# Estimating indoor occupancy through low-cost BLE devices

arXiv: https://arxiv.org/abs/2102.03351 · HTML: https://mlsanity.com/paper/2102.03351/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 30 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey](https://mlsanity.com/paper/2004.08821/) (2020)
- [Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges](https://mlsanity.com/paper/1610.05424/) (2016)
- [Exploiting WiFi Channel State Information for Residential Healthcare Informatics](https://mlsanity.com/paper/1712.03401/) (2017)
- [B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows](https://mlsanity.com/paper/2101.10870/) (2021)
- [Precise Energy Modeling for the Bluetooth Low Energy Protocol](https://mlsanity.com/paper/1403.2919/) (2014)
- [How Reliable is Smartphone-based Electronic Contact Tracing for COVID-19?](https://mlsanity.com/paper/2005.05625/) (2020)
- [Identifying the BLE Advertising Channel for Reliable Distance Estimation on Smartphones](https://mlsanity.com/paper/2006.09099/) (2020)

## Built on by (most cited first, 3 of 3)

- [Modeling Classroom Occupancy using Data of WiFi Infrastructure in a University Campus](https://mlsanity.com/paper/2104.10667/) (2021)
- [ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity Recogntion](https://mlsanity.com/paper/2408.09527/) (2024)
- [mmFlux: Crowd Flow Analytics with Commodity mmWave MIMO Radar](https://mlsanity.com/paper/2507.07331/) (2025)

## Abstract (arXiv)

Detecting the presence of persons and estimating their quantity in an indoor environment has grown in importance recently. For example, the information if a room is unoccupied can be used for automatically switching off the light, air conditioning, and ventilation, thereby saving significant amounts of energy in public buildings. Most existing solutions rely on dedicated hardware installations, which involve presence sensors, video cameras, and carbon dioxide sensors. Unfortunately, such approaches are costly, are subject to privacy concerns, have high computational requirements, and lack ubiquitousness. The work presented in this article addresses these limitations by proposing a low-cost occupancy detection system. Our approach builds upon detecting variations in Bluetooth Low Energy (BLE) signals related to the presence of humans. The effectiveness of this approach is evaluated by performing comprehensive tests on five different datasets. We apply several pattern recognition models and compare our methodology with systems building upon IEEE 802.11 (WiFi). On average, in multifarious environments, we can correctly classify the occupancy with an accuracy of 97.97%. When estimating the number of people in a room, on average, the estimated number of subjects differs from the actual one by 0.32 persons. We conclude that our system's performance is comparable to that of existing ones based on WiFi, while significantly reducing cost and installation effort. Hence, our approach makes occupancy detection practical for real-world deployments.