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.
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 [
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 [
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.
Artificial neural networks (ANN) is a mathematical functioning model of neural networks that
are traditional for living organisms and represent networks of nerve cells [
Anthropometric parameters of a person performing a fitness exercise are key indicators in
determining and recognizing images when performing fitness exercises [
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
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 [
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 [
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: 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). Business Process Doing the exercise by the user in front of the camera and getting the information about the execution technique
User To turn on the camera and to do the exercise in front of it in a strictly defined position and from the best angle To return the errors in execution technique for the exercise filmed on a camera to the User
The modelled business processes and defined views allow us to form a model of
entityrelationship 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 [
The defined attributes and their description for the database under development are described in
the table 2 [
Entity
For views, let's define the key entities that represent the nature of a fitness trainer [
Queries for creating user data types: create type VideoFileFormatType as enum ('mp4', 'avi', 'mov'); create type ErrorGrossType as enum ('blunder', 'average', 'slight'); create type ExerciseGroupType as enum ('legs', 'arms', 'chest', 'back', 'abs')
Other views are implemented according to the presented types of corresponding entities (fig. 3).
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.
The next step will be the decomposition process for normalizing the relation to the 4th normal
form [
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: 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)
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 );
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.
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.
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:
The execution plans without and with indices are illustrated on figures 6 and 7.
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 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.
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.