Hybrid IoT-Edge-Cloud Computing-based Athlete Healthcare Framework: Digital Twin Initiative

Alsubai, Shtwai; Sha, Mohemmed; Alqahtani, Abdullah; Bhatia, Munish

doi:10.1007/s11036-023-02200-z

Cited by 6 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The availability of data built over time would allow the training system to assess multiple features using computational methods, advanced physical and mental state estimations, and their comparisons to the goals the user sets. The training system would contain a digital twin of the player [34,35]. The digital twin runs in virtual space together with the competitor running in real space.…”

Section: Border Area Of Reality and Simulated Reality Between Traditi...mentioning

confidence: 99%

Virtuality engineering in esports

Białecki,

Gajewski,

Romaniuk

2024

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

Traditional sports and esports benefit from the development of Information and Communications Technologies (ICT), including gaming, 4D image/video processing, augmented reality (AR), virtual reality (VR), machine learning (ML), artificial intelligence (AI), big data, high-performance computing (HPC), and cloud computing. On the fuzzy border between the areas of physical and modified reality, both types of sports can coexist. The hardware layer of esports includes PC, consoles, smartphones, and peripherals used to interface with computers, including sensors and feedback devices. The IT layer of esports includes algorithms required in the development of games, online platforms, and virtual reality. The esports community includes amateur and professional players, spectators, esports organizers, sponsors, and other stakeholders. Esports and gaming research spans throughout law (intellectual rights, insurance, safety, and age restrictions), administration (teams, clubs, organizations, league regulations, and tournaments) biology (medicine, psychology, addiction, training and education) Olympic and non- Olympic disciplines, ethical issues, game producers, finance, gambling, data acquisition and analysis. Our article aims to presents selected research issues of esports in the ICT virtualization layer.

show abstract

Section: Border Area Of Reality and Simulated Reality Between Traditi...mentioning

confidence: 99%

Virtuality engineering in esports

Białecki,

Gajewski,

Romaniuk

2024

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

show abstract

“…A unique architectural framework was developed and executed, combining four deep learning techniques using edge computing devices [38]. The present study presents a novel framework for athlete healthcare that leverages IoT-edge computing technologies to enhance the provision of medical treatment [39].As healthcare difficulties rise, ML, Edge AI,IoMT, 6G, and cloud computing can help to solve issues [40], [41], [42] including deep learning [43].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Healthcare Efficacy Through IoT-Edge Fusion: A Novel Approach for Smart Health Monitoring and Diagnosis

Izhar,

Naqvi,

Ahmed

et al. 2023

IEEE Access

View full text Add to dashboard Cite

This paper presents an innovative framework that leverages cutting-edge technologies to revolutionize healthcare systems, focusing on data security, privacy, and efficient medical diagnosis. Our approach integrates distributed ledger technology (DLT), artificial intelligence (AI), and edge computing to create a robust and dependable medical ecosystem. In our proposed system, patients' health data is securely managed using a combination of elliptic curve cryptography-based identity-based cryptosystems and edge nodes, ensuring both privacy and integrity. These edge nodes, designed for lowpower and short-range communication, play a pivotal role in in-vivo data collection and monitoring within the human body.The DLT model at the core of our framework utilizes peer-to-peer networks, enabling seamless information exchange while eliminating the need for centralized servers. We emphasize public edge DLTs, such as Ethereum, to ensure accessibility and data ownership for all stakeholders. Furthermore, our system incorporates a hybrid machine learning model for early detection and prediction of security threats, enhancing overall system efficiency. Our findings demonstrate a remarkable 99.7% accuracy in classification using this approach. In conclusion, this framework's multidisciplinary approach bridges the gap between healthcare, edge computing, and DLT, promising real-time data processing, enhanced security, and privacy preservation. With the rise of the Internet of Things, this innovation holds the potential to transform the future of healthcare technology.

show abstract