most citedDetecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

28 citations · 42 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC2020

AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

Anton Smerdov, Evgeny Burnaev, Andrey Somov +1

The emerging progress of eSports lacks the tools for ensuring high-quality analytics and training in Pro and amateur eSports teams. We report on an Artificial Intelligence (AI) ena…

cs.IR2020★ 28 cited

Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

Anton Smerdov, Andrey Somov, Evgeny Burnaev +2

Current research in eSports lacks the tools for proper game practising and performance analytics. The majority of prior work relied only on in-game data for advising the players on…

cs.HC2020★ 13 cited

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

Anton Smerdov, Bo Zhou, Paul Lukowicz +1

Proper training and analytics in eSports require accurately collected and annotated data. Most eSports research focuses exclusively on in-game data analysis, and there is a lack of…

cs.HC2019★ 1 cited

Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario

Anton Smerdov, Anastasia Kiskun, Rostislav Shaniiazov +2

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 - a…

cs.HC2019

eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair

Anton Smerdov, Evgeny Burnaev, Andrey Somov

Today's competition between the professional eSports teams is so strong that in-depth analysis of players' performance literally crucial for creating a powerful team. There are two…