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An Intelligent Children Healthcare System in the Context of Internet of
Things
Conference Paper · June 2018
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An Intelligent Children Healthcare System in the
Context of Internet of Things
[1]
Nishargo Nigar, [2]Mohammed Nazim Uddin
Department of Computer Science and Engineering, East Delta University, Chittagong,
Bangladesh ,[2] School of Science, Engineering and Technology, East Delta University, Chittagong,
Bangladesh
[1]
E-mail: somcnish@gmail.com,[2]nazim@eastdelta.edu.bd
[1]

lot when it comes to latest technologies. Collecting data
from users is really important and the process should be
automatic and fast. We can utilize edutainment and
implement knowledge of nutrition, serving sizes of food
and health habits for children in our system. Most of the
children feel bored to eat nutritious food, but edutainment
has its own attributes to make it fun and enjoyable. We
can evaluate the usage pattern of our healthcare system to
get better results.
In this paper, we present an intelligent children
healthcare system based on IoT. The remainder of the
paper is organized as follows. Section II discusses the
literature review. Section III describes the methodology of
our children healthcare system. Section IV presents the
results. Finally, Section V demonstrates the discussion and
conclusion.
Abstract— Malnutrition and childhood obesity have already
been identified as major public health concerns. Not being
aware of such problems can cause various diseases and
eventually death among children. In the paper, we propose a
framework for an intelligent children healthcare system based
on Internet of Things (IoT) that works as a shield for children
against diseases, monitors progress, tracks records and
promotes nutrition through edutainment and mHealth features.
After analyzing our data, we recorded the results with big data
analytics tool called Hadoop and compared them with Naïve
Bayes and Logistic Regression algorithms where the latter
performed better.
Index Terms—Internet of Things, Big Data Analytics, mHealth,
Edutainment
I. INTRODUCTION
For the past few years, obesity and malnutrition rate
among children have been increased and gradually turned
into a serious matter. The healthcare strategy of children is
different from adults. They require proper care and
attention when they develop their behavioral and physical
abilities. Hence, developing a healthcare system for
children has become a necessity. Of course, it is a
troublesome job to keep up the whole procedure. Creating
efficient solution for children is difficult when the
environment is constantly changing with newer challenges
[1]. But technology has the capability of altering unstable
healthcare systems to stable ones. One of the latest
technologies is mHealth, where preventing diseases
through mobile phones and wireless technology is the
primary goal. If parents and health instructors can
supervise children’s smartphone activities, we can apply
the usage towards better care and disease management.
Introducing mHealth to children can make them aware of
increasing diseases and teach them significance of proper
nutrition. The research conducted by [2] shows the
possibilities of mHealth technology for children who are
less than 5 years old in low and middle income nations.
In our system, we put an emphasis on edutainment in a
great manner. Combining edutainment and IoT will surely
help us setting a new landmark in children healthcare as
researchers were successful integrating serious games and
IoT together previously [3] [4]. User experience matters a
II. LITERATURE REVIEW
To improve the quality of community healthcare, newer
solutions are being implemented. Researchers stated that
IoT can be applied to e-health and it will make a bigger
impact in near future [5]. In [6], Mother and Child Tracking
System (MCTS) is designed to uphold maternal and child
healthcare services. It includes SMS and web-based
application services as the communication medium
between patients and caregivers. In [7], authors presented
a mobile application for obesity management where
healthy eating is promoted among children. Mobile health
architecture has been established to prevent childhood
obesity issues with mHealth applications in [8]. The
research conducted in [9] demonstrated a child behavior
and health monitoring system using IoT and Hadoop. The
system components are Arduino board, therapeutic
android games for psychotherapy and wireless sensors.
