Health and Environment Monitoring System for Viral Respiratory Diseases

Health and Environment Monitoring System for Viral Respiratory Diseases

The proposed system in this work is a contactless health and environment monitoring system designed after conducting a literature review on the most recent research on viral respiratory diseases and their impact on businesses, risk factors of a location, and surveillance of people for various safety...

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Bibliographic Details
Published in:2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1 - 7
Main Authors: S, Shwetha, N, Nithuna, B, Oviya, J, Swetha, N, Harini
Format: Conference Proceeding
Language:English
Published: IEEE 06-07-2023
Subjects:
Android Application
Body Temperature Detection
Contactless Vitals Measurements
Disease Prediction
Face recognition
Hardware
Health Monitoring System
Human factors
Image Processing
Mask detection
Organizations
Oxygen Saturation Measurement
PPG Signals
Pulmonary diseases
Pulse rate Detection
Python OpenCV
Social factors
Temperature measurement
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Summary:The proposed system in this work is a contactless health and environment monitoring system designed after conducting a literature review on the most recent research on viral respiratory diseases and their impact on businesses, risk factors of a location, and surveillance of people for various safety measures and causes leading to its spread. The proposed system takes the form of an Android Mobile Application, designed to function as a safety entry checkpoint for various organizations, including businesses and educational institutions. It aims to detect viral respiratory diseases by leveraging the capabilities of a smartphone camera without requiring any additional external hardware specifications. Furthermore, the system incorporates users' vaccination information, enabling a comprehensive assessment of health and safety. The key algorithms presented in this work focus on measuring critical body vital signs, including heart rate, temperature, and oxygen saturation (Oxygen saturation (SpO2)) levels. By utilizing advanced techniques such as mask detection and social distance monitoring, the system ensures the recognition of disease indicators and triggers appropriate actions to minimize the risk of transmission. To assess the safety of an area or environment, the proposed system employs the Euclidean distance metric and MobileNetV2, a state-of-the-art deep learning model. This combination enables real-time evaluation and analysis of the surroundings, providing crucial information for making informed decisions regarding the level of safety in a given location.
ISSN:2473-7674
DOI:10.1109/ICCCNT56998.2023.10306600