@inproceedings{abf5e65f5712449aa908b86052d47a48,
title = "A motion rehabilitation self-training and evaluation system using Kinect",
abstract = "Stroke patients usually have difficulties to conduct rehabilitation training themselves, due to no rehabilitation evaluation in time and dependence on doctors. In order to solve this problem, this paper proposes a motion rehabilitation and evaluation system based on the Kinect gesture measuring technology combining VR technology as well as traditional method of stroke rehabilitation. Real-time rehabilitation motion feedback is achieved by using Kinect motion capturing, customized skeleton modeling, and virtual characters constructed in Unity3D. The jitter problem of virtual characters following motion using Kinect is solved. Fidelity and interactivity of virtual rehabilitation training is improved. Our experiment validated the feasibility of this system preliminarily.",
keywords = "Kinect, Motion rehabilitation, Stroke, Virtual guidance",
author = "Wei Pei and Guanghua Xu and Min Li and Hui Ding and Sicong Zhang and Ailing Luo",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016 ; Conference date: 19-08-2016 Through 22-08-2016",
year = "2016",
month = oct,
day = "21",
doi = "10.1109/URAI.2016.7734059",
language = "英语",
series = "2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "353--357",
booktitle = "2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016",
}