Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures : Linking Hand Movement, Proximity, and Gaze Data
Heilala, V., Lehesvuori, S., Hämäläinen, R., & Kärkkäinen, T. (2024). Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures : Linking Hand Movement, Proximity, and Gaze Data. In SAC '24 : Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (pp. 3-10). ACM. https://doi.org/10.1145/3605098.3635929
Päivämäärä
2024Tekijänoikeudet
© 2024 Copyright held by the owner/author(s).
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.
Julkaisija
ACMEmojulkaisun ISBN
979-8-4007-0243-3Konferenssi
ACM/SIGAPP Symposium on Applied ComputingKuuluu julkaisuun
SAC '24 : Proceedings of the 39th ACM/SIGAPP Symposium on Applied ComputingAsiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/215926857
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Profilointi, SALisätietoja rahoituksesta
The work was supported by the Research Council of Finland under Grant number 353325.Lisenssi
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