ABSTRACT
Investigating features from gait biometric is an essential step for solving human identification problems, medical diagnosis, monitoring and rehabilitation. This chapter addresses the use of a low-cost Kinect V2.0 sensor along with hand-crafted and deep learning techniques for the investigation. As deep neural network is a representation of the most effective machine learning technology in biomedical domain, its usage in this work is extremely significant to determine the accuracy, reliability and feasibility of observational assessments of spatio-temporal features of gait. A novel approach for human detection and tracking is proposed in this chapter, which involves gait feature learning principles from Color Depth videos. A proposed semi-dynamic object tracking algorithm is used to extract the human object. Gait energy image (GEI) representation is exemplified to train a 2D Convolutional Neural Network for automatic feature extraction in one case. In another case, kinematic hip joint angles are extracted via the stick model generated using body aspect ratios. A maximum accuracy of 95.1% is attained from the CNN models, which infers that the gait features automatically extracted are informative enough. Reliability analysis of all features is carried out using statistical methods such as hypothesis testing (T-test as sample size > 30), Kolmogorov-Smirnov (KS) test, Pearson’s r test and energy distance test to ensure feature learning for biometric applications. Almost all the outcomes illustrate that the features come from the same population mean (as critical region value of 0.05 is crossed) and hence are promising for assessing the strategies that impact the inter-record contrasts among motion signatures.
