ArticleSensors (Basel, Switzerland)2025
Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Occupancy monitoring (OM) and the localization of individuals within indoor environments using wearable devices offer a very promising data communication solution in applications such as home automation, smart office management, outbreak monitoring, and emergency operating plans. OM is challenging when developing solutions that focus on reduced power consumption and cost. Bluetooth low energy (BLE) technology is energy- and cost-efficient compared to other technologies. Integrating BLE Received Signal Strength Indicator (RSSI) signals with machine learning (ML) introduces a new Artificial Intelligence- (AI-) enhanced OM approach. In this paper, we propose an Intelligent Bluetooth Virtual Door (IBVD) OM system for the indoor/outdoor tracking of individuals using the interaction between a BLE device worn by the occupant and two BLE beacons located at the entrance/exit points of a doorway. ML algorithms are used to perform intelligent OM through pattern detection from the BLE RSSI signal(s). This approach differs from other technologies in that it does not require any floorplan information. The developed OM system achieves a range between 96.6% and 97.3% classification accuracy for all tested ML models, where the error translates to a minor delay in the time in which an individual's location is classified, introducing a highly reliable indoor/outdoor tracking system.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.