Abstract:
t Rapid development of wearable devices and
mobile cloud computing technologies has led to new opportunities
for large scale e-healthcare systems. In these systems,
individuals? health information are remotely detected
using wearable sensors and forwarded through wireless
devices to a dedicated computing system for processing
and evaluation where a set of specialists namely, hospitals,
healthcare agencies and physicians will take care of
such health information. Real-time or semi-real time health
information are used for online monitoring of patients
at home. This in fact enables the doctors and specialists
to provide immediate medical treatments. Large scale
e-healthcare systems aim at extending the monitoring coverage
from individuals to include a crowd of people who
live in communities, cities, or even up to a whole country.
In this paper, we propose a large scale e-healthcare monitoring system that targets a crowd of individuals in a
wide geographical area. The system is efficiently integrating
many emerging technologies such as mobile computing,
edge computing, wearable sensors, cloud computing, big
data techniques, and decision support systems. It can offer
remote monitoring of patients anytime and anywhere in a
timely manner. The system also features some unique functions
that are of great importance for patients? health as
well as for societies, cities, and countries. These unique
features are characterized by taking long-term, proactive,
and intelligent decisions for expected risks that might arise
by detecting abnormal health patterns shown after analyzing
huge amounts of patients? data. Furthermore, it is
using a set of supportive information to enhance the decision
support system outcome. A rigorous set of evaluation
experiments are conducted and presented to validate the
efficiency of the proposed model. The obtained results show
that the proposed model is scalable by handling a large
number of monitored individuals with