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Wireless heart-beat monitoring system with supervised learning / Lye Wei Liang... [et al.]

Most of the hospitals in Malaysia still utilise manual inspection by medical personnel to determine the health conditions of the patients. The data collected from the medical equipment would have to be analysed and verified by the hospital. Frequently, many patients need medical inspections. However...

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Main Authors: Liang, Lye Wei, Yusuf Fadhlullah, Solahuddin, Abdullah, Samihah, Abdul Hamid, Shabinar
Format: Article
Language:English
Published: Universiti Teknologi Mara Cawangan Pulau Pinang 2020
Subjects:
Pharmaceutical technology
Industrial research. Research and development
Online Access:http://ir.uitm.edu.my/id/eprint/33233/1/33233.pdf
http://ir.uitm.edu.my/id/eprint/33233/
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spelling my.uitm.ir.332332020-08-13T06:19:35Z http://ir.uitm.edu.my/id/eprint/33233/ Wireless heart-beat monitoring system with supervised learning / Lye Wei Liang... [et al.] Liang, Lye Wei Yusuf Fadhlullah, Solahuddin Abdullah, Samihah Abdul Hamid, Shabinar Pharmaceutical technology Industrial research. Research and development Most of the hospitals in Malaysia still utilise manual inspection by medical personnel to determine the health conditions of the patients. The data collected from the medical equipment would have to be analysed and verified by the hospital. Frequently, many patients need medical inspections. However, to provide a precise diagnosis, medical personnel requires more time. This limitation can be addressed by the development of automated and wireless health monitoring systems with health diagnostic feature supported by artificial intelligence (AI). In this project, the objective is to develop a prototype of a wireless (non-invasive) heartbeat monitoring system with supervised learning. This system monitors the heartbeat activity and predicts the condition of the user's heartbeat. Technically, a photoplethysmography-based (PPG-based) heartbeat sensor is used to build a heartbeat sensing device with a Bluetooth feature that communicates with an Android application. The Android application is developed to receive heartbeat data from the device and feed the data into an AI classification model to predict the heartbeat condition of the user. This AI classifier was built from heartbeat data collected from 10 healthy people. The additional heartbeat dataset was generated based on a sound source of heartbeat information to increase the volume of the training dataset. The completion of this project implementation results in a wireless heartbeat monitoring system that can be applied regardless of location and time. The accuracy of the AI prediction is 99 % when evaluated with a testing dataset. The empirical accuracy obtained by testing the system with actual implementation is 90 %. Universiti Teknologi Mara Cawangan Pulau Pinang 2020-06-30 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/33233/1/33233.pdf Liang, Lye Wei and Yusuf Fadhlullah, Solahuddin and Abdullah, Samihah and Abdul Hamid, Shabinar (2020) Wireless heart-beat monitoring system with supervised learning / Lye Wei Liang... [et al.]. ESTEEM Academic Journal, 16. pp. 1-14. ISSN 2289-4934
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Pharmaceutical technology
Industrial research. Research and development
spellingShingle Pharmaceutical technology
Industrial research. Research and development
Liang, Lye Wei
Yusuf Fadhlullah, Solahuddin
Abdullah, Samihah
Abdul Hamid, Shabinar
Wireless heart-beat monitoring system with supervised learning / Lye Wei Liang... [et al.]
description Most of the hospitals in Malaysia still utilise manual inspection by medical personnel to determine the health conditions of the patients. The data collected from the medical equipment would have to be analysed and verified by the hospital. Frequently, many patients need medical inspections. However, to provide a precise diagnosis, medical personnel requires more time. This limitation can be addressed by the development of automated and wireless health monitoring systems with health diagnostic feature supported by artificial intelligence (AI). In this project, the objective is to develop a prototype of a wireless (non-invasive) heartbeat monitoring system with supervised learning. This system monitors the heartbeat activity and predicts the condition of the user's heartbeat. Technically, a photoplethysmography-based (PPG-based) heartbeat sensor is used to build a heartbeat sensing device with a Bluetooth feature that communicates with an Android application. The Android application is developed to receive heartbeat data from the device and feed the data into an AI classification model to predict the heartbeat condition of the user. This AI classifier was built from heartbeat data collected from 10 healthy people. The additional heartbeat dataset was generated based on a sound source of heartbeat information to increase the volume of the training dataset. The completion of this project implementation results in a wireless heartbeat monitoring system that can be applied regardless of location and time. The accuracy of the AI prediction is 99 % when evaluated with a testing dataset. The empirical accuracy obtained by testing the system with actual implementation is 90 %.
format Article
author Liang, Lye Wei
Yusuf Fadhlullah, Solahuddin
Abdullah, Samihah
Abdul Hamid, Shabinar
author_facet Liang, Lye Wei
Yusuf Fadhlullah, Solahuddin
Abdullah, Samihah
Abdul Hamid, Shabinar
author_sort Liang, Lye Wei
title Wireless heart-beat monitoring system with supervised learning / Lye Wei Liang... [et al.]
title_short Wireless heart-beat monitoring system with supervised learning / Lye Wei Liang... [et al.]
title_full Wireless heart-beat monitoring system with supervised learning / Lye Wei Liang... [et al.]
title_fullStr Wireless heart-beat monitoring system with supervised learning / Lye Wei Liang... [et al.]
title_full_unstemmed Wireless heart-beat monitoring system with supervised learning / Lye Wei Liang... [et al.]
title_sort wireless heart-beat monitoring system with supervised learning / lye wei liang... [et al.]
publisher Universiti Teknologi Mara Cawangan Pulau Pinang
publishDate 2020
url http://ir.uitm.edu.my/id/eprint/33233/1/33233.pdf
http://ir.uitm.edu.my/id/eprint/33233/
_version_ 1685651032205426688
score 13.211869

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