly instantaneous prediction speed. Experiments with three healthy subjects were devised to study the training speed and real-time classification accuracy. Results indicate an average training time of less than 95 seconds and a real time accuracy of approximately 95%. The study reveals the successful detection of four different hand motions and a high level of intuitive user intention-based control over the robotic glove.","PeriodicalId":235222,"journal":{"name":"2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":"{\"title\":\"Design of a wearable FMG sensing system for user intent detection during hand rehabilitation with a soft robotic glove\",\"authors\":\"H. Yap, Andrew Mao, J. Goh, C. Yeow\",\"doi\":\"10.1109/BIOROB.2016.7523722\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the design of a wearable feedback system based on force myography (FMG) for user intent detection during hand rehabilitation with a soft robotic glove. We present the development of a form-fitting FMG sensor band using force sensitive resistor (FSR). A supervised learning classifier, Artificial Neural Network (ANN), was implemented to classify four different hand motions with nearly instantaneous prediction speed. Experiments with three healthy subjects were devised to study the training speed and real-time classification accuracy. Results indicate an average training time of less than 95 seconds and a real time accuracy of approximately 95%. The study reveals the successful detection of four different hand motions and a high level of intuitive user intention-based control over the robotic glove.\",\"PeriodicalId\":235222,\"journal\":{\"name\":\"2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"34\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIOROB.2016.7523722\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIOROB.2016.7523722","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of a wearable FMG sensing system for user intent detection during hand rehabilitation with a soft robotic glove
This paper presents the design of a wearable feedback system based on force myography (FMG) for user intent detection during hand rehabilitation with a soft robotic glove. We present the development of a form-fitting FMG sensor band using force sensitive resistor (FSR). A supervised learning classifier, Artificial Neural Network (ANN), was implemented to classify four different hand motions with nearly instantaneous prediction speed. Experiments with three healthy subjects were devised to study the training speed and real-time classification accuracy. Results indicate an average training time of less than 95 seconds and a real time accuracy of approximately 95%. The study reveals the successful detection of four different hand motions and a high level of intuitive user intention-based control over the robotic glove.