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https://doi.org/10.1145/369968...
Article . 2025 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2025
License: CC BY
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Conference object . 2025
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Preprint . 2025
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Assessing Medical Training Skills via Eye and Head Movements

Authors: Kayhan Latifzadeh; Luis A. Leiva; Klen Copic Pucihar; Matjaz Kljun; Iztok Devetak; Lili Steblovnik;

Assessing Medical Training Skills via Eye and Head Movements

Abstract

We examined eye and head movements to gain insights into skill development in clinical settings. A total of 24 practitioners participated in simulated baby delivery training sessions. We calculated key metrics, including pupillary response rate, fixation duration, or angular velocity. Our findings indicate that eye and head tracking can effectively differentiate between trained and untrained practitioners, particularly during labor tasks. For example, head-related features achieved an F1 score of 0.85 and AUC of 0.86, whereas pupil-related features achieved F1 score of 0.77 and AUC of 0.85. The results lay the groundwork for computational models that support implicit skill assessment and training in clinical settings by using commodity eye-tracking glasses as a complementary device to more traditional evaluation methods such as subjective scores.

Keywords

Human-Computer Interaction, FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Vision and Pattern Recognition, Human-Computer Interaction (cs.HC)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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