Ensemble Learning for Seated People Counting using WiFi Signals: Performance Study and Transferability Assessment

Bernaola, Jose Ramon Merino; Sobrón, Iker; Ser, Javier Del; Landa, Iratxe; Eizmendi, Iñaki; Vélez, Manuel

doi:10.1109/gcwkshps52748.2021.9682014

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…Bernaola et al [81] discovered that by investigating the features of wireless signals, such as those used by WiFi systems, machine learning models can recognize and classify a wide range of human behaviors without the use of intrusive equipment. The authors concentrate on identifying sitting people in an interior environment using ensemble learning, a subset of supervised learning machine learning models that rely on aggregating the results of many predictors.…”

Section: ) Wifi 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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Section: ) Wifi 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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“…A study focused in ensemble learning for seated people counting using Wi-Fi signals by Bernaola et al [20]. The study explores the use of ensample learning, a branch of supervised machine learning that combines the outputs of individual predictors for detecting seated people.…”

Section: Wifi Signals Applicationmentioning

confidence: 99%

A Contactless Smart WiFi-Based Application Presence or Fall Detection System: Analyzing Channel State Information (CSI) Signals

Al-Rajab,

Al Zraiqat,

John

et al. 2023

IJEMD-CSAI

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Falls are considered to be the most common accident among people of determination and the elderly. Recently, many solutions have been proposed, whether wearable or noncontact, for people presence or falling detection (FD). These solutions can use wearable sensors to effectively monitor the health condition of elderly people at home and ensure their performance. However, all of these solutions require users to always wear specialized devices and sensors in their bodies, which limits the deployment of large-scale systems. Additionally, camera-based systems can raise privacy concerns. Recently, the non-contact Wi-Fi approach is becoming more and more popular because of its ubiquitous and non-invasiveness. In this paper, we propose a smart contactless system that uses Artificial Intelligence (AI) to analyze the Channel State Information (CSI) signals extracted from Wi-Fi signals. Our proposed application can help the people of determination and senior citizens (e.g., remote monitoring of the elderly) to be engaged all the time through closed monitoring based on ability to analyse the CSI signals extracted from Wi-Fi signals. This system can detect the presence, and falls of users without requiring them to wear any specialized devices or sensors. We believe that this application can help elderly and disabled people to remain engaged and monitored at all times, providing their communities with the means to better care for and serve them.

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A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things

et al. 2023

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Ensemble Learning for Seated People Counting using WiFi Signals: Performance Study and Transferability Assessment

Cited by 7 publications

References 26 publications

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

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

A Contactless Smart WiFi-Based Application Presence or Fall Detection System: Analyzing Channel State Information (CSI) Signals

A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things

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