Abstract:Nurses welcome innovative training and assessment methods to effectively interpret physiological vital signs. The objective is to determine if eye-tracking technology can be used to develop biometrics for automatically predict the performance of nurses whilst they interact with computer-based simulations. 47 nurses were recruited, 36 nursing students (training group) and 11 coronary care nurses (qualified group). Each nurse interpreted five simulated vital signs scenarios whilst 'thinking-aloud'. The participa… Show more
“…A fixation is when the participant is fixating on single location using their fovea vision, and a saccade can be a vector between two fixations or rapid movements between fixations [ 24 ]. The following eye tracking metrics were calculated which have been used in similar studies [ 25 – 28 ]: Fig. 1 Main image: Mentice VIST-Lab simulator, with the four AOIs identified.…”
IntroductionUnobtrusive metrics that can auto-assess performance during clinical procedures are of value. Three approaches to deriving wearable technology-based metrics are explored: (1) eye tracking, (2) psychophysiological measurements [e.g. electrodermal activity (EDA)] and (3) arm and hand movement via accelerometry. We also measure attentional capacity by tasking the operator with an additional task to track an unrelated object during the procedure.MethodsTwo aspects of performance are measured: (1) using eye gaze and psychophysiology metrics and (2) measuring attentional capacity via an additional unrelated task (to monitor a visual stimulus/playing cards). The aim was to identify metrics that can be used to automatically discriminate between levels of performance or at least between novices and experts. The study was conducted using two groups: (1) novice operators and (2) expert operators. Both groups made two attempts at a coronary angiography procedure using a full-physics virtual reality simulator. Participants wore eye tracking glasses and an E4 wearable wristband. Areas of interest were defined to track visual attention on display screens, including: (1) X-ray, (2) vital signs, (3) instruments and (4) the stimulus screen (for measuring attentional capacity).ResultsExperts provided greater dwell time (63% vs 42%, p = 0.03) and fixations (50% vs 34%, p = 0.04) on display screens. They also provided greater dwell time (11% vs 5%, p = 0.006) and fixations (9% vs 4%, p = 0.007) when selecting instruments. The experts’ performance for tracking the unrelated object during the visual stimulus task negatively correlated with total errors (r = − 0.95, p = 0.0009). Experts also had a higher standard deviation of EDA (2.52 µS vs 0.89 µS, p = 0.04).ConclusionsEye tracking metrics may help discriminate between a novice and expert operator, by showing that experts maintain greater visual attention on the display screens. In addition, the visual stimulus study shows that an unrelated task can measure attentional capacity.Trial registration This work is registered through clinicaltrials.gov, a service of the U.S. National Health Institute, and is identified by the trial reference: NCT02928796.
“…A fixation is when the participant is fixating on single location using their fovea vision, and a saccade can be a vector between two fixations or rapid movements between fixations [ 24 ]. The following eye tracking metrics were calculated which have been used in similar studies [ 25 – 28 ]: Fig. 1 Main image: Mentice VIST-Lab simulator, with the four AOIs identified.…”
IntroductionUnobtrusive metrics that can auto-assess performance during clinical procedures are of value. Three approaches to deriving wearable technology-based metrics are explored: (1) eye tracking, (2) psychophysiological measurements [e.g. electrodermal activity (EDA)] and (3) arm and hand movement via accelerometry. We also measure attentional capacity by tasking the operator with an additional task to track an unrelated object during the procedure.MethodsTwo aspects of performance are measured: (1) using eye gaze and psychophysiology metrics and (2) measuring attentional capacity via an additional unrelated task (to monitor a visual stimulus/playing cards). The aim was to identify metrics that can be used to automatically discriminate between levels of performance or at least between novices and experts. The study was conducted using two groups: (1) novice operators and (2) expert operators. Both groups made two attempts at a coronary angiography procedure using a full-physics virtual reality simulator. Participants wore eye tracking glasses and an E4 wearable wristband. Areas of interest were defined to track visual attention on display screens, including: (1) X-ray, (2) vital signs, (3) instruments and (4) the stimulus screen (for measuring attentional capacity).ResultsExperts provided greater dwell time (63% vs 42%, p = 0.03) and fixations (50% vs 34%, p = 0.04) on display screens. They also provided greater dwell time (11% vs 5%, p = 0.006) and fixations (9% vs 4%, p = 0.007) when selecting instruments. The experts’ performance for tracking the unrelated object during the visual stimulus task negatively correlated with total errors (r = − 0.95, p = 0.0009). Experts also had a higher standard deviation of EDA (2.52 µS vs 0.89 µS, p = 0.04).ConclusionsEye tracking metrics may help discriminate between a novice and expert operator, by showing that experts maintain greater visual attention on the display screens. In addition, the visual stimulus study shows that an unrelated task can measure attentional capacity.Trial registration This work is registered through clinicaltrials.gov, a service of the U.S. National Health Institute, and is identified by the trial reference: NCT02928796.
“…Cohen et al [21] advocated that the inter-subject correlation of the electroencephalogram can be used to measure students' neural engagement in video watching, therefore would be predictive of their academic performances. Currie et al [22] highlight the use of eye movement as a mean of gauging student engagement for predicting academic outcome. Many recent studies [23]- [25] reveal the need to use engagement data in the predictive model development.…”
Timeliness was a missing factor in many studies on Academic Performance Prediction to identify at-risk students. This study embarked on a search to evaluate the feasibility of predicting students’ performance based on heart rate data collected during classes. This dimension of data was collected in the first four weeks after semester commencement to validate accurate prediction that will enable educationists to introduce remedial intervention to at-risk students. Another aim of this study is to determine the best threshold values for the different types of heart rate fluctuations that can be used in predicting academic achievements. The threshold values were tested further to verify whether the prediction model for individual course or combined courses was more accurate. Results revealed that heart rate data alone can achieve a maximum prediction accuracy of 88% and recall of 100%. Threshold values calculated in derived heart rate fluctuation types produces the best results. Prediction models for individual courses outperform the model using average threshold values of all courses.
“…Eye-tracking technology is commonly used in human-computer interaction (HCI) research to measure visual engagement. Eye-tracking has been used in the fields of medical research and healthcare for training, simulation and assessment of clinical decision-making processes (Bond et al, 2014;Currie et al, 2017;McLaughlin et al, 2017), but there has been little research conducted on the use of eye-tracking on medical device design. Using eye-tracking analysis to investigate the user interfaces of medical devices will enable objective quantitative measurements of the variability of the AED user interfaces, as well as insight into their complexity.…”
Sudden cardiac arrest (SCA) is a leading cause of death worldwide. It is treated with a defibrillating shock from an automated external defibrillator (AED) and cardiopulmonary resuscitation. AEDs are commonly used by minimally-trained rescuers to treat patients of SCA, and need to be designed to be intuitive to use. However, there are no guidelines or recommendations for the user interface design of these devices, and as such, there are obvious inconsistencies in the aesthetics and functionality, of AEDs currently on the market. This study was designed to assess the variability and determine the visual hierarchy of a range of public access AEDs. This was done by analysing the user's eye gaze behaviour in 400 AED sense-making sessions (10 AEDs * 40 subjects/users).
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