Using a Human-Shaped Input Device for Telerehabilitation

Tayama, Yuki; Hagino, Misaki; Okada, Kenichi

doi:10.1109/ichi.2014.25

Cited by 2 publications

(1 citation statement)

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“…Although the performance of these approaches may be lower than KSS ones because they depend on the angle of vision of the camera [14], they can operate automatically, being a non-intrusive approach whilst providing privacy, if the system has the ability to decide whether or not to stream the video [20,21]. Moreover, vision-based systems offer many interesting research applications in this field, and the use of the Kinect-like cameras provides a step forward in telerehabilitation and telecare, allowing patients to be remote and automatically assisted in their particular therapies or attention [7,8,22,23,24,25,26]. …”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Approach for Building Telecare and Telerehabilitation Services

Barriga

Conejero

Hernández

et al. 2016

Sensors

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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.

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