=Paper=
{{Paper
|id=Vol-3179/Paper_20.pdf
|storemode=property
|title=An IoT Solution: A Fitness Trainer
|pdfUrl=https://ceur-ws.org/Vol-3179/Paper_20.pdf
|volume=Vol-3179
|authors=Yaroslav Hladkyi,Myroslava Gladka,Mykola Kostikov,Rostyslav Lisnevskyi
|dblpUrl=https://dblp.org/rec/conf/iti2/HladkyiGKL21
}}
==An IoT Solution: A Fitness Trainer==
https://ceur-ws.org/Vol-3179/Paper_20.pdf
An IoT Solution: A Fitness Trainer
Yaroslav Hladkyia, Myroslava Gladkab, Mykola Kostikovb,c and Rostyslav Lisnevskyib
a
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Prosp. Peremohy, 37,
Kyiv, 01601, Ukraine
b
Taras Shevchenko National University of Kyiv, Volodymyrska Street, 60, Kyiv, 01601, Ukraine
c
National University of Food Technologies, Volodymyrska Street, 68, Kyiv, 01601, Ukraine
Abstract
An IoT system of motion detection and control for fitness exercises can be urgent for people
who do sports at home. Using such a system as a mobile application allows the user to get the
assessment of technique when doing exercises demonstrating them in front of the smartphone
camera, as well as recommendations which may help improve doing exercises. As a result,
the efficiency of exercises will be increased. These functions are implemented via a neural
network able to recognize images. Besides, users will see their progress and information on
exercises made with flaws, which will help to fix the execution technique.
The IoT system configures business rules and scenarios, necessary artefacts for users, the
exercises they perform, errors and deviations that are committed while doing the exercises, as
well as analyzing video files. The IoT solution is built using a neural network that is capable
of recognizing the user's body posture during the exercise using a video file captured on the
camera of their own smartphone. With the help of mathematical calculations, artificial
intelligence will return the result of the video file analysis, where the user will be able to see
their flaws while doing a certain task, and get recommendations. A special factor in pattern
recognition is the individual initial anthropometric data of each user, which must be taken
into account in the analysis.
The use of IoT system of monitoring and control over the performance of fitness exercises
will positively affect the trend of a healthy lifestyle in today's world without the involvement
of personal fitness trainers.
Keywords 1
IoT, system, neural network, image recognition, fitness, training, technique, mobile
application
1. Introduction
Nowadays, when most professions involve sedentary work, stressful situations, and performing
tasks remotely using a PC, the question of recovery and support of full functioning of an organism.
emerges. Fitness has become widespread due to its positive effect on the human body: the activation
of anabolism, i.e. the accumulation of plastic substances that form body tissues, and energy
substances, to ensure vital functions. Full implementation of this process through fitness leads to
health improvement when a human body functions in a way that provides complete physical and
mental well-being. Therefore, doing fitness exercises, including cardio trainers and various other
procedures, has a positive effect on human health state and feelings [1].
The supervision of a coach while doing fitness exercises can eliminate the need for deep
knowledge in the field of biomechanics, motion physiology, methods of the training process
organization, theoretical points of physical activities and sports [2]. Psychological, methodical,
nutrition counselling and pedagogical support of the coach will help to avoid the personal experience
of trial and error. No less important is also the psychological aspect. However, the constant
Information Technology and Implementation (IT&I-2021), December 01–03, 2021, Kyiv, Ukraine
EMAIL: hladkky@gmail.com (A. 1); gladkam@fit.knu.ua (A. 2); kostikov@knu.ua (A. 3); lisnevskyir@fit.knu.ua (A. 4)
ORCID: 0000-0003-2914-8685 (A. 1); 0000-0001-5233-2021 (A. 2); 0000-0002-1569-8179 (A. 3); 0000-0002-9006-6366 (A. 4)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
215
supervision of the training process by a fitness trainer limits the time and number of workouts.
Therefore, it is important to develop an image recognition system where the position of a fitness
trainer will be replaced in part or in full by a highly developed artificial intelligence [3].
Among the mobile application in Ukraine, some offer their users an adaptive schedule for certain
exercises. So, the people who don't have the funds to buy a gym membership, or don't have enough
free time, have the opportunity to use such applications and maintain the good physical state of the
body. The users of such applications follow the instructions of a virtual trainer who develops exercise
routines depending on the user's characteristics. But users don't have an opportunity to know how
correct is their technique of doing a certain exercise, while it is an integral factor in their efficiency.