C4.5 decision tree algorithm is used to predict the
probability of any disorder. Various studies have been
conducted previously using Raspberry Pi to implement IoT
in healthcare services [10] [11] [12] [13]. In [14], the
authors developed a multiplatform system for mother and
children by enabling remote monitoring and early
detection of diseases. In [15], hospitalization is taken to be
an agitating incident for children. The paper focused on
edutainment to a great extent and declared that games
can be a learning opportunity in an adverse situation like a
1
hospital. They developed a totem for children at a hospital
environment servicing entertainment and education to
ensure better hospitalization experience. The totem helps
children to be conscious of their physical health, building
bonds with doctors and get familiar with unknown medical
equipments in a friendly way. It uses authentication
mechanism through webcam sensor that recognizes the
child and offers him/her personalized meals. The study in
[16] demonstrates the use of IoT concept to create
awareness among children regarding health. A mHealth
framework is displayed where the food intake is tracked
and alerts are sent according to their food selections. The
platform used Automatic Identification and Data Capture
Techniques (AIDC) to simplify inputs. The paper followed
two implementation approaches; one with using RFID and
another one is QR code. In [17], an IoT enabled intelligent
system was proposed which records data from sensors,
chatbot, questionnaires, recommendation system and
lesson plans. A smart nutrition card is generated for
emergency hospitalization and disease management.
In [18], the researchers were able to design a mobile
phone application to assess real time balance of calories
with the context of increased obesity rates. The paper
aims to solve the problem with the functionality of
self-monitoring. Wellness management has been done by
[19] where a mobile phone (Model: Symbian Series 60) is
used. Collaborative games have been developed for
obesity prevention targeting youth and adults [20] [21].
Other examples might include recent smartphone
applications like exercise and nutrition facts apps for
children, which are available on Google Play Store. These
applications promote a child’s access to knowledge of
nutrition and learning through games. An exceptional
related work is Pedometer [22], which tracks the number
of steps, calories, speed and distance while walking,
running or cycling. The problem is most of the available
applications are static and contain very limited functions.
For that reason, we applied our own methodology to
redesign the current state of existing children healthcare
systems.
wearables or tracking monitors is also rising. Traditional
mobile applications will come to an end eventually. On the
other hand, connectivity increases security concerns. The
primary security concern of IoT is that is has the ability to
increase the number of devices behind the firewall of any
network. In order to secure our system, we need to apply
few techniques.
Healthcare Apps
Heart Rate
Wearable
Location
AsteroidOS
Device
No of Steps
arable
Calories Burnt
Device
Smartphone
Camera
Food Detection
User Detection
Activities
Attendance Rate
Emotional & Physical State
Nutrition Info
BMI & TDEE Updates
Calorie Intake
Hadoop
Classification
Fig. 1. Framework of the proposed system
A. The Three-Party System
In our system, we have used AsteroidOS; an
open-source operating system for smart watches. The
operating system provides a ground for developers to
build personal apps for the watch. The app development
process requires a SDK by OpenEmbedded. Building the
cross compilation toolchain is essential. Configuration of
QtCreator for cross compilation is done after the
installation of SDK. AsteroidOS works as our core
component where healthcare apps developed by us have
been hosted. We used smartphone sensor as camera and a
wearable device. All the hardware and software units are
connected and eventually data from the units are collected
for analysis.
We are considering three types of users participating in
our system: 1. Children (Age: 9-15), 2. Parents and 3.
Health Tutor. The child is the main participant who uses
the healthcare apps, eats according to the recommended
food feature, goes through the lessons and performs
various activities. Parents receive alerts and notifications
so that they can observe their performances. The health
tutor is an important character as he helps in advanced
mHealth features and provides necessary modifications if
needed. Parents and health instructor will be equipped
with particular mobile phone application connected with
the smart watch worn by the child.
Earlier it was discovered that engagement rate among a
particular group like students can be measured by
III. METHODOLOGY
In our method, a healthcare system is developed among
children, parents and health instructors. The total system
consists of three parties and dynamic features. The system
suggests a mobile platform on background and using
wearable device and built in smartphone sensor such as
camera. A child’s physical activity, heart rate and location
are tracked through the wearable device. Smartphone
sensor like camera is mandatory to establish food
detection and security feature. The system has a lot of
components including users, sensors, artificial intelligence
and recommendation methods. It offers various features
such as notifications, alerts, lesson plan, chatbot, food
detection, user tracking, BMI & TDEE calculator and real
time health monitoring.
