使用3D视觉传感器和移动传感器监测家中的行动障碍

F. Kashani, G. Medioni, Khanh Nguyen, Luciano Nocera, C. Shahabi, Ruizhe Wang, Cesar Blanco, Yi-An Chen, Yu-Chen Chung, Beth Fisher, Sara Mulroy, Phil Requejo, C. Winstein
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引用次数: 1

摘要

在本文中,我们提出PoCM2 (Point-of-Care Mobility Monitoring),一个通用的和可扩展的家庭移动评估和监测系统。PoCM2使用3D视觉传感器(如微软Kinect)和移动传感器(即嵌入/连接到移动设备(如智能手机)的内部和外部传感器)进行补充数据采集,以及一系列允许评估存档和实时移动数据的分析。我们展示了PoCM2的性能,并开发了一种特定的应用程序,用于帕金森病流动性数据的冻结检测和定量,作为估计PD患者的药物水平并可能建议调整的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring mobility disorders at home using 3D visual sensors and mobile sensors
In this paper, we present PoCM2 (Point-of-Care Mobility Monitoring), a generic and extensible at-home mobility evaluation and monitoring system. PoCM2 uses both 3D visual sensors (such as Microsoft Kinect) and mobile sensors (i.e., internal and external sensors embedded with/connected to a mobile device such as a smartphone) for complementary data acquisition, as well as a series of analytics that allow evaluation of both archived and real-time mobility data. We demonstrate the performance of PoCM2 with a specific application developed for freeze detection and quantification from Parkinson's Disease mobility data, as an approach to estimate the medication level of the PD patients and potentially recommend adjustments.
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