Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures : Linking Hand Movement, Proximity, and Gaze Data
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.
Main Authors
Format
Conferences
Conference paper
Published
2024
Subjects
Publication in research information system
Publisher
ACM
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202405273957Use this for linking
Parent publication ISBN
979-8-4007-0243-3
Review status
Peer reviewed
DOI
https://doi.org/10.1145/3605098.3635929
Conference
ACM/SIGAPP Symposium on Applied Computing
Language
English
Is part of publication
SAC '24 : Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing
Citation
- 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
License
Funder(s)
Research Council of Finland
Funding program(s)
Research profiles, AoF
Profilointi, SA
Additional information about funding
The work was supported by the Research Council of Finland under Grant number 353325.
Copyright© 2024 Copyright held by the owner/author(s).