D. Jarchi, Amy Peters, Benny P. L. Lo, E. Kalliolia, I. D. Giulio, P. Limousin, B. Day, Guang-Zhong Yang
{"title":"Assessment of the e-AR sensor for gait analysis of Parkinson;s Disease patients","authors":"D. Jarchi, Amy Peters, Benny P. L. Lo, E. Kalliolia, I. D. Giulio, P. Limousin, B. Day, Guang-Zhong Yang","doi":"10.1109/BSN.2015.7299396","DOIUrl":null,"url":null,"abstract":"This paper analyses gait patterns of patients with Parkinson's Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency as a gait parameter derived from the estimated heel-contacts is calculated and validated using the CODA motion-capture system. Intersession variability of step-frequency for each patient and the overall variability across patients demonstrate a good agreement between estimations from the e-AR and CODA systems.","PeriodicalId":447934,"journal":{"name":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSN.2015.7299396","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
This paper analyses gait patterns of patients with Parkinson's Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency as a gait parameter derived from the estimated heel-contacts is calculated and validated using the CODA motion-capture system. Intersession variability of step-frequency for each patient and the overall variability across patients demonstrate a good agreement between estimations from the e-AR and CODA systems.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
