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Improving BLE-Based Passive Human Sensing with Deep Learning — arXiv:2609.02915

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02915
License (as archived): CC0-1.0
Archived: 2026-10-17T03:22:57.222Z
SHA-256 of the archived text: 45a0a09ccb91d13a2c9ed3d757974f9cbada6783b411ef575ad2d49c8d2387ad
Stable URL of this page: https://metraipolis.com/documento/dc_1007ac37d327

How to cite

Improving BLE-Based Passive Human Sensing with Deep Learning — arXiv:2609.02915 — arXiv (en), https://arxiv.org/abs/2609.02915 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_1007ac37d327 (Metraipolis archive dc_1007ac37d327, sha256 45a0a09ccb91d13a2c9ed3d757974f9cbada6783b411ef575ad2d49c8d2387ad)

BibTeX

@misc{dc1007ac37d327,
  title = {Improving BLE-Based Passive Human Sensing with Deep Learning — arXiv:2609.02915},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02915},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_1007ac37d327; sha256 45a0a09ccb91d13a2c9ed3d757974f9cbada6783b411ef575ad2d49c8d2387ad},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02915. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Passive Human Sensing (PHS) is an approach to collecting data on human presence, motion or activities that does not require the sensed human to carry devices or participate actively in the sensing process. In the literature, PHS is generally performed by exploiting the Channel State Information variations of dedicated WiFi, affected by human bodies obstructing the WiFi signal propagation path. However, the adoption of WiFi for PHS has some drawbacks, related to power consumption, large-scale deployment costs and interference with other networks in nearby areas. Bluetooth technology and, in particular, its low-energy version Bluetooth Low Energy (BLE), represents a valid candidate solution to the drawbacks of WiFi, thanks to its Adaptive Frequency Hopping (AFH) mechanism. This work proposes the application of a Deep Convolutional Neural Network (DNN) to improve the analysis and classification of the BLE signal deformations for PHS using commercial standard BLE devices. The proposed approach was applied to reliably detect the presence of human occupants in a large and articulated room with only a few transmitters and receivers and in conditions where the occupants do not directly occlude the Line of Sight between transmitters and receivers. This paper shows that the proposed approach significantly outperforms the most accurate technique found in the literature when applied to the same experimental data.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-04 in eess.SP).
Categorie arXiv: Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2609.02915 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T03:09:04.234Z dalla pagina https://arxiv.org/catchup/eess.SP/2026-09-04?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).