ABSTRACT. Despite the relevance of immersive shopping experiences in the blockchain-based virtual economy, only limited research has been conducted on this topic. In this article, we cumulate previous research findings indicating that machine vision algorithms, immersive technologies, and customer personalization tools enable personalized digital shopping experiences in the virtual commerce. Throughout May 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “metaverse” + “haptic and biometric sensor technologies,” “deep learning-based image classification algorithms,” and “movement and behavior tracking tools.” As we inspected research published between 2021 and 2022, only 152 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 27, 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, Distiller SR, and MMAT.

Keywords: haptic and biometric sensor technologies; deep learning; image classification; movement and behavior tracking; metaverse

How to cite: Kovacova, M., Horak, J., and Popescu, G. H. (2022). “Haptic and Biometric Sensor Technologies, Deep Learning-based Image Classification Algorithms, and Movement and Behavior Tracking Tools in the Metaverse Economy,” Analysis and Metaphysics 21: 176–192. doi: 10.22381/am21202211.

Received 18 June 2022 • Received in revised form 20 December 2022
Accepted 24 December 2022 • Available online 30 December 2022