A Novel Compensatory Motion Detection Method Using Multiple Signals and Machine Learning

Abstract

Harmful compensatory motion will seriously influence the upper limb rehabilitation effect of stroke patients. Existing compensatory motion pattern detection methods, which are mainly based on accelerometers, inertial measurement units (IMUs), and vision, have problems such as high installation requirements, calibration difficulty, object occlusion, and complex operations. To solve these problems, by analyzing the principle of compensatory motion, this paper designs a trunk restraint belt, compensation motion mode detection system that is composed of a force sensor (F-S), angular displacement sensor (AD-S), and surface electromyography (sEMG). Fifteen healthy subjects without dyskinesia completed normal motion and simulated compensatory movement modes (trunk rotation, trunk forward tilt, and scapular elevation) based on the system. Machine learning (ML) methods are used to classify three compensated motion and normal motion (NM). Among them, the support vector machine (SVM) classifier shows good classification performance for trunk forward tilt (F1-score = 0.9256), shoulder blade elevation (F1-score = 0.9190), trunk rotation (F1-score = 0.8262) and normal motion (F1-score = 0.9758). This method is a new compensation detection method with obvious advantages: it is easy to wear, and without considering the problems of calibration and target occlusion, it can not only identify the patient motion mode but also limit the large range of compensatory motion.