Skip to main content

Advertisement

SpringerLink
Log in

Assessment of mental fatigue and stress on electronic sport players with data fusion

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Stress and mental fatigue are in existence constantly in daily life, and decrease our productivity while performing our daily routines. The purpose of this study was to analyze the states of stress and mental fatigue using data fusion while e-sport activity. In the study, ten volunteers performed e-sport duty which required both physical and mental effort and skills for 2 min. Volunteers’ electroencephalogram (EEG), galvanic skin response (GSR), heart rate variability (HRV), and eye tracking data were obtained before and during game and then were analyzed. In addition, the effects of e-sports were evaluated with visual analogue scale and d2 attention tests. The d2 tests are performed after the game, and the game has a positive effect on attention and concentration. EEG from the frontal region indicates that the game is partly caused by stress and mental fatigue. HRV analysis showed that the sympathetic and vagal activities created by e-sports on people are different. By evaluating HRV and GSR together, it was seen that the emotional processes of the participants were stressed in some and excited in others. Data fusion can serve a variety of purposes such as determining the effect of e-sports activity on the person and the appropriate game type.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Cacioppo JT, Tassinary LG, Berntson GG (2007) Handbook of psychophysiology. Cambridge University Press, New York 914 p

    Google Scholar 

  2. Wan B, Wang Q, Su K, Dong C, Song W, Pang M (2021) Measuring the impacts of virtual reality games on cognitive ability using EEG signals and game performance data. IEEE Access 9:18326–18344

    Article  Google Scholar 

  3. Baddeley AD, Logie RH (1999) Working memory: the multiple component model. In: Miyake A, Shah P (eds) Models of working memory: mechanisms of active maintenance and executive control. Cambridge Univ. Press, Cambridge, pp 28–61

    Chapter  Google Scholar 

  4. Xiong R, Kong F, Yang X, Liu G, Wen W (2020) Pattern recognition of cognitive load using EEG and ECG signals. Sensors 20:5122

    Article  PubMed Central  Google Scholar 

  5. Anderson CW, Bratman JA (2008) Translating thoughts into actions by finding patterns in brainwaves, in Proc. 14th Yale Workshop Adapt. Learn. Syst., 2008, pp. 1-6

  6. Mohamed Z, El Halaby M, Said T, Shawky D, Badawi A (2018) Characterizing Focused Attention and Working Memory Using EEG. Sensors (Basel) 18(11):3743

    Article  Google Scholar 

  7. Antonenko PD, Niederhauser DS (2010) The influence of leads on cognitive load and learning in a hypertext environment. Comput Hum Behav 26(2):140–150

    Article  Google Scholar 

  8. Scharinger C, Kammerer Y, Gerjets P (2015) Pupil dilation and EEG alpha frequency band power reveal load on executive functions for link-selection processes during text reading. PLoS One 10(6):e0130608

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Khader PH, Jost K, Ranganath C, Rösler F (2010) Theta and alpha oscillations during working-memory maintenance predict successful long-term memory encoding. Neurosci Lett 468(3):339–343

    Article  PubMed  CAS  Google Scholar 

  10. Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research. Brain Res Rev 29(2–3):169–195

    Article  PubMed  CAS  Google Scholar 

  11. Castro-Meneses LJ, Kruger JL, Doherty S (2020) Validating theta power as an objective measure of cognitive load in educational video. Education Tech Research Dev 68:181–202

    Article  Google Scholar 

  12. Gündoğdu S, Doğan EA, Gülbetekin E, Çolak ÖH, Polat Ö (2019) Evaluation of the EEG signals and eye tracker data for working different N-back modes. Traitement du Signal 36(6):493–500

    Article  Google Scholar 

  13. Sharma N, Gedeon T (2012) Objective measures, sensors and computational techniques for stress recognition and classification: a survey. Comput Methods Prog Biomed 108(3):1287–1301

    Article  Google Scholar 

  14. Lin T, John L (2006) Quantifying mental relaxation with EEG for use in computer games. International Conference on Internet Computing, pp. 409–415, Las Vegas, Nevada, USA

  15. Pelegrina S, Lechuga MT, Madruga JAG, Elosúa MR, Macizo P, Carreiras M, Fuentes LJ, Bajo MT (2015) Normative data on the n-back task for children and young adolescents. Front Psychol 6(1544):1–11

    Google Scholar 

  16. Wilhelm O, Hildebrandt A, Oberauer K (2013) What is working memory capacity, and how can we measure it? Front Psychol 433(4):1–22

