Capacity Control in Indoor Spaces Using Machine Learning Techniques Together with BLE Technology

Beato, M. Encarnación; Sánchez, Montserrat Mateos; Gallinas, Roberto Berjón; García, Ana Fermoso

doi:10.3390/jsan10020035

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Machine Learning techniques include both classification and regression algorithms, so they can be used both for presence detection and counting people. For example, K-Means and Gradient Boosting were adopted in [67], whereas Artificial Neural Networks, regression models and Decision Trees (Random Forest) were adopted in [68], and Random Forest were applied in [69]. Passive, BLE-based presence detection systems in the literature exploit Logistic Regression, k-Nearest Neighbor (KNN), and Support Vector Machine (SVM) with linear, polynomial and Radial Basis Function kernel.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

A Perspective on Passive Human Sensing with Bluetooth

Iannizzotto

Milici

Nucita

et al. 2022

Sensors

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Passive human sensing approaches based on the analysis of the radio signals emitted by the most common wireless communication technologies have been steadily gaining momentum during the last decade. In this context, the Bluetooth technology, despite its widespread adoption in mobile and IoT applications, so far has not received all the attention it deserves. However, the introduction of the Bluetooth direction finding feature and the application of Artificial Intelligence techniques to the processing and analysis of the wireless signal for passive human sensing pave the way for novel Bluetooth-based passive human sensing applications, which will leverage Bluetooth Low Energy features, such as low power consumption, noise resilience, wide diffusion, and relatively low deployment cost. This paper provides a reasoned analysis of the data preprocessing and classification techniques proposed in the literature on Bluetooth-based remote passive human sensing, which is supported by a comparison of the reported accuracy results. Building on such results, the paper also identifies and discusses the multiple factors and operating conditions that explain the different accuracy values achieved by the considered techniques, and it draws the main research directions for the near future.

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Section: Machine Learning Methodsmentioning

confidence: 99%

A Perspective on Passive Human Sensing with Bluetooth

Iannizzotto

Milici

Nucita

et al. 2022

Sensors

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“…The authors undertake tests to validate the proposed methodologies and assess their usefulness in industrial safety monitoring. Gutiérrez et al propose a novel solution to capacity control in indoor spaces that blends machine learning approaches with BLE technology [69]. In a real experiment done at a university, the authors investigate three distinct prediction models using machine learning approaches, specifically logistic regression, decision trees, and artificial neural networks.…”

Section: ) Radar Basedmentioning

confidence: 99%

Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

Shahbazian

Trubitsyna

2023

IEEE Access

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Applications for human sensing, also known as (human) occupancy detection, include energy management systems for intelligent buildings, intruder detection, e-health systems, the identification of everyday activity, and the monitoring of vital signs. These applications require intelligent decisionmaking that relies on human sensing. Multiple technologies based on vision, sensors, or radio signals can be used to detect occupancy. Vision-based systems use a multitude of cameras to recognize the human presence, but they are restricted by light availability, line-of-sight coverage, expensive equipment, and privacy concerns. Sensor-based techniques refer to a prospective method that employs various combinations of sensors. These solutions are static and necessitate costly equipment installation and maintenance. Due to technical advancements, radio-based signals, such as WiFi, have been integrated into various forms of infrastructure, including homes, offices, and constructions. Due to how human body movements affect wireless signal propagation, it is possible to detect human motions by analyzing the received wireless signals (such as reflection, diffraction, and scattering). Due to its low cost and non-intrusive nature, wirelessbased human activity detection has received substantial attention and become a key topic of study. This article reviews the underlying principles, methodologies, and system architectures of radio-frequency-based occupancy detection systems. We classify the reviewed research studies based on the technical measures and applications they employ. In addition to focusing on the security aspects of occupancy detection and discussing future trends and difficulties, we also discuss practical considerations.

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“…Using a network of BLE beacons to record the RSSI values of neighboring devices to infer the occupant's zone-level location was proposed in [37]. Another regression model and decision trees (random forest) based on BLE beacon networks were adopted in [38]. All these approaches require the occupants to carry permanently connected devices which have limitations in privacy concerns, with users forgetting to turn on the Bluetooth of their devices and the fact that they had to carry the sensor with them all the time.…”

Section: Introductionmentioning

confidence: 99%

Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration

Naghdi

O’Keefe

2022

Sensors

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The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Indoor positioning approaches apply different types of sensors to increase the accuracy of the user’s position. Among these technologies, Bluetooth Low Energy (BLE) appeared as a popular alternative due to its low cost and energy efficiency. However, BLE faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This work presents a method to compensate RSSI values by applying Artificial Neural Network (ANN) algorithms to RSSI measurements from three BLE advertising channels and a wearable camera as an additional source of information for the presence or absence of human obstacles. The resulting improved RSSI values are then converted into ranges using path loss models, and trilateration is applied to obtain indoor localization. The proposed artificial system provides significantly better localization solutions than fingerprinting or trilateration using uncorrected RSSI values.

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Capacity Control in Indoor Spaces Using Machine Learning Techniques Together with BLE Technology

Cited by 4 publications

References 18 publications

A Perspective on Passive Human Sensing with Bluetooth

A Perspective on Passive Human Sensing with Bluetooth

Human Sensing by Using Radio Frequency Signals: A Survey on Occupancy and Activity Detection

Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration

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