Pupil diameter (PD) has been suggested as a reliable parameter for identifying an individual’s emotional state. In this paper, we introduce a learning machine technique to detect and differentiate between positive and negative emotions. We presented 30 participants with positive and negative sound stimuli and recorded pupillary responses. The results showed a significant increase in pupil dilation during the processing of negative and positive sound stimuli with greater increase for negative stimuli. We also found a more sustained dilation for negative compared to positive stimuli at the end of the trial, which was utilized to differentiate between positive and negative emotions using a machine learning approach which gave an accuracy of 96.5% with sensitivity of 97.93% and specificity of 98%. The obtained results were validated using another dataset designed for a different study and which was recorded while 30 participants processed word pairs with positive and negative emotions.
Background
Augmented reality (AR) is an interactive technology that uses persuasive digital data and real-world surroundings to expand the user's reality, wherein objects are produced by various computer applications. It constitutes a novel advancement in medical care, education, and training.
Objective
The aim of this work was to assess how effective AR is in training medical students when compared to other educational methods in terms of skills, knowledge, confidence, performance time, and satisfaction.
Methods
We performed a meta-analysis on the effectiveness of AR in medical training that was constructed by using the Cochrane methodology. A web-based literature search was performed by using the Cochrane Library, Web of Science, PubMed, and Embase databases to find studies that recorded the effect of AR in medical training up to April 2021. The quality of the selected studies was assessed by following the Cochrane criteria for risk of bias evaluations.
Results
In total, 13 studies with a total of 654 participants were included in the meta-analysis. The findings showed that using AR in training can improve participants' performance time (I2=99.9%; P<.001), confidence (I2=97.7%; P=.02), and satisfaction (I2=99.8%; P=.006) more than what occurs under control conditions. Further, AR did not have any effect on the participants’ knowledge (I2=99.4%; P=.90) and skills (I2=97.5%; P=.10). The meta-regression plot shows that there has been an increase in the number of articles discussing AR over the years and that there is no publication bias in the studies used for the meta-analysis.
Conclusions
The findings of this work suggest that AR can effectively improve performance time, satisfaction, and confidence in medical training but is not very effective in areas such as knowledge and skill. Therefore, more AR technologies should be implemented in the field of medical training and education. However, to confirm these findings, more meticulous research with more participants is needed.
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