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Effective Modeling and Numerical Simulation of Triboelectric Nanogenerator for Blood Pressure Measurement Based on Wrist Pulse Signal Using Comsol Multiphysics Software

[Image: see text] Among the wearable sensor family, the triboelectric nanogenerator has excellent potential in human healthcare systems due to its small size, self-powered, and low cost. Here is the design and simulation of the triboelectric nanogenerator using the 3D model in COMSOL Multiphysics so...

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Autores principales: Venugopal, Karthikeyan, Shanmugasundaram, Vivekanandan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352328/
https://www.ncbi.nlm.nih.gov/pubmed/35936394
http://dx.doi.org/10.1021/acsomega.2c03281
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author Venugopal, Karthikeyan
Shanmugasundaram, Vivekanandan
author_facet Venugopal, Karthikeyan
Shanmugasundaram, Vivekanandan
author_sort Venugopal, Karthikeyan
collection PubMed
description [Image: see text] Among the wearable sensor family, the triboelectric nanogenerator has excellent potential in human healthcare systems due to its small size, self-powered, and low cost. Here is the design and simulation of the triboelectric nanogenerator using the 3D model in COMSOL Multiphysics software for blood pressure measurement. As a reliable indicator of human physiological health, blood pressure (BP) has been utilized in more and more cases to predict and diagnose potential diseases and the dysfunction caused by hypertension. The main focus of this study is to prognosis and preserve human health against BP. It is one of the significant challenges in predicting and diagnosing BP in the human lifestyle. The self-powered triboelectric nanogenerator can diagnose BP using the wrist pulse pressure. To optimize the performance of the modeled triboelectric nanogenerator, the known wrist pulse pressure is applied explicitly, which converts the applied pressure into an equivalent electrical signal across the output terminals. An output open circuit voltage for the applied pulse pressure is 26 V. The generated output electrical signal is proportional to the applied pulse pressure, which is used to know the BP range. It ensures that the triboelectric nanogenerator is an opted sensor to sense the minute nadi pressure signal. This work validates that the simulated model has the potential to act as several health care monitors such as respiratory rate, heart rate, glucose range, joint motion sensing, gait, and CO(2) detectors.
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spelling pubmed-93523282022-08-05 Effective Modeling and Numerical Simulation of Triboelectric Nanogenerator for Blood Pressure Measurement Based on Wrist Pulse Signal Using Comsol Multiphysics Software Venugopal, Karthikeyan Shanmugasundaram, Vivekanandan ACS Omega [Image: see text] Among the wearable sensor family, the triboelectric nanogenerator has excellent potential in human healthcare systems due to its small size, self-powered, and low cost. Here is the design and simulation of the triboelectric nanogenerator using the 3D model in COMSOL Multiphysics software for blood pressure measurement. As a reliable indicator of human physiological health, blood pressure (BP) has been utilized in more and more cases to predict and diagnose potential diseases and the dysfunction caused by hypertension. The main focus of this study is to prognosis and preserve human health against BP. It is one of the significant challenges in predicting and diagnosing BP in the human lifestyle. The self-powered triboelectric nanogenerator can diagnose BP using the wrist pulse pressure. To optimize the performance of the modeled triboelectric nanogenerator, the known wrist pulse pressure is applied explicitly, which converts the applied pressure into an equivalent electrical signal across the output terminals. An output open circuit voltage for the applied pulse pressure is 26 V. The generated output electrical signal is proportional to the applied pulse pressure, which is used to know the BP range. It ensures that the triboelectric nanogenerator is an opted sensor to sense the minute nadi pressure signal. This work validates that the simulated model has the potential to act as several health care monitors such as respiratory rate, heart rate, glucose range, joint motion sensing, gait, and CO(2) detectors. American Chemical Society 2022-07-18 /pmc/articles/PMC9352328/ /pubmed/35936394 http://dx.doi.org/10.1021/acsomega.2c03281 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Venugopal, Karthikeyan
Shanmugasundaram, Vivekanandan
Effective Modeling and Numerical Simulation of Triboelectric Nanogenerator for Blood Pressure Measurement Based on Wrist Pulse Signal Using Comsol Multiphysics Software
title Effective Modeling and Numerical Simulation of Triboelectric Nanogenerator for Blood Pressure Measurement Based on Wrist Pulse Signal Using Comsol Multiphysics Software
title_full Effective Modeling and Numerical Simulation of Triboelectric Nanogenerator for Blood Pressure Measurement Based on Wrist Pulse Signal Using Comsol Multiphysics Software
title_fullStr Effective Modeling and Numerical Simulation of Triboelectric Nanogenerator for Blood Pressure Measurement Based on Wrist Pulse Signal Using Comsol Multiphysics Software
title_full_unstemmed Effective Modeling and Numerical Simulation of Triboelectric Nanogenerator for Blood Pressure Measurement Based on Wrist Pulse Signal Using Comsol Multiphysics Software
title_short Effective Modeling and Numerical Simulation of Triboelectric Nanogenerator for Blood Pressure Measurement Based on Wrist Pulse Signal Using Comsol Multiphysics Software
title_sort effective modeling and numerical simulation of triboelectric nanogenerator for blood pressure measurement based on wrist pulse signal using comsol multiphysics software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352328/
https://www.ncbi.nlm.nih.gov/pubmed/35936394
http://dx.doi.org/10.1021/acsomega.2c03281
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