Research Project 4 will develop novel, objective, and passive measures of Parkinson disease features

摘要 新的传感设备可以对帕金森病进行客观、频繁、敏感的评估。然而，许多设备， 就像智能手机一样，需要个人完成活动。例如，因为PD与结肠癌不同，它有 明确的外部特征(例如，缓慢的步态，频繁的睡眠中断)，它非常适合被动评估。 在研究项目4中，我们将评估三种主要评估PD的领先技术(但不是 排他性的)通过被动的方式。这些技术包括可以测量电机的可穿戴传感器 和自主神经功能，这是一种视频分析工具，可以测量标准局部放电电机的元素 还有一种“看不见的”无线电波感应工具，可以评估家中帕金森病的自然病史。 这三个工具将增进我们的理解，并产生可用于 加快治疗发展和改善护理。 第一个项目将研究一种嵌入了加速计、陀螺仪和心电的可穿戴传感器 评估诊所内外功能的能力。在最大的PD传感器研究之一中，我们将 寻求证实和扩大研究结果，即帕金森病患者表现出不同的昼夜活动模式 控制(例如，多躺，少走)，评估自主神经功能，并与华盛顿大学合作 密歇根的Udall中心，探索低胆碱能状态的人的步态功能是否更差。 第二个项目将评估最近开发的应用机器学习的视频分析工具 用于评估常规评估的运动评估(例如，面部表情、手指敲击)的算法 通过马达部分的MDS-UPDRS来查看这些在没有调查员的情况下进行的评估， 可以区分帕金森病患者和非帕金森病患者，并与传统评估相关联。 第三个项目将使用一种新的方法来评估帕金森病患者的运动和非运动功能。 无线电波传感装置。该设备可以评估现有的测量(例如，步速、呼吸频率)和 将为疾病提供新见解的新措施(例如，路径曲折、独处时间)。

SUMMARY New sensing devices can generate objective, frequent, sensitive assessments of PD. However, many devices, like smartphones, require individuals to complete activities. Because PD, unlike colon cancer, for example, has clear external features (e.g., slow gait, frequent sleep interruptions), it is well suited for passive assessment. In Research Project 4, we will evaluate three leading technologies to assess PD principally (but not exclusively) through passive means. These technologies include wearable sensors that can measure motor and autonomic function, a video analytical tool that can measure elements of the standard PD motor examination, and an “invisible” radio wave sensing tool that can assess the natural history of PD in the home. These three tools will enhance our understanding and generate objective measures of PD that can be used to accelerate therapeutic development and improve care. The first project will examine a wearable sensor that has embedded accelerometers, gyroscope, and ECG capabilities to assess function inside and outside the clinic. In one of the largest PD sensor studies, we will seek to confirm and extend findings that individuals with PD exhibit distinct diurnal activity patterns from controls (e.g., lie down more, walk less), evaluate autonomic function, and in partnership with the University of Michigan's Udall Center, explore whether gait function is worse in individuals with a hypocholinergic state. The second project will evaluate a recently developed video analytics tool that applies machine learning algorithms to evaluate motor assessments (e.g., facial expression, finger tapping) that are routinely assessed through the motor portion the MDS-UPDRS to see if these assessments, conducted without an investigator, can differentiate those with PD from those without and correlate with traditional assessments. The third project will evaluate motor and non-motor function of individuals with PD in their homes using a novel radio wave sensing device. This device can assess existing measures (e.g., gait speed, respiratory rate) and novel measures (e.g., path tortuosity, time alone) that will provide novel insights into the disease.