University of Houston

Department of Computer Science

In Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy

Chang H. Yun

Will defend his dissertation

Abstract

Designing well-adjusted levels of game difficulty to maximize the users’ gaming experiences is one of the major challenges in the game development process.   A successful design substantially increases the probability that the players will retain their interests in the game for several initial game stages.  However, the time spent and the efforts made may cause the players to modify their expectations, resulting in desire to adjust the difficulty level as the game continues to progress.  We introduce a novel methodology that improves the individual users’ gaming experiences by automatically adjusting the game difficulty continuously, based on the users’ physiological feedbacks.  The physiological data of the player – blood flow in the supraorbital region – is continuously monitored using StressCam, a contact-free, real-time, thermal image-based monitoring and analysis system.  This stress level is utilized in adjusting the game difficulty to satisfy the player throughout the game.

The usability evaluations measure the successful gamer experience improvement by analyzing two major factors:  (1) the interaction of the users’ physiological state and the level of difficulty experienced and (2) the validity of the performance and the in-game subjective feedback readings along with the post-game evaluations.  The results show successful real-time automatic game difficulty adjustments based on corresponding physiological feedbacks from the gamers.  Furthermore, the results also demonstrate an increase in gamer performances as well as improvements in subjective ratings of perceptions of the game difficulty and the game entertainment values.  We conclude that our user-centric approach successfully adjusted game difficulty automatically to accommodate the individual gamers and, consequently, improved gaming experience of the gamers who had individually unique gaming expertise or preference. 

This thesis is based on the joint research with Dr. Dvijesh Shastri who is a member of Dr. Ioannis Pavlidis’ Computation Physiology Lab (CPL) group.