  User engagement modeling for manipulating actions in vision-based interfaces
is one of the most important case studies of user mental state detection. In a
Virtual Reality environment that employs camera sensors to recognize human
activities, we have to know when user intends to perform an action and when
not. Without a proper algorithm for recognizing engagement status, any kind of
activities could be interpreted as manipulating actions, called "Midas Touch"
problem. Baseline approach for solving this problem is activating gesture
recognition system using some focus gestures such as waiving or raising hand.
However, a desirable natural user interface should be able to understand user's
mental status automatically. In this paper, a novel multi-modal model for
engagement detection, DAIA, is presented. using DAIA, the spectrum of mental
status for performing an action is quantized in a finite number of engagement
states. For this purpose, a Finite State Transducer (FST) is designed. This
engagement framework shows how to integrate multi-modal information from user
biometric data streams such as 2D and 3D imaging. FST is employed to make the
state transition smoothly using combination of several boolean expressions. Our
FST true detection rate is 92.3% in total for four different states. Results
also show FST can segment user hand gestures more robustly.
