Fitomatic: A Web Based Automated Healthcare Supervision and Monitoring App

© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 58
Fitomatic: A Web Based Automated Healthcare Supervision and
Monitoring App
Kingshuk Debnath1, Anusha Sunilkumar2, Neha Bhange3, Elrisha Dsilva4, Dilip Dalgade
1Student, Computer Engineering, MCT’s Rajiv Gandhi Institute of Technology, Mumbai, India.
Assistant Professor, Computer Engineering, MCT’s Rajiv Gandhi Institute of Technology, Mumbai, India.
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Abstract - In recent times, people all around the world
have realized the importance of maintaining a healthy
lifestyle. Thus, more and more people have decided to focus
on having a healthy and fit body. However, monitoring
health and fitness without proper assistance could be
confusing and difficult. To achieve a healthy and fit body,
not only exercise but also a proper diet should be followed.
Also, every individual has different fitness goals and thus, for
every individual the fitness regimen differs. Keeping all these
constraints in mind, we have proposed a system that helps
beginners as well as fitness enthusiasts take the first step to
achieve their fitness goals. Our solution aims to help
individuals improve their quality of life, by recommending
healthier diet and exercise plans by analyzing their BMI and
monitoring the exercises done by the user. Since many
individuals cannot make time out of their busy schedules to
visit the gym, this system is beneficial to them because they
can perform exercises and also get them monitored virtually
without the need of a physical trainer.
Key Words: Machine Learning, MediaPipe, KNN,
Fitness, Pose Detection, Recommendation, BMI.
1.INTRODUCTION
In our work, we introduce Fitomatic, a web app which
tracks the fitness activities of users, their diet and meal
tracking, and detects the users exercise posture. Owing to
busy schedules and work pressure people are not paying
attention to their health and fitness. Physical inactiveness
is the most important problem in today’s generation. It is
important to understand that diet and exercise varies from
users having different lifestyles, height, weight, sex, age,
and activity level, however diet and exercise are both
correlated.
1.1 Importance of Fitness
The importance of having good physical fitness cannot be
stressed any further in the times that we find ourselves
right now. People have been struggling with various
health-related problems [1] such as eye strain, mental
stress, irregular sleep patterns, obesity, decreased
immunity, etc. Immense emphasis has been put on by
bodies like WHO (World Health Organization) since the
spread of COVID-19 started increasing, on improving our
health and immunity for being safe from the coronavirus
and proper diet and exercise plays a pivotal role in making
our bodies healthier. Some mobile applications provide
expert support and sessions on a paid basis to get a more
personalized and focused option for training and guidance.
Thus, a product that is free of cost is needed so that it can
be used by all.
1.2 Research Studies
Although people are becoming more and more health-
conscious, they still do not have the time to dedicate to
going to the gym. This explains why working people all
around the world prefer health and fitness tracking apps.
Recent Statistical studies show that within the first week
of lockdown, the Daily Active Users (DAU) in Health &
Fitness Apps category saw an upsurge of almost 14%. This
led to a tremendously high download growth rate, nearly
157% was observed in-home fitness apps in India [2].
Therefore, a method is required which is much more
accessible and at the same time, is reliable. In this work,
we aim to:
● Provide a platform to satisfy all of users’ needs at
one place.
● Provide accurate and proper training, all at the
convenience of users.
● Provide constant feedback to improve the quality
of performance of users.
● Provide healthy diet plans which suit the user,
taking into consideration their allergies and
workout regimen.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072

