Mobile gait analysis

Abstract

Getting your gait analysed is quite a tedious process. With a very limited number of professionals and a generally lengthy procedure. Typically, expensive and time-consuming marker-based technologies are employed in conjunction with several infrared cameras to generate kinematic data that concisely quantifies a person’s walking behaviour. Many people get discouraged in getting their gait checked out. This project aims to develop an extension to automated gait analysis that makes gait analysis available on smart devices. The alternative may serve as a baseline for future implementations that are cheaper, user-friendly and accessible to an ordinary smartphone or web browser. Accessibility of gait analysis on a web application encourages people to check their walking patterns more regularly, and if the issue is very severe, they can take the next step of contacting a specialist. For mobile gait analysis to be made possible, a substantial amount of data was required. By collaborating with the Podiatry Department of the University of Malta and the Chinese Academy of Sciences Institute of Automation (CASIA), a considerable amount of gait data was acquired. The data consists of videos of people walking regularly or irregularly. But videos are not enough for the development of our system. The videos were inputted into a pose estimator whose goal was to outline the skeleton of the person throughout the video. Additionally, the pose estimator was modified to record the coordinates of the main joints concerning a gait cycle (hip, knee and ankle). These coordinates were then plotted as a scatter plot where the gait cycle is generated. With the coordinates extracted, kinematics were also extracted to create another model which detects different features for gait analysis. After the gait cycle of each video was extracted, the next step was to classify whether that gait cycle was either regular or irregular. This goal is achieved by passing the labelled gait cycles into a pattern recognition architecture (model) which was then tested for accuracy with satisfactory results. The application was tested out on people which have either bad, good or slightly bad gaits to investigate the rigidity of the system. After a series of experiments, it can be concluded that the system performs with 94% accuracy just by using a mobile phone.