Augmented Reality Models Research Articles

Caregiver Preferences for Three-Dimensional Printed or Augmented Reality Craniosynostosis Skull Models: A Cross-Sectional Survey.

Recent advances in three-dimensional (3D) printing and augmented reality (AR) have expanded anatomical modeling possibilities for caregiver craniosynostosis education. The purpose of this study is to characterize caregiver preferences regarding these visual models and determine the impact of these models on caregiver understanding of craniosynostosis. The authors constructed 3D-printed and AR craniosynostosis models, which were randomly presented in a cross-sectional survey. Caregivers rated each model's utility in learning about craniosynostosis, learning about skull anatomy, viewing an abnormal head shape, easing anxiety, and increasing trust in the surgeon in comparison to a two-dimensional (2D) diagram. Furthermore, caregivers were asked to identify the fused suture on each model and indicate their preference for generic versus patient-specific models. A total of 412 craniosynostosis caregivers completed the survey (mean age 33 years, 56% Caucasian, 51% male). Caregivers preferred interactive, patient-specific 3D-printed or AR models over 2D diagrams (mean score difference 3D-printed to 2D: 0.16, P < 0.05; mean score difference AR to 2D: 0.17, P < 0.01) for learning about craniosynostosis, with no significant difference in preference between 3D-printed and AR models. Caregiver detection accuracy of the fused suture on the sagittal model was 19% higher with the 3D-printed model than with the AR model (P < 0.05) and 17% higher with the 3D-printed model than with the 2D diagram (P < 0.05). Our findings indicate that craniosynostosis caregivers prefer 3D-printed or AR models over 2D diagrams in learning about craniosynostosis. Future craniosynostosis skull models with increased user interactivity and patient-specific components can better suit caregiver preferences.

Case Study on Augmented Reality, Digital and Physical Modelling with Mathematical Learning Disabilities Students in an Elementary School in Luxemburg

This paper reports on a case study of two elementary school students with mathematical learning disabilities (MLD) (ages 10 and 11) using augmented reality (AR), digital and physical modelling in mathematics class. MLD students worked on modelling geometric shapes (cubes, cuboids, squared pyramids, and octahedrons) and forms (polygons) by combining real-world objects with AR and creating a copy of geometric shapes or missing parts with a 3D printing device. The study focused on the development of process skills and mathematical concepts, tried to identify changes in the visualspatial memory, and documented the learning behaviour in class. Further, we collected data through task-based interviews with both students. Based on our findings, we present settings and manipulatives which are likely to foster process skills and mathematical concepts in geometry tasks suitable for MLD elementary school students.

Using Three-Dimensional Augmented Reality to Enhance General Aviation Weather Training

This work evaluates augmented reality (AR) training materials for general aviation (GA) weather training. Reviews of GA weather training identify gaps where students lack opportunities to experience weather patterns and lack the ability to correlate weather knowledge in weather-related situations. Three-dimensional (3D) visual models may help close the gaps by visualizing information about weather processes, hazards, and visual cues. A 3D AR thunderstorm model visualizes a single-cell thunderstorm clouds, winds, precipitation, lightning, and advective movement. Preliminary evaluation of the model was conducted through a subject matter expert (SME) review and a usability study. The SME review identified improvements to the model and areas for future content design. The usability study identified usability issues with the model. Insights about the design of weather visualizations are developed into recommendations. The approach will integrate 3D AR models into the weather training curriculum to create interactive print training.

No Microscope? No Problem: A Systematic Review of Microscope-Free Microsurgery Training Models.