2. RELATED WORK
A decent amount of work has been done for developing
designs for health monitoring applications. In [3], a system
is proposed which can help doctors to recommend diet
and exercise to the patients. Deals with health monitoring
of disease like diabetes etc. based on patients’ latest
reports using the Machine learning Technique i.e C4.5.
They conclude C4.5 is better than the ID3 algorithm with
respect to both the data-sets that were used.
S. Agarwal et al. [4], designed an application called FitMe,
which aims to reduce the dependency on actual trainers
and provide health benefits anywhere, anytime, free of
cost and with limited hardware support. FitMe utilizes
lightweight deep learning models for accurate pose
estimation of the users. In addition to checking the
accuracy of poses, it provides instant feedback to users so
that they can maintain the right postures on the fly. The
quality results obtained are shown in this work and
further proved that it has massive scope for adoption by
people for their fitness needs being inside their homes.
In [5] Gourangi Taware, Rohit Agrawal, Pratik Dhende ,
Prathamesh Jondhalekar, Shailesh Hule, introduce
Fitcercise, an application that detects the exercise position
of the user counts the prescribed exercise repetitions and
gives individualized, comprehensive analysis about
enhancing the user's body posture.
D. Shah, V. Rautela, C. Sharma and A. Florence A, "Yoga
Pose Detection Using Posenet and k-NN [6] designed a
project that carries a non-profit system that strives to
develop core muscles using yoga-like poses. Virtual yoga
asana practice is possible thanks to the totally accurate
position detection provided by the proposed method. The
cosine similarity technique is used to consider the
deviation of the angle created with the original values.
This study uses computer vision algorithms and the open
pose to evaluate human poses and a person's yoga stance
(open-source library). The proposed model was trained
with 90% of data and tested with 10% of the same with
real-time testing, resulting in 94 % accuracy.
A. Singh, S. Agarwal, P. Nagrath, A. Saxena and N. Thakur
[7], an article that covers the problems with estimating
human posture and provides an overview of extensive
research on the subject, including deep learning
methodology and conventional image-based algorithms,
has been offered. The author has created a straightforward
model using a convolutional neural network that
estimates the postures and exemplifies the potential of
CNNs after examining numerous findings and identifying
the constraints.
An application is designed by Prof. Prajkta Khaire,
Rishikesh Suvarna, Ashraf Chaudhary in [8], that provides
the user with a complex algorithm which can provide the
user with a diet plan based on his/her characteristics like
height, weight, BMI. With just one button click, users will
be able to register an account, manage their account, and
access the diet through the suggested application's user-
friendly User-Interface. It also offers the option to get in
touch with a real nutritionist for advice if the user has a
food allergy.
In another work presented by A. Henning, B. Alvarez, C.
Brady, J. Kopec and E. Tkacz [9], have designed a Elasto-
Trak that combines the cardiovascular workout of a
treadmill with the resistance training of springs, thereby
enabling users to achieve the benefits of both exercises
simultaneously. The strength of the device's frame, the
device's ability to successfully boost the user's heart rate
into the cardiovascular training range, and the device's
usability will all be tested.
Using a professional workout as a reference, Nagarkoti, R.
Teotia, A. K. Mahale, and P. K. Das suggested a system in
[10] to analyze a user's body position during exercise. In
order to identify mistakes and offer the user corrective
action, we depict the human body as a collection of limbs
and examine angles between limb pairs.
Last but not least, S. Bian, V. F. Rey, P. Hevesi, and P.
Lukowicz studied the potential of this sensory modality in
gym workouts in [11], where they also detailed the
physical theory underlying the pervasive electric coupling
between the human body and surroundings.
2.1 Limitations
● Some mobile applications provide expert support
and sessions on a paid basis to get a more
personalized and focussed option for training and
guidance.
● Tedious task of searching for integrity in the
manual systems before.
● All existing systems are not well integrated.
Rather they are good in their own respected work.
● Existing apps that used ML models for monitoring
would only be able to estimate or identify the
pose from a static image.
● Generation of the feedback in the form of
paragraphs.
● Complex hardware infrastructure is neither
affordable by users, nor is easy to use.

