Shivanthan A. C. Yohanandan, Chathura Perera, Mary Jones, R. Peppard, T. Perera
{"title":"目的利用开源软件对运动障碍的视频震颤进行评估","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":"{\"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}","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
摘要
震颤是一种由专家主观评估的不自主节律性肌肉运动。为了提高准确性和减少偏差,必须对震颤进行视频记录,并由多位专家进行评级。现有的基于视频的运动跟踪技术可用于量化震颤评估;然而,这种方法依赖于复杂而昂贵的仪器以及专门的皮肤标记。本文介绍了一种使用可访问硬件和开源软件的低成本无标记方法。在8名接受深部脑刺激治疗的震颤患者队列中,我们发现基于视频的技术与专家震颤评分有很强的一致性(r = 0.93, p < 0.001)。这使得它适用于即时护理评估以及在未来的结构化临床试验中使用。
Objective video-based tremor assessment for movement disorders using open-source software
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.