Things are getting connected everyday and use of
2
receiving qualitative feedbacks from them [23][24]. As our
aim is to engage children in a spontaneous way, we want
to develop a simple and entertaining platform by applying
a ‘game level’ technique. Children will be scored regarding
their activities, performances, food choices and answers to
several questionnaires. A lot of new activities will be
unlocked when a child reaches the next level. The parents
and health tutor will automatically receive their scorecard
upon completing each level. To assess their performance,
we use a simple equation per level:
T  L  A  C  Q (1)
out any app for the first time, it is necessary for him to
take a picture or set it from the phone memory for
security purpose. With each new login, the camera will
recognize the child with formerly stored photo. In other
words, camera will capture the face model to match the
previously entered photo as the ID. It will also assist in
calculating attendance rate. All messages, media like
videos, photos, important documents and calls are secured
with the help of end to end encryption method.
D. Interactive Chatbot System
A child’s physical state is easy to be understood due to
various common features like lesson plans, activities,
exercises and performances. In fact, devices like
smartphone and wearables have made it quite simpler to
receive such data. But in order to be on familiar terms
with a child’s emotional state, we must try creating a
feature that adds value to his overall health profile. To do
so, we intended to create a chatbot. Chatbot has become
very popular these days because of its interesting
functionalities, facilities and applications. A mobile app
named Wysa [25] features an emotionally intelligent
chatbot and it is powered by artificial intelligence.
While using our system, there might be situations where
the child needs to talk about his physical or mental state.
This might happen when he is at school or away from
home. To solve the problem, we considered designing a
strategy that aligns with learning, perception and planning
parameters as they are the building blocks of an intelligent
chatbot. Natural language capability is a must when it
comes to dynamic chatbots. We planned to use Recast.AI
[26] as our platform. The core components of Recast.AI are
machine learning, artificial intelligence and Natural
Language Processing (NLP).
where, T = total score, L = lesson plan, A = activities, C =
calorie intake and Q = questionnaire answers
Equation 1 sums up the scores and the higher the
discrete value, the better the participation rate of a child.
No negative value is measured.
B. Food Detection
To implement food detection functionality in our
system, we used the concept of object detection. That is
why; You only look once (YOLO) algorithm has been
utilized. In this algorithm, only one network is applied to
an image. Then, a number of regions are created in the
image by the network. The network will predict confidence
scores, probabilities and bounding boxes for all of the
regions. Firstly, an input image will be divided into an SxS
grid. The technique is when a center of an object covers or
wraps any space of a grid cell; we can detect the object
from it easily. We can define confidence as Pr (Object) *
Intersection over union. Each bounding box will produce
five predictions: x, y, w, h and confidence; (x, y) meaning
the center of the bounding box, w indicating the width and
h representing the height.
Fig. 2. Food detection example
To detect a food, we have to follow a few steps that
include collecting data, annotating them, training our
model using GPU, preprocessing and eventually predicting
new images. A docker image is brought into play to reduce
complexities and quantization is needed to compress the
data. To avoid the use of hardware, we are using a tool
called NanoNets. Our food detection functionality is
capable of providing the user with important nutrition
information. In addition, it can label the food with proper
timing categories like snacks, breakfast, lunch and dinner.
C. User Tracking & Security
To enable user tracking, the employment of phone
camera is a must. All the healthcare apps require
verification when a user hits the login button. While trying
Fig. 2. Development of Intelligent Chatbot System
3
To develop our personalized chatbot, we ought to follow
a few steps like training, building, coding, connecting and
monitoring. For the chatbot to work out perfectly,
necessary intents and expressions are required to be
adjoined. The next part is to handle the flow of the
conversation and finally mandatory skills of the chatbot
must be inserted. An intelligent chatbot must reply to a
user’s questions sensibly. As a result, producing all replies
is vital. To track the performance of our chatbot, we may
put it together with a well known messaging platform.
activity) to receive their BMI and TDEE results and further
suggestions for future.
