SQUAT EXERCISE ABNORMALITY DETECTION BY ANALYZING JOINT ANGLE FOR KNEE OSTEOARTHRITIS REHABILITATION

Abstract

Normally, osteoarthritic knee patients experienced 1) difficulties controlling their fine motors, 2) lack of muscle strength, and 3) limited range of motion. The limitations can be improved by physiotherapy exercises to 1) enhance flexibility and mobility of joints and 2) increase strength and endurance of the muscles. However, the patients should be individually monitored so that the exercises are performed correctly, effectively and efficiently. This paper focuses on squat exercise monitoring for knee osteoarthritis rehabilitation. The patient’s movement is captured by using a low cost 3D camera, Kinect sensor for skeletal tracking to recognize and track people without using marker. 3D coordinates of each joint is retrieved from the skeleton data, where a joint angle is derived based on two intersecting human body segments. Time series of the joint angles during the squat exercise are recorded, which are then smoothed by Double Exponential Smoothing technique to find the variability between them. The proposed method is validated by using simulated videos of squat exercise performed by 10 healthy volunteers of various physiques and gender to simulate the normal and abnormal conditions. Mean Squared Error (MSE) is calculated between the measured and smoothed angles to classify the movement either normal or abnormal. The parameters for smoothing and trend control used are 0.8928 and 0.7256, respectively, which are derived based on optimal MSE of the 10 volunteers. The simulation results show that the average MSE for each 10 samples of normal and abnormal conditions are 3.1358 and 10.5205, respectively. Hence, a simple threshold method has been developed to detect movement abnormality while doing squat exercise.