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Utilization of Neurophysiological Data to Classify Player Immersion to Distract from Pain

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HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12425))

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Abstract

Painful experiences during clinical procedures can have detrimental effects on the physical and mental health of a patient. Current pain reduction methods can be effective in reducing pain, however these methods are not without fault. Active distraction via computer games have been proven to effectively reduce the experience of pain. However, the potential of this distraction to effectively alleviate pain is dependent on players’ engagement with the game, which is determined by the difficulty of the game and the skill of the player. This paper aims to model and classify immersion through increasingly difficult levels of game play, in the presence of pain, using functional Near Infrared Spectroscopy (fNIRS) and heart rate data. Twenty people participated in a study wherein fNIRS data (4 channels located at the prefrontal cortex, four channels located at the somatosensory cortex) and heart rate data were collected whilst participants were subjected to experimental pain, via the Cold Pressor Test (CPT). Participants played a computer game at varying difficultly levels as a distraction. Data were then pre-processed using an Acceleration Based Movement Artefact Reduction Algorithm (AMARA) and Correlation Based Signal Improvement (CBSI). Classification was subsequently undertaken using Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Recursive Partitioning (rPart). The results demonstrate a maximum accuracy of 99.2% for the binary detection of immersion in the presence of pain.

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Acknowledgments

The authors would like to thank Onteca Inc., who provided an adapted version of their title Space Ribbon for use in this study. The authors would also like to thank all of the participants for agreeing to take part in the study.

Author information

Authors and Affiliations

  1. Department of Computer Science, Liverpool John Moores University, Liverpool, L3 3AF, UK

    Kellyann Stamp

  2. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, 4072, Australia

    Chelsea Dobbins

  3. School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, L3 3AF, UK

    Stephen Fairclough

Authors
  1. Kellyann Stamp
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  2. Chelsea Dobbins
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  3. Stephen Fairclough
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Corresponding author

Correspondence to Kellyann Stamp .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece

    Constantine Stephanidis

  2. Coventry University, Coventry, UK

    Don Harris

  3. Cranfield University, Cranfield, UK

    Wen-Chin Li

  4. Soar Technology Inc., Orlando, FL, USA

    Dylan D. Schmorrow

  5. Design Interactive, Inc., Orlando, FL, USA

    Cali M. Fidopiastis

  6. Cyprus University of Technology, Limassol, Cyprus

    Panayiotis Zaphiris

  7. Cyprus University of Technology, Limassol, Cyprus

    Andri Ioannou

  8. DePaul University, Chicago, IL, USA

    Xiaowen Fang

  9. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  10. Fraunhofer FKIE, Wachtberg, Germany

    Jessica Schwarz

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Stamp, K., Dobbins, C., Fairclough, S. (2020). Utilization of Neurophysiological Data to Classify Player Immersion to Distract from Pain. In: Stephanidis, C., et al. HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games. HCII 2020. Lecture Notes in Computer Science(), vol 12425. Springer, Cham. https://doi.org/10.1007/978-3-030-60128-7_55

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  • DOI: https://doi.org/10.1007/978-3-030-60128-7_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60127-0

  • Online ISBN: 978-3-030-60128-7

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