Virtual Healthcare Visits, Wearable Devices and Ambient Sensors, and Biomedical Big Data in the Metaverse Environment Cover Image
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Virtual Healthcare Visits, Wearable Devices and Ambient Sensors, and Biomedical Big Data in the Metaverse Environment
Virtual Healthcare Visits, Wearable Devices and Ambient Sensors, and Biomedical Big Data in the Metaverse Environment

Author(s): Kimberly Hancock
Subject(s): Health and medicine and law, ICT Information and Communications Technologies
Published by: Addleton Academic Publishers
Keywords: virtual; healthcare; wearable; sensor; biomedical; metaverse;

Summary/Abstract: Based on an in-depth survey of the literature, the purpose of the paper is to explore metaverse healthcare data, wearable technology sensors, and smart healthcare devices and applications furthering digital twin-based patient care units. In this research, previous findings were cumulated showing that anthropomorphic 3D virtual avatars in immersive clinical settings require big healthcare data analytics, smart wearable Internet of Medical Things technologies, and medical artificial intelligence algorithms, and I contribute to the literature by indicating that Internet of Things-based healthcare systems require wearable medical devices and digital twin simulation tools in virtual metaverse spaces. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “the metaverse environment” + “virtual healthcare visits,” “wearable devices and ambient sensors,” and “biomedical big data.” As research published in 2022 was inspected, only 136 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 25 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

  • Issue Year: 9/2022
  • Issue No: 2
  • Page Range: 73-88
  • Page Count: 16
  • Language: English