Abstract
Analysis of human gait to detect walking abnormality has recently gained growing interest. It carries profound impact in medical diagnosis and rehabilitation engineering. In this study we have explored different regression modeling techniques to detect pathological gait. Inexpensive Microsoft Kinect (V2) sensor was used for data acquisition. Comparative analysis was performed between logistic regression, SVM and multiple adaptive regression splines (MARS) models. Kinematic time series, extracted from different lower limb joints, were fed into the models and used to detect the abnormal pattern. Feature vectors were constructed from 6 joint angles (hip, knee and ankle angles of both sides) and merged on the range of multiple time instance for greater accuracy. An attempt was also made to investigate the statistical significance of the feature vectors. MARS model was found to be comparatively better than others with 88.3% of detection accuracy.
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