Modeling Multiple Sclerosis at Different Levels Using Reinforcement Learning

Abstract

Background: Multiple sclerosis (MS) represents one of the most common disorders of the central nervous system, which leads to the dysfunction of different body systems and generates a myriad of problems for the affected individuals. Given the progressive nature of this disease, it can divide into several levels. The progression rate of the disease at each stage is essential for specialists, as it can help them to adopt appropriate therapeutic measures. Methods: One of the methods used in many MS neurological treatments is Expanded Disability Status Scale (EDSS), which allows physicians to give an estimate of the severity of the disease to patients, learn about the stage of the patient’s disease and prescribe appropriate medicines accordingly. Given the importance and impact of this disease on the quality of life of patients, researchers look for inexpensive and simple models with minimum side effects for examining different levels of MS and providing treatment solutions. Results: In this study, patients were asked to stand on a force plate. Then, the time series of the center of pressure and body oscillations of patients at various levels were recorded using a motion analyzer device, and a closed loop control system was proposed using the reverse pendulum (representing human body) and reinforcement learning. Conclusion: Based on the feedback received from the environment, the necessary rules for maintaining the balance of pendulum obtained, and, by observing the ankle torque at the output, a model presented that could examine different levels of MS.