Augmented reality in sport and healthcare sciences

Abstract

The term "augmented reality," refers to the adding of new data to an existing environment. When contrasted to other innovations, augmented reality may provide benefits not previously available. This research aimed to describe and get a better understanding of the advantages that augmented reality may bring to the training of athletes. In this review we propose the findings were evaluated in terms of their significance in athletics in sports and healthcare sectors. According to the findings of our study, a variety of augmented reality methods could be employed for teaching as well as delivering feedback. It may be possible to change some of the rules in order to level the playing field amongst players with varying levels of expertise. The perception of the audience might be enhanced further by the addition of more information. We also investigated the limits of existing augmented reality systems, as well as their usefulness in training, and offered some pointers for the creation of various types of training situations. This paper describes augmented reality techniques and reviews augmented reality tools for sport and health sciences. Current study describes augmented reality tools. Firstly, we explain what the augmented reality stands for.

Development of Deep Learning Enabled Augmented Reality Framework for Monitoring the Physical Quality Training of Future Trainers-Teachers

“…The proposed system holds immense promise in fitness training programs. It can serve as a virtual personal trainer, offering real-time guidance on exercise form, posture, and range of motion [44]. Individuals looking to improve their fitness levels can benefit from accurate feedback, reducing the risk of injuries and enhancing the effectiveness of workouts [44].…”

“…It can serve as a virtual personal trainer, offering real-time guidance on exercise form, posture, and range of motion [44]. Individuals looking to improve their fitness levels can benefit from accurate feedback, reducing the risk of injuries and enhancing the effectiveness of workouts [44]. Additionally, the system can be adapted for use in rehabilitation programs, assisting patients in performing therapeutic exercises correctly and safely.…”

Enhanced U-Net Architecture for Lung Segmentation on Computed Tomography and X-Ray Images

“…Among the several computational models proposed, U-Net, a convolutional neural network (CNN) architecture, has gained prominence for its efficacy in biomedical image segmentation [3]. The standard U-Net model, adapted specifically for medical imaging, excels due to its symmetric expansive path, which enables precise localization combined with a contractive path that captures context [4]. However, while the model has proven its competence in segmenting various biological structures, researchers have identified limitations in its application to lung CT images, particularly concerning the segmentation of intricate lung parenchyma and pathological structures [5].…”