Eye Tracking the Visual Attention of Nurses Interpreting Simulated Vital Signs Scenarios: Mining Metrics to Discriminate Between Performance Level
- Submitting institution
-
University of Ulster
- Unit of assessment
- 12 - Engineering
- Output identifier
- 76399649
- Type
- D - Journal article
- DOI
-
10.1109/THMS.2017.2754880
- Title of journal
- IEEE Transactions on Human-Machine Systems
- Article number
- -
- First page
- 113
- Volume
- 48
- Issue
- 2
- ISSN
- 2168-2291
- Open access status
- Not compliant
- Month of publication
- October
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
A - Healthcare Sensor Systems
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work employed the novel use of eye tracking technology to gain an understanding of the actions of human operators of medical device technology. Significantly, it was shown that eye-tracking can predict performance of trainee and qualified nurses in the coronary care setting. More broadly this has provided significant insight into medical device UX and human factors issues relating to automation bias. The team has since applied this knowledge in the development of UX aspects of portable defibrillator technology – working alongside Heartsine/Stryker as part of the £3.7m Biodevices Rapid Prototyping Laboratory.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
