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一种基于视觉的远程护理和远程康复服务构建方法。

A Vision-Based Approach for Building Telecare and Telerehabilitation Services.

作者信息

Barriga Angela, Conejero José M, Hernández Juan, Jurado Elena, Moguel Enrique, Sánchez-Figueroa Fernando

机构信息

Quercus Software Engineering Group, University of Extremadura, Cáceres 10071, Spain.

出版信息

Sensors (Basel). 2016 Oct 18;16(10):1724. doi: 10.3390/s16101724.

Abstract

In the last few years, telerehabilitation and telecare have become important topics in healthcare since they enable people to remain independent in their own homes by providing person-centered technologies to support the individual. These technologies allows elderly people to be assisted in their home, instead of traveling to a clinic, providing them wellbeing and personalized health care. The literature shows a great number of interesting proposals to address telerehabilitation and telecare scenarios, which may be mainly categorized into two broad groups, namely wearable devices and context-aware systems. However, we believe that these apparently different scenarios may be addressed by a single context-aware approach, concretely a vision-based system that can operate automatically in a non-intrusive way for the elderly, and this is the goal of this paper. We present a general approach based on 3D cameras and neural network algorithms that offers an efficient solution for two different scenarios of telerehabilitation and telecare for elderly people. Our empirical analysis reveals the effectiveness and accuracy of the algorithms presented in our approach and provides more than promising results when the neural network parameters are properly adjusted.

摘要

在过去几年中,远程康复和远程护理已成为医疗保健领域的重要话题,因为它们通过提供以个人为中心的技术来支持个人,使人们能够在自己家中保持独立。这些技术使老年人能够在家中得到帮助,而无需前往诊所,为他们提供福祉和个性化医疗保健。文献中展示了大量针对远程康复和远程护理场景的有趣提议,这些提议主要可分为两大类,即可穿戴设备和情境感知系统。然而,我们认为这些看似不同的场景可以通过单一的情境感知方法来解决,具体而言是一种基于视觉的系统,该系统可以以非侵入性方式为老年人自动运行,这就是本文的目标。我们提出了一种基于3D摄像头和神经网络算法的通用方法,该方法为老年人远程康复和远程护理的两种不同场景提供了一种有效的解决方案。我们的实证分析揭示了我们方法中所提出算法的有效性和准确性,并且当神经网络参数得到适当调整时,能提供非常有前景的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/757f/5087511/aa8b959339ad/sensors-16-01724-g005.jpg

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