Detail publikačního výsledku

Enhancing Extended Reality assisted surgery through a Field-of-View video delivery optimization

ALEKSEEVA, D.; MEZINA, A.; BURGET, R.; ARPONEN, O.; LOHAN, E.; OMETOV, A.

Originální název

Enhancing Extended Reality assisted surgery through a Field-of-View video delivery optimization

Anglický název

Enhancing Extended Reality assisted surgery through a Field-of-View video delivery optimization

Druh

Článek WoS

Originální abstrakt

Emerging Extended Reality (XR) applications bring new opportunities for digital healthcare systems, i.e., eHealth. XR-assisted surgery is one of the most outstanding examples of future technology that has a high social impact on the healthcare and medical educational system. The current work presents the intelligent design for remote XR-assisted surgery. The study presents the Field-of-View (FoV)-based viewport model empowered with behavioral data. It applies the viewport prediction model based on the behavioral data by applying Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). In the final analysis, LSTM showed lower errors and a higher coefficient of determination, but ANN performed much faster. Finally, the study defines the dynamic system’s states for adaptive and fast video delivery concerning Quality of Experience (QoE). The presented approach aims to mitigate the delay to ensure smooth playback and display high-quality images.

Anglický abstrakt

Emerging Extended Reality (XR) applications bring new opportunities for digital healthcare systems, i.e., eHealth. XR-assisted surgery is one of the most outstanding examples of future technology that has a high social impact on the healthcare and medical educational system. The current work presents the intelligent design for remote XR-assisted surgery. The study presents the Field-of-View (FoV)-based viewport model empowered with behavioral data. It applies the viewport prediction model based on the behavioral data by applying Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). In the final analysis, LSTM showed lower errors and a higher coefficient of determination, but ANN performed much faster. Finally, the study defines the dynamic system’s states for adaptive and fast video delivery concerning Quality of Experience (QoE). The presented approach aims to mitigate the delay to ensure smooth playback and display high-quality images.

Klíčová slova

Extended Reality; Remote surgery; Artificial Intelligence; Video delivery; Network Management

Klíčová slova v angličtině

Extended Reality; Remote surgery; Artificial Intelligence; Video delivery; Network Management

Autoři

ALEKSEEVA, D.; MEZINA, A.; BURGET, R.; ARPONEN, O.; LOHAN, E.; OMETOV, A.

Rok RIV

2026

Vydáno

06.02.2025

Periodikum

Computer Networks

Svazek

March 2025

Číslo

259

Stát

Nizozemsko

Strany počet

18

URL

https://www.sciencedirect.com/science/article/pii/S1389128625000611

BibTex

@article{BUT196731,
  author="Daria {Alekseeva} and Anzhelika {Mezina} and Radim {Burget} and Otso {Arponen} and Elena Simona {Lohan} and Aleksandr {Ometov}",
  title="Enhancing Extended Reality assisted surgery through a Field-of-View video delivery optimization",
  journal="Computer Networks",
  year="2025",
  volume="March 2025",
  number="259",
  pages="18",
  issn="1389-1286",
  url="https://www.sciencedirect.com/science/article/pii/S1389128625000611"
}