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Presentation
Real-Time Stealth Intervention for Motor Learning Using Player Flow-State
6th IEEE International Conference on Serious Games and Applications for Health (SeGAH) (2019)
  • Ramin Tadayon, Arizona State University
  • Ashish Amresh, Arizona State University
  • Troy McDaniel, Arizona State University
  • Sethuraman Panchanathan, Arizona State University
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.
Keywords
  • real-time adaptation,
  • motor learning,
  • serious games
Publication Date
July, 2019
Location
Vienna, Austria
DOI
https://doi.org/10.1109/SeGAH.2018.8401360
Citation Information
Ramin Tadayon, Ashish Amresh, Troy McDaniel and Sethuraman Panchanathan. "Real-Time Stealth Intervention for Motor Learning Using Player Flow-State" 6th IEEE International Conference on Serious Games and Applications for Health (SeGAH) (2019)
Available at: http://works.bepress.com/ashish-amresh/34/