Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario
This addresses the need for unobtrusive data collection in the emerging eSports domain, though it is incremental as it applies existing machine learning methods to new sensor data.
The study tackled the problem of understanding eSports athletes' behavior by developing a smart chair platform to collect and analyze data from accelerometers and gyroscopes during CS:GO gameplay, demonstrating that professional athletes can be identified based on their chair behavior.
eSports is the rapidly developing multidisciplinary domain. However, research and experimentation in eSports are in the infancy. In this work, we propose a smart chair platform - an unobtrusive approach to the collection of data on the eSports athletes and data further processing with machine learning methods. The use case scenario involves three groups of players: `cyber athletes' (Monolith team), semi-professional players and newbies all playing CS:GO discipline. In particular, we collect data from the accelerometer and gyroscope integrated in the chair and apply machine learning algorithms for the data analysis. Our results demonstrate that the professional athletes can be identified by their behaviour on the chair while playing the game.