{"title":"New Approach of Cycling Phases Detection to Improve FES-Pedaling in SCI Individuals","authors":"R. Baptista, Benoît Sijobert, C. Azevedo","doi":"10.1109/IROS.2018.8594162","DOIUrl":null,"url":null,"abstract":"FES allows spinal cord injured individuals to propel tricycles by means of their own leg power. The stimulation patterns are in most of the cases predefined and muscle activation triggered on the basis of the pedal position. This requires an empirical tuning to fit the pattern to the pilot sitting position and distance to crank with no possible generalization and no adaptive properties. The aim of the present article is to introduce a new approach of motion segmentation based on inertial measurement units located on the cyclist legs with the final aim to predict the optimal pedaling force evolution. Results obtained with one healthy subject in different cycling conditions are presented and the application to FES-cycling discussed.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"143 1","pages":"5181-5186"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IROS.2018.8594162","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
FES allows spinal cord injured individuals to propel tricycles by means of their own leg power. The stimulation patterns are in most of the cases predefined and muscle activation triggered on the basis of the pedal position. This requires an empirical tuning to fit the pattern to the pilot sitting position and distance to crank with no possible generalization and no adaptive properties. The aim of the present article is to introduce a new approach of motion segmentation based on inertial measurement units located on the cyclist legs with the final aim to predict the optimal pedaling force evolution. Results obtained with one healthy subject in different cycling conditions are presented and the application to FES-cycling discussed.