Revolutionizing Simulation Education With Smart Glass Technology

Frederick, Julie; Gelderen, Stacey Van

doi:10.1016/j.ecns.2020.12.007

Cited by 17 publications

(5 citation statements)

References 5 publications

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“…Te construction of an online teaching environment can be personalized according to the teaching purpose, and compared with the traditional ofine teaching mode, online teaching can obtain a better teaching presentation with the help of teaching environment construction. Te advantage of online teaching is that it can be done with simple electronic devices for both students and teachers, which can greatly save the cost and time for teachers and students to move around [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Neural Network for the Online Course Quality Assessment System

Bai

Yong-guo

2022

Mathematical Problems in Engineering

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Under the influence of COVID-19, online office and online education has ushered in a golden period of development. The teaching quality of online education has been a controversial issue. Our study takes online course teaching quality assessment as the starting point, explores the influencing factors of online course quality assessment with online courses as the research object, and analyzes the latest research proposal for an online course quality index. To make the online course quality assessment more intelligent, we propose an online course quality assessment method based on a fuzzy neural network. The method uses fuzzy rules as the baseline and adds a TSK perception mechanism to expand the perception domain of the fuzzy neural network and improve the course quality index prediction accuracy. At the input side of the fuzzy neural network, we preclassify the online course data into four parts, and each part of the data represents a different assessment domain. Due to the large data cost, we expanded the collective amount of data using data augmentation methods. In addition, we parse the structure of the fuzzy neural network hierarchy and introduce the construction and role of the TSK perception mechanism in the fuzzy rules. An optimal learning strategy is proposed in the fuzzy neural network training. Finally, in the experimental session, we verify the effectiveness of data augmentation and explore the distribution of course quality assessment weights. In the comparison of the model prediction results with the actual assessment results, our method achieves an excellent matching rate, which proves the high efficiency of our method in the online course quality assessment system.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Neural Network for the Online Course Quality Assessment System

Bai

Yong-guo

2022

Mathematical Problems in Engineering

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“…Over the last few years, numerous studies have investigated the use of smart glasses (with [33][34][35][36][37][38][39][40][41][42][43][44][45][46] and without augmented reality [5][6][7][8][9][10][11][12][13][14][15][16]) in healthcare. Given that our study participants lived in rural/remote areas with an expected digital divide issue [47][48][49], the more advanced use of smart glasses, i.e., augmented reality with superimposing virtual objects on video of real world was not used for our XROs to obtain remote support in their radiography practice from their supervising radiographers [33][34][35][36][37][38][39][40][41][42][43][44][45][46]. Among the recent studies on the use of smart glasses without any augmented reality [5][6][7]...…”

Section: Discussionmentioning

confidence: 99%

Use of Smart Glasses (Assisted Reality) for Western Australian X-ray Operators’ Continuing Professional Development: A Pilot Study

Ng,

Baldock,

Newman

2024

Healthcare

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Previous studies have explored use of smart glasses in telemedicine, but no study has investigated its use in teleradiography. The purpose of this study was to implement a six-month pilot program for Western Australian X-ray operators (XROs) to use smart glasses to obtain assisted reality support in their radiography practice from their supervising radiographers, and evaluate its effectiveness in terms of XROs’ competence improvement and equipment usability. Pretest–posttest design with evaluation of the XROs’ competence (including their X-ray image quality) and smart glasses usability by XROs in two remote centers and their supervising radiographers from two sites before and after the program using four questionnaire sets and X-ray image quality review was employed in this experimental study. Paired t-test was used for comparing mean values of the pre- and post-intervention pairs of 11-point scale questionnaire and image quality review items to determine any XROs’ radiography competence improvements. Content analysis was used to analyze open questions about the equipment usability. Our study’s findings based on 13 participants (11 XROs and 2 supervising radiographers) and 2053 X-ray images show that the assisted reality support helped to improve the XROs’ radiography competence (specifically X-ray image quality), with mean post-intervention competence values of 6.16–7.39 (out of 10) and statistical significances (p < 0.001–0.05), and the equipment was considered effective for this purpose but not easy to use.

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“…During clinical surgery, surgeons need to monitor and view multiple visualization instruments, including endoscopes, laparoscopes, cardiac monitors, and others [1]. The surgeons always observe the display data and images by means of rotating their head, as far as to result in torso distortion due to of maintaining a fixed position for long periods of time [2]. Data and images are transmitted to the glasses through WIFI when using smart glasses for surgery [3].…”

Section: Introductionmentioning

confidence: 99%

Technology Behavior Model—Beyond Your Sight with Extended Reality in Surgery

Gong

JosephNg

2022

ASI

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Extended Reality Smart Glasses is a new pattern that uses extended reality technology to present a visual environment that combines the physical and virtual worlds. However, the surgical technique using Smart Glasses implementation is still unknown, to the infancy in clinical surgery, derived to the limits of existing technology. This study researched the acceptability and possibility of XRSG for medical experts. It combines human seen behavioral control with information technology research to construct a new “Extended Reality Technology Behavior Model” using method Technology Acceptance Model and Theory of Planned Behavior. To improve the accuracy of the study, statistical analysis, exploratory analysis, and cross-sectional research triangulation were used to collect data in five hospitals in Malaysia using a convenience sampling method and a questionnaire on behavioral influences. From the collected data, PLS-SEM analysis was used to reflect the relationship between variables. The strong positive results suggest that using XRSG by medical experts helps to improve the composition, interactivity, standardization, and clarity of medical images, resulting in increased efficiency and reduced procedure time and felt the usefulness and ease of use of XRSG through their behavior, providing a basis for technology acceptance in surgery.

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Revolutionizing Simulation Education With Smart Glass Technology

Cited by 17 publications

References 5 publications

Fuzzy Neural Network for the Online Course Quality Assessment System

Fuzzy Neural Network for the Online Course Quality Assessment System

Use of Smart Glasses (Assisted Reality) for Western Australian X-ray Operators’ Continuing Professional Development: A Pilot Study

Technology Behavior Model—Beyond Your Sight with Extended Reality in Surgery

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