2.2 Problem Statement
Most users must utilize various applications to keep track
of their workouts, routines, and diet preparation.
Consumers eventually lose interest since they find it quite
difficult to use several apps and maintain track of it.
Although people are becoming more and more health-
conscious, they still do not have the time to dedicate to
going to the gym. This explains why working people all
around the world prefer health and fitness tracking apps.
3. PROPOSED SYSTEM
3.1. System Design
To achieve the desired goal of recommending personalized
diets along with exercise tracking, we use the following
methodology, containing two phases; Phase 1: Diet
recommendation and calorie tracking, Phase 2 : Exercise
live monitoring and feedback generation.
Fig-1: System Architecture
3.1.1. Diet Recommendation and Calorie Tracking
Diet recommendation is implemented using a content-
based approach. A recommendation engine that bases its
suggestions on an item's qualities or content is known as a
content-based recommendation engine. It works by
analyzing the content of items, such as text, images, or
audio, and identifying patterns or features that are
associated with certain items. The following step involves
comparing goods and suggesting comparable ones to users
using these patterns or attributes.
The procedure is as follows:
a) Taking user Data: Starting with entering patient’s
details such as height, weight, age, gender, activity level.
b) Calculating BMI: Calculation of BMI and calories
required with formula using the personal details taken as
input.
BMI (Body Mass Index) and Calories Requirement
Calculation
BMI = [Body Weight (Kg)]/[Sq of body weight in m]
=kg/m^2 Where, Underweight < 18.5
Normal Weight = 18.5 - 24.9
Overweight = 25 - 29.9
Obesity > 30 Calories:
For Men: 66.5 + 13.8(W) + 5.0(H) - 6.8(A)
For Women: 66.51 + 9.6(W) + 1.9(H) - 4.7(A)
Where, W = Weight in lbs. H = Height in inches. A = Age in
years.
c) Content – based Filtering: The Recommendation
engine uses information about the nutritional values and
ingredients of foods to make personalized
recommendations to users. Also, it takes into
consideration an individual's dietary restrictions and
preferences, such as allergies or food preferences.
d) Recommendation: Users are provided with a
customized exclusive experience which will help them
make better choices about what to eat and improve their
overall health that is a diet is recommended.

Fig -2: Diet Recommendation and Calorie Tracking
3.1.2. Exercise Recommendations and Pose Detection
Monitoring of user exercises is done by the method of pose
estimation. Pose estimation refers to a computer vision
technique that detects and tracks human figures or objects
in videos and images. In the case of humans, it could help
determine the location of the key body points.
Fig -3: Exercise Monitoring and Pose Detection
3.2 Framework/Algorithm
3.2.1. Nearest Neighbor for Recommendation
The Nearest Neighbors model is utilized in the diet
recommendation section for prediction, with the cosine
metric being used for categorical data and the brute force
technique being employed for a thorough search. The KNN
model will curate a diet in accordance with the nutrient
limit received from the user and advise it.
Based on their nutritional value, locate the foods or meals
that are the closest to a specific food or meal.
Nearest neighbors can be used in a diet recommendation
system to determine which foods are the most comparable
in terms of nutrients. The concept is that if two foods have
comparable nutrient profiles—for example, comparable
levels of protein, fat, carbs, vitamins, and minerals—then
they are probably going to have comparable impacts on
the body in terms of nutrition and health.
In our project, we use a pre-trained KNeighborsClassifier
on the data to unsupervised identify the samples that are
most comparable.
The fig explains the deviation and distribution of the data
points from a normal distribution and according to the test
input, most similar samples from the dataset are
recommended.

Chart-1: Probability plot of the dataset used
Fig -4: Results from Diet Recommendation System
3.2.2. Mediapipe holistic Framework
Mediapipe Holistic Framework enables live perception of
simultaneous human pose, face landmarks, and hand
tracking in real-time. It integrates separate models for
pose, face and hand components, each of which are
optimized for their particular domain. It is known to offer
fast and accurate, yet separate, solutions for these tasks.
The steps to identify a success movement are:
a) Phone camera to capture a (or a series of) real-time
images.
b) The python module then identifies the users’ skeleton
and joint position from the captured images.
c) When the skeleton and joint positions are pinned, the
success of a movement is calculated.
d) If the movement is a success, the number counts. Once
a set of work-out is done, the record is refreshed and
kept for further advice. The fitness records that show
one’s improvement and achievements can be used for
further advice.
Fig -5: Body Pose Landmarks Detected by Mediapipe
Fig -6: Recognition using Mediapipe
Chart -2: Training Loss and Accuracy