However, there are no analogues in the modern market of mobile fitness applications that could
offer a real time support for doing exercises by an artificial intelligence. Therefore, such an IoT
solution can potentially be an achievement in the field of sports mobile applications.
The aim of the IoT solution under development consists in the right placement of the relations
between the most important entities, optimization of performing queries and ensuring the
implementation of business processes of various kinds.
2. Methods for Solving the Problem
Artificial neural networks (ANN) is a mathematical functioning model of neural networks that
are traditional for living organisms and represent networks of nerve cells [4, 5]. Both biological and
artificial neural networks have neurons as their main element. The neurons are interconnected and
form layers. The number of layers may be different depending on the complexity of the network and
its aim (tasks solved) [6]. Probably the most popular task of the neural networks is visual images
recognition [7]. Nowadays the networks are created in which machines are capable of successful
recognition of symbols on the paper and bank cards, signatures on official documents, objects
detection, etc. These functions allow to facilitate human work significantly, as well as increase
reliability and accuracy of various work processes due to avoiding mistakes caused by the human
factor [8, 9]. A neural network (NN) is a mathematical model in a form of software and hardware
which is based on the functioning principles of biological neural networks [10]. Thus it is appropriate
to use this network for image recognition during the training process. A convolutional NN (CNN) has
a special architecture that allows it to recognize images most effectively [11, 12] The very idea of
CNN is based on the alternation of convolutional and subsampling layers, and the structure is
unidirectional. CNN got its name from the convolution operation, which implies that each image
fragment will be multiplied by the convolution core element by element, and the result must be added
up and written in a similar position of the original image. This architecture provides invariance of
recognition regarding the object shift, gradually increasing the "window", which "faces" the
convolution, revealing larger and larger structures and patterns in the image [13].
2.1. Positioning for Image Scanning
Anthropometric parameters of a person performing a fitness exercise are key indicators in
determining and recognizing images when performing fitness exercises [14, 15]. Therefore, the basic
initial values for the correct calculations of the performance quality should be the following
parameters: weight, height, length of different body parts (Fig. 1: 1x, 2x, 3x, 4x, 5x), circumferences
of the chest, waist, hips, limbs (Fig. 1: 1v, 2v, 3v, 4v, 5v, 6v, 7v, 8v), as well as physiological
indicators of key articular joints locations and distances between them. To do this, it is necessary to
measure and use these indicators as a basis for calculations (Fig. 1, e.g., the length of 2r-4r, 6l-7l, etc.)
[16]. It should be noted that all points parameters, except for 1 and 3, are represented by double
values: separately for the right and left side of the body.
2.2. A Neural Network for User's Motion Recognition
For the correct work of a neural network for motion detection and recognition, the system uses
LMST architecture. It is based on anthropometric indicators and representing images of exercise
216
execution. This includes sets of body parts motion and also individual elements recorded while being
done by 5 fitness coaches and 24 performers who were not professional athletes. The training process
of this neural network is based on patterns depending on indicators of individual body part motion in
correlation to the motion range expected when doing a certain exercise element [10, 15]. After this
training, we get an encoder that allows predicting an acceptable motion range for each exercise
according to the given individual parameters of the user. The function of adding new exercises is
implemented by the same principles, with each exercise being represented as a set of individual
elements (or parts of elements) of the performer's motion.
Figure 1: Key points for anthropometric measurements
2.3. Neural Networks in Mobile Applications
Mobile applications development reaches a new level every year. Applying machine learning
algorithms in this field becomes a problem which is being solved more and more often [17, 18]. The
main aim lies in optimization of certain computing resources. This leads to the possibility of
integrating such algorithms into a mobile application. Another solution may consist in transferring all
the computing mechanisms to the server. In this case, smartphone users need to have a stable internet
connection in order to use the system functions [19, 20]. Nowadays many services that use the
possibilities of machine learning are implemented according to the latter scenario [21].
2.4. Business Processes of a Fitness Trainer
All the functioning of the fitness trainer must be described as a set of rules and algorithms that
allow to create a set of exercises, monitor the execution and analyze the results. To develop the
algorithms, we need to create business processes that are shown in the Table 1.
