Smartphone Based Digital Stethoscope for Connected Health -- A Direct Acoustic Coupling Technique

2016 ◽  
Author(s):  
Arijit Sinharay ◽  
Deb Ghosh ◽  
Parijat Deshpande ◽  
Shahnawaz Alam ◽  
Rohan Banerjee ◽  
...  
Keyword(s):  
Coupling Technique ◽  
Acoustic Coupling ◽  
Connected Health

(Preprint)

2018 ◽  
Author(s):  
Ram Dixit ◽  
Sahiti Myneni
Keyword(s):  
Cancer Survivors ◽  
Mental Models ◽  
Health Systems ◽  
Information Needs ◽  
Health Data ◽  
Information Architecture ◽  
Personal Health ◽  
User Centered Design ◽  
Card Sort ◽  
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.

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

Electronics ◽  
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.

Investigation of zirconium nanowire by elastic, thermal and ultrasonic analysis

2020 ◽  
Vol 75 (12) ◽  
pp. 1077-1084
Author(s):  
Bhawan Jyoti ◽  
Shakti Pratap Singh ◽  
Mohit Gupta ◽  
Sudhanshu Tripathi ◽  
Devraj Singh ◽  
...  
Keyword(s):  
Ultrasonic Attenuation ◽  
Thermal Relaxation ◽  
Coupling Constants ◽  
Physical Parameters ◽  
Third Order ◽  
Jones Potential ◽  
Acoustic Coupling ◽  
Anisotropic Factor ◽  
Third Order Elastic Constants ◽  
Ultrasonic Analysis

AbstractThe elastic, thermal and ultrasonic properties of zirconium nanowire (Zr-NW) have been investigated at room temperature. The second and third order elastic constants (SOECs and TOECs) of Zr-NW have been figured out using the Lennard–Jones Potential model. SOECs have been used to find out the Young’s modulus, bulk modulus, shear modulus, Poisson’s ratio, Pugh’s ratio, Zener anisotropic factor and ultrasonic velocities. Further these associated parameters of Zr-NW have been utilized for the evaluation of the Grüneisen parameters, thermal conductivity, thermal relaxation time, acoustic coupling constants and ultrasonic attenuation. On the basis of the above analyzed properties of Zr-NW, some characteristics features of the chosen nanowire connected with ultrasonic and thermo-physical parameters have been discussed.

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

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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