A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable Diseases

• Published in: IEEE sensors journal Vol. 23; no. 2; pp. 981 - 988
• Main Authors: Khan, Haroon Ahmed, Nguyen, Tu N., Shafiq, Ghufran, Mirza, Jawad, Javed, Muhammad Awais
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anxiety disorders; Artificial intelligence; Biomedical monitoring; Cloud computing; Coronaviruses; COVID-19; Critical components; Energy efficient; Human factors; Hypertension; Machine learning; machine learning (ML); Monitoring; Pandemics; physical-layer security (PLS); physiological signals; Sensors; stress detection; Telemedicine; Viral diseases; Wearable sensors; Wearable technology
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3204586

The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences on people's physical and mental well-being. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension among the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even an increased frequency of suicides. Stress monitoring is a critical component of any strategy used to intervene in the case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines, and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an artificial intelligence (AI)-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors, and, finally, the proposed use of a combination of machine-learning (ML) classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19.