TY - GEN
T1 - Measuring vigilance decrement using computer vision assisted eye tracking in dynamic naturalistic environments
AU - Bodala, Indu P.
AU - Abbasi, Nida I.
AU - Sun, Yu
AU - Bezerianos, Anastasios
AU - Al-Nashash, Hasan
AU - Thakor, Nitish V.
N1 - Funding Information:
*This work was supported by the National University of Singapore for Cognitive Engineering Group at Singapore Institute for Neurotechnology (SINAPSE) under Grant R-719-001-102-232.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/13
Y1 - 2017/9/13
N2 - Eye tracking offers a practical solution for monitoring cognitive performance in real world tasks. However, eye tracking in dynamic environments is difficult due to high spatial and temporal variation of stimuli, needing further and thorough investigation. In this paper, we study the possibility of developing a novel computer vision assisted eye tracking analysis by using fixations. Eye movement data is obtained from a long duration naturalistic driving experiment. Source invariant feature transform (SIFT) algorithm was implemented using VLFeat toolbox to identify multiple areas of interest (AOIs). A new measure called 'fixation score' was defined to understand the dynamics of fixation position between the target AOI and the non target AOIs. Fixation score is maximum when the subjects focus on the target AOI and diminishes when they gaze at the non-target AOIs. Statistically significant negative correlation was found between fixation score and reaction time data (r =-0.2253 and p<0.05). This implies that with vigilance decrement, the fixation score decreases due to visual attention shifting away from the target objects resulting in an increase in the reaction time.
AB - Eye tracking offers a practical solution for monitoring cognitive performance in real world tasks. However, eye tracking in dynamic environments is difficult due to high spatial and temporal variation of stimuli, needing further and thorough investigation. In this paper, we study the possibility of developing a novel computer vision assisted eye tracking analysis by using fixations. Eye movement data is obtained from a long duration naturalistic driving experiment. Source invariant feature transform (SIFT) algorithm was implemented using VLFeat toolbox to identify multiple areas of interest (AOIs). A new measure called 'fixation score' was defined to understand the dynamics of fixation position between the target AOI and the non target AOIs. Fixation score is maximum when the subjects focus on the target AOI and diminishes when they gaze at the non-target AOIs. Statistically significant negative correlation was found between fixation score and reaction time data (r =-0.2253 and p<0.05). This implies that with vigilance decrement, the fixation score decreases due to visual attention shifting away from the target objects resulting in an increase in the reaction time.
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U2 - 10.1109/EMBC.2017.8037359
DO - 10.1109/EMBC.2017.8037359
M3 - Conference contribution
C2 - 29060401
AN - SCOPUS:85032178855
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2478
EP - 2481
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -