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

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. © 2001-2012 IEEE.

Optical Chaos Based Secure Optical Body Area Network Employing Polarization Multiplexing and Free Space Optics for e-Health Applications

We propose and demonstrate an optical chaos secured optical body area network (OBAN) employing polarization multiplexing and free space optics links. The physiological data of patient coded in non-return to zero on-off keying (NRZ-OOK) format from two on-body nodes modulated on orthogonal polarization states of a continuous wave (CW) laser is secured by using additive chaos masking (ACM) technique with chaotic waveforms generated through direct modulation of semiconductor chaotic lasers (CLs). After polarization multiplexing, the secure NRZ- OOK modulated optical signals are transmitted over indoor and outdoor free space optics (FSO) links based on GammaGamma channel model towards remote healthcare center. After chaos subtraction, the NRZ-OOK modulated optical signals are photodetected and passed on to bit error rate (BER) estimator for performance analysis. The electronic health (e-health) system based on the proposed OBAN provides adequate privacy for classified patient related information with added advantages of acceptable BER results, cost efficiency, speedy installation and suitable for use in current pandemic situation. © 2022 IEEE.

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

The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences in people’s physical and mental wellbeing. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension amongst the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even increased frequency in suicides. Stress monitoring is a critical component of any strategy used to intervene in 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 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 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. IEEE

Religious marginality, covid-19, and redress of targeting and inequalities

This article interrogates whether we should consider ‘religious marginality’ as a qualifier much like the exploration of how gender, ethnicity, and class inequalities are explored when examining Covid-19-related vulnerabilities and their implications for building back better. Drawing on a case study of Pakistan as well as evidence from India, Uganda, and Iraq, this article explores the accentuation of vulnerabilities in Pakistan and how different religious minorities experience the impact of the interplay of class, caste, ethnicity, and religious marginality. The article argues that where religious minorities exist in contexts where the broader political and societal policy is one of religious ‘othering’ and where religious marginality intersects with socioeconomic exclusion, they experience particular forms of vulnerability associated directly or indirectly with Covid-19 consequences that are acute and dire in impact. Building back better for religiously inclusive societies will require both broad-based as well as more specific redress of inequalities. © 2021 The Authors. IDS Bulletin © Institute of Development Studies.