Estimation of behavioral user state based on eye gaze and head pose—application in an e-learning environment

A Game-Based Corpus for Analysing the Interplay between Game Context and Player Experience

“…A series of head movement parameters have been extracted [1] (please note that these parameters are not part of the data included in the corpus, but rather, features extracted from the video recordings). More specifically, the parameters extracted for each player were: Horizontal H h and Vertical V h components of head pose, the horizontal component of eye gaze H e , head roll H r and the fraction of the inter-ocular distance with regards to a frame where the algorithm starts.…”

“…Although physiological measurement devices have been used extensively within the affective computing research for emotion recognition [17] and despite the great efforts that have been devoted to make these devices wearable, their use in commercial computer games is still limited. Facial expressions [7] and head movements [1] are rich non intrusive sources of information regarding the issue of capturing the emotional or behavioral state of a person while interacting with a machine or undertaking certain tasks. A lot of work has been done in recent bibliography for modeling such cues in a variety of environments.…”

“…A lot of work has been done in recent bibliography for modeling such cues in a variety of environments. For example, Asteriadis et al in [1] investigate the issue of using head movements, as well as gaze patterns in order to achieve a mapping between these and a child's state towards learning material, aiming at the adaptation of the learning process to the child's needs. Smith et al [22] propose a scheme for mapping gaze and eye blinks to driver state, in the context of driving conditions, utilizing Finite State Automata.…”

Context Analysis for Situation Assessment in Automotive Applications

“…Various approaches can be categorized, according to the different proposed methodologies, by pointing out the condition of usage, the assumptions and the obtainable performances. In (Asteriadis et al, 2009) gaze is estimated by analyzing the motion of some relevant features in the eyes and mouth area. This last method, however, does not take into account possible illumination changes since it is designed for indoor Human-Computer Interaction applications.…”

User modeling via gesture and head pose expressivity features

“…The above system can perform real time and the rules employed for re-initialization render it robust to sudden changes in lighting and spontaneous. Further details of the system are given in [1].…”