Abstract

We present a novel approach to real-time adaptation in serious games for at-home motor learning. Our approach assesses and responds to the 'flow-state' of players by tracking and classifying facial emotions in real-time using the Kinect camera. Three different approaches for stealth assessment and adaptation using performance and flow-state data are defined, along with a case-study evaluation of these approaches based on their effectiveness at maintaining positive affective interaction in a subject.

Original languageEnglish (US)
Title of host publication2018 IEEE 6th International Conference on Serious Games and Applications for Health, SeGAH 2018
EditorsJoao L. Vilaca, Duarte Duque, Nuno Rodrigues, Nuno Dias, Thomas Grechenig
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
ISBN (Electronic)9781538662984
DOIs
StatePublished - Jun 29 2018
Event6th IEEE International Conference on Serious Games and Applications for Health, SeGAH 2018 - Vienna, Austria
Duration: May 16 2018May 18 2018

Other

Other6th IEEE International Conference on Serious Games and Applications for Health, SeGAH 2018
CountryAustria
CityVienna
Period5/16/185/18/18

Keywords

  • affective design
  • autonomous training
  • flow-state evaluation
  • serious games
  • stealth adaptation

ASJC Scopus subject areas

  • Health(social science)
  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Tadayon, R., Amresh, A., McDaniel, T., & Panchanathan, S. (2018). Real-time stealth intervention for motor learning using player flow-state. In J. L. Vilaca, D. Duque, N. Rodrigues, N. Dias, & T. Grechenig (Eds.), 2018 IEEE 6th International Conference on Serious Games and Applications for Health, SeGAH 2018 (pp. 1-8). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SeGAH.2018.8401360