Application of Machine Learning Methods for Human Gait Analysis

The majority of human gait analysis methods are limited to clinical gait laboratories. The calculation of gait parameters for athletes, during running in open environment, has endless possibilities of performance analysis to keep track of training. This thesis demonstrates a method to capture three-dimensional measurements of multidimensional human body movements during walking and running by means of GPS-aided-INS equipped data logger and also describes the two-dimensional (forward and vertical) analysis of captured three-dimensional movement. The gait segmentation based on the vertical velocity has been presented and the built data processing software can compute majority of traditional gait metrics such as stride duration, average speed, stride length, cadence and vertical oscillation. The equipment uses inexpensive pressure insoles to generate foot pressure data for model training and indirect estimation of vertical ground reaction force and ground contact time. Both machine and deep learning approaches are detailed for indirect estimation of vertical ground reaction force and ground contact time. The possibilities are also explored to make interpersonal gait parameter estimation by means of generalised prediction models. Both machine leaning and deep learning solution are presented to generate continuous vertical ground reaction force curves along with gait components. The methods, presented in this thesis, help to analyse human motion by means of gait segmentation and to calculate or estimate numerous spatio-temporal gait parameters. The intra-step variations in motion parameters are great help to analyse the different aspects of running in outdoor. The encouraging results reported in this thesis demonstrate the feasibility of device that provides detailed analysis about the performance of an athlete in outdoor running environment.