AMAR: Lightweight Attention-Based Multi-User Activity Recognition from Wi-Fi CSI
The paper introduces AMAR, a lightweight attention-based framework for multi-user activity recognition using Wi-Fi channel state information. It addresses challenges in recognizing overlapping activities from multiple users by employing a transformer-based architecture. The proposed system significantly improves activity prediction accuracy and reduces bandwidth requirements compared to existing methods.
- ▪AMAR formulates human activity recognition as a set prediction problem to handle multi-user scenarios.
- ▪The framework uses learnable query embeddings for simultaneous identification of multiple activities.
- ▪AMAR achieves an F1-score of 53.4%, outperforming the best benchmark by 7.8%.
- ▪The system reduces occupancy estimation error by 74% while minimizing bandwidth usage.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20649 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary source text | contentText |
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| Cluster | o28UR-1vo50j |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Electrical Engineering and Systems Science > Signal Processing arXiv:2605.20649 (eess) [Submitted on 20 May 2026] Title:AMAR: Lightweight Attention-Based Multi-User Activity Recognition from Wi-Fi CSI Authors:Amirhossein Mohammadi, Hina Tabassum View a PDF of the paper titled AMAR: Lightweight Attention-Based Multi-User Activity Recognition from Wi-Fi CSI, by Amirhossein Mohammadi and Hina Tabassum View PDF HTML (experimental) Abstract:Wi-Fi-based human activity recognition (HAR) has emerged as a promising approach for contactless sensing, leveraging channel state information (CSI) collected from wireless transceivers.
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