Authors :
Mohammed Golam Sarwar Bhuyan; Must. Asma Yasmin
Volume/Issue :
Volume 7 - 2022, Issue 10 - October
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3Vy463r
DOI :
https://doi.org/10.5281/zenodo.7215581
Abstract :
The comfort and ease of human lives are
significantly improved by the Internet of Things (IoT)
devices' integration of medical signal processing
capabilities with cutting-edge sensors. Providing
healthcare services to every patient, especially the
elderly person those are living in the remote areas and
suffering chronic diseases need to monitor in real-time.
Remote patient monitoring systems are designed to
obtain a number of physiological data from the patients.
Most common data are Electrocardiogram (ECG),
Electroencephalogram (EEG), heart beats and
respiration rate, oxygen volume in blood or pulse
oximetry, signals from the nervous system, blood
pressure, body/skin temperature and blood glucose
level. In this research work, a FOG computing
architecture is suggested for the real-time deployment of
a remote patient monitoring system. Reducing latency
and network consumption is the main driver behind the
suggested approach's use of the FOG paradigm. Due to
the large amount of data involved in healthcare and the
enormous value that accurate predictions hold, the
integration of machine learning (ML) algorithms is
imperative. The use of Bayesian Belief Network (BBN)
classifier algorithms in the healthcare applications that
are covered in this work has demonstrated experimental
evidence of accuracy and usefulness.
Keywords :
Internet of Things (IoT), FOG, Remote Patient Monitoring, ECG, Machine Learning, Bayesian Belief Network.
The comfort and ease of human lives are
significantly improved by the Internet of Things (IoT)
devices' integration of medical signal processing
capabilities with cutting-edge sensors. Providing
healthcare services to every patient, especially the
elderly person those are living in the remote areas and
suffering chronic diseases need to monitor in real-time.
Remote patient monitoring systems are designed to
obtain a number of physiological data from the patients.
Most common data are Electrocardiogram (ECG),
Electroencephalogram (EEG), heart beats and
respiration rate, oxygen volume in blood or pulse
oximetry, signals from the nervous system, blood
pressure, body/skin temperature and blood glucose
level. In this research work, a FOG computing
architecture is suggested for the real-time deployment of
a remote patient monitoring system. Reducing latency
and network consumption is the main driver behind the
suggested approach's use of the FOG paradigm. Due to
the large amount of data involved in healthcare and the
enormous value that accurate predictions hold, the
integration of machine learning (ML) algorithms is
imperative. The use of Bayesian Belief Network (BBN)
classifier algorithms in the healthcare applications that
are covered in this work has demonstrated experimental
evidence of accuracy and usefulness.
Keywords :
Internet of Things (IoT), FOG, Remote Patient Monitoring, ECG, Machine Learning, Bayesian Belief Network.