4. CONCLUSIONS
This work proposes an application designed specifically
for fitness enthusiasts. By utilizing a web camera, machine
learning modules and recommendation engines can help
users achieve their fitness goals all at one place. The future
work would consist of a system for tracking of diet and
exercise and in continuation would provide alternate
options with respect to the user’s ailments to a particular
food item or exercise in case of change of user preferences
and creating a regular and emergency alert system to
remind the user before every follow-up session and in
alert user in cases of extreme reports.
ACKNOWLEDGEMENT
We wish to state that the work embodied in this project
titled “Fitomatic: A Web Based Automated Healthcare
Supervision and Monitoring App” forms our own
contribution to the work carried out under the guidance of
'Prof. Dilip Dalgade’s direction at MCT's Rajiv Gandhi
Institute of Technology. We affirm that this written
submission contains our ideas in our own words, and that
when other people's thoughts or words are used, they are
properly acknowledged and cited.
REFERENCES
[1] "Covid-19 lockdown has negatively impacted kids’ diet,
sleep and physical activity: Study", The Indian
Express, 2020. [Online].Available: Covid-19 lockdown
has negatively impacted kids’ diet, sleep and physical
activity: Study | Lifestyle News,The Indian Express
[2] C. Ang, "Fitness apps grew by nearly 50% during the
first half of 2020, study finds", WorldEconomic Forum,
2020.[Online].Available: Fitness app downloads grew
by 46% worldwide in COVID-19 | World Economic
Forum (weforum.org)
[3] D. Mogaveera, V. Mathur and S. Waghela, "e-Health
Monitoring System with Diet and Fitness
Recommendation using Machine Learning", 2021 6th
International Conference on Inventive Computation
Technologies (ICICT), 2021.
[4] S.Agarwal et al., "FitMe: A Fitness Application for
Accurate Pose Estimation Using Deep Learning", 2021
2nd International Conference on Secure Cyber
Computing and Communications (ICSCC), 2021.
[5] Gourangi Taware , Rohit Agrawal , Pratik Dhende ,
Prathamesh Jondhalekar, Shailesh Hule, 2021, AI-based
Workout Assistant and Fitness guide, INTERNATIONAL
JOURNAL OF ENGINEERING RESEARCH &
TECHNOLOGY (IJERT) Volume 10, Issue 11 (November
2021).
[6] D. Shah, V. Rautela, C. Sharma and A. Florence A, "Yoga
Pose Detection Using Posenet and k-NN," 2021
International Conference on Computing,
Communication and Green Engineering (CCGE), 2021.
[7] A. Singh, S. Agarwal, P. Nagrath, A. Saxena and N.
Thakur, "Human Pose Estimation Using Convolutional
Neural Networks," 2019 Amity International
Conference on Artificial Intelligence (AICAI), 2019.
[8] Prof. Prajkta Khaire, Rishikesh Suvarna, Ashraf
Chaudhary, “Virtual Dietitian: An Android based
Application to Provide Diet”, International Research
Journal of Engineering and Technology (IRJET),
Volume: 07 Issue: 01 | Jan 2020
[9] A. Henning, B. Alvarez, C. Brady, J. Kopec and E. Tkacz,
"Workout Machine that Combines Cardiovascular
Exercise with Strength Training," 2020 39th Annual
Northeast Bioengineering Conference, 2020
[10] A. Nagarkoti, R. Teotia, A. K. Mahale and P. K. Das,
"Realtime Indoor Workout Analysis Using Machine
Learning & Computer Vision," 2019 41st Annual
International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC), 2019.
[11] S. Bian, V. F. Rey, P. Hevesi and P. Lukowicz, "Passive
Capacitive based Approach for Full Body Gym Workout
Recognition and Counting," 2019 IEEE International
Conference on Pervasive Computing and
Communications.
[12] B. Sainz-De-Abajo, J. M. García-Alonso, J. J. Berrocal-
Olmeda, S. Laso-Mangas and I. De La Torre-Díez,
"FoodScan: Food Monitoring App by Scanning the
Groceries Receipts.”
BIOGRAPHIES
Kingshuk Debnath, Undergraduate
Student, BE Computer Engineering,
MCT’s Rajiv Gandhi Institute of
Technology, Mumbai University,
Mumbai.
kingshuk.d16@gmail.com
Anusha Sunilkumar, Undergraduate
Mumbai.
anushasunil71201@gmail.com
1’st
Author
Photo
2nd
Aut
hor

3rd
Aut
hor
Neha Bhange, Undergraduate
Mumbai.
neha05bhange@gmail.com
Elrisha Dsilva, Undergraduate
Mumbai.
elrishad30@gmail.com
Dilip Dalgade, Assistant Professor,
Computer Engineering, Expertise in
Data Structures and Algorithm and
Machine Learning MCT’s Rajiv Gandhi
Institute of Technology, Mumbai
University, Mumbai.
dilip.dalgade@mctrgit.ac.in

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