iGait: Vision-based Low-Cost, Reliable Machine Learning Framework for Gait Abnormality Detection

Abstract

Human gait has shown to be a strong indicator of health issues under a wide variety of conditions. For that reason, gait analysis has become a powerful tool for clinicians to assess functional limitations due to neurological or orthopedic conditions that are reflected in gait. Therefore, accurate gait monitoring and analysis methods have found a wide range of applications from diagnosis to treatment and rehabilitation. This thesis focuses on creating a low-cost and non-intrusive vision-based machine learning framework dubbed as iGait to accurately detect CLBP patients using 3-D capturing devices such as MS Kinect. To analyze the performance of the system, a precursor analysis for creating a feature vector is performed by designing a highly controlled in-lab simulation of walks. Furthermore, the designed framework is extensively tested on real- world data acquired from volunteer elderly patients with CLBP. The feature vector presented in this thesis show very high agreement in getting the pathological gait disorders (98% for in-lab settings and 90% for actual CLBP patients), with a thorough research on the contribution of each feature vector on the overall classification accuracy.