Real-time wearable device for predicting a long covid patient's condition - AbdelRahman, AbdelGawad Tamer; Nordin, Nor Hidayati Diyana; Toha, Siti Fauziah; Idris, Ahmad Syahrin (IET, 2022)
This paper aims to develop a wearable device that can be able to Predict the long covid-19 patients’ conditions, to notify the doctors on a real-time basis. Long covid-19 patients suffer a lot during their daily activities especially if the lasting symptom is related to the respiratory system. By developing a system, that is easy and comfortable to wear during normal daily life, we believe that we will be able to predict the long covid-19 patients’ condition. The system should first detect and analyze the patient’s breathing pattern using artificial intelligence then store the patient’s breathing pattern along with his status in an online database, then notify the doctors in case of a critical situation. To train the model the breathing pattern of current long covid patients and normal people was captured during doing daily activities such as walking, sitting, and climbing stairs. We hope that the developed system will help in easing the suffering of long covid patients by providing better monitoring of their health.