The presented business rules are connected with the next views:
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entities: Video, Smartphone Camera, Neural Network, Message, Errors when Doing Exercise,
Exercise Execution Result, Execution Assessment, Fitness Exercise, IS User.
activities: User does the exercise, User fims the exercise with the smartphone camera, Naural
network analyzes the execution process from the smartphone camera, Neural network counts the
errors when doing the certain exercise, User gets the message from the neural network.
The neural network is an actor behind the scenes that is external to the system under development.
Thus, it isn't being modelled. Only the input and output data of this component are described. They
are given in the form of anthropometric indicators (fig. 1).
Table 1
Business rules for a mobile fitness trainer functioning
Business User Aim Task Scenario
Process
Adding a new Main: DB To add a new To save the new The DB Administrator gets the
exercise for Administrator pattern for parameters for the parameters in the form of digital data
possible detecting neural network in the from a neural network that was
analysis body parts in DB trained to a new pattern which
a new pose allows to record the deviations in the
Auxiliary: Neural To learn a To create new body posture of the IS User, and
Network new pattern parameters for stores them in the DB. The IS User
recognizing deviations can use the new functions after
in the body posture starting a new fitness exercise in
according to a new front of the camera. The video from
pattern the camera will be analyzed by the
Behind the To use the Having the possibility to Neural Network that will use the
scenes: IS User new functions learn about the parameters stored in the DB and will
of the system deviation in the posture alert about the possible deviations
when doing a new (assess the exercise execution on a
fitness exercise certain scale)
Buying a Main: IS User To buy a To make a payment in The User wants to get a premium
premium premium order to get new account and makes a payment. The
account account that functions from the IS DB Administrator gets a message
enhances the (e.g., some patterns) about the payment and checks it. In
available case of the successful funds transfer,
functions he gives the user the access to the
Auxiliary: DB To add the IS To get a message about premium account. Otherwise, does
Administrator user to the user's payment for nothing. The IS User gets the
premium the premium account message about the successful
users in the and give him the access purchase of the account or
DB to premium tools of the cancellation of the purchase
IS operation cancellation in case of an
error
Correction of Main: IS User To redo To get a list of exercises The IS User does the exercises in
mistakes exercises with with the highest front of the camera. A neural
the highest number of errors during network analyzes the execution of
number of execution that need to each exercise and counts the number
errors be redone of errors. Then the data about the
Auxiliary: Neural Each exercise To transfer the exercise execution are obtained by
Network is assessed, information about the the DB Administrator who organizes
and the errors deviations during exercises prioritizing ones which have
number is execution of each to be done better and saves them
counted exercise to the DB into the DB. The IS User gets the
Administrator information about exercises with the
Auxiliary: DB To save the To get the data about maximum number of committed
Administrator information the exercises from the errors and corrects them by doing
about errors Neural Network. To sort the problematic exercises in front of
in the DB the IDs in descending the camera.
order by the key of
committed errors. To
save the exercise order
into the DB
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Business User Aim Task Scenario
Process
Doing the Main: IS User To do an To turn on the camera The IS User logs in to the IS with their
exercise by exercise and and to do the exercise login and password, turns on the
the user in get the in front of it in a strictly camera and starts filming an arbitrary
front of the assessment of defined position and fitness exercise with the camera. The
camera and the execution from the best angle exercise execution is being analyzed
getting the technique by the Neural Network for a certain
information Behind the To analyze the To return the errors in time period. After the end of
about the scenes: Neural exercise done execution technique for exercise, the User gets the message
execution Network by the user the exercise filmed on a about the execution technique and
technique camera to the User counts the committed errors.
2.5. Representation of the Fitness Trainer Model
The modelled business processes and defined views allow us to form a model of entity-
relationship dependencies (fig. 2.). The created diagram includes 6 entities with 14 attributes, and
8 relationships between entities. This is the basis for building a class diagram (fig. 3) that is presented
in UML notation [22].
2.6. Data Elements Specification
The defined attributes and their description for the database under development are described in
the table 2 [23].
Table 2
Entities and attributes of the database under development
№ Entity Attributes Entity Description
1 User login The main user of the system who directly uses
password its functions
2 Exercise name An exercise that can be done and filmed by the
exercise group user
description
3 Error scale An error made by the user when doing the
description exercise
4 Videofile size A video file that contains doing the exercise by
duration the user
format
address
5 Result result date A message presented to the user after doing
Message message text the exercise and analyzing the video file
6 Result indicator of the analysis The result of analyzing the user's video file
3. Practical Implementation
3.1. Views
For views, let's define the key entities that represent the nature of a fitness trainer [24]. The first
user data type is the video file format which is described in the Table 3. Another view is the data type
that defines the scale of the error made (Table 4). The exercise list that includes certain fitness
activities are described in the Table 5.
