Real-time stealth intervention for motor learning using player flow-state

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 assess...

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Bibliographic Details
Main Authors: Tadayon, Ramin, Amresh, Ashish, McDaniel, Troy, Panchanathan, Sethuraman
Format: Conference Proceeding
Language:English
Subjects:
affective design
autonomous training
Cameras
flow-state evaluation
Games
Measurement
Physiology
Real-time systems
serious games
stealth adaptation
Task analysis
Training
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Summary: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.
ISSN:2573-3060
DOI:10.1109/SeGAH.2018.8401360