Non-invasive hypertension monitoring in smart furniture

Hypertension is a silent killer as it is difficult to be detected. Daily monitoring blood pressure is a good way for early diagnose hypertension. The common commercial hypertension monitoring device only consists of local data storage and normally equipped with cuff The data from the device is diffi...

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Main Author: Lum, Shirley
Format: Undergraduates Project Papers
Language:English
Published: 2017
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/25928/1/Non-invasive%20hypertension%20monitoring.pdf
http://umpir.ump.edu.my/id/eprint/25928/
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spelling my.ump.umpir.259282021-05-24T03:58:48Z http://umpir.ump.edu.my/id/eprint/25928/ Non-invasive hypertension monitoring in smart furniture Lum, Shirley TS Manufactures Hypertension is a silent killer as it is difficult to be detected. Daily monitoring blood pressure is a good way for early diagnose hypertension. The common commercial hypertension monitoring device only consists of local data storage and normally equipped with cuff The data from the device is difficult to be retrieved automatically and the repetition of pressure inflation during measurement may cause trauma. Recently, application of Internet of Things (loT) with home health monitoring system becomes a trend. This project proposed a cuffless hypertension monitoring, which embedded in the armchair, with a feature of automatically upload the recorded blood pressure and pulse rate readings to a cloud storage. An optical sensor is used to detect the photoplethysmography (PPG) wave from the fingertip of the patient. The raw PPG wave is filtered and amplified. Three parameters are taken from the PPG wave, including, systolic upstroke time (ST), diastolic time (DT), and time taken between systolic peak and diastolic peak (P2P) to estimate systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse rate (PR). Correlation and linear regression analysis are used to correlate this PPG parameter with blood pressure. The pulse rate is estimated by using the time taken between two successive optimum peaks in PPG wave. The mean difference of estimated SBP and DBP comparing to reference is 3.5909 ± 0.5478 mmHg and 3.6769 ± 0.6095 mmHg respectively. The mean difference of estimated PR comparing to reference is 5.914 ± 0.6970 BPM. The output, SBP, DBP, and PR, is uploaded to a cloud storage, Azure Blob Storage, and presented in Power BI dashboard. By providing cuffless blood pressure and pulse rate measurement and instantaneous upload BP and PR readings to cloud storage, the proposed solution has overcome the problem of common hypertension monitoring device. 2017-06 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25928/1/Non-invasive%20hypertension%20monitoring.pdf Lum, Shirley (2017) Non-invasive hypertension monitoring in smart furniture. Faculty of Manufacturing Engineering, Universiti Malaysia Pahang.
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TS Manufactures
spellingShingle TS Manufactures
Lum, Shirley
Non-invasive hypertension monitoring in smart furniture
description Hypertension is a silent killer as it is difficult to be detected. Daily monitoring blood pressure is a good way for early diagnose hypertension. The common commercial hypertension monitoring device only consists of local data storage and normally equipped with cuff The data from the device is difficult to be retrieved automatically and the repetition of pressure inflation during measurement may cause trauma. Recently, application of Internet of Things (loT) with home health monitoring system becomes a trend. This project proposed a cuffless hypertension monitoring, which embedded in the armchair, with a feature of automatically upload the recorded blood pressure and pulse rate readings to a cloud storage. An optical sensor is used to detect the photoplethysmography (PPG) wave from the fingertip of the patient. The raw PPG wave is filtered and amplified. Three parameters are taken from the PPG wave, including, systolic upstroke time (ST), diastolic time (DT), and time taken between systolic peak and diastolic peak (P2P) to estimate systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse rate (PR). Correlation and linear regression analysis are used to correlate this PPG parameter with blood pressure. The pulse rate is estimated by using the time taken between two successive optimum peaks in PPG wave. The mean difference of estimated SBP and DBP comparing to reference is 3.5909 ± 0.5478 mmHg and 3.6769 ± 0.6095 mmHg respectively. The mean difference of estimated PR comparing to reference is 5.914 ± 0.6970 BPM. The output, SBP, DBP, and PR, is uploaded to a cloud storage, Azure Blob Storage, and presented in Power BI dashboard. By providing cuffless blood pressure and pulse rate measurement and instantaneous upload BP and PR readings to cloud storage, the proposed solution has overcome the problem of common hypertension monitoring device.
format Undergraduates Project Papers
author Lum, Shirley
author_facet Lum, Shirley
author_sort Lum, Shirley
title Non-invasive hypertension monitoring in smart furniture
title_short Non-invasive hypertension monitoring in smart furniture
title_full Non-invasive hypertension monitoring in smart furniture
title_fullStr Non-invasive hypertension monitoring in smart furniture
title_full_unstemmed Non-invasive hypertension monitoring in smart furniture
title_sort non-invasive hypertension monitoring in smart furniture
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/25928/1/Non-invasive%20hypertension%20monitoring.pdf
http://umpir.ump.edu.my/id/eprint/25928/
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score 13.18916