Fever Status Detection using Artificial Neuron Network
Linos Nchena and
Dagmar Janacova
Department of Automation and Control Engineering, Tomas Bata University in Zlín, Czech Republic
Keywords: Assistive Technologies, Senior Citizen Assistance, Incident Detection System, Health Status Monitor,
Artificial Intelligence, Artificial Neural Networks, Machine Learning.
Abstract: This research paper proposes a monitoring system and a prototype that has been developed for detecting if a
when fever is present in senior citizens or any other specific groups of people requiring continuous care. With
various issues affecting the health of senior citizens, it is imperative to continuously monitor their health
status. The monitoring system is beneficial as it will make it feasible to enable the real time detection of fever
and thus allowing for the early treatment. Delaying treatment can lead to the underlining health issue going
beyond the remediable condition. Thus, quick detection is vital. There are various issues that might causes
illness in people. Some of the issues include virus outbreak, seasonal infections, disease, and old age. In this
paper our focus is mainly on old age. This group of people is much more at risk of getting ill or frequently
need more attention. In this project, the presence of fever or illness has been detected by using artificial
intelligence (AI). The AI technique that is utilized in this project is artificial neural networks. The computation
is done by first training the system and then secondly validating the trained system. After the training, the
system is supplied with a new set of data, with a known state, to validate that the training was successful. To
validate the system, it is provided with sample data to test its efficiency. If the system is well trained the
validation data would label that data correctly. That label is known before the validation test, as the sample
data had known labels. These known labels were not given to training but not validation system. The system
is function properly if its label matched the sample data label. The conducted experiment demonstrated a
successful detection with an efficiency rate of 82 percent.
A status detection system is a computation system
used to monitor data activity and then assign a one
label on the occurring activity from given possible
data labels. An example of a data labelling is biodata
testing procedure which is intended to label the
sample as either infection found or not infection
detected. An automatic status detection system can be
used to monitor senior citizens activity status to avail
them quick support when needed. In most developed
countries, senior citizens make up a large portion of
the total population. Due to the advanced age, senior
citizens require more care and support more than
younger citizens. When taking care of senior citizen,
we must monitor their physical and health status to be
able to respond to their issues in the shortest possible
time frame. It is feasible to identify an issue, before it
becomes severe, and be able to respond to that issue.
An example of one such issue is a senior citizen fells
down and fails to get up on their own. A fell status
system can notify a caregiver who is away from the
house and then the caregiver can get back home to
assist the fallen senior in good time. A delay in this
situation could be fatal. Therefore, this work intent to
conduct early detection of fever before the situation
is out of controllable stage. This detection would
make it to response to issue at a stage when the issues
are still in remediable stages (Garçon et al., 2016).
There are four advantages why this system is very
helpful; Firstly, this system can help decongest care
giving facilities. Caregiver’s facility would get
congested, in a case where every person needing care,
goes to live at the facility for physical monitoring and
attention. Therefore, a system that can remotely
monitor issues can benefit caregivers’ facilities by
attending to more clients since remote monitoring
accommodates more clients than on-premises
monitoring. The second advantage is that for some
groups of people, for example senior citizen, such
fever is so much common that they require frequent
medical attention. Therefore, this monitoring system
would allow for identifying the right moment when to
Nchena, L. and Janacova, D.
Fever Status Detection using Artificial Neuron Network.
DOI: 10.5220/0010485407760781
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 776-781
ISBN: 978-989-758-509-8
2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
receive direct human attention. Without such a
monitoring system, such a needy person might delay
or ignore to identify the moment when to seek direct
human attention. Thus, a remote monitoring system
that can identify the fever in its earliest possible time
would allow treatment before the issue goes out of
control. The third advantage is that a detection system
can reduce the stress level on care givers. Caregivers
usually make frequent checks on the person under
their monitoring. In a case that a caregiver attends to
other works, it because difficult to monitor their
subject. Using an intelligent monitoring system, the
caretaker could receive real-time data about the
subject’s health status. Therefore, caregiver would
have less chances of missing status record data since
the system would be sending status updates in real-
time. Lastly, the importance of a monitoring system
is the freedom and independence that the subject
person gained from using the system. Since the
caregiver will not have to constantly monitor them,
this allows the subject to feel independent. They are
relieved of the feeling of having someone constantly
physically monitoring then. Some subjects would feel
guilty when caregivers give them more attention as
they feel they are inconveniencing the caregiver.
With this system applied, the subject might feel they
are not a burden to caregivers as the system assumes
most of the caregivers’ responsibilities (Paudel et
al.,2018; Hussein et al., 2014; Das et al., 2015).
This study is composed of the following parts:
The second chapter present fundamentals of artificial
neural networks. The third chapter presents related
works done by previous research works. Then the
fourth chapter presents a proposed methodology of
solving the problem. The fifth chapter gives the
experimental results. Sixth chapter presents
discussion of the results and prospects of the research.
