Prediction of the Impact of Physical Exercise on Knee Osteoarthritis Patients using Kinematic Signal Analysis and Decision Trees

Abstract

The evaluation of knee biomechanics provides valuable clinical information. This can be done by means of a knee kinesiography exam which measures the three-dimensional rotation angles during walking, thus providing objective knowledge about knee function (3D kinematics). 3D kinematic data is quantifiable information that provides opportunities to develop automatic and objective methods for personalized computer-aided treatment systems. The purpose of this study is to explore a decision tree based method for predicting the impact of physical exercise on a knee osteoarthritis population. The prediction is based on 3D kinematic data i.e., flexion/extension, abduction/adduction and internal/external rotation of the knee. Experiments were conducted on a dataset of 309 patients who have engaged in physical exercise for 6 months and have been grouped into two classes, Improved state (I) and not-Improved state (nI) based on their state before (t 0) and after the exercise (t 6). The method developed was able to predict I and nI patien with knee osteoarthritis using 3D kinematic data with an accuracy of 82%. Results show the effectiveness of 3D kinematic signal analysis and the decision tree technique for predicting the impact of physical exercise based on patient knee osteoarthritis pain level.