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An Improved IoT-Based System for Detecting the Number of People and Their Distribution in a Classroom.
Matuska, Slavomir; Machaj, Juraj; Hudec, Robert; Kamencay, Patrik.
  • Matuska S; Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia.
  • Machaj J; Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia.
  • Hudec R; Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia.
  • Kamencay P; Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia.
Sensors (Basel) ; 22(20)2022 Oct 18.
Article in English | MEDLINE | ID: covidwho-2071713
ABSTRACT
This paper presents an improved IoT-based system designed to help teachers handle lessons in the classroom in line with COVID-19 restrictions. The system counts the number of people in the classroom as well as their distribution within the classroom. The proposed IoT system consists of three parts a Gate node, IoT nodes, and server. The Gate node, installed at the door, can provide information about the number of persons entering or leaving the room using door crossing detection. The Arduino-based module NodeMCU was used as an IoT node and sets of ultrasonic distance sensors were used to obtain information about seat occupancy. The system server runs locally on a Raspberry Pi and the teacher can connect to it using a web application from the computer in the classroom or a smartphone. The teacher is able to set up and change the settings of the system through its GUI. A simple algorithm was designed to check the distance between occupied seats and evaluate the accordance with imposed restrictions. This system can provide high privacy, unlike camera-based systems.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: S22207912

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: S22207912