Seventh chapter is the last and gives the conclusion.
An artificial neural network (ANN) is a data
processing system that is inspired by how the human
or animals’ brain processes data (Nasser et al., 2019).
The brain has several processing units, that are
interconnected and using these interconnections, they
can map input data to specified output data. Each one
of the units in these interconnections is known as a
neuron. The human brain has over 100 billion
neurons, which are interconnected in several ways. It
operates in such a way that it gets data from sensors,
and then passes that data to its processing mechanism.
The processing mechanism then manipulate this
collected data to generate output information. In a
similar approach ANN imitates the brains’ data
processing structure. Like the brain, the ANN has
three components: input layer, hidden layer, and
output layer (Nasser et al., 2019; Chatzimichail et
al.,2013; Ajerla et al., 2016).
An ANN has multiple layers, and each layer
processes data as a component of a processing layer
group. The data gets processed at every single layer.
The data gets processed as many times as there are
layers. If the network has only one layer, then the
processing would only occur in one step. In case of a
multilayer ANN, the processing is performed from
one layer to the next layer and then next layer until all
layers have had a turn in processing the data. This
means the data is processed repeatedly based on
number of hidden layers in that network. In a network
the number of neurons or number of layers are
designed based on the problem that is been solved.
However, the number of neurons should be decided
appropriately. Simply increasing the number of
neurons without some corresponding importance in
features used in the problems would not automatically
improve the performance of the network. Below is
Figure 1, which shows the three layers of an ANN.
Figure 1: The three main layers in ANN topology.
The hidden layer is usually the most complex of the
three layers of a network. The mapping of input to
output is achieved after the ANN has undergone some
training sessions. After the training, the network can
then be used to solve the problem it was trained to
solve. It solves the problem after it has mastered the
link between every input and to corresponding
outputs. ANN are used in solving a variety of
problems. Common problems solved by using ANN
include handwriting recognition, disease diagnosis,
process control, financial viability predictions, stock
market predictions, complex systems modeling, error
compensation in industrial processes, fire control,
Fever Status Detection using Artificial Neuron Network
security surveillance, etc. (Nasser et al.,2019; Ajerla
et al., 2019; Janku et al., 2018; Mehr et al., 2016).
Within the existing literature, the detection problem
has been solved in different ways by various
researchers. A list of research of particular interest
have been incorporated in presenting this research.
Chatzimichail et al. (2013), have discussed the
detection ways to determine the presence of Asma in
children under the age of five. The is done based on
recognized symptoms as features of presence of
Asma disease. The experiment was conducted by
collecting a sample from 112 records which have 48
features. To solve the issue the researchers decided to
reduce the number of features to nine from 48. This
was done because the removed features had little
impact on the results of the experiment. For analysis
purposes, the experiment was performed twice. First
with the full number of features at 48 and then
secondly only with the nine to illustrate the need to
have some features removed. During pre-processing,
data was divided in ten equal sets. Ten cycles of
training were performed using these ten datasets. For
every cycle, one data set is used as testing data where
the remaining nine sets are used as training data. The
total results obtained are then summated to obtain an
overage of the training accuracy. The experiment
results showed that removing the features that had a
smaller impact on results of experiment made the
ANN much more effective by raising accuracy from
83.87% to 96.77%.
Ajerla et al. (2016), considers an application for
providing various service to senior citizens using
artificial intelligence detection. The system offers
services that include fire detection, gas leak detection
and unaccompanied monitoring. The task was to
improve the performance of an algorithm if the sensor
was place on the waist rather than on the head or
wrist. This was because head or wrist is more accurate
but is less comfortable for the subject compared to the
wrist. The rest has more vector movements that the
head of waist of which these movements are the input
of the ANN. 525 data sets where collected. Because
they were of different sizes, some of the data was
disused and some of the data was normalized but
adding zeros where they had no entry to make all the
dataset have same size. The final data used was 120.
The 120 sets were divided into 90 as training data set
and 30 as testing set. An ANN of three hidden layers.
The ANN is trained to detect the occurrence or no-
occurrence of a fall. The experiment concludes that
the detection of fall from the waist and head in
previous experiment was at 95% while in this
experiment it was at 75%. The 75% detection
accuracy for the sensor on the wrist was considered
an improvement as the waist position is more
convenient than the head. A similar research is
conducted by Yoo et al. (2016). Both these systems
are used as a real-time motoring system for falling
and hence caregivers are updated immediately on
occurrence of falling.
Janku et al. (2018), presents a research about a
new method of fire detecting technique using neural
networks It focuses on the issue with current systems
that they have difficult in differentiating controlled
fires from dangerous fires. Controlled fires are fires
that are specifically started and are not a danger to life
or property. For instance, a fire from a welding
machine when using a welding thing in a warehouse.
