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

总结 新的传感设备可以产生客观，频繁，灵敏的PD评估。然而，许多设备， 像智能手机一样，需要个人完成活动。因为帕金森病不像结肠癌，例如， 清晰的外部特征（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的技术来评估PD（但不 （1）通过被动方式。这些技术包括可穿戴传感器， 和自主功能，一个视频分析工具，可以测量标准PD运动的元素 检查，和一个“看不见的”无线电波传感工具，可以评估PD的自然历史在家里。 这三个工具将增强我们对PD的理解，并产生可用于 加速治疗发展和改善护理。 第一个项目将研究一种可穿戴传感器，该传感器具有嵌入式加速度计，陀螺仪和ECG 评估诊所内外功能的能力。在最大的PD传感器研究中，我们将 寻求证实和扩展以下发现：PD患者表现出与PD患者不同的日间活动模式 控制（例如，多躺，少走），评估自主神经功能，并与密歇根大学合作， 密歇根大学的Udall中心，探索是否步态功能是在个人与胆碱能状态差。 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 评估运动评估的算法（例如，面部表情，手指敲击）， 通过运动部分的MDS-EADS，看看这些评估，没有研究者进行， 可以区分患有PD的患者和没有PD的患者，并与传统评估相关联。 第三个项目将评估运动和非运动功能的个人与PD在他们的家园使用一种新的 无线电波感应装置该设备可以评估现有的措施（例如，步态速度、呼吸频率）和 新颖的措施（例如，路径曲折，时间单独），这将提供对疾病的新见解。

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.