Estimating indoor occupancy through low-cost BLE devices

TL;DR

This paper presents a low-cost BLE-based system for indoor occupancy detection that achieves high accuracy and can estimate the number of people, offering a privacy-preserving alternative to traditional sensor methods.

Contribution

It introduces a novel, affordable BLE signal analysis approach for occupancy detection, reducing costs and complexity compared to existing WiFi-based systems.

Findings

97.97% average classification accuracy

0.32 persons average error in counting

Comparable performance to WiFi-based systems

Abstract

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.