ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore mobile edge computing and deep learning-based generative networks, context-aware augmented reality and bio-inspired artificial vision systems, and emotion detection and recognition technologies articulating extended reality environments. In this research, previous findings were cumulated showing that the decentralized and interconnected metaverse integrates convolutional neural and digital twin-enabled edge networks, movement and behavior tracking tools, and immersive 3D and machine perception technologies. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “the decentralized and interconnected metaverse” + “context-aware augmented reality and bio-inspired artificial vision systems,” “situational awareness and deep learning forecasting algorithms,” and “scene recognition and spatial computing technologies.” As research published in 2022 was inspected, only 139 articles satisfied the eligibility criteria, and 28 sources were selected. 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: context-aware augmented reality; bio-inspired; artificial vision; situational awareness; deep learning; forecasting; scene recognition; spatial computing; metaverse

How to cite: Cug, J., Rowland, Z., and Barker, M. (2022). “Context-aware Augmented Reality and Bio-inspired Artificial Vision Systems, Situational Awareness and Deep Learning Forecasting Algorithms, and Scene Recognition and Spatial Computing Technologies in the Decentralized and Interconnected Metaverse,” Smart Governance 1(3): 71–86. doi: 10.22381/sg1320225.

Received 26 June 2022 • Received in revised form 23 September 2022
Accepted 27 September 2022 • Available online 30 September 2022