3D Kinematics and Decision Trees to Predict the Impact of a Physical Exercise Program on Knee Osteoarthritis Patients

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.

Effect of Eight Weeks Plyometric Training on Some Kinematic Parameters, Horizontal Jumping Power, Agility, and Body Composition in Elite Parkour Athletes

Objective: A high correlation between lower limb explosive power and muscular strength, production of high power levels in the shortest time, and high level of agility are essential to achieve optimal performance in Parkour. It seems that polymetric exercises can make it possible to achieve the highest performance. In this regard, the aim of the present study was to investigate the effect of an 8-week plyometric exercise program on knee kinematic parameters, body composition, agility and horizontal jumping power of Parkour athletes. Methods: In this quasi-experimental study with pre-test and post-test design, 20 elite Parkour athletes aged 19-26 years were selected and randomly divided into two groups of exercise (n=10) and control (n=10). The exercise group carried out the program for eight weeks, three sessions per week, each for one hour. Before and after exercise, measurements of kinematic parameters of knee, agility, and horizontal jumping power, and body composition in subjects were performed. The collected data were analyzed using t-test considering a significant level of P≤0.05. Results: Plyometric exercise for eight weeks had a significant effect on knee kinematic parameters of Parkour athletes (P=0.003) and significantly improved their horizontal jump, agility and reduced body fat percentage (P≤0.05). Conclusion: Plyometric exercise can significantly improve kinematic parameters of the knee, increase the jumping power and agility, and reduce body fat percentage in Parkour athletes; however, since Parkour movements are very similar to plyometric exercises, more study is needed.

A Combined Visualization Method for Multivariate Data Analysis. Application to Knee Kinematic and Clinical Parameters Relationships

This paper aims to analyze the correlation structure between the kinematic and clinical parameters of an end-staged knee osteoarthritis population. The kinematic data are a set of characteristics derived from 3D knee kinematic patterns. The clinical parameters include the answers of a clinical questionnaire and the patient’s demographic characteristics. The proposed method performs, first, a regularized canonical correlation analysis (RCCA) to evaluate the multivariate relationship between the clinical and kinematic datasets, and second, a combined visualization method to better understand the relationships between these multivariate data. Results show the efficiency of using different and complementary visual representation tools to highlight hidden relationships and find insights in data.

A Sample-Encoding Generalization of the Kohonen Associative Memory and Application to Knee Kinematic Data Representation and Pathology Classification

Knee kinematic data consist of a small sample of high-dimensional vectors recording repeated measurements of the temporal variation of each of the three fundamental angles of knee three-dimensional rotation during a walking cycle. In applications such as knee pathology classification, the notorious problems of high-dimensionality (the curse of dimensionality), high intra-class variability, and inter-class similarity make this data generally difficult to interpret. In the face of these difficulties, the purpose of this study is to investigate knee kinematic data classification by a Kohonen neural network generalized to encode samples of multidimensional data vectors rather than single such vectors as in the standard network. The network training algorithm and its ensuing classification function both use the Hotelling T 2 statistic to evaluate the underlying sample similarity, thus affording efficient use of training data for network development and robust classification of observed data. Applied to knee osteoarthritis pathology discrimination, namely the femoro-rotulian (FR) and femoro-tibial (FT) categories, the scheme improves on the state-of-the-art methods.