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Biometrics and classifier fusion to predict the fun-factor in video gaming
- Source :
- CIG
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- The key to the development of adaptive gameplay is the capability to monitor and predict in real time the players experience (or, herein, fun factor). To achieve this goal, we rely on biometrics and machine learning algorithms to capture a physiological signature that reflects the player's affective state during the game. In this paper, we report research and development effort into the real time monitoring of the player's level of fun during a commercially available video game session using physiological signals. The use of a triple-classifier system allows the transformation of players' physiological responses and their fluctuation into a single yet multifaceted measure of fun, using a non-linear gameplay. Our results suggest that cardiac and respiratory activities provide the best predictive power. Moreover, the level of performance reached when classifying the level of fun (70% accuracy) shows that the use of machine learning approaches with physiological measures can contribute to predicting players experience in an objective manner.
- Subjects :
- Biometrics
business.industry
Computer science
05 social sciences
Feature extraction
ComputingMilieux_PERSONALCOMPUTING
050801 communication & media studies
02 engineering and technology
Machine learning
computer.software_genre
Session (web analytics)
0508 media and communications
Factor (programming language)
0202 electrical engineering, electronic engineering, information engineering
Predictive power
Key (cryptography)
020201 artificial intelligence & image processing
State (computer science)
Artificial intelligence
business
computer
Video game
computer.programming_language
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2016 IEEE Conference on Computational Intelligence and Games (CIG)
- Accession number :
- edsair.doi...........8bb040fabd7f7ad16eef19d825a83389
- Full Text :
- https://doi.org/10.1109/cig.2016.7860418