{"title":"Real-time Intent Recognition for a Powered Knee and Ankle Transfemoral Prosthesis","authors":"H. A. Varol, M. Goldfarb","doi":"10.1109/ICORR.2007.4428400","DOIUrl":null,"url":null,"abstract":"This paper describes a real-time gait intent recognition approach for use in controlling a fully powered transfemoral prosthesis. Rather than utilize an \"echo control\" as proposed by others, which requires instrumentation of the sound-side leg, the proposed approach infers user intent based on the characteristic shape of the force and moment vector of interaction between the user and prosthesis. The real-time intent recognition approach utilizes a K-nearest neighbor algorithm with majority voting and threshold biasing schemes to increase its robustness. The ability of the approach to recognize in real time a person's intent to stand or walk at one of three different speeds is demonstrated on measured biomechanics data.","PeriodicalId":197465,"journal":{"name":"2007 IEEE 10th International Conference on Rehabilitation Robotics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2007-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE 10th International Conference on Rehabilitation Robotics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICORR.2007.4428400","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 31
Abstract
This paper describes a real-time gait intent recognition approach for use in controlling a fully powered transfemoral prosthesis. Rather than utilize an "echo control" as proposed by others, which requires instrumentation of the sound-side leg, the proposed approach infers user intent based on the characteristic shape of the force and moment vector of interaction between the user and prosthesis. The real-time intent recognition approach utilizes a K-nearest neighbor algorithm with majority voting and threshold biasing schemes to increase its robustness. The ability of the approach to recognize in real time a person's intent to stand or walk at one of three different speeds is demonstrated on measured biomechanics data.