Table I. Activity Factor Table [28]
Amount
Activity
Sedentary
of
Lightly active
E. Lesson Plan & Recommender
Assessing a child’s physical, behavioral and mental
capabilities necessitate proper lesson plan and sticking to
it. An admin panel will prepare appropriate contents
regarding nutrition, health, diseases, vitamins and food in
an entertaining way. The sole purpose of edutainment at
this juncture is to encourage the children to be able to
appreciate nutritious and healthy food, as well as the
soundness of body and mind. The applications will contain
such informative contents for a child user with
recommendation feature. As an intelligent system, we aim
to personalize the contents for a child. To comprehend
consumer behavior, lots of business organizations utilize
recommendation software. We will be using Recombee
[27] that acts as a recommender as a service powered by
artificial intelligence. The engine proffers SDKs and APIs
for several languages. While building our recommender,
we had to consider three primary points: a. user b. content
and c. user-content interaction. The content can also be
referred as the item. To receive an outcome from the user,
we will let him bookmark or rate the lessons. To filter or
boost recommendations, we have to define particular
properties.
Moderately
active
Very active
Extremely active
Description
TDEE
Little
or
no
exercise
Light
exercise/sports or
games 1-3 days/
week/workout at
weekend
Moderate
exercise/sports or
games 3-5 days/
week
Heavy
exercise/sports
6-7 days/week
Very heavy or
Extreme
exercise/training/
sports or games 2
times/day
1.2 x BMR
1.375 x BMR
1.55 x BMR
1.725 x BMR
1.9 x BMR
G. Alerts & Notifications
The success of the system lies in taking appropriate
actions after observing a child’s performance. To let the
parents and health instructor know about the child’s
activities, alerts and notifications are mandatory. These
can be urgent or simple according to the level of its
importance. When a child is in danger, an immediate alert
is generated to aware the parents or instructor. But when
it is about their scorecard, it will be sent like a simple
notification. Parents can set safe zone over Google Maps
using the interface of the app. They will be notified
whenever their child is entering or leaving the zone. A
child can also be told to drink water at a certain time, like
within the duration of 2 hours. To implement such
capabilities, triggers must be created. IFTTT (if this, then
that) [29] is a great tool do so. By combining IFTTT and
ThingSpeak together, we can apply the mechanism. IFTTT
also offers services like syncing sleep readings from smart
watch and logging one’s meals and exercises.
F. BMI & TDEE Updates
BMI refers to Body Mass Index and it quantifies the total
tissue mass amount in a person. Tissue mass designates
bone, fat or muscle. BMI is quite easy to calculate. We only
require the height, weight and age of any user. Though
calculating a child’s BMI is almost as simi r as la an adult, it
is compared to typical numeric values for other children of
same age after getting the result. Percentile is measured
while calculating; a BMI less than 5th percentile denotes
underweight and above 95th percentile symbolizes having
obesity issues. On the other hand, Total Daily Energy
Expenditure (TDEE) represents the calories burnt when
exercise is taken into consideration. To calculate TDEE of
an individual, we also need to figure out one’s Basal
Metabolic Rate (BMR) and then multiply it with an activity
factor. BMR is dependent on factors like one’s gender,
height, weight and age. Activity factor includes amount of
activity
of
a
person
such
as
sedentary,
lightly/moderately/very/extremely active and so on. In our
platform, a user does not have to go through
mathematical equations to learn about their BMI and
TDEE. They can simply enter the input (height, weight, age,
H. Real-time Health Monitoring
The most practical function of our system is real-time
health tracking. With the help of a smart watch, constant
heart rate monitoring facility is available at present. Our
healthcare apps integrated within the smart watch will
support real-time health monitoring of our child users.
One of the healthcare apps is called Pain Diary which can
log the total description of current health conditions,
symptoms and possible body positions where the pain is
occurring. To enable real-time system features, the
4
employment of GPS is to be done. The concept of remote
sensing and triggers play a major role in real-time tracking
of patient data.
correct, the algorithm generates a good result in practice.
While creating a classifier model, we look for the
probability of given inputs for all the possible values of
class variable.
