An automated approach for video-based motor assessment in Parkinson's disease

帕金森病基于视频的运动评估的自动化方法

• 批准号: 10572002
• 负责人: Ryan Thomas Roemmich
• 金额: $ 20.27万
• 依托单位: HUGO W. MOSER RES INST KENNEDY KRIEGER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10572002
• 关键词: 3-Dimensional; Acceleration; Agreement; Algorithms; Artificial Intelligence; Body part; Cellular Phone; Clinic; Clinical; Communities; Computer Vision Systems; Computers; Data; Degenerative Disorder; Development; Devices; Dyskinetic syndrome; Emerging Technologies; Equipment; Fingers; Frequencies; Functional disorder; Gait; Goals; Healthcare; Home; Household; Human; Impairment; Individual; Investments; Joints; Leg; Length; Limb structure; Lower Extremity; Measurement; Measures; Methods; Motion; Motor; Movement; Movement Disorder Society Unified Parkinson' s Disease Rating Scale; Movement Disorders; Nature; Nerve Degeneration; Neurologist; Parkinson Disease; Pattern; Performance; Persons; Population; Process; Research; Series; Severities; Specialist; Speed; System; Tablets; Technology; Testing; Time; Toes; Tremor; Underserved Population; Upper Extremity; Video Recording; Visual; Walking; Work; clinical care; clinical decision-making; clinical implementation; clinical movement analysis; cost; digital; digital video recording; high reward; improved; innovation; instrument; kinematics; laptop; mHealth; mobile application; motor control; motor deficit; motor disorder; motor symptom; novel strategies; open source; spatiotemporal; technology validation; tool; virtual; walking speed; wearable device

Project Summary Parkinson’s disease (PD) is neurodegenerative movement disorder that causes a series of motor deficits. Due to the degenerative nature of PD, effective management of motor symptoms requires frequent and accurate motor assessment. Current approaches for PD motor assessment rely on subjectively rated clinical scales or research-grade equipment that is expensive or data-limited. These limitations significantly restrict the ability to perform frequent, granular assessments of motor function and highlight an important need for new approaches that enable objective motor assessments in any setting with minimal costs of time, money, or effort. Here, we propose an automated, video-based approach to PD motor assessment by using a state of the art pose estimation algorithm. Briefly, pose estimation is an emerging technology that is capable of quantitatively tracking human movement from simple digital videos recorded using common household devices (e.g., smartphones, tablets). The development of pose estimation algorithms for tracking human movement has progressed rapidly in the artificial intelligence community, and there is now significant untapped potential for leveraging this technology to perform rapid, automated motor assessments directly in home or clinic. In Aim 1.1, we will test a pose estimation approach for gait assessment in persons with PD. Gait dysfunction is common in PD, but it remains difficult to assess gait quantitatively in the home or clinic. Gait assessment is not represented adequately in clinical scales of motor function in PD, and there are no good methods for objective measurement of whole-body gait kinematics that can be used directly in the home or clinic. We will address this need by using a video-based pose estimation workflow to perform gait assessments in persons with PD. We will compare these results to motion capture measurements to examine how well the pose estimation approach approximates ground-truth measurements. In Aim 1.2, we will use our pose estimation approach to track repetitive movements in persons with PD. PD often causes difficulty in performing repetitive movements (e.g., finger tapping); accordingly, assessment of repetitive movements constitutes a significant portion of the MDS-Unified Parkinson’s Disease Rating Scale (the standard clinical scale for motor assessment in PD). This assessment is done through subjective human ratings; here, we will test an automated, objective, video-based approach for tracking repetitive movements in persons with PD and again compare to ground-truth motion capture measurements. In Aim 2, we will compare and contrast motor deficits identified by pose estimation and human assessment. Subjective visual assessment is the most common means of assessing motor function in PD, but computer vision approaches (e.g., pose estimation) have significant potential to automate and objectivize this process. Here, we will compare how well deficits in gait and repetitive movements detected by our pose estimation approach align with neurologist ratings of these tasks on the Unified Parkinson’s Disease Rating Scale.

项目摘要 帕金森病(PD)是一种神经退行性运动障碍，会导致一系列运动障碍。到期 由于帕金森病的退行性本质，有效的运动症状的处理需要频繁和准确 运动评估。目前的帕金森病运动评估方法依赖于主观评定的临床评分或 研究级别的设备，价格昂贵或数据有限。这些限制极大地限制了 经常进行精细的运动功能评估，并强调新方法的重要需求 这使得在任何环境下都能以最少的时间、金钱或精力进行客观的运动评估。 在这里，我们提出了一种自动化的，基于视频的方法，通过使用最先进的技术来评估PD运动 位姿估计算法。简而言之，位姿估计是一种新兴的技术，它能够定量地 从使用普通家用设备记录的简单数字视频跟踪人体运动(例如， 智能手机、平板电脑)。用于跟踪人体运动的姿势估计算法的发展已经 在人工智能领域取得了快速进展，现在有很大的未开发潜力 利用这项技术直接在家中或诊所进行快速、自动化的运动评估。 在目标1.1中，我们将测试一种姿势估计方法，用于帕金森病患者的步态评估。步态障碍是 在帕金森病中很常见，但在家庭或临床中仍然很难定量评估步态。步态评估不是 在帕金森病患者的临床运动功能量表中得到充分体现，但目前尚无较好的客观评价方法 可直接用于家庭或临床的全身步态运动学测量。我们将解决 这需要使用基于视频的姿势估计工作流程来执行帕金森病患者的步态评估。 我们将把这些结果与运动捕捉测量结果进行比较，以检验姿势估计的准确性。 该方法与地面真实测量结果接近。 在Aim 1.2中，我们将使用我们的姿势估计方法来跟踪帕金森病患者的重复运动。PD 经常造成执行重复动作的困难(例如，手指敲击)；因此，评估 重复运动是MDS-统一帕金森氏病评定量表的重要组成部分 (帕金森病患者运动功能评估的标准临床量表)。这种评估是通过主观的人类完成的 在这里，我们将测试一种自动的、客观的、基于视频的方法来跟踪重复的运动 帕金森病患者，并再次与地面真实运动捕获测量进行比较。 在目标2中，我们将比较和对比姿势估计和人体评估确定的运动缺陷。 主观视觉评估是评估帕金森病患者运动功能最常用的手段，但计算机 视觉方法(例如，姿势估计)具有使这一过程自动化和客观化的巨大潜力。 在这里，我们将比较我们的姿势估计检测到的步态缺陷和重复运动的程度 方法与神经科医生在统一帕金森氏病评定量表上对这些任务的评分一致。