Uses Of Artificial
Intelligence In Personalizing Sports Training
Usos De La Inteligencia Artificial En La
Personalización Del Entrenamiento Deportivo
Leobardo Zerpa Marquina *
SUMMARY
This research addresses,
from a global analytical approach, the transformative capacity of artificial
intelligence (AI) in the personalization of physical training, including both
its own basic technological principles, as well as its broad socio-sporting
implications. To explore the topic ontoepistemically, a methodological strategy
is constructed that pursues the definition and conceptualization of AI, the
analysis of its influence on physical-sports practice, the description of its
applications and the characterization of emerging technologies in this line;
for this purpose, AI models applied to the following sports are reviewed in
different websites: athletics, basketball, baseball, gymnastics, soccer,
fitness, swimming and field tennis. The applications consulted show AI-based
platforms that enable the creation of personalized training routines, adapted
to the needs and personal training objectives of athletes, support for referee
decisions and injury prevention, projecting a holistic and rigorous margin of a
field in continuous development that warns of a serious risk between sports
organizations with an abundance of resources and sports organizations with
limited resources that do not have the means to acquire the latest technology.
Keywords: Sports, Artificial intelligence, Health, Technology.
RESUMEN
En la presente investigación se aborda, desde un
enfoque analítico global, la capacidad transformativa que presenta la
inteligencia artificial (IA) en la personalización del entrenamiento físico,
incluyendo tanto sus propios principios tecnológicos de base, como sus amplias
implicaciones socio deportivas. Para explorar ontoepistémicamente el tema, se
construye una estrategia metodológica que persigue la definición y
conceptualización de la IA, el análisis de su influencia en la práctica
físico-deportiva, la descripción de sus aplicaciones y la caracterización de
las tecnologías emergentes en esta línea; para ello, se revisan, en diversos
sitios web, modelos de IA aplicadas a los deportes: atletismo, baloncesto,
béisbol, gimnasia, fútbol, fitness, natación y tenis de campo. Las aplicaciones
consultadas evidencian plataformas basadas en IA que posibilitan la creación de
rutinas de entrenamiento personalizadas, adaptadas a las necesidades y al
objetivo de entrenamiento personal de los atletas, apoyo a las decisiones
arbitrales y prevención de lesiones, proyectándose un margen holístico y
riguroso de un campo en continuo desarrollo que advierte un grave riesgo entre
las organizaciones deportivas con una abundancia de recursos y las
organizaciones deportivas con recursos limitados que no disponen para adquirir
tecnología de última generación.
Palabras
Clave: Deporte, Inteligencia
artificial, Salud, Tecnología.
INTRODUCTION
The research explores the implementation and benefits
of AI in the personalization of sports training. From the methodological
perspective, a qualitative and documentary approach was adopted by conducting a
comprehensive review of scientific and technical literature published between
2015 and 2025. The search strategy focused on keywords such as "AI",
"sport", "technologies", "physical activity" and
"health". The critical analysis of the selected texts allowed
categorizing the different technologies and methodologies, synthesis of
comparison results on advantages and limitations in the optimization of
physical activity. The theoretical framework reflected how AI uses a set of
techniques that simulate the cognitive processes developed by people in order
to carry out the automation of functions and thus improve the efficiency of the
various sports fields in which it is used.
This scenario shows that AI can process significant
volumes of data that are collected through sensors and wearable devices, thus
personalizing workouts and preventing injuries. The research contemplates the
use of AI applications in athletics, basketball, baseball, fitness, soccer,
gymnastics, swimming and table tennis, indicating that in these sports AI is
used in sports training to improve performance by analyzing biometric data and
tracking athletes in 3D. In refereeing, semi-automated systems improve the
efficiency of decision making, while in sports health, AI enables accurate
diagnosis and injury prevention through analysis of training patterns. In
conclusion, AI redefines the practice to which athletes are exposed, improves
performances, prevents injuries and promotes healthy lifestyles, modifying the
sports practice and the experience of the professional athlete or practitioner.
MATERIALS AND METHODS
The research has an exploratory and descriptive
character, as well as a qualitative-documental approach, on the analysis of the
benefits and implementation of artificial intelligence in personalized sports
training. For this purpose, a systematic review of scientific and technical
literature was conducted, which collected from specialized websites, to
academic articles and technical reports, published between 2015 and 2025, to
ensure the timeliness and relevance of the information compiled.
As a search strategy for the selection, the following
keywords were used: artificial intelligence, sport, technology, physical
activity and health, both in Spanish and English, and with the support of
Boolean AND/OR operators.