Queries for creating user data types:
create type VideoFileFormatType as enum ('mp4', 'avi', 'mov');
create type ErrorGrossType as enum ('blunder', 'average', 'slight');
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create type ExerciseGroupType as enum ('legs', 'arms', 'chest', 'back', 'abs')
Other views are implemented according to the presented types of corresponding entities (fig. 3).
Figure 2: Entity-Relationship Diagram
Figure 3: UML Class Diagram
220
Table 3 Table 4
Fields of the list "VideoFileFormatType" Fields of the list "ErrorGrossType"
№ Field Description № Field Name Description
Name 1 blunder A blunder
1 mp4 A possible videofile format 2 average An average arror
2 avi A possible videofile format 3 slight A slight error
3 mov A possible videofile format
Table 5
Fields of the list "ExerciseGroup"
№ Field Name Description
1 legs Legs development exercise group
2 arms Arms development exercise group
3 body Chest development exercise group
4 back Back development exercise group
5 abs Abdominal muscles development exercise group
3.2. Functional and Ambiguous Dependencies
The user has a login and a password connected to it. They are defined by a unique user ID:
userid → login(1), passwordhash (2).
Given the fitness exercise ID, it is possible to determine its name, exercise group, and the
description of its execution: exerciseid → exercisename(3), exercisegroup(4), exercisedescription(5).
Given the video file ID, it is possible to determine all of its attributes: videofileid → videofilesize
(6), videofileformat (7), videofileduration (8), videofileuri(9).
The result ID contains the information about the success of the analysis: resultid →
analysisperformed(10).
In addition, this result determines the user whose video was analyzed, the video file itself, the
exercise performed on it, and the message that will be sent to the user: resultid → userid(11),
videofileid(12), exerciseid(13), resultmessageid(14).
Besides, the result ID defines errors detected during the analysis: resultid ↠ errorid(15).
The ID of the message about the result gives the information about the message date and its text:
resultmessageid → resultmessagedate(16), resultmessagetext(17).
In turn, the error ID can help to determine all of its attributes: errorid → errorgross (18),
errordescription (19).
3.3. Normalization
The next step will be the decomposition process for normalizing the relation to the 4th normal
form [25]. The initial relation is as follows:
R (userid login passwordhash exerciseid exercisename exercisegroup exercisedescription
videofileid videofilesize videofileformat videofileduration videofileuri resultid analysisperformed
resultmessageid resultmessagedate resultmessagetext errorid errorgross errordescription)
Key: resulted.
Step 1: FD 6-9 to R:
R1 (videofileid videofilesize videofileformat videofileduration videofileuri).
Key: videofileid.
R2 (userid login passwordhash exerciseid exercisename exercisegroup exercisedescription
videofileid resultid analysisperformed resultmessageid resultmessagedate resultmessagetext errorid
errorgross errordescription).
Key: resulted.
Step 2: FD 18-19 to R2:
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R3 (errorid errorgross errordescription).
Key: errored.
R4 (userid login passwordhash exerciseid exercisename exercisegroup exercisedescription
videofileid resultid analysisperformed resultmessageid resultmessagedate resultmessagetext errorid).
Key: resulted.
Step 3: FD 16-17 to R4:
R5 (resultmessageid resultmessagedate resultmessagetext).
Key: resultmessageid.
R6 (userid login passwordhash exerciseid exercisename exercisegroup exercisedescription
videofileid resultid analysisperformed resultmessageid errorid).
Key: resulted.
The normalization of other views is made similarly. The obtained relations in the 4th normal form:
R1 (videofileid videofilesize videofileformat videofileduration videofileuri)
R3 (errorid errorgross errordescription)
R5 (resultmessageid resultmessagedate resultmessagetext)
R7 (userid login passwordhash)
R9 (exerciseid exercisename exercisegroup exercisedescription)
R11 (resultid errorid)
R12 (resultid analysisperformed userid exerciseid videofileid resultmessageid)
3.4. Tables and Subject Area Constraints
The table videofile represents the video file entity and corresponds to the relation R1. It has a
unique identifier for each row. All other attributes cannot be null, and the video file address is unique:
create table videofile (
videofileid serial primary key,
videofilesize integer not null,
videofileformat VideoFileFormatType not null,
videofileduration integer not null,
videofileuri varchar(128) unique not null
);
The table error represents the error entity and corresponds to the relation R3. It has a unique
identifier for each row. The error description and the error scale attribute cannot be null:
create table error (
errorid serial primary key,
errorgross ErrorGrossType not null,
errordescription text not null
);
The table resultmessage represents the result message entity and corresponds to the relation R5. .
