Development of smart shin guards for soccer performance analysis based on MEMS accelerometers, machine learning, and GNSS

The performance analysis of a soccer team has become an important topic for soccer coaches. Parameters like the number of shots, passes or sprints during a match provides information about the game quality. However, currently available systems are based on cost-expensive video analysis, which requires a pre-installed infrastructure. Consequently, such systems are only open to professional teams. The use of low-cost wearables represents an alternative to make such performance analysis accessible to hobby teams. This paper focuses on the evaluation of different Machine Learning (ML) approaches for the classification of simple, soccer-specific activities (such as standing, walking, running, passing and shooting) based on Micro-Electro-Mechanical Systems (MEMS) accelerometers. To do so, the sensors are mounted on the soccer player's shin guards. Diverse ML algorithms as well as different dimensionality reduction algorithms are investigated. The best approach shows a macro-precision score of 97% and a macro-recall score of 96%. The final goal is to develop smart shin guards, which can georeference soccer-specific activities in conjunction with sports statistics. The positioning system is based on Global Navigation Satellite Systems (GNSS). This scientific study is part of the Austrian Space Applications Programme (ASAP) 15 and funded by the Federal Ministry of Transport, Innovation and Technology via the Austrian Research Promotion Agency (FFG).