Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures: Linking Hand Movement, Proximity, and Gaze Data
December 08, 2023 Β· Declared Dead Β· π ACM Symposium on Applied Computing
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Authors
Ville Heilala, Sami Lehesvuori, Raija HΓ€mΓ€lΓ€inen, Tommi KΓ€rkkΓ€inen
arXiv ID
2312.05368
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.CY,
cs.HC,
cs.LG
Citations
2
Venue
ACM Symposium on Applied Computing
Last Checked
4 months ago
Abstract
This study employed multimodal learning analytics (MMLA) to analyze behavioral dynamics during the ABCDE procedure in nursing education, focusing on gaze entropy, hand movement velocities, and proximity measures. Utilizing accelerometers and eye-tracking techniques, behaviorgrams were generated to depict various procedural phases. Results identified four primary phases characterized by distinct patterns of visual attention, hand movements, and proximity to the patient or instruments. The findings suggest that MMLA can offer valuable insights into procedural competence in medical education. This research underscores the potential of MMLA to provide detailed, objective evaluations of clinical procedures and their inherent complexities.
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