Deep Learning Based Muscle Intent Classification in Continuous Passive Motion Machine for Knee Osteoarthritis Rehabilitation

Abstract

Knee-osteoarthritis is one of the most common forms of arthritis that people above age 45 suffer from. Physiotherapy and post-surgery rehabilitation are essential stages of the treatment to gain control over the knees and strengthen muscles around the knees. These are conducted under the guidance of therapists and physicians. Robotic therapeutic tools such as CPM machines cut down the massive expenditure of frequent consultations with physicians. However, the available devices in the market are passive as they do not dynamically adapt to a patient's needs as it follows pre-set functions. In this paper, a novel approach is presented to control and actuate a CPM machine by integrating a deep learning based control strategy using CNNs. EMG and IMU sensors are interfaced with the patient's thigh muscles to classify the patient's intent as three states: forward, backward and rest. For implementing the algorithms, a low cost, ecofriendly alpha prototyped CPM machine is developed. Dataset is collected by performing experiments on three healthy subjects under different conditions. Experimental performance shows the feasibility of this home rehabilitation device and accurate intuitive motion predictions with CNN.