ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||4|
|Section||Session 1: Robotics|
|Published online||05 September 2017|
Automated Brunnstrom Assessment for Home Rehabilitation Based on GRNN Model
1 Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences, 215163 Suzhou, China
2 Shanghai University, 200444, Shanghai
To realize the upper extremity rehabilitation assessment for post-stroke intelligently, a new movement assessment model is established in this paper. A WBSN system consisted of two inertial sensors is employed to acquisit patients’ rehabilitation data. The data is stored in both local and server database. An intelligent Brunnstrom assessment based on GRNN model is built on the server. In order to test the accuracy and reliability of the model, twenty patients and four physicians were chosen as volunteers to finish a standard rehabilitation action – touching shoulder with affected hand. The accuracy of the assessment model can be 93.6%. The purpose to build an intelligent assessment is achieved. It makes patients train in the home setting and community possible.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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