Third International Conference on Advances in Computing, Electronics and Electrical Technology - CEET 2015
Author(s) : JAMES MOUNTSTEPHENS , TOH CHIA MING
Keywords-component; formatting; style; styling; insert (key words) Knowledge of mental fatigue has been a mystery to the field of psychology and neuroscience as we still don’t know the exact mechanism of mental fatigue. Mental fatigue can be caused by prolonged use of focus for any sustained attention task. When fatigued, human performance of task is affected and may cause discomfort and some tasks may have safety risk due to decreased performance. An underlying mechanic of attention has been identified and categorized into bottom-up mode and top-down mode where a fight between these 2 modes may cause Directed Attention Fatigue (DAF). DAF would appear due to the inability to inhibit cues of bottom-up mode in favor of top-down ones therefore leading to a drop in performance. An eye-tracking based research would be to verify this. This paper attempts to study this by using existing visual attention computational models together with human eye gaze data. By having human participants to perform a customized sustained attention task, their performance and eye gaze details can be recorded. The obtained data can be modeled and integrated into existing visual attention model to reproduce the results of human data. In order to achieve that, the model had to have the capability to reproduce human perception, recognition and also decision making. The model chosen to as the base of this project would be the Itti and Koch Model (IKM) and heavily modified to suit the purpose of this project. The human data yielded insights into the gaze characteristics of DAF and was used to test the predictions of the overall model, with encouraging results for the proposed DAF.