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Measuring knee biomechanics provides valuable clinical information for defining patient-specific treatment options, including patient-oriented physical exercise programs. It can be done by a knee kinesiography test measuring the three-dimensional rotation angles (3D kinematics) during walking, thus providing objective knowledge about knee function in dynamic and weight-bearing conditions. The purpose of this study was to assess whether 3D kinematics can be efficiently used to predict the impact of a physical exercise program on the condition of knee osteoarthritis (OA) patients. The prediction was based on 3D knee kinematic data, namely flexion/extension, adduction/abduction and external/internal rotation angles collected during a treadmill walking session at baseline. These measurements are quantifiable information suitable to develop automatic and objective methods for personalized computer-aided treatment systems. The dataset included 221 patients who followed a personalized therapeutic physical exercise program for 6 months and were then assigned to one of two classes, Improved condition (I) and not-Improved condition (nI). A 10% improvement in pain was needed at the 6-month follow-up compared to baseline to be in the improved group. The developed model was able to predict I and nI with 84.4% accuracy for men and 75.5% for women using a decision tree classifier trained with 3D knee kinematic data taken at baseline and a 10-fold validation procedure. The models showed that men with an impaired control of their varus thrust and a higher pain level at baseline, and women with a greater amplitude of internal tibia rotation were more likely to report improvements in their pain level after 6 months of exercises. Results support the effectiveness of decision trees and the relevance of 3D kinematic data to objectively predict knee OA patients’ response to a treatment consisting of a physical exercise program.

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.

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