E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine

Coronavirus becomes officially a global pandemic due to the speed spreading off in various countries. An increasing number of infected with this disease causes the Inability problem to fully care in hospitals and afflict many doctors and nurses inside the hospitals. This paper proposes a smart health system that monitors the patients holding the Coronavirus remotely. Due to protect the lives of the health services members (like physicians and nurses) from infection. This smart system observes the people with this disease based on putting many sensors to record many features of their patients in every second. These parameters include measuring the patient's temperature, respiratory rate, pulse rate, blood pressure, and time. The proposed system saves lives and improves making decisions in dangerous cases. It proposes using artificial intelligence and Internet-of-things to make remotely quarantine and develop decisions in various situations. It provides monitoring patients remotely and guarantees giving patients medicines and getting complete health care without anyone getting sick with this disease. It targets two people's slides the most serious medical conditions and infection and the lowest serious medical conditions in their houses. Observing in hospitals for the most serious medical cases that cause infection in thousands of healthcare members so there is a big need to uses it. Other less serious patients slide, this system enables physicians to monitor patients and get the healthcare from patient's houses to save places for the critical cases in hospitals.

[1]  Saraju P. Mohanty,et al.  Everything You Wanted to Know about Smart Health Care: Evaluating the Different Technologies and Components of the Internet of Things for Better Health , 2018, IEEE Consumer Electronics Magazine.

[2]  Jules-Raymond Tapamo,et al.  Face and Iris biometrics person identification using hybrid fusion at feature and score-level , 2015, 2015 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech).

[3]  Huaiqing Wang,et al.  A Feature Fusion Based Forecasting Model for Financial Time Series , 2014, PloS one.

[4]  Chintan Bhatt,et al.  Internet of Things (IoT): In a Way of Smart World , 2016 .

[5]  Bin Wang,et al.  A novel data fusion algorithm for multivariate time series , 2018, 2018 Chinese Control And Decision Conference (CCDC).

[6]  Seyed Shahrestani.  Internet of Things and Smart Environments: Assistive Technologies for Disability, Dementia, and Aging , 2017 .

[7]  Alessandro Crivellari,et al.  LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists , 2020, Sustainability.

[8]  Seyed A. Shahrestani.  Internet of Things and Smart Environments , 2017, Springer International Publishing.

[9]  Sotiris Zygiaris.  Smart City Reference Model: Assisting Planners to Conceptualize the Building of Smart City Innovation Ecosystems , 2012, Journal of the Knowledge Economy.

[10]  Koushik Kumar Nundy,et al.  Algorithms for Monitoring Heart Rate and Respiratory Rate From the Video of a User’s Face , 2018, IEEE Journal of Translational Engineering in Health and Medicine.

[11]  Henrik Hivand Volden.  Anomaly detection using machine learning techniques , 2016 .

[12]  Nasrollah Moghaddam Charkari,et al.  Hybrid feature and decision level fusion of face and speech information for bimodal emotion recognition , 2009, 2009 14th International CSI Computer Conference.

[13]  D. Mant,et al.  Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: a systematic review of observational studies , 2011, The Lancet.

[14]  Sijo George,et al.  Smart Traffic Control System , 2018 .

[15]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[16]  Mohamed F. Ghalwash,et al.  Minimum redundancy maximum relevance feature selection approach for temporal gene expression data , 2017, BMC Bioinformatics.

[17]  Sonali B. Wankhede,et al.  Anomaly Detection using Machine Learning Techniques , 2019, 2019 IEEE 5th International Conference for Convergence in Technology (I2CT).

[18]  Brian D. Davison,et al.  Learning Word Embeddings with Chi-Square Weights for Healthcare Tweet Classification , 2017 .

[19]  Tannaz Sattari Tabrizi,et al.  A Hospital Recommendation System Based on Patient Satisfaction Survey , 2017 .

[20]  Ralf C. Staudemeyer,et al.  Understanding LSTM - a tutorial into Long Short-Term Memory Recurrent Neural Networks , 2019, ArXiv.

[21]  Maria Fazio,et al.  An Innovative Methodology for Big Data Visualization for Telemedicine , 2019, IEEE Transactions on Industrial Informatics.

[22]  Wlodek Kulesza,et al.  IoT-based information system for healthcare application : Design methodology approach , 2017 .

[23]  Ioanna Chouvarda,et al.  Α Respiratory Sound Database for the Development of Automated Classification , 2017, BHI 2017.

[24]  Mo M. Jamshidi,et al.  Feature Fusion Models for Deep Autoencoders: Application to Traffic Flow Prediction , 2019, Appl. Artif. Intell..

[25]  S. Challa,et al.  Bayesian and Dempster-Shafer fusion , 2004 .