Real-Time Quantification of Group Attention via Multimodal Sensors and Spatio-Temporal GNN

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Real-time quantification of group attention can be achieved using a combination of multimodal sensors and spatio-temporal Graph Neural Networks. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Haoran Zhu; Bing Song

doi:10.1504/ijsnet.2026.10078832

January 1, 2026International Journal of Sensor Networks

Real-Time Quantification of Group Attention via Multimodal Sensors and Spatio-Temporal GNN

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Key Points

The aim is to develop a method for real-time quantification of group attention using advanced sensor technologies.
Utilized multimodal sensors to capture diverse data on group attention.
Implemented spatio-temporal graph neural networks (GNN) for data analysis and processing.
Conducted real-time analysis to provide immediate feedback on group attention levels.
Demonstrated effective real-time tracking of group attention with high accuracy.
Showed significant improvements in attention measurement compared to traditional methods.

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Implication

Quantifies group attention in real-time using multimodal sensors, indicating potential improvements in interactive settings.

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Cite This Study

Zhu et al. (2026) studied this question.

synapsesocial.com/papers/6a1a82640307b7850943409e https://doi.org/10.1504/ijsnet.2026.10078832

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Real-Time Quantification of Group Attention via Multimodal Sensors and Spatio-Temporal GNN

Key Points

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Abstract

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Cite This Study

Real-Time Quantification of Group Attention via Multimodal Sensors and Spatio-Temporal GNN

Key Points

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Implication

Abstract

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