Multimodal Sensing for Live-Stand Analytics: A Design-Oriented Literature Synthesis and Reference Architecture

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Abstract
Tourism, cultural events, and trade exhibitions increasingly need real-time, privacy-risk-reducing methods for quantifying visitor behavior and satisfaction at physical stands. This article addresses a fragmented evidence base by (i) presenting a focused, design-oriented literature synthesis of multimodal sensing for stand-like environments and (ii) proposing a unified, deployment-oriented reference architecture. It analyses a 60-paper design corpus (2022–2026), together with selected benchmark/context references across RGB-D, thermal cameras, LiDAR, mmWave radar, microphone arrays, and multimodal fusion, and maps the analytic corpus to event-relevant key performance indicators (KPIs). The synthesis identifies gaps in the corpus: scarce stand-specific, multimodal KPI-labeled event datasets, conceptual inconsistencies in satisfaction inference, and limited direct evidence for commercially important KPIs such as feedback, conversion, and retention. In response, the article introduces the Stand Multimodal Behavior and Satisfaction Analysis (SMBSA) architecture, a five-layer edge-AI conceptual reference architecture with hierarchical fusion and candidate analytics heads for future validation of behavior and satisfaction KPIs under GDPR-aware, privacy-by-design requirements.
Funding Information
  • NOVA Laboratory for Computer Science and Informatics (UID/04516/2025)
  • AI.INSIGHTS: AI-Powered Real-Time Anonymous User Echo Monitoring (ALGARVE-FEDER-02964500)
  • ALGARVE 2030
  • Portugal 2030
  • European Commission

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