For the experiment, the research required to use three
different types of sensors. A sensor for smoke, a
sensor for colour and a sensor for movement
direction. The three sensors would collect data from
the environment and then send it to the centre of the
ANN. The networks are of two kinds, the shallow nets
and deep leaning machine. The two of them differ in
the sense that the shallow nets are consist of only
three layers, while the deep learning machines has
more than three layers. The basic layers are input
layer, hidden layer, and output layer. In the deep
learning machines the hidden lawyer is not one but
several layers. The data from each of the three sensors
was used in this experiment. The researcher also
stated current systems use one sensor compared to the
three that this experiment is utilizing. Furthermore,
this work intended to remove a scenario of having a
high error values in the detection system. The cited
previous research works are said to have a lot of false
negatives and false positives. The study experiment
provides interesting results that proves a better
method to detect fires. The new method has provided
results fire detection with accuracy of 93%. This
system operated online hence a real-time motoring
system for fire and hence care takers are updated
immediately on the occurrence of fire.
In implementing our experiment, we shall take the
following direction. In training data, we shall set the
size of training data at 80% instead of 60% used by
Kajan et al., (2014) and 75% by Yoo et al., (2016).
This makes the system more specific and less generic
a good preference in this problem. We shall also limit
the parameters we select to those that have the highest
impact among the list of probable parameters.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Senior citizens face several health issues. Neural
networks can be utilized to protect them from such
issues that they face in their daily lives (Shahid et
al.,2007; Amato et al., 2007; Mehr et al., 2016). The
application of ANN could improve and enhance
lifespan for the seniors. To develop a monitoring and
alert notification system, we shall design a prototype
for detecting fever in senior citizens and other citizen
in risk category. We shall use pseudonymized and
anonymized data to train and test this application.
4.1 Method and Procedure
To help identify whether a medication alarm should
be alerted, the following three tasks must be
performed. (1) The features of the sample data are
evaluated for relevancy. We use only those features
which have high relevancy to the identification of
medical condition. Using the square-mean error of
each features’ error, the features with small square-
mean error changes are removed. The regression
method is the method we shall apply to identify these
less significant features. (2) After this feature removal
operation, a feedforward-propagation ANN is trained
on this dataset to help identifies the status of the
medical condition. The dataset is divided into five
equal sub-datasets. In iterations, each of these five
sub-datasets is used as a test set while the rest is used
as a training set. This iteration is used to avoid cross-
validation and to make sure that every sub-dataset is
used at least once as a testing set. (3) After the training
sessions, the test dataset is feed into the system for
testing procedure. If the tests results are of some
specified accuracy level, then the design is successful.
The designed systems can then be used for a subject
dataset to predict status of medical condition. This is
the final step of the experiment. In summary, we used
three different datasets in this network. First is
training dataset, second is testing data set and last is
the subject dataset. The third dataset is the candidate
dataset that is under investigation for the status of
medical condition.
4.2 Experiment Data and Used Tools
The artificial neural network was developed in
Python programming language. Python has
supporting libraries for ANN implementation. In this
experiment the libraries that we used include keras,
matplotlib, pandas and TensorFlow. The computer
used had the following specifications: Processor:
Intel(R) Core (TM) i7-4510U, CPU: 2.00GHz, 2
cores, RAM Memory: 4 GB DDR3 1600 MHz, OS:
Windows 64 bits.
The data we used consists of one thousand
medical records. The records have 8 fields, each
representing a unique data feature of the record. We
divided the records into two groups. One group is to
be used as training dataset and the other as a
validation dataset. The records ratio is 8 to 2; where
training set is (80%) and validation set is (20%). The
experiment used data from different sources that
corelated to feature detection as advised by medical
specialists. The data’s features selected are not
selected at random. These are features that have been
identified to be direct or indirectly linked to the
presence or absence of fevers (Chatzimichail et
al.,2013). The ANN model has TensorFlow backend.
it has three layers, the first layer has 12 neurons,
second layer has 8 and last layer has two neurons. The
training dataset is online database available from
https://data.world/anaozp/diabetes. This dataset has
been used to train and test the ANN.
Based on the collected data we trained the neural
networks and attained an accuracy of 79.8%. Several
experiment trials were conducted. The accuracy of
79.8% is acceptable to successfully determine
presence or absence of fever.