Benchtop microsurgical training models that use digital tools (smartphones, tablets, and virtual reality [VR]) for magnification are allowing trainees to practice without operating microscopes. This systematic review identifies existing microscope-free training models, compares models in their ability to enhance microsurgical skills, and presents a step-by-step protocol for surgeons seeking to assemble their own microsurgery training model. We queried PubMed, Embase, and Web of Science databases through November 2020 for microsurgery training models and performed a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We collected data including training model characteristics (cost, magnification, and components) and outcomes (trainee satisfaction, image resolution, and faster suturing speed). We also conducted a complimentary Google search to identify commercially available microscope-free microsurgical training models or kits not reported in peer-reviewed literature. Literature search identified 1,805 publications; 24 of these met inclusion criteria. Magnification tools most commonly included smartphones (n = 10), VR simulators (n = 4), and tablets (n = 3), with magnification ranging up to ×250 magnification on digital microscopy, ×50 on smartphones, and ×5 on tablets. Average cost of training models ranged from $13 (magnification lens) to $15,000 (augmented reality model). Model were formally assessed using workshops with trainees or attendings (n = 10), surveys to end-users (n = 5), and single-user training (n = 4); users-reported satisfaction with training models and demonstrated faster suturing speed and increased suturing quality with model training. Five commercially available microsurgery training models were identified through Google search. Benchtop microsurgery trainers using digital magnification successfully provide trainees with increased ease of microsurgery training. Low-cost yet high magnification setups using digital microscopes and smartphones are optimal for trainees to improve microsurgical skills. Our assembly protocol, "1, 2, 3, Microsurgery," provides instructions for training model set up to fit the unique needs of any microsurgery trainee.

Designing a Patient Room as a Fall Protection Strategy: The Perspectives of Healthcare Design Experts.

Despite decades of research into patient falls, there is a dearth of evidence about how the design of patient rooms influences falls. Our multi-year study aims to better understand how patient room design can increase stability during ambulation, serving as a fall protection strategy for frail and/or elderly patients. The aim of this portion of the study was to ascertain the architect’s perspective on designing a room to mitigate the risk of falls, as well as to evaluate the face validity of a predictive algorithm to assess risk in room design using the input of a design advisory council (AC). The purpose of this paper is to provide insight into the design process and decision-making for patient rooms; summarize the impressions of industry experts about the configurations and layout of the patient rooms tested in a preliminary augmented reality model; establish the face validity of modeled heat maps depicting risk; and report the results of a pre-meeting and post-meeting survey of expert opinions. Feedback was coded using human factors/ergonomic (HF/E) design principles, and the findings will be used to guide further development of an “optimal” prototype room for human subject testing. The results confirm the challenges that architects face as they balance competing priorities and reveal how a participatory process focusing on preventing falls can shift assumptions about design strategies, especially subtle changes (e.g., toilet orientation).

Development of an inside-out augmented reality technique for neurosurgical navigation.

With the advancement of 3D modeling techniques and visualization devices, augmented reality (AR)-based navigation (AR navigation) is being developed actively. The authors developed a pilot model of their newly developed inside-out tracking AR navigation system. The inside-out AR navigation technique was developed based on the visual inertial odometry (VIO) algorithm. The Quick Response (QR) marker was created and used for the image feature-detection algorithm. Inside-out AR navigation works through the steps of visualization device recognition, marker recognition, AR implementation, and registration within the running environment. A virtual 3D patient model for AR rendering and a 3D-printed patient model for validating registration accuracy were created. Inside-out tracking was used for the registration. The registration accuracy was validated by using intuitive, visualization, and quantitative methods for identifying coordinates by matching errors. Fine-tuning and opacity-adjustment functions were developed. ARKit-based inside-out AR navigation was developed. The fiducial marker of the AR model and those of the 3D-printed patient model were correctly overlapped at all locations without errors. The tumor and anatomical structures of AR navigation and the tumors and structures placed in the intracranial space of the 3D-printed patient model precisely overlapped. The registration accuracy was quantified using coordinates, and the average moving errors of the x-axis and y-axis were 0.52 ± 0.35 and 0.05 ± 0.16 mm, respectively. The gradients from the x-axis and y-axis were 0.35° and 1.02°, respectively. Application of the fine-tuning and opacity-adjustment functions was proven by the videos. The authors developed a novel inside-out tracking-based AR navigation system and validated its registration accuracy. This technical system could be applied in the novel navigation system for patient-specific neurosurgery.

