Estimation of Upper Extremity Movement Performance in Stroke Patients with Artificial Learning Techniques

Abstract

The main reason why people are directed to rehabilitation services after stroke-like neurological diseases are to bring individuals' daily abilities to a normal level. Measuring the activities of people in their daily lives ensures that these rehabilitation services progress more healthily. In our study, Brunnstrom Hemiplegia Recovery Staging, which is widely used by doctors to evaluate the movement function of stroke patients during rehabilitation, was examined. The study was specifically adapted to the upper extremity stage 4a movement of the Brunnstrom Staging. Daily movements of patients were evaluated with accelerometer sensors. With this methodology, sensor data was collected from 15 volunteer stroke patients and 80 healthy individuals. These sensor data were interpreted by the medical professional. Thus, consistency between movement data of healthy and sick individuals was analyzed. The data obtained as a result of the analysis process were examined with artificial learning methods and classified as healthy/unhealthy. The methodology of the study is suitable for research designed to increase upper / lower extremity performance in the daily life of individuals.