It has a unique identifier for each row. All the other attributes cannot be null. Besides, the result date
cannot be later than today:
create table resultmessage (
resultmessageid serial primary key,
resultmessagedate date not null check (resultmessagedate <= current_date),
resultmessagetext text not null
);
The table user represents the user entity and corresponds to the relation R7. It has a unique
identifier for each individual user, and the login field cannot be null:
create table "user" (
userid serial primary key,
login varchar(16) unique not null,
passwordHash text
);
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Besides, the password must have at least 8 symbols. Otherwise, a hashed password is entered into
the table:
create or replace function user_password_hashing_trigger()
returns trigger as
$$
begin
if length(new.passwordhash) < 8 then
raise exception 'Too short password';
end if;
new.passwordHash = crypt(new.passwordHash, gen_salt('md5'));
return new;
end;
$$ language PLPGSQL;
create trigger user_password_hashing
before insert on "user"
for each row
execute procedure user_password_hashing_trigger();
The table result represents the result entity and corresponds to the relation R12. It has not null
foreign keys for corresponding tables, except for the result message ID, as if the attribute responsible
for the analysis (which also cannot be null) takes 'false' value, there will be no message for this result:
create table result (
resultid serial primary key,
analysisperformed boolean not null,
userid integer not null references "user"(userid),
exerciseid integer not null references exercise(exerciseid),
videofileid integer not null references videofile(videofileid),
resultmessageid integer references resultmessage(resultmessageid)
);
After creating the result table, in the table errorsonresult it is necessary to perform the restriction
of the foreign key to the former table:
alter table errorsonresult
add constraint resultid_fk
foreign key(resultid) references result(resultid)
on delete cascade;
Similarly, we form views, triggers and algorithms for other entities.
4. Result
When implementing the fitness trainer mobile application, we perform the analysis of the views
for business processes, presented in the table 1.
The first execution plan responds to a query that returns all gross errors made by users:
explain analyze
select count(*) from error e
where errorgross = 'blunder';
Execution plans without and with indices are illustrated on fig. 4 and 5 respectively.
Figure 4: A query execution plan before indexing
223
An execution plan for a query that returns all the users and the exercise group along with the
number of exercises from this group that were done today, sorted by the number of exercises in
descending order:
select login, exercisegroup, count(*) as total from result
natural join resultmessage r
natural join exercise e2
natural join "user" u
where resultmessagedate = current_date
group by (login, exercisegroup)
order by 3 desc;
The execution plans without and with indices are illustrated on figures 6 and 7.
Fig.5 A query execution plan after indexing
Figure 6: A query execution plan before indexing
An execution plan for a query that returns all the video files larger than 30 MB, file format MP4
and containing at least 3 errors.
select videofileid, count(e.errorid) from videofile
natural join result
natural join errorsonresult e
where videofilesize > 30000000
224
and videofileformat = 'mp4'
group by videofileid
having count(errorid) > 2
order by 2 desc;
Query plans are developed for working with each view of the system.
5. Conclusions
The development of the IoT fitness trainer resulted in designing a mobile application that stores
the information about exercise types, parameters and execution features, anthropometric parameters
of the user. It allows users to monitor the correctness of doing fitness exercises in real time thanks to
using neural networks for image recognition. To develop this solution, DBMS PostgreSQL was used.
All the scripts in SQL language were written and executed in a free cross-platform tool named
DBeaver and in the terminal of the DBMS itself (PSQL Shell).
The product is based on the described business processes related to the fitness trainer IoT solution.
An important concept of the presented solution is the support of the people health under quarantine
restrictions, lack of time or opportunities to attend fitness classes with a coach. Using the presented
IoT solution will fully ensure the accessibility for everyone, and the introduction of image recognition
technology implements the mechanism of correct performance and correlation of user performance,
which eliminates the risk of damage from improper fitness technique.
Figure 7: A query execution plan after indexing
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