Assessing Medical Training Skills via Eye and Head Movements
May 12, 2025 Β· Declared Dead Β· π User Modeling, Adaptation, and Personalization
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Authors
Kayhan Latifzadeh, Luis A. Leiva, Klen ΔopiΔ Pucihar, MatjaΕΎ Kljun, Iztok Devetak, Lili Steblovnik
arXiv ID
2507.16819
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CV
Citations
1
Venue
User Modeling, Adaptation, and Personalization
Last Checked
4 months ago
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.
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