Remote monitoring of Covid-19 patients using IoT and AI

The covid-19 pandemic has brought changes in various sectors like healthcare, business, education and economy. Due to the large spread of covid-19 in a lot of countries there is shortage of hospital beds, oxygen supply and healthcare workers. So, the pandemic generated need to use smart pioneering technologies like Artificial Intelligence and Internet of Things to monitor patient in an effective way. In this research paper a prototype is proposed based on IoT and AI for monitoring home quarantine covid-19 patients. Wearable IoT devices automatically collect information like oxygen level, temperature of body, etc. with the help of integrated sensors. Coughing is one of the most noticeable symptoms of people infected with covid-19. Frequency of cough is detected using Tensor flow library of Deep learning model. This prototype is a way to make IoT sensors smarter enough to detect coughs with the help of a trained model. Coughing dataset is collected and labelled manually. Dataset is self-created and categorized into cough and noise. Cough detection is based on MFCC features using DNN and CNN. The use of these technologies can bring a quick transition in healthcare to avoid risks caused with the life of human beings.