Virtual Reality Intelligent Hardware最新文献

Background

Currently, many simulator systems for medical procedures are under development. These systems can provide new solutions for training, planning, and testing medical practices, improve performance, and optimize the time of the exams. However, to achieve the best results, certain premises must be followed and applied to the model under development, such as usability, control, graphics realism, and interactive and dynamic gamification.

Methods

This study presents a system for simulating a medical examination procedure in the nasal cavity for training and research purposes, using a patient′s accurate computed tomography (CT) as a reference. The pathologies that are used as a guide for the development of the system are highlighted. Furthermore, an overview of current studies covering bench medical mannequins, 3D printing, animals, hardware, software, and software that use hardware to boost user interaction, is given. Finally, a comparison with similar state-of-the-art studies is made.

Results

The main result of this work is interactive gamification techniques to propose an experience of simulation of an immersive exam by identifying pathologies present in the nasal cavity such as hypertrophy of turbinates, septal deviation adenoid hypertrophy, nasal polyposis, and tumor.

Background

The problem of visualizing a hierarchical dataset is an important and useful technique in many real-life situations. Folder systems, stock markets, and other hierarchical-related datasets can use this technique to better understand the structure and dynamic variation of the dataset. Traditional space-filling(square)-based methods have the advantages of compact space usage and node size as opposed to diagram-based methods. Spacefilling-based methods have two main research directions: static and dynamic performance.

Methods

This study presented a treemapping method based on balanced partitioning that enables excellent aspect ratios in one variant, good temporal coherence for dynamic data in another, and in the third, a satisfactory compromise between these two aspects. To layout a treemap, all the children of a node were divided into two groups, which were then further divided until groups of single elements were reached. After this, these groups were combined to form a rectangle representing the parent node. This process was performed for each layer of the hierarchical dataset. For the first variant from the partitioning, the child elements were sorted and two groups, sized as equally as possible, were built from both big and small elements (size-balanced partition). This achieved satisfactory aspect ratios for the rectangles but less so temporal coherence (dynamic). For the second variant, the sequence of children was taken and from this, groups, sized as equally as possible, were created without the need for sorting (sequence-based, good compromise between aspect ratio and temporal coherency). For the third variant, the children were split into two groups of equal cardinalities, regardless of their size (number-balanced, worse aspect ratios but good temporal coherence).

Results

This study evaluated the aspect ratios and dynamic stability of the employed methods and proposed a new metric that measures the visual difference between rectangles during their movement to represent temporally changing inputs.

Conclusion

This study demonstrated that the proposed method of treemapping via balanced partitioning outperformed the state-of-the-art methods for several real-world datasets.

Background

The accurate (quantitative) analysis of 3D face deformation is a problem of increasing interest in many applications. In particular, defining a 3D model of the face deformation into a 2D target image to capture local and asymmetric deformations remains a challenge in existing literature. A measure of such local deformations may be a relevant index for monitoring the rehabilitation exercises of patients suffering from Parkinson’s or Alzheimer’s disease or those recovering from a stroke.

Methods

In this paper, a complete framework that allows the construction of a 3D morphable shape model (3DMM) of the face is presented for fitting to a target RGB image. The model has the specific characteristic of being based on localized components of deformation. The fitting transformation is performed from 3D to 2D and guided by the correspondence between landmarks detected in the target image and those manually annotated on the average 3DMM. The fitting also has the distinction of being performed in two steps to disentangle face deformations related to the identity of the target subject from those induced by facial actions.

Results

The method was experimentally validated using the MICC-3D dataset, which includes 11 subjects. Each subject was imaged in one neutral pose and while performing 18 facial actions that deform the face in localized and asymmetric ways. For each acquisition, 3DMM was fit to an RGB frame whereby, from the apex facial action and the neutral frame, the extent of the deformation was computed. The results indicate that the proposed approach can accurately capture face deformation, even localized and asymmetric deformations.

Conclusion

The proposed framework demonstrated that it is possible to measure deformations of a reconstructed 3D face model to monitor facial actions performed in response to a set of targets. Interestingly, these results were obtained using only RGB targets, without the need for 3D scans captured with costly devices. This paves the way for the use of the proposed tool in remote medical rehabilitation monitoring.

