Automated Fugl-Meyer Assessment using SVR model

Abstract

A simple, objective and quantitative unsupervised outcome measure is considered vital in the home-based rehabilitation for stroke patients. The Fugl-Meyer Assessment (FMA) scale is widely utilized in the clinical practice, while not suitable in the home settings due to its subjective and time-consuming property. In this paper, a Support Vector Regression (SVR) based evaluation model was presented to automatically estimate the FMA scores for Shoulder-Elbow movement. The estimation was obtained by analyzing accelerometer data recorded during the performance of 4 tasks from Shoulder-Elbow FMA. A combined feature selection method based on ReliefF-SVR was implemented to simplify the calculation and improve the model performance. Twenty-four subjects were involved in this study and results showed that it was possible to achieve accurate estimation of Shoulder-Elbow FMA scores using the proposed model and a cross-validation prediction error value of 2.1273 was achieved.