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Author: search.filters.author.Rohil, Mukesh KumarSubject: search.filters.subject.Augmented reality (AR)

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    Utilization of Augmented Reality Visualizations in Healthcare Education: Trends and Future Scope
    (IEEE, 2023) Rohil, Mukesh Kumar
    Augmented reality (AR) is a recent technology with applications in multiple sectors, including the healthcare industry. AR incorporates (or overlays) digital or computer-generated data such as audio, video, images, and contact or haptic sensations into a real-time setting. Innovations in augmented reality can assist medical personnel in diagnosing, treating, and operating on patients more precisely. However, healthcare differs from other disciplines because it focuses on something that is unique, unpredictable, simultaneously extremely complex, and fragile: The Human Body. This paper aims to comprehend the application of augmented reality and visualizations in medical and healthcare education and practice. The novelty of the paper is that in addition to providing an overview of recent successful augmented reality applications in healthcare, we emphasize that educational use of these in the present form (or after appropriate adaptation) provide benefits to teachers, students and working healthcare professionals. some of the key benefits are fast learning and better knowledge retention
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    Advanced 3D Modeling for Augmented Reality Visualizations in Engineering Education: Issues, Challenges, and Future
    (IEEE, 2023) Rohil, Mukesh Kumar
    Augmented Reality (AR), the superimposing of a virtual object on the view of the real environment, assists in visualizing engineering concepts in real-time. Computer graphics-based 3D modeling techniques evolved over the past five decades are being used in AR systems. Representations of 3D objects using polygons, surface patches, points (or voxels), and lines are insufficient to visualize the intricacies of objects having curved surfaces. To enable high-level control and accuracies pertaining to 3D representational details of 3D models, advanced 3D modeling approaches are needed. In this paper, we discuss (along with their suitability, examples, and advantages) several advanced 3D modeling techniques for AR visualizations. We emphasize that the use of AR visualizations for teaching and learning can help learners with a better understanding of concepts in engineering education. Additionally, we discuss some issues, challenges, and the future scope of use of AR in Engineering Education.
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    VOEDHgesture: A Multi-Purpose Visual Odometry/ Simultaneous Localization and Mapping and Egocentric Dynamic Hand Gesture Data-Set for Virtual Object Manipulations in Wearable Mixed Reality
    (SciTech, 2024) Rohil, Mukesh Kumar
    Visual Odometry/ Simultaneous Localization and Mapping (VO/ SLAM) and Egocentric hand gesture recognition are the two major technologies for wearable computing devices like AR (Augmented Reality)/ MR (Mixed Reality) glasses. However, the AR/MR community lacks a suitable dataset for developing both hand gesture recognition and RGB-D SLAM methods. In this work, we use a ZED mini Camera to develop challenging benchmarks for RGB-D VO/ SLAM tasks and dynamic hand gesture recognition. In our dataset VOEDHgesture, we collected 264 sequences using a ZED mini camera, along with precisely measured and time-synchronized ground truth camera positions, and manually annotated the bounding box values for the hand region of interest. The sequences comprise both RGB and depth images, captured at HD resolution (1920 × 1080) and recorded at a video frame rate of 30Hz. To resemble the Augmented Reality environment, the sequences are captured using a head-mounted ZED mini camera, with unrestricted
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    Fast and robust virtual try-on based on parser-free generative adversarial network
    (Springer, 2024-01) Rohil, Mukesh Kumar
    Image-based virtual try-on models have recently become popular leading to many new developments, especially in the past three years. The problem of virtual try-on requires trying on a cloth image on a target person’s image. Implementing the same turns out to be a complicated task. It involves calculating the position, angle, and texture for the cloth to be placed on the target that could be in varying orientations. Also, texture may change as a result of any change in orientation. Therefore, generating textures for the cloth also poses a major challenge. In this article, we propose a generative adversarial network-based virtual try-on network that is robust, fast, and parser-free. We dive into some of the latest developments in the field of virtual try-on models and discuss their market feasibility as well as techniques. It is observed that the performance of our proposed network is comparable to the state-of-the-art models, and it outperforms the latter in terms of execution speed owing to its low time complexity. Moreover, it uses a parser-free architecture. It does not require any external input or processing while testing or applying a trained model. It uses a “teacher-student” approach to learn from existing models. The loss function is based on final output of the model. Therefore, it can also learn its shortcomings from the output of the model, unlike other architectures where much of the training is done in a self-supervised manner from the real person’s image.
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    An architecture to intertwine augmented reality and intelligent tutoring systems: towards realizing technology-enabled enhanced learning
    (Springer, 2024-08) Rohil, Mukesh Kumar
    Intelligent Tutoring Systems (ITS) and Augmented Reality (AR) have become greatly popular in current scenario, especially for helping students in mastering difficult subjects through a variety of different methods with the implementation of smart algorithms. There are many papers in the current literature that discuss the ITS architecture and the AR architecture independently; a few papers have even proposed designs for combining these systems, but the need for this article arises in order to suggest improvements that could theoretically increase the performance and overall robustness of the system for learning basic, complex, domain-specific and AR related concepts. This article discusses the existing ITS and AR systems and their flaws, followed by some potential benefits that can be achieved by combining ITS and AR effectively. We propose a novel architecture for improving the combined AR and ITS system scalable for supporting interaction for the diverse users and domain. The proposed system makes an effective use of three tier architecture, load sharing algorithms, data management techniques, multiple servers, marker-less AR, and modeling 3D object models on the fly, in order to make the system more effective, secure, reliant, and seamless for the users. For realizing 3D object modeling on the fly, the article presents an improved method by combining Level of Detail and Rasterization techniques in order to render in steps in accordance with the demand (i.e. processing up to adequate and sufficient level of details), which will help us use the architecture for small scale to large scale systems. Although 3D object modeling on the fly needs storage up to 33% more than the conventional geometrical structure of the mesh, the speed-up achieved can be as high as six times for coarse mesh and up to 1.46 times for fine mesh. At the core of the proposed system, is to make the ITS extendible to multiple domains of learning and education, and to reduce the response time and latency.