The Implementation of Augmented Reality and First Person Method of Promotional Media in Kemang Pratama Housing Bekasi

The promotional media used in the Kemang Pratama Bekasi housing complex uses brochures with 2D images, causing a lack of promotion of the housing. And if you want to see the condition or appearance of the house, you have to come to the location of the house in the Kemang Pratama Bekasi housing complex as a clear picture. But by using the implementation model of Augmented Reality and First Person Mode as a media for promoting housing in Kemang Pratama, Bekasi, it makes it easier for buyers to see the condition and appearance of the house in terms of home specifications, because it can display a 3D-shaped house using a smartphone camera as an initial image before seeing the house to the location. So with this technology it becomes more effective and efficient in terms of time. Augmented Reality is used to view the exterior with a brochure (as a marker) which is scanned using a camera and First person mode to see the interior of the house.

Delta.AR: An augmented reality-based visualization platform for 3D genome

Delta.AR: An augmented reality-based visualization platform for 3D genome

Fully Automatic Adaptive Meshing Based Segmentation of the Ventricular System for Augmented Reality Visualization and Navigation

Effective image segmentation of cerebral structures is fundamental to 3-dimensional techniques such as augmented reality. To be clinically viable, segmentation algorithms should be fully automatic and easily integrated in existing digital infrastructure. We created a fully automatic adaptive-meshing-based segmentation system for T1-weighted magnetic resonance images (MRI) to automatically segment the complete ventricular system, running in a cloud-based environment that can be accessed on an augmented reality device. This study aims to assess the accuracy and segmentation time of the system by comparing it to a manually segmented ground truth dataset. A ground truth (GT) dataset of 46 contrast-enhanced and non-contrast-enhanced T1-weighted MRI scans was manually segmented. These scans also were uploaded to our system to create a machine-segmented (MS) dataset. The GT data were compared with the MS data using the Sørensen-Dice similarity coefficient and 95% Hausdorff distance to determine segmentation accuracy. Furthermore, segmentation times for all GT and MS segmentations were measured. Automatic segmentation was successful for 45 (98%) of 46 cases. Mean Sørensen-Dice similarity coefficient score was 0.83 (standard deviation [SD]= 0.08) and mean 95% Hausdorff distance was 19.06 mm (SD= 11.20). Segmentation time was significantly longer for the GT group (mean= 14405 seconds, SD= 7089) when compared with the MS group (mean= 1275 seconds, SD= 714) with a mean difference of 13,130 seconds (95% confidence interval 10,130-16,130). The described adaptive meshing-based segmentation algorithm provides accurate and time-efficient automatic segmentation of the ventricular system from T1 MRI scans and direct visualization of the rendered surface models in augmented reality.

Potentials of Augmented Reality in a BIM based building submission process

The digital transformation in the construction industry, which is also referred to as Construction 4.0, is gaining impact in recent years. BIM as the central point for model-based planning and communication is at the centre of this development. This paves the way for the implementation of further technologies, such as augmented reality (AR) or artificial intelligence (AI) in the construction process. However, the submission process currently receives little attention in the BIM project cycle, even though it has a significant impact on a building project. There is a huge potential for the application of BIM and AR in the building submission process. BIM allows partly automatic compliance checks to be performed. The BIM model and the results of these checks can be used as basis for the AR model. This paper investigates an application for AR in a BIM submission process. Here we show that especially the plan check process and hearings in the approval process can be improved and sped up by combining BIM and the visualization technology of AR. In this process, AR can support the building authority in checking the compliance with building regulations. Additionally, non-experts involved often have problems grasping the planned building project just based on the technical 2D plans. In this case, AR helps them to visualise the impact of the planned project and allows for better judgement. Use-cases are presented, showing strategies to improve the plan check process and assist the building authority as well as the persons involved, for both experts as well as non-experts. Finally, the requirements, technical implementations, and effects of an AR application for the plan check process are described. An AR-supported plan checking process can vastly improve the presentation of technical content. This leads to better understanding and more transparency for non-experts. Because AR is relatively easy to use, it might become ubiquitous not only to experts, but also to the general public.

A comparison of pre-clinical instructional technologies: Natural teeth, 3D models, 3D printing, and augmented reality.

