ABSTRACT. The objective of this paper is to systematically review immersive decentralized networking and virtual navigation tools, photorealistic synthetic images and data, and metaverse assets and services. The findings and analyses highlight that cognitive computing and multimodal sensing systems, environment mapping and predictive modeling algorithms, and digital twin and tactile sensing technologies articulate extended reality environments. Throughout December 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “the metaverse economy” + “deep learning-based object detection and holographic virtual imaging technologies,” “mobile biometric and sentiment data,” and “digital twin simulation and modeling tools.” As research published between 2022 and 2023 was inspected, only 168 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, we selected 32 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.

Keywords: deep learning-based object detection and holographic virtual imaging technologies; mobile biometric and sentiment data; digital twin simulation and modeling tools; metaverse

How to cite: Frajtova Michalikova, K., Ondrejka, R., and Johnson, E. (2023). “Deep Learning-Based Object Detection and Holographic Virtual Imaging Technologies, Mobile Biometric and Sentiment Data, and Digital Twin Simulation and Modeling Tools in the Metaverse Economy,” Linguistic and Philosophical Investigations 22: 247–263. doi: 10.22381/lpi22202315.

Received 25 January 2023 • Received in revised form 21 May 2023
Accepted 23 May 2023 • Available online 30 May 2023