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Gait Abnormality Detection Using Deep Convolution Network

Human gait analysis plays a significant role in clinical domain for diagnosis of musculoskeletal disorders. It is an extremely challenging task for detecting abnormalities (unsteady gait, stiff gait, etc.) in human walking if the prior information is unknown about the gait pattern. A low-cost Kinect sensor is used to obtain promising results on human skeletal tracking in a convenient manner. A model is created on human skeletal joint positions extracted using Kinect v2 sensor in place using Kinect-based color and depth images. Normal gait and abnormal gait are collected from different persons on treadmill. Each trial of gait is decomposed into cycles. A convolutional neural network (CNN) model was developed on this experimental data for detection of abnormality in walking pattern and compared with state-of-the-art techniques.