    Google Scholar 

  17. Tanaka M, Shigihara Y, Ishii A, Funakura M, Kanai E, Watanabe Y (2012) Effect of mental fatigue on the central nervous system: An electroencephalography study. Behav Brain Funct 8(48):1–8

    Google Scholar 

  18. Tanaka M, Ishii A, Watanabe Y (2015) Effects of mental fatigue on brain activity and cognitive performance: A magnetoencephalography study. Anat Physiol 5(S4):1–5

    CAS  Google Scholar 

  19. Bates ME, Lemay E (2004) The d2 test of attention: construct validity and extensions in scoring techniques. J Int Neuropsychol Soc 10(3):392–400

    Article  PubMed  Google Scholar 

  20. Tarnowski P, Kołodzıej M, Majkowski A, Rak R (2016) A system for synchronous acquisition of selected physiological signals aimed at emotion recognition. Przegląd Elektrotechniczny 92(12):327–331

    Google Scholar 

  21. Gündoğdu S, Doğan EA, Gülbetekin E, Çolak ÖH, Polat Ö (2019) Bulmaca Video Oyunu Oynama Süresinin Stres ve Odaklanma Üzerindeki Etkilerinin Galvanik Deri Tepkisi, KHD ve Göz Takip Tabanlı Değerlendirilmesi. 4th International Mediterranean Scıence And Engıneerıng Congress. April 25-27, Alanya / TURKEY.

  22. Newzoo. Global Esports Audience Growth. Available online: https://newzoo.com/key-numbers (Accessed on 30 March 2021)

  23. Rudolf K, Bickmann P, Froböse I, Tholl C, Wechsler K, Grieben C (2020) Demographics and Health Behavior of Video Game and eSports Players in Germany: The eSports Study 2019. Int J Environ Res Public Health 17(6):1870

    Article  PubMed Central  Google Scholar 

  24. Wewers ME, Lowe NK (1990) A critical review of visual analogue scales in the measurement of clinical phenomena. Res Nurs Health 13:227–236

    Article  PubMed  CAS  Google Scholar 

  25. May T, Pridmore S (2020) A visual analogue scale companion for the six-item Hamilton Depression Rating Scale. Aust Psychol 55:3–9

    Article  Google Scholar 

  26. Lesage FX, Berjot S, Deschamps F (2012) Clinical stress assessment using a visual analogue scale. Occup Med 62(8):600–605

    Article  Google Scholar 

  27. Lee KA, Hicks G, Nino-Murcia G (1991) Validity and reliability of a scale to assess fatigue. Psychiatry Res 36(3):291–298

    Article  PubMed  CAS  Google Scholar 

  28. Matthews G, Desmond PA (2002) Task-induced fatigue states and simulated driving performance. Q J Exp Psychol A 55(2):659–686

    Article  PubMed  Google Scholar 

  29. Guo W, Ren J, Wang B, Zhu Q (2015) Effects of relaxing music on mental fatigue induced by a continuous performance task: behavioral and ERPs evidence. PLoS One 10(8):e0136446. https://doi.org/10.1371/journal.pone.0136446

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Spreen O, Strauss E (1998) A Compendium of Neuropsychological Tests, 2nd edn. Oxford University Press, New York 736 p

    Google Scholar 

  31. Hoskinson P, Toomim J (2010) Brain Workshop: Brain Workshop - a Dual N-Back game (Version 4.8.1). http://brainworkshop.sourceforge.net/download.html. Accessed 15 Apr 2019

  32. Zhang R (2019) The Effect of Meditation on Concentration Level and Cognitive Performance. Global J Health Sci 11(1):134–140

    Article  Google Scholar 

  33. Bird JJ, Faria DR, Manso LJ, Ekárt A, Buckingham CD (2019) A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction. Complexity:1–14. https://doi.org/10.1155/2019/4316548

  34. Garcia-Moreno FM, Bermudez-Edo M, Garrido JL, Rodríguez-Fórtiz MJ (2020) Reducing Response Time in Motor Imagery Using A Headband and Deep Learning. Sensors 20:6730

    Article  PubMed Central  Google Scholar 

  35. Wiechert G et al (2016) Identifying users and activities with cognitive signal processing from a wearable headband," 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Palo Alto, CA, USA, 2016, pp. 129-136

  36. Zhao D, MacDonald S, Gaudi T, Uribe-Quevedo A, Martin MV, Kapralos B (2018) Facial expression detection employing a brain computer interface. In Proceedings of the 9th International Conference on Information, Intelligence, Systems and Applications (IISA), Zakynthos, Greece, 23–25 July; pp. 1–2

