Proximity Sensor for Measuring Social Interaction in a School Environment

Author:

Hernández-Heredia Tania Karina1ORCID,Reyes-Manzano Cesar Fabián2ORCID,Flores-Hernández Diego Alonso1ORCID,Ramos-Fernández Gabriel34ORCID,Guzmán-Vargas Lev1ORCID

Affiliation:

1. Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Instituto Politécnico Nacional, Mexico City 07340, Mexico

2. Tecnológico Nacional de México, Tecnológico de Estudios Superiores de Ixtapaluca, Km. 7 Carretera Ixtapaluca-Coatepec S/N San Juan, Ixtapaluca 56580, Mexico

3. Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autonoma de Mexico, Mexico City 04510, Mexico

4. Centro de Ciencias de la Complejidad, Universidad Nacional Autonoma de Mexico, Mexico City 04510, Mexico

Abstract

Social interactions are characterized by being very diverse and changing over time. Understanding this diversity and dynamics, as well as their emerging patterns, is of great interest from social, health, and educational perspectives. The development of new devices has been made possible in recent years by advances in applied technology. This paper presents the design and development of a novel device composed of several sensors. Specifically, we propose a proximity sensor integrated by three devices: a Bluetooth sensor, a global positioning system (GPS) unit and an accelerometer. By means of this sensor it is possible to detect the presence of neighboring sensors in various configurations and operating conditions. Profiles based on the Received Signal Strength Indicator (RSSI) exhibit behavior consistent with that reported by empirical relationships. The present sensor is functional in detecting the proximity of other sensors and is thus useful for the identification of interactions between people in relevant contexts such as schools.

Funder

Consejo Nacional de Humanidades, Ciencias y Tecnologías

Secretaría de Investigación y Progrado, Instituto Politécnico Nacional, Mexico

Publisher

MDPI AG

Reference58 articles.

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3. Ramírez, L.G.C., Jiménez, G.S.A., and Carreño, J.M. (2014). Sensores y Actuadores, Grupo Editorial Patria.

4. Robust Indoor Positioning of Automated Guided Vehicles in Internet of Things Networks with Deep Convolution Neural Network Considering Adversarial Attacks;Elsisi;IEEE Trans. Veh. Technol.,2024

5. Sharma, A., Chauhan, R.C.S., and Kaur, J. (2024). Performance Evaluation of SC-FDMA in Fading Channels with PAPR, QAM, and RSSI Analysis. Res. Sq.

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