A Remote Quantitative Fugl-Meyer Assessment Framework for Stroke Patients Based on Wearable Sensor Networks

To extend the use of wearable sensor networks for stroke patients training and assessment in non-clinical settings, this paper proposes a novel remote quantitative Fugl-Meyer assessment (FMA) framework, in which two accelerometer and seven flex sensors were used to monitoring the movement function of upper limb, wrist and fingers. The extreme learning machine based ensemble regression model was established to map the sensor data to clinical FMA scores while the RRelief algorithm was applied to find the optimal features subset.

Source: Computer Methods and Programs in Biomedicine - March 2, 2016 Category: Bioinformatics Authors: Lei Yu, Daxi Xiong, Liquan Guo, Jiping Wang Source Type: research