Automated Brunnstrom Assessment for Home Rehabilitation Based on GRNN Model

Abstract

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.