Digital Twin Modeling and Spatial Awareness Tools, Acoustic Environment Recognition and Visual Tracking Algorithms, and Deep Neural Network and Vision Sensing Technologies in Blockchain-based Virtual Worlds
Pavol Durana1, Zdenka Musova2, and Adela-Claudia Cuțitoi3ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore virtual twin modeling, computer-generated images, and biometric authentication features enabling metaverse brand experiences. In this research, previous findings were cumulated showing that immersive visualization systems require virtual navigation tools, computer-generated images, and text mining techniques in retail livestreaming. Throughout July 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “blockchain-based virtual worlds” + “digital twin modeling and spatial awareness tools,” “acoustic environment recognition and visual tracking algorithms,” and “deep neural network and vision sensing technologies.” As research published in 2022 was inspected, only 131 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 27 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: digital twin modeling; spatial awareness; acoustic environment recognition; visual tracking; vision sensing; blockchain
How to cite: Durana, P., Musova, Z., and Cuțitoi, A.-C. (2022). “Digital Twin Modeling and Spatial Awareness Tools, Acoustic Environment Recognition and Visual Tracking Algorithms, and Deep Neural Network and Vision Sensing Technologies in Blockchain-based Virtual Worlds,” Analysis and Metaphysics 21: 261–277. doi: 10.22381/am21202216.
Received 16 August 2022 • Received in revised form 14 December 2022
Accepted 17 December 2022 • Available online 30 December 2022