  37. Salehzadeh A, Calitz AP, Greyling J (2020) Human activity recognition using deep electroencephalography learning. Biomed Signal Process Control 62:102094

    Article  Google Scholar 

  38. Alrige M, Chatterjee S (2015) Toward a taxonomy of wearable technologies in healthcare. In: New horizons in design science: Broadening the research agenda. Springer, Cham, pp 496–504

    Chapter  Google Scholar 

  39. Abujelala M, Abellanoza C, Sharma A, Makedon F (2016) Brain-EE: Brain enjoyment evaluation using commercial EEG headband. In: Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments. ACM, New York, pp 33:1–33:5

    Google Scholar 

  40. Przegalinska A, Ciechanowski L, Magnuski M, Gloor P (2018) Muse Headband: Measuring Tool or a Collaborative Gadget? In: Grippa F, Leitão J, Gluesing J, Riopelle K, Gloor P (eds) Collaborative Innovation Networks. Studies on Entrepreneurship, Structural Change and Industrial Dynamics. Springer, Cham

    Google Scholar 

  41. Kasperiuniene J, Jariwala M, Vaskevicius E, Satkauskas S (2016) Affective engagement to virtual and live lectures. In: Dregvaite G, Damasevicius R (eds) Information and software technologies. Springer, Cham, pp 499–508

    Chapter  Google Scholar 

  42. Arnsten AF (2009) Stress signalling pathways that impair prefrontal cortex structure and function. Nature reviews. Neuroscience 10(6):410–422

    PubMed  CAS  Google Scholar 

  43. Castillo O, Sotomayor S, Kemper G, Clement V (2021) Correspondence Between TOVA Test Results and Characteristics of EEG Signals Acquired Through the Muse Sensor in Positions AF7–AF8. In: Iano Y, Arthur R, Saotome O, Kemper G, Borges Monteiro AC (eds) Proceedings of the 5th Brazilian Technology Symposium. Smart Innovation, Systems and Technologies, vol 202. Springer, Cham

    Google Scholar 

  44. Mesquita RNO, Kyröläinen H, Olstad DS (2017) Reliability and validity of time domain heart rate variability during daily routine activities – an alternative to the morning orthostatic test? Biomed Hum Kinetics 9(1):64–68

    Article  Google Scholar 

  45. Kim PW, Lee S (2017) Audience real-time bio-signal-processing-based computational intelligence model for narrative scene editing. Multimed Tools Appl 76(23):24833–24845

    Article  Google Scholar 

  46. Zhang Xizheng Z, Ling Y, Weixiong W (2010) Wavelet Time-frequency Analysis of Electro-encephalogram (EEG) Processing. (IJACSA). Int J Adv Comput Sci Appl 1(5):1–5

    Google Scholar 

  47. Olkkonen JT (2011) Discrete Wavelet Transforms - Theory and Applications

  48. Orosco L, Correa AG, Laciar E (2013) Review: a survey of performance and techniques for automatic epilepsy detection. J Med Biol Eng 33(6):526–537

    Article  Google Scholar 

  49. Geethanjali B, Adalarasu K, Mohan J, Seshadri NPG (2018) Music induced brain functional connectivity using eeg sensors: a study on Indian Music. IEEE Sensors J 19(4):1–9

    Google Scholar 

  50. Gall S et al (2017) Associations between selective attention and soil-transmitted helminth infections, socioeconomic status, and physical fitness in disadvantaged children in Port Elizabeth, South Africa: an observational study. PLoS Negl Trop Dis 11(5):1–19

    Article  Google Scholar 

  51. Sethi JK, Nagendra HR, Ganpat TS (2013) Yoga improves attention and self-esteem in underprivileged girl student. J Educ Health Promot 2(55):1–4

    Google Scholar 

  52. Craig A, Tran Y, Wijesuriya N, Nguyenb H (2012) Regional brain wave activity changes associated with fatigue. Psychophysiology 49(4):574–582

    Article  PubMed  Google Scholar 

  53. Gharagozlou F, Saraji GN, Mazloumi A, Nahvi A, Nasrabadi AM, Foroushani AR, Kheradmand AA, Ashouri M, Samavati M (2015) Detecting driver mental fatigue based on EEG alpha power changes during simulated driving. Iran J Public Health 44(12):1693–1700

