A Comparative Study of End-To-End Discriminative Deep Learning Models for Knee Joint Kinematic Time Series Classification

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Abstract
One of the main motivations for classifying knee 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, abduction/adduction, and internal/external rotation, is to assist diagnosis of knee pathologies. These signals are informative but high dimensional, and highly variable, which has posed difficulties that have been addressed by machine learning algorithms. The purpose of this study is to investigate classification of knee kinematic signals through the entire gait using deep neural networks. The signals are first pre-processed to identify representative patterns, which are then used for deep learning of discriminative classifiers. This paper describes an efficient means of distinguishing between knee osteoarthrisis patients and asymptomatic participants, and our methods and experiments which validate it.

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