A Pilot Study for Investigating Gait Signatures in Multi-Scenario Applications

Human pose estimation in a gait sequence is an essential step for solving human identification problems, medical diagnosis, monitoring, and rehabilitation. In this paper, a low-cost Kinect V2.0 sensor is used for investigating motion signatures obtained from normal healthy adults. The purpose of this study is to determine the accuracy and reliability of observational assessments of spatio-temporal features. A novel approach for human detection and tracking is proposed, which involves gait feature learning principles from depth and RGB video. In the first step, a human object from the depth image is extracted using the proposed semi-dynamic object tracking algorithm, and a stick model is generated using body aspect ratios to extract hip angles. In the second step, the gait energy image (GEI) representation is utilized for training a 2D Convolutional Neural Network (AlexNet) for automatic feature extraction. A key point detection algorithm is proposed for estimating knee, hip, and ankle joints from RGB gait videos. The reliability analysis of motion signatures is performed using various statistical methods to ensure feature learning for multi-scenario applications. The statistical results are promising for evaluating the methods which influence the inter-record differences among motion signatures.

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