IV. RESULTS
C. Used Tools & Languages
A. Logistic Regression
A lot of tools, frameworks and languages have been
used to build up the whole system. First of all, to work
with AsteroidOS, QtCreator is a must and it requires the
knowledge of C++. To develop the mobile platform for
parents and health instructor, android programming
language (Java) is mandatory. To execute food detection
capabilities, NanoNets is used. It uses deep learning as its
core element and it frees us from using any hardware.
Recast.AI is needed to design a chatbot for the child users.
Recombee assists in developing the recommendation
system. IFTTT is a great service that permits us to connect
our apps and devices together. To analyze data from local
database, Sqoop is used. MapReduce framework by
Hadoop can decrease large amount of data. Thus, the
dataset is formed and fed into Orange; a machine learning
and data science toolkit.
Logistic regression refers to a statistical process to
analyze any dataset where there are one or more
independent variables that may determine an outcome.
This outcome is calculated with a dichotomous variable.
The dependent variable is usually binary or dichotomous.
Logistic regression is broadly used where the target or
dependent variable is categorical. The method creates
coefficients of an equation to predict a logit
transformation where:
(2)
Here, p = probability of presence of characteristic of
interest. The coefficient ( ) is equal to the change of logit
with one unit change in X. The logistic function always
generates an S shaped curve. Logit transformation is also
known as the logged odds where:
odds 
D. Performance Analysis
p
(3)
1 p
After feeding the received data to Orange, we observed
the performance of logistic regression and naïve bayes
algorithms. AUC refers to the area under curve; it is
broadly used in classification to detect which model
predicts the classes best. According to our results, the
AUC of both the algorithms are similar. CA stands for
accuracy; where logistic regression performs slightly better
than naïve bayes. F1 is also known as F-score or
F-measure; it is the weighted average of precision and
recall. As a result, it depends on the precision and recall
values. Precision is calculated with the ratio tp/(tp+fp)
where tp = true positives and fp = false positives and recall
is measured with the ratio tp/(tp+fn) where tp = true
positives and fn = false negatives. In both of the cases, the
best case is 1 and the worst case is 0. Although recall of
logistic regression is less than that of naïve bayes;
averaging both precision and recall results in higher F1 or
F-score in case of logistic regression method.
where, p is the probability of presence of characteristic
and 1-p is the probability of absence of characteristic. The
odds ratio can be between 0 and ∞. Values near 0 and ∞
designate very low and very high probabilities of p. Finally,
(4)
In logistic regression, the parameters are chosen in such
a way where the chances of observing the sample values
are maximized.
B. Naïve Bayes
Naïve Bayes classification algorithm is not a single
algorithm, rather it is a collection of algorithms sharing a
common principle; all pair of features that are being
classified is independent of each other. In this algorithm,
Bayes rule is used which says;
(5)
Table I. Results & Comparison
Here, H = hypothesis, E = evidence relating to H, P(H) =
probability of hypothesis H, P(E) = probability of evidence,
P(E|H) = probability of E given that H holds and P(H|E) =
probability of H given E.
Method
Logistic
Regression
Naïve
Bayes
Our dataset has two parts; feature matrix and response
vector. Feature matrix refers to dependent features while
response vector indicates class variable (output or
prediction). If all attributes are equally independent, then:
AUC
0.909
CA
0.848
F1
0.827
Precision
0.866
Recall
0.791
0.909
0.838
0.823
0.826
0.820
V. DISCUSSION & CONCLUSION
Technologies like IoT, Big Data and mHealth have been
extensively used in our system. We have been able to
achieve a successful result with an accuracy of 84.8%,
precision 86.6%, recall 79.1%, 82.7% F-score and 90.9%
AUC. Our results demonstrate that the system will be
(6)
In the algorithm, we assume that the pair of features is
independent and all the attributes contribute equally to
the outcome. Even if the assumptions are usually not
5
highly effective for children, parents and healthcare
systems because of its progressive performance, better
decision making processes and well-organized resources.
We plan to join our system with pediatric wards of hospital
management systems so that even after discharging a child
patient, doctors can stay connected to convey additional
advices.
[13]
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