Mikhail Usanin and Aleksei Smirnov presented the study «AI-based approach to burnout identification and it’s implication for performance in eSports». Co-authors of this research: Marina Zavertiaeva, Petr Parshakov, Sofia Paklina.
This study introduces an AI-based methodology utilizing natural language processing (NLP) to detect burnout through Russian-language texts and analyze it’s relationship with performance, applying it to the eSports context with professional CS:GO players. By the use of fine-tuned RuBERT model, the research identifies a significant negative correlation between burnout probabilities and eSports performance metrics. The findings illustrate that increased burnout corresponds with reduced athlete performance and that older players exhibit a higher likelihood of burnout. This innovative approach provides a scalable and objective tool for monitoring burnout, offering substantial implications for enhancing workplace mental health and performance management in high-stress environments.
