naa a22 4500 510776930 CHVBK 20180411083233.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.3758/s13428-012-0294-x doi (NATIONALLICENCE)springer-10.3758/s13428-012-0294-x Capturing and evaluating blinks from video-based eyetrackers [Elektronische Daten] [Xianta Jiang, Geoffrey Tien, Da Huang, Bin Zheng, M. Atkins] Blinks are related to several emotional states, and the present report describes a simple, reliable way to measure blinks from the video stream of an eye obtained during eyetracking, where the source of the eye video is a video camera attached to a head-mounted eyetracker. Computer vision techniques are employed to determine the moments that a blink starts and ends, for the purpose of calculating blink frequency and duration. The video is first processed to show blocks of eyelid and pupil movements, and is then analyzed for blink starts and ends. The moment of a blink start is reported when the eyelid starts to move quickly, exceeding a predetermined threshold. The end of a blink arises when the pupil size increases by less than a separate threshold. We observed several different blink patterns from different subjects, and our algorithm was designed to work for all of these patterns. We evaluated our algorithm by manually measuring the true blinks of five different subjects while they were eyetracked. To test the sensitivity and specificity of the algorithm, we employed a series of threshold values to plot the receiver operating characteristic curves. Using the best thresholds, we achieved excellent sensitivity (>90%) and specificity (>99%) over the five subjects. Potential applications of this research include real-time, nonintrusive, continuous and automated measurements of mental workload and other emotional states related to blink rates and durations. Psychonomic Society, Inc., 2012 Blinks nationallicence Eyetracking nationallicence Computer vision nationallicence Video processing nationallicence Image-guided tasks nationallicence Mental workload nationallicence Jiang Xianta College of Computer Science and Technology, Zhejiang University, Hangzhou, China aut Tien Geoffrey School of Computing Science, Simon Fraser University, V5A 1S6, Burnaby, BC, Canada aut Huang Da School of Computing Science, Simon Fraser University, V5A 1S6, Burnaby, BC, Canada aut Zheng Bin Department of Surgery, University of Alberta, Edmonton, AB, Canada aut Atkins M. School of Computing Science, Simon Fraser University, V5A 1S6, Burnaby, BC, Canada aut Behavior Research Methods Springer US; http://www.springer-ny.com 45/3(2013-09-01), 656-663 45:3<656 2013 45 13428 https://doi.org/10.3758/s13428-012-0294-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.3758/s13428-012-0294-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Jiang Xianta College of Computer Science and Technology, Zhejiang University, Hangzhou, China aut NATIONALLICENCE 700 1- Tien Geoffrey School of Computing Science, Simon Fraser University, V5A 1S6, Burnaby, BC, Canada aut NATIONALLICENCE 700 1- Huang Da School of Computing Science, Simon Fraser University, V5A 1S6, Burnaby, BC, Canada aut NATIONALLICENCE 700 1- Zheng Bin Department of Surgery, University of Alberta, Edmonton, AB, Canada aut NATIONALLICENCE 700 1- Atkins M. School of Computing Science, Simon Fraser University, V5A 1S6, Burnaby, BC, Canada aut NATIONALLICENCE 773 0- Behavior Research Methods Springer US; http://www.springer-ny.com 45/3(2013-09-01), 656-663 45:3<656 2013 45 13428 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer