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 帕金森氏病（PD）是神经退行性运动障碍，导致一系列运动缺陷。到期的 对于PD的退化性质，有效的运动症状管理需要经常准确 电动机评估。当前的PD运动评估方法依赖于主观评级的临床量表或 昂贵或限制的研究级设备。这些限制显着限制了 经常对运动功能进行颗粒状评估，并强调对新方法的重要需求 在任何环境中，其时间，金钱或精力最少的环境中都可以进行客观的电机评估。 在这里，我们通过使用最先进的状态提出了一种基于视频的自动化，基于视频的PD电机评估方法 姿势估计算法。简而言之，姿势估计是一种能够定量的技术 跟踪使用常见家用设备录制的简单数字视频的人类运动（例如 智能手机，平板电脑）。用于跟踪人类运动的姿势估计算法的发展具有 在人工智能社区中迅速发展，现在有巨大的未开发潜力 利用这项技术直接在家庭或诊所进行快速自动的运动评估。 在AIM 1.1中，我们将测试PD患者的步态评估的姿势估计方法。步态功能障碍是 在PD中很常见，但是在家庭或诊所中定量评估步态仍然很难。步态评估不是 在PD中适当地表示运动功能的临床尺度，并且没有好的方法 全身的测量会收集可以直接在家庭或诊所中使用的运动学。我们将解决 通过使用基于视频的姿势估计工作流来进行PD人员进行收集评估，从而进行这种需求。 我们将将这些结果与运动捕获测量结果进行比较，以检查姿势估计的效果 方法近似地面真相测量值。 在AIM 1.2中，我们将使用姿势估计方法来跟踪PD人的重复运动。 通常在执行重复运动（例如，手指敲击）方面引起困难；因此，评估 重复运动构成了MDS统一的帕金森氏病评级量表的很大一部分 （PD中运动评估的标准临床量表）。该评估是通过主观人进行的 评分；在这里，我们将测试一种自动化，基于视频的方法，用于跟踪重复运动 具有PD的人，并将其与地面真相运动捕获测量值进行比较。 在AIM 2中，我们将通过姿势估计和人类评估来比较和对比电机定义。 主观视觉评估是PD评估运动功能的最常见手段，但计算机 视觉方法（例如，姿势估计）具有自动化和客观化此过程的重要潜力。 在这里，我们将比较我们的姿势估计检测到的步态和重复运动的定义良好 在统一的帕金森氏病评级量表上，方法与这些任务的神经科医生评分保持一致。