Editorial: Immersive Media in Connected Health

BACKGROUND Connected Health technologies are a promising solution for chronic disease management. However, the scope of connected health systems makes it difficult to employ user-centered design in their development, and poorly designed systems can compound the challenges of information management in chronic care. The Digilego Framework addresses this problem with informatics methods that complement quantitative and qualitative methods in system design, development, and architecture. OBJECTIVE To determine the accuracy and validity of the Digilego information architecture of personal health data in meeting cancer survivors’ information needs. METHODS We conducted a card sort study with 9 cancer survivors (patients and caregivers) to analyze correspondence between the Digilego information architecture and cancer survivors’ mental models. We also analyzed participants’ card sort groups qualitatively to understand their conceptual relations. RESULTS We observed significant correlation between the Digilego information architecture and cancer survivors’ mental models of personal health data. Heuristic analysis of groups also indicated informative discordances and the need for patient-centric categories relating health tracking and social support in the information architecture. CONCLUSIONS Our pilot study shows that the Digilego Framework can capture cancer survivors’ information needs accurately; we also recognize the need for larger studies to conclusively validate Digilego information architectures. More broadly, our results highlight the importance of complementing traditional user-centered design methods and innovative informatics methods to create patient-centered connected health systems.

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

Electronics ◽

10.3390/electronics10131558 ◽

2021 ◽

Vol 10 (13) ◽

pp. 1558

Author(s):

Muhammad Bilal Khan ◽

Mubashir Rehman ◽

Ali Mustafa ◽

Raza Ali Shah ◽

Xiaodong Yang

Keyword(s):

Psychological Health ◽

Software Defined Radio ◽

Orthogonal Frequency Division Multiplexing ◽

Additive White Gaussian Noise ◽

Vital Signs ◽

Wireless Channel ◽

Machine Learning Algorithms ◽

Breathing Patterns ◽

Connected Health ◽

Innovative Solution

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.

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Guest Editorial: Special Issue on Deep Representation and Transfer Learning for Smart and Connected Health

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2021.3049931 ◽

2021 ◽

Vol 32 (2) ◽

pp. 464-465

Author(s):

Vasile Palade ◽

Stefan Wermter ◽

Ariel Ruiz-Garcia ◽

Antonio De Padua Braga ◽

Clive Cheong Took

Keyword(s):

Transfer Learning ◽

Guest Editorial ◽

Special Issue ◽

Connected Health ◽

Editorial Special Issue

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Rural Healthcare IoT Architecture Based on Low-Energy LoRa

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18147660 ◽

2021 ◽

Vol 18 (14) ◽

pp. 7660

Author(s):

Ace Dimitrievski ◽

Sonja Filiposka ◽

Francisco José Melero ◽

Eftim Zdravevski ◽

Petre Lameski ◽

...

Keyword(s):

Low Power ◽

Rural Areas ◽

Urban Areas ◽

Well Being ◽

Battery Life ◽

Ultra Low Power ◽

Term Operation ◽

Doctor Patient Communication ◽

Connected Health ◽

Iot Devices

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.

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A Sensitivity Analysis of Biophysiological Responses of Stress for Wearable Sensors in Connected Health

IEEE Access ◽

10.1109/access.2021.3082423 ◽

2021 ◽

pp. 1-1

Author(s):

Talha Iqbal ◽

Pau Redon ◽

Andrew J Simpkin ◽

Adnan Elahi ◽

Sandra Ganly ◽

...

Keyword(s):

Sensitivity Analysis ◽

Wearable Sensors ◽

Connected Health

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

Diagnostics ◽

10.3390/diagnostics11030556 ◽

2021 ◽

Vol 11 (3) ◽

pp. 556

Author(s):

Talha Iqbal ◽

Adnan Elahi ◽

Pau Redon ◽

Patricia Vazquez ◽

William Wijns ◽

...

Keyword(s):

Cardiovascular Events ◽

Medical Conditions ◽

Review Of The Literature ◽

Stress Monitoring ◽

Preventive Healthcare ◽

Connected Health ◽

Root Cause ◽

Chemical Indicators ◽

Life Threatening ◽

Assessment Of Stress

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.

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