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用于伴有步态冻结发作的帕金森病患者的非侵入性医疗设备。

A Non-Invasive Medical Device for Parkinson's Patients with Episodes of Freezing of Gait.

机构信息

Telecommunications Research Group, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador.

Networks and Applied Telematics Group, Universidad Simón Bolívar, Caracas 89000, Venezuela.

出版信息

Sensors (Basel). 2019 Feb 12;19(3):737. doi: 10.3390/s19030737.

Abstract

A critical symptom of Parkinson's disease (PD) is the occurrence of Freezing of Gait (FOG), an episodic disorder that causes frequent falls and consequential injuries in PD patients. There are various auditory, visual, tactile, and other types of stimulation interventions that can be used to induce PD patients to escape FOG episodes. In this article, we describe a low cost wearable system for non-invasive gait monitoring and external delivery of superficial vibratory stimulation to the lower extremities triggered by FOG episodes. The intended purpose is to reduce the duration of the FOG episode, thus allowing prompt resumption of gait to prevent major injuries. The system, based on an Android mobile application, uses a tri-axial accelerometer device for gait data acquisition. Gathered data is processed via a discrete wavelet transform-based algorithm that precisely detects FOG episodes in real time. Detection activates external vibratory stimulation of the legs to reduce FOG time. The integration of detection and stimulation in one low cost device is the chief novel contribution of this work. We present analyses of sensitivity, specificity and effectiveness of the proposed system to validate its usefulness.

摘要

帕金森病(PD)的一个关键症状是步态冻结(FOG)的发生,这是一种间歇性障碍,会导致 PD 患者频繁摔倒和受伤。有各种听觉、视觉、触觉和其他类型的刺激干预措施可用于诱导 PD 患者摆脱 FOG 发作。在本文中,我们描述了一种低成本的可穿戴系统,用于非侵入性步态监测和外部传递下肢表面振动刺激,由 FOG 发作触发。目的是减少 FOG 发作的持续时间,从而能够迅速恢复行走,防止严重受伤。该系统基于 Android 移动应用程序,使用三轴加速度计设备进行步态数据采集。采集到的数据通过基于离散小波变换的算法进行处理,可实时精确检测 FOG 发作。检测会激活腿部的外部振动刺激,以减少 FOG 时间。将检测和刺激集成在一个低成本设备中是这项工作的主要新颖贡献。我们提出了对所提出系统的敏感性、特异性和有效性的分析,以验证其有用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60d6/6387047/f21538607ede/sensors-19-00737-g001.jpg

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