Developing an AI-driven multimodal approach to visualising resilient team performance: joint attentional engagement with gaze and speech in simulated emergency scenarios
Although the accuracy of automatic annotation (approximately. 40%-60%) should be improved before applying it to medical training, the research method could provide preliminary insight into elements of good team performance.
Authors
- Frank Coffey (ORCID: https://orcid.org/0000-0002-2096-6895)
- Stephen Timmons (ORCID: https://orcid.org/0000-0002-3731-1350)
- Takeru Abe (ORCID: https://orcid.org/0000-0003-3496-1953)
- Miharu Fuyuno
- Kazuya Bise
- Hitoshi Sato
- Atsushi Miyazaki
- Andrew K Mackenzie
- Kyota Nakamura
- Takeshi Saitoh
- Keiko Tsuchiya
Institutions
- Fukushima Medical University (JP)
- Nottingham University Hospitals NHS Trust (GB)
- Kyushu University (JP)
- University of Nottingham (GB)
- Kyushu Institute of Technology (JP)
- Yokohama City University Medical Center (JP)
- Osaka University Hospital (JP)
- Nottingham Trent University (GB)
- Yokohama City University (JP)
Publication Details
- Journal
- BMJ Open Quality
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1136/bmjoq-2025-003658
- Primary Topic
- Human-Automation Interaction and Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science