A Wearable Device for Respiratory Diseases Monitoring in Crowded Spaces. Case Study of COVID-19

The necessary measures need to be put in place for the fulfillment of good health and hygiene's Sustainable Development Goals (SDG). As reported by (WHO), chronic respiratory diseases are on the rise, especially in developing countries resulting in over four million deaths annually across the globe. In addition to the COVID-19 pandemic, some other highly contagious respiratory diseases include tuberculosis and influenza which can be also monitored. Since it is difficult to identify an infected person in order to avoid transmissions, this research is proposed as a measure to control and minimize the spread of such diseases. It aims at developing an artificial intelligence (AI)-powered system able to monitor the risk of respiratory diseases spreading using a wearable device. In this study, we propose a contactless system to detect an environment with a high risk of contamination. The device applies Internet of Things (IoT) sensing technologies for data collection from the user's immediate surrounding environment (cough sound and body temperature) and machine learning (ML) algorithms for data analysis to predict a possible exposure to respiratory diseases. The user will get alerts to take appropriate actions to avoid possible infections. The collected data is also sent to the cloud from time to time via cellular networks for further analytics and future research. This will be an improvement to existing cloud-based solutions considering connectivity and energy constraints in Africa and the need for real-time response. AI capabilities will move from the cloud to the data source (edge devices) using TinyML.