Kinematic gait analysis of workers exposed to knee straining postures by Bayes decision rule

Abstract Deep knee flexion postures such as kneeling and squatting have been demonstrated, in recent review of occupational knee disorders, as a risk factor of developing knee osteoarthritis (OA). This study investigates a probabilistic method to analyze knee gait kinematics measurements of workers exposed to knee straining postures to determine if they are in any way similar to those of knee OA patients. The measurements we use are clinically relevant kinematic signals, namely the variation during a locomotion gait cycle of the angles the knee makes with respect to the three-dimensional (3D) planes of flexion/extension, internal/external rotation, and abduction/adduction. Three groups of participants were used: a set of 24 workers exposed to knee straining postures (KS workers) acting as a test group, a control group of 25 non-KS posture workers, and a reference knee OA group of 29 subjects. We compared the kinematic data of KS workers to those of knee OA patients and non-KS subjects using the Bayes decision theory. The results shows that, using the 3D data taken together or the abduction/adduction data, the KS workers resembles often to the OA patients. The analysis on the transverse plane and on sagittal plane, i.e., the flexion/extension and the internal/external rotation, are not conclusive as the similarities are not significant. The kinematic gait analysis by Bayes decision rule shows the similarity of workers exposed to knee straining postures to OA gait pattern and justifies further prospective studies of KS workers in order to assess if gait pattern could be modified even before the onset of the disease. Key Words: Bayesian decision rule, Biomechanics, Kinematic gait analysis, Knee osteoarthritis, Knee straining posture