ABSTRACT. The aim of this systematic review is to synthesize and analyze immersive and interactive technologies, asset maintenance simulations, and real-time data-based digital twins in the industrial metaverse. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout August 2022, with search terms including “the industrial metaverse” + “multi-modal synthetic data fusion and analysis,” “virtual immersive and cognitive neuro-engineering technologies,” and “bio-inspired computational intelligence and deep learning algorithms.” As we analyzed research published in 2022, only 151 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 20, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: multi-modal synthetic data fusion and analysis; virtual immersive and cognitive neuro-engineering technologies; bio-inspired computational intelligence; deep learning algorithms; industrial metaverse

How to cite: Aldridge, S., Geambazi, P., and Alexandru, B. (2022). “Multi-Modal Synthetic Data Fusion and Analysis, Virtual Immersive and Cognitive Neuro-Engineering Technologies, and Bio-inspired Computational Intelligence and Deep Learning Algorithms in the Industrial Metaverse,” Journal of Self-Governance and Management Economics 10(4): 22–36. doi: 10.22381/jsme10420222.

Received 26 September 2022 • Received in revised form 25 December 2022
Accepted 27 December 2022 • Available online 30 December 2022