Shivanthan A. C. Yohanandan, Chathura Perera, Mary Jones, R. Peppard, T. Perera
{"title":"Objective video-based tremor assessment for movement disorders using open-source software","authors":"Shivanthan A. C. Yohanandan, Chathura Perera, Mary Jones, R. Peppard, T. Perera","doi":"10.1109/HIC.2017.8227617","DOIUrl":null,"url":null,"abstract":"Tremor is an involuntary rhythmic muscle movement assessed subjectively by specialists. To improve accuracy and mitigate bias, tremor must be video recorded and rated by multiple experts. Existing video-based motion tracking techniques can be applied to quantify tremor assessment; though, such methods rely on sophisticated and expensive instrumentation as well as specialized skin markers. This paper describes a low-cost markerless method using accessible hardware and open-source software. In a cohort of 8 subjects with tremor undergoing deep brain stimulation therapy, we show our video-based technique has strong concordance (r = 0.93, p < 0.001) with expert tremor ratings. This makes it suitable for point-of-care assessment as well as use in future structured clinical trials.","PeriodicalId":120815,"journal":{"name":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HIC.2017.8227617","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Tremor is an involuntary rhythmic muscle movement assessed subjectively by specialists. To improve accuracy and mitigate bias, tremor must be video recorded and rated by multiple experts. Existing video-based motion tracking techniques can be applied to quantify tremor assessment; though, such methods rely on sophisticated and expensive instrumentation as well as specialized skin markers. This paper describes a low-cost markerless method using accessible hardware and open-source software. In a cohort of 8 subjects with tremor undergoing deep brain stimulation therapy, we show our video-based technique has strong concordance (r = 0.93, p < 0.001) with expert tremor ratings. This makes it suitable for point-of-care assessment as well as use in future structured clinical trials.