Running is a popular form of recreation. Running clubs and shops commonly provide services related to video analysis of running. The most usual service is determining pronation type of a runner. The normal method is measuring the eversion angle of an ankle, usually measured manually from a backside video of a runner on a treadmill. Therefore, there is a need to develop applications for automatic analysis of running gait.
In this thesis we have developed methods for automatic measurement of the eversion angle of an ankle of runners on a treadmill, which automatically measure the angle and the type of pronation. Tested on a group of 15 runners, these methods produced reasonably accurate result for roughly determining the pronation type. However they are not accurate enough for wider commercial use and for research in sports. We propose some guidelines and improvements for further work to minimise the shortcomings and enable efficient use in applications for general use and in research.