A 360° video stream provide users a choice of viewing one's own point of interest inside the immersive contents. Performing head or hand manipulations to view the interesting scene in a 360° video is very tedious and the user may view the interested frame during his head/hand movement or even lose it. While automatically extracting user's point of interest (UPI) in a 360° video is very challenging because of subjectivity and difference of comforts. To handle these challenges and provide user's the best and visually pleasant view, we propose an automatic approach by utilizing two CNN models: object detector and aesthetic score of the scene. The proposed framework is three folded: pre-processing, Deepdive architecture, and view selection pipeline. In first fold, an input 360° video-frame is divided into three subframes, each one with 120° view. In second fold, each sub-frame is passed through CNN models to extract visual features in the sub-frames and calculate aesthetic score. Finally, decision pipeline selects the subframe with salient object based on the detected object and calculated aesthetic score. As compared to other state-of-the-art techniques which are domain specific approaches i.e., support sports 360° video, our system support most of the 360° videos genre. Performance evaluation of proposed framework on our own collected data from various websites indicate performance for different categories of 360° videos.

Teaching science through computer games, simulations, and artificial intelligence (AI) is an increasingly active research field. To this end, we conducted a systematic literature review on serious games for science education to reveal research trends and patterns. We discussed the role of virtual reality (VR), AI, and augmented reality (AR) games in teaching science subjects like physics. Specifically, we covered the research spanning between 2011 and 2021, investigated country-wise concentration and most common evaluation methods, and discussed the positive and negative aspects of serious games in science education in particular and attitudes towards the use of serious games in education in general.

The recent advancements in the field of Virtual Reality (VR) and Augmented Reality (AR) have a substantial impact on modern day technology by digitizing each and everything related to human life and open the doors to the next generation Software Technology (Soft Tech). VR and AR technology provide astonishing immersive contents with the help of high quality stitched panoramic contents and 360° imagery that widely used in the education, gaming, entertainment, and production sector. The immersive quality of VR and AR contents are greatly dependent on the perceptual quality of panoramic or 360° images, in fact a minor visual distortion can significantly degrade the overall quality. Thus, to ensure the quality of constructed panoramic contents for VR and AR applications, numerous Stitched Image Quality Assessment (SIQA) methods have been proposed to assess the quality of panoramic contents before using in VR and AR. In this survey, we provide a detailed overview of the SIQA literature and exclusively focus on objective SIQA methods presented till date. For better understanding, the objective SIQA methods are classified into two classes namely Full-Reference SIQA and No-Reference SIQA approaches. Each class is further categorized into traditional and deep learning-based methods and examined their performance for SIQA task. Further, we shortlist the publicly available benchmark SIQA datasets and evaluation metrices used for quality assessment of panoramic contents. In last, we highlight the current challenges in this area based on the existing SIQA methods and suggest future research directions that need to be target for further improvement in SIQA domain.

Background

Augmented reality (AR), virtual reality (VR), and remote-controlled devices are driving the need for a better 5G infrastructure to support faster data transmission. In this study, mobile AR is emphasized as a viable and widespread solution that can be easily scaled to millions of end-users and educators because it is lightweight and low-cost and can be implemented in a cross-platform manner. Low-efficiency smart devices and high latencies for real-time interactions via regular mobile networks are primary barriers to the use of AR in education. New 5G cellular networks can mitigate some of these issues via network slicing, device-to-device communication, and mobile edge computing.

Methods

In this study, we use a new technology to solve some of these problems. The proposed software monitors image targets on a printed book and renders 3D objects and alphabetic models. In addition, the application considers phonetics. The sound (phonetic) and 3D representation of a letter are played as soon as the image target is detected. 3D models of the Turkish alphabet are created by using Adobe Photoshop with Unity3D and Vuforia SDK.

Results

The proposed application teaches Turkish alphabets and phonetics by using 3D object models, 3D letters, and 3D phrases, including letters and sounds.

Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways, including, for example, using augmented reality (AR) applications. Wearable technology uses electronic devices that may be carried as accessories, clothes, or even embedded in the user's body. Although the potential benefits of smart wearables are numerous, their extensive and continual usage creates several privacy concerns and tricky information security challenges. In this paper, we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors. We highlight the fundamental features of security and privacy for wearable device applications. Then, we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors. We also present a case study on privacy-preserving machine learning techniques. Herein, we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance. We explain the implementation details of a case study of a secure prediction service using the convolutional neural network (CNN) model and the Cheon-Kim-Kim-Song (CHKS) homomorphic encryption algorithm. Finally, we explore the obstacles and gaps in the deployment of practical real-world applications. Following a comprehensive overview, we identify the most important obstacles that must be overcome and discuss some interesting future research directions.