Monitoring Rehabilitation Parameters In Stroke Patients.

Abstract

This research presents the development and testing of a system for monitoring functional parameters in stroke patients undergoing rehabilitation. Benefits of real-time automated monitoring will improve measurement consistency and accuracy, reduce consultant time, earlier discharge, less hospital beds required and delivery of controlled, repetitive training. The system uses three devices: (1) the Myo gesture control armband (Thalmic Labs) to detect EMG signals, angles and acceleration; (2) the Arm Motion Monitoring and Recovery Improvement Toolkit (AMMRIT) (custom built) arm exoskeleton to monitor the whole arm angles and (3) the Kinect Sensor (Microsoft) to detect facial expressions. Stroke is the second most common cause of death and the leading cause of disability in Europe. The incidence rate is approximately 16 per 10,000 per year in the UK. One of the most effective treatments following stroke is physiotherapy which can help the patient relearn how to move the limbs. Quantifying the progress of functional recovery is technically complex because of the multi-joined structure of the arm. Currently there is no single portable system available that can provide objective measurement and analysis to monitor functional recovery for Early Supported Discharge (ESD). We developed the AMMRIT which can guide as well as assess the arm’s functional recovery of patients. In this research we combine the device with Myo armband and Kinect sensor to monitor a wide range of functional parameters to accurately assess the rehabilitation progression.