hQChain_Leveraging towards Blockchain and Queueing Model for Secure Smart Connected Health

Blockchain facilitates a broad spectrum of applications such as transaction of cryptocurrency, catering to financial services, designing and constructing smart cities and so on. It has astounding benefits including accountability, consistency and decentralization. Smart healthcare can be exemplified as utilizing propitious electronic technology safeguarded with blockchain for superior diagnosis of the disorders, improvised and cost-effective treatment of the patients, and enhanced quality of lives. Since, blockchain in smart healthcare architecture hosts substantial amount of patient data queueing models play a pivotal role to efficiently process the data. In this paper, it highlights the concepts of blockchain, then delve into the smart healthcare architecture and then deal with the several queueing models that already exist. It proposes the model i.e. hQChain which is inculcating M1,b/Mb/1 queueing model into blockchain based smart healthcare architecture. It offers a queuing mathematical and analytical model to analyze and study the performance measurement of hQChain model.

Rural Healthcare IoT Architecture Based on Low-Energy LoRa

Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers’ well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.

Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.

Democratizing Global Healthcare through Scalable, Emergent (Beyond-the-Mobile) Wireless Technologies (Preprint)

UNSTRUCTURED Advances in mobile phone technologies coupled with availability of modern wireless networks is beginning to have a marked impact on digital health through the growing array of apps and connected devices. This said limited deployment outside of developed nations will require additional approaches in order to collectively reach the some 8 billion people on earth. Another consideration for development of digital health centered around mobile devices lies in the need for pairing steps, firmware updates and a variety of user-inputs which can increase friction for the patient. An alternate, so called ‘Beyond the Mobile’ (BTM) approach where medicaments, devices, and health services communicate directly to the cloud offers an attractive means to expand and fully realize our connected health utopia. In addition to offering highly personalized experiences, such approaches could address cost, security, and convenience concerns associated with smartphone based systems, translating to improved engagement and adherence rates with patients. Furthermore, by connecting these so-called Internet of Medical Things (IoMT) instruments through next generation networks, it offers the potential to reach patients with acute needs in non-urban regions of developing nations. Herein we outline how deployment of BTM technologies through Low Power Wide Area Networks (LPWAN) could offer a scalable means to democratize digital health and contribute to improved patient outcomes globally

“Everything Is Connected”: Health Lifestyles and Teenagers’ Social Distancing Behaviors in the COVID-19 Pandemic

Social distancing during the COVID-19 pandemic requires people to engage in new health behaviors that are public, monitored, and often contested. Parents are typically considered responsible for controlling their children’s behavior and instilling norms. We investigated how parents and teens managed teenagers’ social distancing behaviors. Analyzing longitudinal (2015–2020), dyadic qualitative interviews with teenagers and their parents in 20 families from two middle-class communities in which social distancing was normative, we found that preexisting health lifestyles were used to link social distancing behaviors to specific identities, norms, and understandings of health. The pandemic presented challenges resulting from contradictory threats to health, differing preferences, and conflicting social judgments. Parents responded to challenges by adhering to community norms and enforcing teens’ social distancing behaviors. They drew on preexisting, individualized health lifestyles as cultural tools to justify social distancing messages, emphasizing group distinctions, morality, and worth in ways that perpetuated inequalities.

A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare

Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies.