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一种用于帕金森病患者长期监测的腰部佩戴式惯性测量单元。

A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson's Disease Patients.

机构信息

Technical Research Centre for Dependency Care and Autonomous Living-CETPD, Universitat Politècnica de Catalunya-BarcelonaTech, Rambla de l'Exposició 59-69, Vilanova i la Geltrú, 08800 Barcelona, Spain.

Unidad de Parkinson y Trastornos del Movimiento (UParkinson), Passeig Bonanova 26, 08022 Barcelona, Spain.

出版信息

Sensors (Basel). 2017 Apr 11;17(4):827. doi: 10.3390/s17040827.

Abstract

Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have been demonstrated to be useful in the monitoring of motor symptoms of Parkinson's disease (PD). In this sense, most of previous works have attempted to assess PD symptoms in controlled environments or short tests. This paper presents the design of an IMU, called 9 × 3, that aims to assess PD symptoms, enabling the possibility to perform a map of patients' symptoms at their homes during long periods. The device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9 × 3, storing inertial information and algorithm outputs, sending messages to external devices and being able to detect freezing of gait and bradykinetic gait. Results obtained in 12 patients exhibit a sensitivity and specificity over 80%. Additionally, the system enables working 23 days (at waking hours) with a 1200 mAh battery and a sampling rate of 50 Hz, opening up the possibility to be used for other applications like wellbeing and sports.

摘要

惯性测量单元(IMU)是一种在医疗应用等领域中使用的设备,因为它们轻巧、小巧且高效。具体来说,IMU 已被证明在监测帕金森病(PD)的运动症状方面非常有用。在这方面,大多数先前的工作都试图在受控环境或短时间测试中评估 PD 症状。本文介绍了一种称为 9 × 3 的 IMU 的设计,该设计旨在评估 PD 症状,使患者在家中长时间进行症状图绘制成为可能。该设备能够获取和存储原始惯性数据,以便进行人工智能算法训练。此外,所提出的 IMU 还能够实时执行开发和嵌入式学习模型。结果表明,9 × 3 具有很大的灵活性,能够存储惯性信息和算法输出,向外部设备发送消息,并能够检测到冻结步态和运动徐缓步态。在 12 名患者中获得的结果显示,敏感性和特异性均超过 80%。此外,该系统能够在使用 1200 mAh 电池和 50 Hz 采样率的情况下工作 23 天(在清醒时间),为其他应用(如健康和运动)提供了可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3426/5422188/eeae568321e2/sensors-17-00827-g001.jpg

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