New Normal: Cooperative Paradigm for Covid-19 Timely Detection and Containment using Internet of Things and Deep Learning

TL;DR

This paper proposes an IoT and deep learning-based connected system for real-time COVID-19 detection, social distancing enforcement, and infection spread tracing to aid in pandemic containment and economic recovery.

Contribution

It introduces a novel integrated IoT and CNN-based framework for detecting social distancing violations and tracking infection spread in real-time.

Findings

Effective social distancing violation detection using YOLO v2 and v3

Successful simulation of infection spread tracing

Potential for real-time pandemic monitoring and response

Abstract

The spread of the novel coronavirus (COVID-19) has caused trillions of dollars in damages to the governments and health authorities by affecting the global economies. The purpose of this study is to introduce a connected smart paradigm that not only detects the possible spread of viruses but also helps to restart businesses/economies, and resume social life. We are proposing a connected Internet of Things ( IoT) based paradigm that makes use of object detection based on convolution neural networks (CNN), smart wearable and connected e-health to avoid current and future outbreaks. First, connected surveillance cameras feed continuous video stream to the server where we detect the inter-object distance to identify any social distancing violations. A violation activates area-based monitoring of active smartphone users and their current state of illness. In case a confirmed patient or a person with high symptoms is present, the system tracks exposed and infected people and appropriate measures are put into actions. We evaluated the proposed scheme for social distancing violation detection using YOLO (you only look once) v2 and v3, and for infection spread tracing using Python simulation.