An IoT-based wearable and AI camera system can successfully monitor vital signs and analyze facial expressions to support the management of COVID-19 patients.
The spread of infectious diseases, such as COVID-19, presents a significant problem for public health and healthcare systems. Digital technology plays an important role in achieving access to healthcare by enhancing device connectivity and information sharing. This study aimed to develop, implement, and demonstrate a tracking and surveillance system to enhance monitoring for emerging infectious diseases, focusing on COVID-19 patient profiling. The system integrates IoT-based wearable devices, an artificial intelligence (AI) camera for real-time monitoring, and a MySQL database for data management. The program uses Charts.js for data visualization and Longdo Map API for mapping, leveraging Jetson Nano boards, webcams, and Python (Version 3.9). We employed a classification technique to categorize patients into two groups: those with a positive mood and those with a negative mood. For comparing accuracies, we utilized three types of models: multilayer perceptron (MLP), support vector machine (SVM), and random forest. Model validation and evaluation were conducted using Python programming. The results of this study fall into three parts. The first part involved testing the monitoring and surveillance system. It was found that the system could receive information from the wearable device, display the received data in graph form, and notify the medical staff when examining symptoms to consider whether the patient should be taken to the hospital. The second part focused on testing the device, and it was found that it could measure body temperature, heart rate, and blood oxygen levels (SpO2) and send those data to the database. The third part involved an AI camera test, and it was found that the most suitable algorithm to analyze the patient’s facial expressions was Random Forest. The results show that the system supports hospitals in managing COVID-19 and similar diseases by enabling timely interventions through facial expression analysis.
Kajornkasirat et al. (Wed,) studied this question.