To assess student perceptions on learning dental anatomy using natural extracted teeth, 3D printed models, 3D virtual models, and augmented reality (AR) technology. METHODS: Eighty first-year dental students enrolled in the dental anatomy course were instructed to examine four stations. The stations included four versions of a mandibular first molar: an extracted natural tooth, a 3D printed model, a 3D virtual model, and a model displayed on a novel AR device. After examining all stations, the students were asked to complete an electronic survey. Bivariate analysis was performed to determine whether students' perceptions are related to their demographic characteristics and technological experience with computer use, 3D modeling, and video games. RESULTS: Seventy students completed the survey (87.5% response rate). Students rated natural teeth to have the highest educational value, the 3D printed tooth to be the most accessible, and the AR application to be the most interesting modality.Students who played little to no video games were more likely to rate AR as high educational value (48.8%vs. 10.3%; p<0.001), while students with little 3D modeling experience were more likely to rate high accessibility of the 3D Model (78.1%vs. 48.3%; p<0.001). No statistically significant associations between the perceptions and students' demographic characteristics or use of computer were noted. The natural extracted tooth was rated highest in educational value, while the 3D printed model was rated easiest to use, and the AR model was the most interesting to the students.

Development and use of augmented reality models to teach medicinal chemistry

Background and purposeStudents in the doctor of pharmacy curriculum have varied backgrounds in their chemical training and also their ability to make mental conversions from two-dimensional chemical representations, on lecture slides or textbook images, to three-dimensional cognitive understanding. In order to bridge the gap, augmented reality (AR) models were developed to provide an alternative learning medium for the students. AR was selected to take advantage of the ubiquitous presence of smartphones, without incurring the expense of Virtual Reality hardware. Educational activity and settingAR models were developed and introduced in the classroom in three phases. Student survey responses were used to improve the utility of the models in between phases. Active learning exercises were developed that required both individual and group interactions to complete. FindingsAn optimized AR model creation workflow was developed that allowed each AR model to be created and posted in about 30 min. Depending on the phase of the study, 69% to 88% of the students found the AR models easy to use and 58% to 83% wanted to see more AR models used in future lectures. A majority (76%) of the students viewed the AR models on their smartphones. SummaryAugmented reality modules were created for use in medicinal chemistry courses in the pharmacy curriculum. Models were introduced in phases and included iterative improvements based on student feedback. The AR exercises provided active learning opportunities and were well received. The majority of students would like additional AR modules used in the course.

Acceptance of dance training system based on augmented reality and technology acceptance model (TAM)

The advancement in Computer Vision (CV) has evolved drastically from image processing to object recognition, tracking video, restoration of images, three-dimensional (3D) pose recognition, and emotion analysis. These advancements have eventually led to the birth of Augmented Reality (AR) technology, which means embedding virtual objects into the real-world environment. The primary focus of this research was to solve the long-term learning retention and poor learning efficiency for mastering a dance skill through the AR technology based on constructivism learning theory, Dreyfus model and Technology Acceptance Model (TAM). The problem analysis carried out in this research had major research findings, in which the retention and learning efficiency of a dance training system were predominantly determined through the type of learning theory adopted, learning environment, training tools, skill acquisition technology and type of AR technique. Therefore, the influential factors for the user acceptance of AR-based dance training system (ARDTS) were based on quantitative analysis. These influential factors were determined to address the problem of knowledge gap on acceptance of AR-based systems for dance education through self-learning. The evaluation and testing were conducted to validate the developed and implemented ARDTS system. The Technology Acceptance Model (TAM) as the evaluation model and quantitative analysis was done with a research instrument that encompassed external and internal variables. TAM consisted of 37 items, in which six factors were used to assess the new developed ARDTS by the authors and its acceptability among 86 subjects. The current study had investigated the potential use of AR-based dance training system to promote a particular dance skill among a sample population with various backgrounds and interests. The obtained results support a general acceptance towards ARDTS among the users who are interested in exploring the cutting-edge technology of AR for gaining expertise in a dance skill.