Figure 2: Accuracy evolution of the training and testing
Figure 2 shows the results for the training and testing
sessions that were conducted.
shows the level of
accuracy from this specific training and testing in the
machine learning experiment. As can be seen from
the figure, the training took 300 epochs with training
accuracy results ranging from 0 % to 82 %. However,
the feasible testing range was shorter, starting from
Fever Status Detection using Artificial Neuron Network
65 % ending at 82 %. Within the first 20 epochs, the
training accuracy rises sharply from 0 to 70 %. From
the 20th epoch, it steadily grows until it reaches 78 %
by the 250th epochs. It then marginally grows and
then stabilizes around 80% till the end of the
experimental period. As proven in the figure, there
are less data spikes in system training than in system
testing. The training-testing deviation starts at 67 %
on the 10th epoch. Spikes grow until around the 120th
epoch. They then gradually reduce to a 10%
difference by the 250th epoch. This spike reduction
indicates acceptable training performance. The
training and testing accuracy start to stabilize at about
the 150th epochs with 78.1% and 79.8% accuracy,
respectively. Accuracy starts from its lowest point
and goes upwards as the epochs increases. It stops
increasing at about 78.1% to 79.8% and stabilizes
there. Therefore, this experiment is a success as the
test performance indicates acceptable performance on
actual subjects’ data.
The training and testing loss start to stabilize at
around after the 120th epochs with 0.140 and 0.145
loss, respectively. The loss starts from the highest
point and starts declining. It continues a steady
decline until it reaches around 0.141 of the loss where
it then stabilizes.
Figure 3: Loss and accuracy evolution of the Training and
testing sessions.
In Figure 3, loss and accuracy metrics are shown.
The accuracy metric increases while the loss metric
decreases with the increase in the number of epochs.
The experiment’s result illustrates the usability of this
neural network in fever status detection as a vital
component of assistive technologies. Instances of
expected benefits of such a system includes. The
system allows early detection of an illness without
having a physically residing at a care facility. ANN
has been applied to identify illness status for subject
senior citizens. ANNs with a success rate at 81% did
manage to label the medical status based on specific
features. The subject citizen had sensors that are
taking record of changes in selected body parameters
(Ajerla et al., 2019; Yoo et al.,2018). The sensors can
be attached or unattached to the body depending on
preference. Rather than subject been located at a
monitoring care facility, sensors send data to the
facility making the monitoring process much more
convenient. This makes subject more self-reliant and
improves quality of life and reduces expensive for
cost of care and attention. This can also make the
monitoring process more robust and effective.
Furthermore, the ATs can make automatic diagnosis
and then alert relevant caregiver for further care and
prescription. This would as a result reduce or even
eliminates workload for personnel at care facilities.
With an increase in the population of senior
citizens, the need for monitoring systems would
increase. According to the World Health
Organization, (2002), in 2025, there would be a total
of about 1.2 billion people over the age of 60. By
2050 this number would increase to 2 billion with
80% of them living in developing countries. The
population growth is faster for older persons than for
the rest of the population (United Nations, 2007). In
1950 the total number of people over 60 years old was
8 percent. By 2007, this percentage had grown to 11
percent. By 2050, this number is projected to be at 22
percent. The increase in elderly population means
more people would require assistive living care than
before. Thus, detection systems are becoming vital
and would require more investment both academic
and financial. They could also become a usual home
appliance for the elderly family member of the home.
In future research we hope to focus on a system,
which includes a notification message and pill taking
schedule. Apart from fever detection system, this
application can be coupled with other application to
form a complex system offering several services. We
wish to integrate these into this fever detection as
separate system components.
In this paper, we have discussed fever status detection
using an artificial neural network. An experiment was
setup to perform fever status detection using neural
networks. The experiment has provided insights on
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
the viability of a fever detection system. The system
could be useful in reduction of cost in the elderly
citizens care and medical services. Additionally, we
outlined three key benefits for the use of assistive
technologies systems in rapid detection of health
status or condition of person under observation.
1) The remote and quick diagnosis would allow easier
and continuous surveillance and enable early
treatment of illness.
2) An automated system could minimise subject
persons’ dependence and reduce stress on both the
care giver and the person receiving care. Less stress
and less dependence for the concerned senior citizen
could be a good remedy for improvement of health.
3) An automated system would reduce the cost for
care and medical services. Employing machines
would costs less than employing human monitoring
assistants. Early diagnosis can also be achieved
without automated technologies; however, such a
method would require more costs than using the
automated detection system.
The common solution to senior citizens
monitoring is to have the citizen reside at a care
facility. Such solution costs more for the senior
citizen and is more workload for the care facility.
Hence a fever detection system is a better option as it
addresses both these issues. This detection services
could be installed at a home, housing a senior citizen.
This system could be able to support in the everyday
lives’ activities and care service delivery to senior
citizens and other vulnerable citizens. With such a
system in common usage, the lives of millions of
senior citizens across the globe could be improved.
This work was supported by IGA/CebiaTech/2021/
001, a research project of the Faculty of Applied
Informatics, Tomas Bata University in Zlín.
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