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基于网络的系统,使用可穿戴传感器对帕金森病患者进行家庭监测。

A web-based system for home monitoring of patients with Parkinson's disease using wearable sensors.

机构信息

Harvard School of Engineering and Applied Sciences, Cambridge, MA 02138, USA.

出版信息

IEEE Trans Biomed Eng. 2011 Mar;58(3):831-6. doi: 10.1109/TBME.2010.2090044. Epub 2010 Oct 28.

Abstract

This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson's disease (PD) using wearable sensors. MercuryLive contains three tiers: a resource-aware data collection engine that relies upon wearable sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when 800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor patients with PD to facilitate the titration of medications in the late stages of the disease.

摘要

这封信介绍了 MercuryLive,这是一个使用可穿戴传感器实现帕金森病(PD)患者家庭监测的平台。MercuryLive 包含三个层次:一个资源感知的数据采集引擎,该引擎依赖于可穿戴传感器、用于实时流传输和传感器数据存储的 Web 服务,以及一个具有视频会议功能的基于 Web 的图形用户界面客户端。此外,该平台还具有分析传感器(即加速度计)数据的功能,能够可靠地估计临床评分,捕捉震颤、运动迟缓、运动障碍的严重程度。测试结果表明,当可用带宽为 800kb/s 且使用 40%的视频压缩时,平均数据延迟小于 400ms,视频延迟约为 200ms,视频帧率约为 13 帧/s,并且在 10 分钟的交互会话后,上传数据特征需要额外 1 分钟的时间。这些结果表明,所提出的平台适合监测帕金森病患者,以方便在疾病晚期调整药物剂量。

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