Pro gamers and amateurs found that `` the way the body moves during the game is different ''


Maxime FORT

A professional gamer who plays a game as a competition is a profession that earns hundreds of millions of yen per year if it becomes a top player. Research has shown that such professional gamers and general amateurs have different ways of moving their bodies when sitting on a chair and playing a game.

Understanding Cyber Athletes Behavior Through a Smart Chair: CS: GO and Monolith Team Scenario-IEEE Conference Publication

[1908.06407] Understanding Cyber Athletes Behavior Through a Smart Chair: CS: GO and Monolith Team Scenario

Predictable eSports: Amateurs and professionals have different sitting postures

This study Sukorukovo University of Science and Technology and the Moscow Physical Institute of Science and Technology (MIPT), and St. Petersburg aerospace university in the joint research of (SUAI), Sukorukovo University of Science and Technology computer and data-intensive science and engineering center in (CDISE) Prof. Andrey Somov and Prof. Evgeny Burnaev are leading the research. The research conducted reveals the relationship between esports player body movements and skill levels.

The research team conducted an experiment on ' Counter-Strike: Global Offensive (CS: GO) ', one of the e-sports FPS where the world competition of high prize money is held. A total of 19 subjects, 9 CS: GO pros and 10 amateurs, participated in the experiment. Subjects were asked to play the game on a chair with an accelerometer and gyroscope so that they could measure their position and weight during the game.

As a result of three-dimensional analysis of the 'movement' that each player showed during the game, professional gamers and beginners 'kill (defeat the opponent)' 'killed (defeated by the opponent)' 'shoot' It turns out that the reaction to the inside event is different. We found that professional players move around more intensively and more frequently than beginner players.

When the movement of each player's body was patterned, 31 types of patterns were confirmed, and when the obtained data was read and learned by a machine learning algorithm, machine learning by

random forest analysis was only 3 minutes of data It is now possible to hit each player's skill level with a probability of 77%.

By Sean Do

In addition to this research, Prof. Somov and Prof. Burnaev are studying the effects of pulse, skin resistance, line of sight, hand movement, room temperature, carbon dioxide concentration, etc. on the game player's psychology and body .

in Science,   Game, Posted by log1k_iy