Real-Time Stealth Intervention for Motor Learning Using Player Flow-State6th IEEE International Conference on Serious Games and Applications for Health (SeGAH) (2019)
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.
- real-time adaptation,
- motor learning,
- serious games
Publication DateJuly, 2019
Citation InformationRamin 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://0-works.bepress.com.library.simmons.edu/ashish-amresh/34/