Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

The World Health Organization has declared the COVID-19 pandemic, with most countries being affected by this virus both socially and economically. It thus became necessary to develop solutions to help monitor and control disease spread by controlling medical workers’ movements and warning them against approaching infected individuals in isolation rooms. This paper introduces a control system that uses improved particle swarm optimization (PSO), and artificial neural network (ANN) approaches to achieve social distancing. The distance between medical workers carrying mobile nodes and the beacon node (isolation room) was determined using the ZigBee wireless protocol’s received signal strength indicator (RSSI). Two path loss models were developed to determine the distance from patients with COVID-19: the first is a log-normal shading model (LNSM), and the second is a polynomial function (POL). The coefficient values of the POL model were controlled based on PSO to improve model performance. A random-nonlinear time variation controller-PSO (RNT-PSO) approach was developed to avoid the local minima of the conventional PSO. As a result, social distancing for COVID-19 can be accurately determined. The measured RSSI and the distance were used as ANN inputs, while three control signals (alarming, warning, and closing) were used as ANN outputs. The results revealed that the hybrid model between POL and RNT-PSO, called RNT-PSO-POL, improved the system’s performance by reducing the mean absolute error of distance to 1.433 m, compared to 1.777 m for the LNSM. The results show that the ANN achieves robust performance in terms of mean squared error.