ABSTRACT. In this article, we cumulate previous research findings indicating that 3D holographic avatars, context modeling and ambient scene detection tools, and tactile sensing and machine intelligence technologies are instrumental in digital hyper-realistic worlds. Throughout May 2023, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “the decentralized metaverse” + “context modeling and ambient scene detection tools,” “machine learning-based object recognition and distributed sensing technologies,” and “immersive visualization and haptic augmented reality systems.” As we inspected research published in 2022 and 2023, only 179 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 30, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, ROBIS, and SRDR.

Keywords: context modeling; ambient scene detection tools; machine learning; object recognition; distributed sensing technologies; immersive visualization; haptic augmented reality systems; metaverse

How to cite: Poliak, M., Poliakova, A., and Nica, E. (2023). “Context Modeling and Ambient Scene Detection Tools, Machine Learning-based Object Recognition and Distributed Sensing Technologies, and Immersive Visualization and Haptic Augmented Reality Systems in the Decentralized Metaverse,” Review of Contemporary Philosophy 21: 261–277. doi: 10.22381/RCP22202315.

Received 23 June 2023 • Received in revised form 24 August 2023
Accepted 26 August 2023 • Available online 30 August 2023