Eye-tracking glasses in face-to-face interactions: Manual versus automated assessment of areas-of-interest

Autor: K Van Beeck, Johannes A. Romijn, Timothy Callemein, Ellen M. A. Smets, Chiara Jongerius, Toon Goedemé, Marij A. Hillen
Přispěvatelé: Graduate School, Medical Psychology, APH - Personalized Medicine, APH - Quality of Care, Amsterdam Gastroenterology Endocrinology Metabolism, Endocrinology, APH - Methodology
Rok vydání: 2021
Předmět:
genetic structures
Eye Movements
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Experimental and Cognitive Psychology
computer.software_genre
Computer vision algorithm
050105 experimental psychology
Article
03 medical and health sciences
Face-to-face
0302 clinical medicine
Person re-identification
Arts and Humanities (miscellaneous)
Developmental and Educational Psychology
Computer vision algorithms
Humans
Gaze behaviour
0501 psychology and cognitive sciences
Eye-Tracking Technology
Pose
General Psychology
Vision
Ocular
Pose estimation
business.industry
05 social sciences
Reproducibility of Results
Gaze
eye diseases
Inter-rater reliability
Eye-tracking glasses
Manual annotation
Eye tracking
Areas-of-interest
Psychology (miscellaneous)
Artificial intelligence
business
computer
030217 neurology & neurosurgery
Natural language processing
Algorithms
Zdroj: Behavior Research Methods
Behavior research methods, 53(5), 2037-2048. Springer Nature
ISSN: 1554-3528
1554-351X
Popis: The assessment of gaze behaviour is essential for understanding the psychology of communication. Mobile eye-tracking glasses are useful to measure gaze behaviour during dynamic interactions. Eye-tracking data can be analysed by using manually annotated areas-of-interest. Computer vision algorithms may alternatively be used to reduce the amount of manual effort, but also the subjectivity and complexity of these analyses. Using additional re-identification (Re-ID) algorithms, different participants in the interaction can be distinguished. The aim of this study was to compare the results of manual annotation of mobile eye-tracking data with the results of a computer vision algorithm. We selected the first minute of seven randomly selected eye-tracking videos of consultations between physicians and patients in a Dutch Internal Medicine out-patient clinic. Three human annotators and a computer vision algorithm annotated mobile eye-tracking data, after which interrater reliability was assessed between the areas-of-interest annotated by the annotators and the computer vision algorithm. Additionally, we explored interrater reliability when using lengthy videos and different area-of-interest shapes. In total, we analysed more than 65 min of eye-tracking videos manually and with the algorithm. Overall, the absolute normalized difference between the manual and the algorithm annotations of face-gaze was less than 2%. Our results show high interrater agreements between human annotators and the algorithm with Cohen’s kappa ranging from 0.85 to 0.98. We conclude that computer vision algorithms produce comparable results to those of human annotators. Analyses by the algorithm are not subject to annotator fatigue or subjectivity and can therefore advance eye-tracking analyses.
Databáze: OpenAIRE
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