Human gait characterization using Kinect

Abstract

Treball de fi de grau en BiomèdicaTutor: Xavier Binefa VallsGait analysis has become a widely used tool to evaluate and assess the human locomotion, aiding to identify and characterize movement disorders from the population. The increasing interest in gait analysis together with the recent development of new technologies has led the progress of the devices and techniques used in motion analysis, providing new opportunities in human motion characterization. In this project, the potential use of Kinect as a tool for gait analysis is presented with the aim to provide reliable information of human motion using this single device, avoiding the use of expensive motion capture systems and strict laboratory conditions usually used for this type of analysis. This project proposes a set of algorithms to effectively extract the gait features of interest, in terms human motion (kinematics) but also in terms of forces (kinetics) during gait. This part has/nbeen one of the most challenging analysis in the project, as at a glance, seems impossible to obtain human forces using a camera. The results obtained have been compared to those obtained with the gold standard equipment and similarities between both approaches indicate that the goal has been positively achieved. These findings encourage even more the use of this technology as an alternative when no equipment or laboratory conditions are available. The information acquired in this project with Kinect sensor could have of great interest to characterize the human gait in two major application fields: sports and healthcare. On one hand, in sports context, these measurements could have an impact in the improvement of the performance during running, contributing to give useful information to exhibit the good technique and correct posture; hence, achieving the maximum efficiency in the performance. On the other hand, in clinical field, these measurements could help to deeply understand how our movement during locomotion can lead to increase the risk of injuries. Specifically, common incidence of injuries such as knee pain, back pain and stress fracture can be analyzed with the results provided in this project