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Assessment of Video Games Players and Teams Behaviour via Sensing and Heterogeneous Data Analysis: Deployment at an eSports Tournament

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Science and Technologies for Smart Cities (SmartCity360° 2020)

Abstract

eSports is video gaming where individual players or teams oppose their physical, psychological and emotional conditions in the game context to achieve a specific goal by the end of the game. However, neither players nor teams have been studied in real scenarios. In this paper, we report on the deployment of sensing system for collecting a player biometric data (a computer mouse and keyboard), voice data, and heart rate in an eSports ‘Team Fortress 2’ tournament. Upon the data analysis we demonstrate that an increased heart rate has a negative impact on the player performance. At the same time, successful teams communicate more during the game. Moreover, team communication in positive tone has a positive contribution in the overall team performance.

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Notes

  1. 1.

    TF2 Moscow LAN official website https://match.tf/tournaments/44.

  2. 2.

    C.Moore ‘Hats of affect: A study of Affect, Achievements and Hats in Team Fortress 2’, available at http://gamestudies.org/1101/articles/moore.

References

  1. Anderson, C.G.: Understanding esports as a stem career ready curriculum in the wild. In: 2018 10th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games), pp. 1–6 (2018)

    Google Scholar 

  2. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Google Scholar 

  3. Ceriotti, M.: Monitoring heritage buildings with wireless sensor networks: the torre aquila deployment. In: 2009 International Conference on Information Processing in Sensor Networks, pp. 277–288 (2009)

    Google Scholar 

  4. Choi, G., Kim, M.: Eye gaze information and game level design according to fps gameplay beats. J. Inform. Commun. Convergence Eng. 16, 189–196 (2018)

    Google Scholar 

  5. Dupont, C., Hermenier, F., Schulze, T., Basmadjian, R., Somov, A., Giuliani, G.: Plug4green: A flexible energy-aware vm manager to fit data centre particularities. Ad Hoc Netw. 25, 505–519 (2015)

    Article  Google Scholar 

  6. Freeman, G., Wohn, D.Y.: Understanding esports team formation and coordination. Comput. Support. Coop. Work 27(3–6), 1019–1050 (2018)

    Google Scholar 

  7. Garcia-Ceja, E., Osmani, V., Mayora, O.: Automatic stress detection in working environments from smartphones’ accelerometer data: a first step. IEEE J. Biomed. Health Inform. 20(4), 1053–1060 (2016)

    Article  Google Scholar 

  8. Guo, J., Zhou, R., Zhao, L., Lu, B.: Multimodal emotion recognition from eye image, eye movement and eeg using deep neural networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3071–3074 (2019)

    Google Scholar 

  9. Haladjian, J., Schlabbers, D., Taheri, S., Tharr, M., Bruegge, B.: Sensor-based detection and classification of soccer goalkeeper training exercises. ACM Trans. Internet Things 1(2), 1–20 (2020)

    Article  Google Scholar 

  10. Hamari, J., Keronen, L.: Why do people play games? A meta-analysis. Int. J. Inf. Manage. 37(3), 125–141 (2017)

    Article  Google Scholar 

  11. Hamari, J., Tuunanen, J.: Player types: a meta-synthesis. Trans. Digital Games Res. Assoc. 1(2), 29–53 (2014). https://doi.org/10.26503/todigra.v1i2.13

  12. Hasan, M.R., Jamil, M., Rahman, M., et al.: Speaker identification using mel frequency cepstral coefficients. Variations 1(4) (2004)

    Google Scholar 

  13. Heinz, E.A., Kunze, K.S., Gruber, M., Bannach, D., Lukowicz, P.: Using wearable sensors for real-time recognition tasks in games of martial arts - an initial experiment. In: 2006 IEEE Symposium on Computational Intelligence and Games, pp. 98–102 (2006). https://doi.org/10.1109/CIG.2006.311687

  14. Hooshyar, D., Yousefi, M., Lim, H.: Data-driven approaches to game player modeling: a systematic literature review. ACM Comput. Surv. 50(6) (2018). https://doi.org/10.1145/3145814

  15. Jeyakumar, J.V., Lai, L., Suda, N., Srivastava, M.: Sensehar: a robust virtual activity sensor for smartphones and wearables. In: Proceedings of the 17th Conference on Embedded Networked Sensor Systems, pp. 15–28. SenSys ’19, Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3356250.3360032

  16. Khromov, N., Korotin, A., Lange, A., Stepanov, A., Burnaev, E., Somov, A.: Esports athletes and players: a comparative study. IEEE Pervasive Comput. 18(3), 31–39 (2019)

    Article  Google Scholar 

  17. Koposov, D., Semenova, M., Somov, A., Lange, A., Stepanov, A., Burnaev, E.: Analysis of the reaction time of esports players through the gaze tracking and personality trait. In: 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), pp. 1560–1565 (2020). https://doi.org/10.1109/ISIE45063.2020.9152422

  18. Livingstone, S.R., Russo, F.A.: The ryerson audio-visual database of emotional speech and song (ravdess): a dynamic, multimodal set of facial and vocal expressions in north american english. PloS one 13(5), e0196391 (2018)

    Google Scholar 

  19. Meyer, M., et al.: Event-triggered natural hazard monitoring with convolutional neural networks on the edge. In: Proceedings of the 18th International Conference on Information Processing in Sensor Networks. pp. 73–84. IPSN ’19, Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3302506.3310390

  20. Paravizo, E., de Souza, R.R.L.: Playing for real: an exploratory analysis of professional esports athletes’ work. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018), pp. 507–515. Springer International Publishing, Cham (2019)

    Google Scholar 

  21. Sifa, R., Drachen, A., Bauckhage, C.: Large-scale cross-game player behavior analysis on Steam. In: Proceedings of the 11th Conference on Artificial Intelligence and Interactive Digital Entertainment (2015)

    Google Scholar 

  22. Smerdov, A., Burnaev, E., Somov, A.: esports pro-players behavior during the game events: Statistical analysis of data obtained using the smart chair. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 1768–1775 (2019)

    Google Scholar 

  23. Stepanov, A., Lange, A., Khromov, N., Korotin, A., Burnaev, E., Somov, A.: Sensors and game synchronization for data analysis in esports. In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), vol. 1, pp. 933–938 (2019)

    Google Scholar 

  24. Velichkovsky, B.B., Khromov, N., Korotin, A., Burnaev, E., Somov, A.: Visual Fixations Duration as an Indicator of Skill Level in eSports. In: Lamas, D., Loizides, F., Nacke, L., Petrie, H., Winckler, M., Zaphiris, P. (eds.) INTERACT 2019. LNCS, vol. 11746, pp. 397–405. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29381-9_25

    Chapter  Google Scholar 

  25. Yannakakis, G.N., Togelius, J.: Modeling Players, pp. 203–255. Springer International Publishing, Cham (2018)

    Google Scholar 

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Acknowledgments

The reported study was funded by RFBR according to the research project No. 18-29-22077\(\backslash \)19.

Authors also thank the organizers of ‘Team Fortress 2’ Moscow LAN 2019 for technical support during the data collection and fruitful discussions of the game insights.

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Correspondence to Andrey Somov .

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Korotin, A. et al. (2021). Assessment of Video Games Players and Teams Behaviour via Sensing and Heterogeneous Data Analysis: Deployment at an eSports Tournament. In: Paiva, S., Lopes, S.I., Zitouni, R., Gupta, N., Lopes, S.F., Yonezawa, T. (eds) Science and Technologies for Smart Cities. SmartCity360° 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-030-76063-2_28

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  • DOI: https://doi.org/10.1007/978-3-030-76063-2_28

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