IoT-based framework for early identification and monitoring of COVID-19 cases

 The proposed system would employ an Internet of Things (IoTs) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. The framework consists of five main components: 

• Symptom Data Collection and Uploading (using wearable sensors), 
• Quarantine/Isolation Center, 
• Data Analysis Center (that uses machine learning algorithms), 
• Health Physicians, and 
• Cloud Infrastructure. 

To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine (SVM), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-NN), Decision Table, Decision Stump, OneR, and ZeroR. An experiment was conducted to test these eight algorithms on a real COVID-19 symptom dataset, after selecting the relevant symptoms. The results show that five of these eight algorithms achieved an accuracy of more than 90 %. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate identification of potential cases of COVID-19, and the framework would then document the treatment response for each patient who has contracted the virus.

An IoT-based framework for earlyidentification and monitoring of COVID-19 cases. Mwaffaq Otoom, et al. Biomed Signal Process Control. 2020 Sep; 62: 102149. doi: 10.1016/j.bspc.2020.102149