Early indication of some diseases such as Parkinson and Multiple Sclerosis often manifests with walking difficulties. Gait analysis provides vital information for assessing the walking patterns during the locomotion, especially when the outcomes are quantitative measures. This paper explores methods that can respond to the changes in the gait features during the swing stage using Kinect Camera, a low cost, marker-free, and portable device offered by Microsoft. Kinect has been exploited for tracking the skeletal positional data of body joints to assess and evaluate the gait performance. Linear kinematic gait features are extracted to discriminate between walking speeds by using five supervised modulation based machine-learning classifiers as follow: Decision Trees (DT), linear/nonlinear Support Vector Machines (SVMs), subspace discriminant and k-Nearest Neighbour (k-NN). The roles of modulation techniques such as Frequency Modulation (FM) for increasing the efficiency of classifiers have been explored. The experimental results show that all five classifiers can successfully distinguish gait futures signal associated with walking patterns with high accuracy (average expected value of 86.19% with maximum of 92.9%). This validates the capability of the presented methodology in detecting key "indicators" of health events.

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