Blended Learning Model Based on Massive Open Online Courses (MOOCs) Assisted by Augmented Reality (BMA) Model as the Electronic Learning Media in the Pandemic Covid-19

This study aims to analyze educators' responses to implementing the blended learning model based on MOOCs assisted by augmented reality or the so-called BMA model. This BMA model is applied to the learning process using the MOOCs system that has been developed with augmented reality assistance in the midst of the COVID-19 pandemic. To determine the response of prospective educators in the study, data collection techniques were carried out using a questionnaire given to 47 students. This questionnaire consists of 17 question items with 4 Likert scales. The data analysis technique used qualitative, quantitative data analysis. To test the quality of the questionnaire given, data analysis was used using the Rasch model and the winsteps application. Based on the research results, it was found that the instrument data used were valid and reliable. The results of student responses obtained are in very good criteria. This is reinforced by the results of the validity calculation in the rasch analysis which was carried out which stated that 17 items were declared valid and no items were issued. In addition, the value of the calculation of data reliability using the alpha cronbach method with a score of 0.91 is higher than the r table value for α = 0.05 0.2876. Thus it can be said that the data used is reliable and prospective educators respond very well to the use of this BMA model in the implementation of the learning process through the MOOCs system during the Covid-19 pandemic. This is reinforced by the results of the validity calculation in the rasch analysis which was carried out which stated that 17 items were declared valid and no items were issued. In addition, the value of the calculation of data reliability using the alpha cronbach method with a score of 0.91 is higher than the r table value for α = 0.05 0.2876. Thus it can be said that the data used is reliable and prospective educators respond very well to the use of this BMA model in the implementation of the learning process through the MOOCs system during the Covid-19 pandemic. This is reinforced by the results of the validity calculation in the Rasch analysis, which was carried out, which stated that 17 items were declared valid and no items were issued. In addition, the value of the calculation of data reliability using the alpha cronbach method with a score of 0.91 is higher than the r table value for α = 0.05 0.2876. Thus, the data used is reliable, and prospective educators respond very well to the use of the BMA model in the implementation of the learning process through the MOOCs system during the Covid-19 pandemic.

Metrics development and modelling the mixed reality and digital twin adoption in the context of Industry 4.0

PurposeThis paper aims to examine the current technology acceptance model (TAM) in the field of mixed reality and digital twin (MRDT) and identify key factors affecting users' intentions to use MRDT. The factors are used as a set of key metrics for proposing a predictive model for virtual, augmented and mixed reality (MR) acceptance by users. This model is called the extended TAM for MRDT adoption in the architecture, engineering, construction and operations (AECO) industry.Design/methodology/approachAn interpretivist philosophical lens was adopted to conduct an inductive systematic and bibliographical analysis of secondary data contained within published journal articles that focused upon MRDT acceptance modelling. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach to meta-analysis were adopted to ensure all key investigations were included in the final database set. Quantity indicators such as path coefficients, factor ranking, Cronbach’s alpha (a) and chi-square (b) test, coupled with content analysis, were used for examining the database constructed. The database included journal papers from 2010 to 2020.FindingsThe extant literature revealed that the most commonly used constructs of the MRDT–TAM included: subjective norm; social influence; perceived ease of use (PEOU); perceived security; perceived enjoyment; satisfaction; perceived usefulness (PU); attitude; and behavioural intention (BI). Using these identified constructs, the general extended TAM for MRDT in the AECO industry is developed. Other important factors such as “perceived immersion” could be added to the obtained model.Research limitations/implicationsThe decision to utilise a new technology is difficult and high risk in the construction project context, due to the complexity of MRDT technologies and dynamic construction environment. The outcome of the decision may affect employee performance, project productivity and on-site safety. The extended acceptance model offers a set of factors that assist managers or practitioners in making effective decisions for utilising any type of MRDT technology.Practical implicationsSeveral constraints are apparent due to the limited investigation of MRDT evaluation matrices and empirical studies. For example, the research only covers technologies which have been reported in the literature, relating to virtual reality (VR), augmented reality (AR), MR, DT and sensors, so newer technologies may not be included. Moreover, the review process could span a longer time period and thus embrace a fuller spectrum of technology development in these different areas.Originality/valueThe research provides a theoretical model for measuring and evaluating MRDT acceptance at the individual level in the AECO context and signposts future research related to MRDT adoption in the AECO industry, as well as providing managerial guidance for progressive AECO professionals who seek to expand their use of MRDT in the Fourth Industrial Revolution (4IR). A set of key factors affecting MRDT acceptance is identified which will help innovators to improve their technology to achieve a wider acceptance.