    PubMed  PubMed Central  Google Scholar 

  54. Yazgı S, ve Yıldız M (2009) Yutkunmanın kalp hızı değişkenliği üzerine etkisinin yok edilmesi, VI. Ulusal Tıp Bilişimi Kongresi, ss. 276-283, 12-15 Kasım, Akdeniz Üniversitesi, Antalya

  55. Ateş O, Keskin B, ve Çotuk, H.B. (2017) İş yerinde zihinsel yüklenme ve egzersizin kalp hızı değişkenliği üzerindeki etkisi. Ulusal Spor Bilimleri Dergisi 1(2):55–65

    Article  Google Scholar 

  56. Acharya UR, Kannathal N, Sing OW, Ping LY, Chua T (2004) Heart rate analysis in normal subjects of various age groups. Biomed Eng Online 36(7):1140–1148

    Google Scholar 

  57. Toledo E, Gurevitz O, Hod H, Eldar M, Akselrod S (2002) Thrombolysis in the eyes of the continuous wavelet transform. Comput Cardiol 29:657–660

    Article  Google Scholar 

  58. Bersak D, McDarby G, Augenblick N, McDarby P, McDonnell D, McDonald B, Karkun R (2001) Intelligent biofeedback using an immersive competitive environment. Designing Ubiquitous Computing Games Workshop at UbiComp, 30 September - 2 October, Atlanta, GA, USA

  59. Lin T, Omato M, Hu W, Imamiya A (2005) Do physiological data relate to traditional usability indexes? Proceedings of the 17th Australia Conference on Computer–Human Interaction: Citizens Online: Considerations for Today and the Future, pp. 1–10, 21-25 November, Canberra, Australia

  60. Shi Y, Ruiz N, Taib R, Choi EHC, Chen F (2007) Galvanic skin response (GSR) as an index of cognitive load. Extended Abstracts Proceedings of the 2007 Conference on Human Factors in Computing Systems, CHI’07, pp. 2651–2656, 28 April-3 May, San Jose, California, USA

  61. Finger B, Murphy RO (2011) Using skin conductance in judgment and decision making research. In: Schulte-Mecklenbeck M, Kuehberger A, Ranyard R (eds) A handbook of process tracing methods for decision research. Psychology Press, New York, pp 163–184

    Google Scholar 

  62. Utkutuğ, Ç.P. ve Alkibay, S. (2013) Nöropazarlama: reklam etkinliğinin psikofizyolojik tekniklerle değerlendirilmesi üzerine yapılmış araştırmalarının gözden geçirilmesi. H.Ü. İktisadi İdari Bilimler Fakültesi Dergisi 31(2):167–195

    Google Scholar 

  63. Klebba JM (1985) Physiological measures of research: a review of brain activity, electrodermal response, pupil dilation, and voice analysis methods and studies. J Curr Issues Res Advert 8(1):53–76

    Google Scholar 

  64. Gökay R, Masazade E, Aydin Ç, Barkana DE (2015) Emotional state and cognitive load analysis using features from bvp and sc sensors. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 178-183, 14-16 Sept, San Diego, CA, USA

Download references

Acknowledgements

We acknowledge the volunteers for their contribution to this study.

Funding

This study was supported by the Research Project Department of Akdeniz University, Antalya, Turkey (project number: FBA-2018-3351).

Author information

Authors and Affiliations

  1. Bergama Vocational School, Dokuz Eylül Üniversity, İzmir, Turkey

    Serdar Gündoğdu

  2. Faculty of Engineering, Department of Electrical and Electronics Engineering, Akdeniz University, Antalya, Turkey

    Ömer Halil Çolak & Övünç Polat

  3. Faculty of Medicine, Department of Neurology, Akdeniz University, Antalya, Turkey

    Ebru Apaydın Doğan

  4. Faculty of Letters, Department of Psychology, Akdeniz University, Antalya, Turkey

    Evrim Gülbetekin

Authors
  1. Serdar Gündoğdu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ömer Halil Çolak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ebru Apaydın Doğan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Evrim Gülbetekin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Övünç Polat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Serdar Gündoğdu.

Ethics declarations

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards (ethical approval date, number: Akdeniz University, Clinical Research Ethics Committee, 06/12/2017–715).

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gündoğdu, S., Çolak, Ö.H., Doğan, E.A. et al. Assessment of mental fatigue and stress on electronic sport players with data fusion. Med Biol Eng Comput 59, 1691–1707 (2021). https://doi.org/10.1007/s11517-021-02389-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-021-02389-9

Keywords

Search

Navigation