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

Neila Mezghani, Nathaly Gaudreault, Amar Mitiche, Leila Ayoubian, Youssef Ouakrim, Nicola Hagemeister, Jacques A Deguise


Deep knee flexion postures such as kneeling and squatting have been demonstrated, in recent review of occupational kneedisorders, as a risk factor of developing knee osteoarthritis (OA). This study investigates a probabilistic method to analyze kneegait kinematics measurements of workers exposed to knee straining postures to determine if they are in any way similar tothose of knee OA patients. The measurements we use are clinically relevant kinematic signals, namely the variation duringa 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 kneestraining postures (KS workers) acting as a test group, a control group of 25 non-KS posture workers, and a reference knee OAgroup of 29 subjects. We compared the kinematic data of KS workers to those of knee OA patients and non-KS subjects using theBayes decision theory. The results shows that, using the 3D data taken together or the abduction/adduction data, the KS workersresembles often to the OA patients. The analysis on the transverse plane and on sagittal plane, i.e., the flexion/extension and theinternal/external rotation, are not conclusive as the similarities are not significant. The kinematic gait analysis by Bayes decisionrule shows the similarity of workers exposed to knee straining postures to OA gait pattern and justifies further prospective studiesof KS workers in order to assess if gait pattern could be modified even before the onset of the disease.

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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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