Augmented reality – A potential enabler for advanced medical device use and safety training

This project designed and built two proof of concept Augmented Reality (AR) models for use in two training scenarios; medical device and radiation safety.

Development of a Virtual Three-Dimensional Assessment Scenario for Anatomical Education.

In anatomical education three‐dimensional (3D) visualization technology allows for active and stereoscopic exploration of anatomy and can easily be adopted into medical curricula along with traditional 3D teaching methods. However, most often knowledge is still assessed with two‐dimensional (2D) paper‐and‐pencil tests. To address the growing misalignment between learning and assessment, this viewpoint commentary highlights the development of a virtual 3D assessment scenario and perspectives from students and teachers on the use of this assessment tool: a 10‐minute session of anatomical knowledge assessment with real‐time interaction between assessor and examinee, both wearing a HoloLens and sharing the same stereoscopic 3D augmented reality model. Additionally, recommendations for future directions, including implementation, validation, logistic challenges, and cost‐effectiveness, are provided. Continued collaboration between developers, researchers, teachers, and students is critical to advancing these processes.

Educators’ Interests, Prior Knowledge and Questions Regarding Augmented Reality, Virtual Reality and 3D Printing and Modeling

Innovative new technologies, such as augmented reality (AR), virtual reality (VR) and 3D printers, provide new affordances for education. While each of these technologies has been shown to positively impact learning, they have not yet been widely adopted in schools. With the interest in these technologies growing among educators, this study explores why educators are motivated to learn about these tools, what they already know about these tools, and what they want to know. This paper presents data from 265 educators who completed a pre-course survey before engaging in an open online course to learn about these technologies. Findings from the data reveal significant variance in educators’ prior knowledge, interests, and motivations. This paper yields new insights that can support the design of professional learning experiences for educators and potentially increase mainstream adoption and use of these tools in education.

Energy-Efficient Task Offloading and Resource Allocation via Deep Reinforcement Learning for Augmented Reality in Mobile Edge Networks

The augmented reality (AR) applications have been widely used in the field of Internet of Things (IoT) because of good immersion experience for users, but their ultralow delay demand and high energy consumption bring a huge challenge to the current communication system and terminal power. The emergence of mobile-edge computing (MEC) provides a good thinking to solve this challenge. In this article, we study an energy-efficient task offloading and resource allocation scheme for AR in both the single-MEC and multi-MEC systems. First, a more specific and detailed AR application model is established as a directed acyclic graph according to its internal functionality. Second, based on this AR model, a joint optimization problem of task offloading and resource allocation is formulated to minimize the energy consumption of each user subject to the latency requirement and the limited resources. The problem is a mixed multiuser competition and cooperation problem, which involves the task offloading decision, uplink/downlink transmission resources allocation, and computing resources allocation of users and MEC server. Since it is an NP-hard problem and the communication environment is dynamic, it is difficult for genetic algorithms or heuristic algorithms to solve. Therefore, we propose an intelligent and efficient resource allocation and task offloading algorithm based on the deep reinforcement learning framework of multiagent deep deterministic policy gradient (MADDPG) in a dynamic communication environment. Finally, simulation results show that the proposed algorithm can greatly reduce the energy consumption of each user terminal.

Investigating the use of visualization to improve public participation in infrastructure projects: how are digital approaches used and what value do they bring?

ABSTRACT Using digital visualization models is suggested as a way of overcoming known barriers in public participation processes. Through a survey conducted among professional practitioners in the Norwegian infrastructure industry, different visualization models were compared in order to determine how they may lead to change in understanding and engagement among the public. The results indicated that digital visualization models were believed to lead to a statistically significant increase in people’s understanding and engagement compared to traditional visualization models. In spite of this indication, it was traditional visualization models that were most commonly used to disseminate information about infrastructure projects to the public. The results also revealed that practitioners believed that 3D models in particular led to better understanding and increased engagement compared to virtual and augmented reality models; however, the latter models had seldom been used in infrastructure projects, and many of the respondents were not familiar with these visualizations. The results suggest that an increase in the use of digital visualizations could be beneficial. Additionally, more frequent, formal, and structured evaluation of the use of visualization should be done in order to ensure that the visualizations used meet specific project objectives.