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Applied Sciences
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Artificial Intelligence and Computer Technologies in Sports and Healthcare
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Artificial Intelligence and Computer Technologies in Sports and Healthcare

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1661

Special Issue Editors

Dr. Chung Hyun Goh

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The University of Texas at Tyler, Tyler, TX 75799, USA
Interests: robotics and automation; virtual design and manufacturing innovation; rehabilitation-focused research; medical device design and mechanical design and optimization; biomaterials and biomechanics; finite element analysis; crystal plasticity finite element method
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mukul Shirvaikar

E-Mail Website
Guest Editor
Department of Electrical Engineering, The University of Texas at Tyler, Tyler, TX 75799, USA
Interests: real time systems; image and signal processing; robotics and computer vision; system-on-a-chip design; pattern recognition; neural networks; medical imaging; engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and computer technologies are transforming sports and healthcare. In this Special Issue, we aim to publish a collection of research contributions illustrating recent achievements in all aspects of the development, study, and understanding of AI and computer technologies in sports and healthcare. High-quality, original research papers are invited in the overlapping fields below:

  • Injury Prevention and Rehabilitation: Predictive Analytics for the Likelihood of Injuries, Rehabilitation Programs, Tracking Recovery Progress.
  • Sports Performance Analysis and Optimization: Player Performance and Training, Game Strategy.
  • Fan Engagement and Experience: Personalized Content, Virtual and Augmented Reality for Immersive Experiences.
  • Sports Officiating and Rule Enforcement: Video Assistant Referee (VAR), Automated Officiating.
  • Healthcare—Diagnostics and Treatment: Medical Imaging and Predictive Diagnostics using AI models.
  • Personalized Medicine: Treatment Plans and Drug Discovery.
  • Efficiency: Workflow Automation for Healthcare and Patient Management, Healthcare Resource Allocation.
  • Remote Monitoring and Telehealth: Wearable Devices, Telemedicine.
  • Mental Health: Monitoring and Therapeutic Interventions.
  • AI in Healthcare Challenges: Data Privacy, Transparency, Regulation and Compliance.

Dr. Chung Hyun Goh
Prof. Dr. Mukul Shirvaikar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • performance analysis and optimization
  • injury prevention and rehabilitation
  • fan engagement and experience
  • officiating and rule enforcement
  • diagnostics and treatment
  • personalized medicine
  • remote monitoring and telehealth
  • mental health

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Published Papers (2 papers)

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Research

20 pages, 54021 KiB  
Article
Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
by Jelle Vanhaeverbeke, Robbe Decorte, Maarten Slembrouck, Sofie Van Hoecke and Steven Verstockt
Appl. Sci. 2024, 14(20), 9246; https://doi.org/10.3390/app14209246 - 11 Oct 2024
Viewed by 620
Abstract
Road cycling races, such as the Tour de France, captivate millions of viewers globally, combining competitive sportsmanship with the promotion of regional landmarks. Traditionally, points of interest (POIs) are highlighted during broadcasts using manually created static overlays, a process that is both outdated [...] Read more.
Road cycling races, such as the Tour de France, captivate millions of viewers globally, combining competitive sportsmanship with the promotion of regional landmarks. Traditionally, points of interest (POIs) are highlighted during broadcasts using manually created static overlays, a process that is both outdated and labor-intensive. This paper presents a novel, fully automated methodology for detecting and tracking POIs in live helicopter video streams, aiming to streamline the visualization workflow and enhance viewer engagement. Our approach integrates a saliency and Segment Anything-based technique to propose potential POI regions, which are then recognized using a keypoint matching method that requires only a few reference images. This system supports both automatic and semi-automatic operations, allowing video editors to intervene when necessary, thereby balancing automation with manual control. The proposed pipeline demonstrated high effectiveness, achieving over 75% precision and recall in POI detection, and offers two tracking solutions: a traditional MedianFlow tracker and an advanced SAM 2 tracker. While the former provides speed and simplicity, the latter delivers superior segmentation tracking, albeit with higher computational demands. Our findings suggest that this methodology significantly reduces manual workload and opens new possibilities for interactive visualizations, enhancing the live viewing experience of cycling races. Full article
(This article belongs to the Special Issue Artificial Intelligence and Computer Technologies in Sports and Healthcare)
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20 pages, 14961 KiB  
Article
Data Augmentation in Histopathological Classification: An Analysis Exploring GANs with XAI and Vision Transformers
by Guilherme Botazzo Rozendo, Bianca Lançoni de Oliveira Garcia, Vinicius Augusto Toreli Borgue, Alessandra Lumini, Thaína Aparecida Azevedo Tosta, Marcelo Zanchetta do Nascimento and Leandro Alves Neves
Appl. Sci. 2024, 14(18), 8125; https://doi.org/10.3390/app14188125 - 10 Sep 2024
Viewed by 779
Abstract
Generative adversarial networks (GANs) create images by pitting a generator (G) against a discriminator (D) network, aiming to find a balance between the networks. However, achieving this balance is difficult because G is trained based on just one value [...] Read more.
Generative adversarial networks (GANs) create images by pitting a generator (G) against a discriminator (D) network, aiming to find a balance between the networks. However, achieving this balance is difficult because G is trained based on just one value representing D’s prediction, and only D can access image features. We introduce a novel approach for training GANs using explainable artificial intelligence (XAI) to enhance the quality and diversity of generated images in histopathological datasets. We leverage XAI to extract feature information from D and incorporate it into G via the loss function, a unique strategy not previously explored in this context. We demonstrate that this approach enriches the training with relevant information and promotes improved quality and more variability in the artificial images, decreasing the FID by up to 32.7% compared to traditional methods. In the data augmentation task, these images improve the classification accuracy of Transformer models by up to 3.81% compared to models without data augmentation and up to 3.01% compared to traditional GAN data augmentation. The Saliency method provides G with the most informative feature information. Overall, our work highlights the potential of XAI for enhancing GAN training and suggests avenues for further exploration in this field. Full article
(This article belongs to the Special Issue Artificial Intelligence and Computer Technologies in Sports and Healthcare)
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