For the first search, a preliminary filter was carried
out based on the chosen titles, selecting only those web pages whose
information was relevant to the subject matter. Likewise, only documents on the
specific application of artificial intelligence in sport and physical activity
were selected, paying special attention to studies on the personalization of
training, biometric data analysis, injury prevention and improvement of sports
performance. In this way, sources without scientific support or those that were
not available in full text were eliminated.
Next, we moved from the critical reading and precision
of the chosen texts to the categorization of the different technologies,
methodologies and applications found. Thus, based on the analysis, the results
were synthesized in order to compare advantages, limitations and success cases
in the use of artificial intelligence for the optimization of sports training.
Finally, these findings were mixed in an informative structure that combined
theoretical foundations with practical examples, in order to support the
conclusions of the study.
Artificial intelligence in sport and physical activity
Artificial intelligence (AI) is commonly defined as a
branch of computer science involved in the development of systems or machines
that are capable of performing tasks that normally require human intelligence;
these tasks include learning, decision making, pattern recognition, language
understanding and complex problem solving. Artificial intelligence is based on
algorithms, neural networks, systems theory, machine learning and, more
recently and especially, deep learning, which allows machines to refine
their performance based on experience and data.
From an academic point of view, AI can be defined as
the set of techniques and methods that allow computers to simulate human
cognitive processes and are aimed at automating functions that optimize
efficiency, precision and speed in various fields of knowledge and industry.
Its development has modified numerous sectors, from medicine to engineering, and
is having a clear influence in the field of physical activity and sports.
The relationship between artificial intelligence and
physical activity is increasingly close and multidimensional. AI in sport is,
as Sanabria et al (2024) point out, the tool that corrects errors, contributes
to decision making, gives rise to new strategies in sports training and
competition, helps prevent sports injuries, helps study opponents and gives
rise to top quality sports situations; this is so because, in the first place,
AI is present in the analysis and improvement of sports performance from the
processing of large volumes of data obtained from sensors, cameras and
electronic and wireless devices. The results of these data allow the analysis
of biomechanical, physiological and tactical aspects of athletes, favoring the
design of training and injury prevention. For example, AI, based on algorithms,
can recognize movement patterns that predispose to injury or can suggest
adjustments to a technique to optimize energy efficiency.
In addition, AI is used for the development of
applications and platforms that encourage sports practice in the non-athletic
population, and does so, for example, with virtual coaches that are
intelligent, "personalized" training programs or gamification systems
that are used to obtain a practice that generates motivation to have healthy
habits by adapting to their needs and their individual progress. In
competition, AI is likewise used for the tactical analysis of opponents, the
simulation of match status or the evaluation of the performance of teams and
athletes, becoming a tactical advantage that can turn a high-level competition
in the direction of a particular team and athlete.
In short, Artificial Intelligence has become a tool
that redefines the way of understanding and practicing physical activity and
sport, and that can give rise to new possibilities to optimize performance,
prevent injuries and encourage the practice of healthy lifestyles; but the
continuous integration of it in the field of physical activity and sports
promises not only to continue to evolve, but also to give rise to a new style
of experience between the professional athlete and the athlete who seeks to
improve his personal well-being, based on physical activity.
Uses of artificial intelligence in physical activity and sports.
The applications of AI in sports are varied.
Technological progress is growing rapidly and incessantly, constantly
increasing the production of information that is stored in large databases that
give rise to sets of information that exceed the cognitive capacity of human
beings. The practice of physical activity and sports as an enhancer of the
human being generates a high production of information on a daily basis. In
this regard, Carrio (2024), points out that AI collaborates in this field in
the following three areas or sectors:
In training and sports preparation
When analyzing the topic of AI in sports, it should be
taken into consideration that improving sports performance and individual
skills is one of the main purposes of training. There are different AI
applications that can be used online, such as the 3D Athlete Tracking (3DAT)
proposed by Intel, which offers different metrics to help high-performance
athletes improve their performance by analyzing factors such as speed or body
position. I believe that this technology will become increasingly important in
different sports, since, on the one hand, it offers objective evaluations
without human bias, and, on the other hand, it allows offering personalized
training plans. Undoubtedly, it thus becomes an invaluable tool for athletes of
all levels who wish to achieve excellence in the disciplines they are involved
in.
In arbitration
In the field of sports arbitration and jurisdiction,
Carrio (2024) highlights the relevance of the refereeing activity being carried
out with the help of artificial intelligence. This same author expresses that
in championships such as the FIFA World Cup 2022, the implementation of the
semi-automated technology system (SOAT), the use of data analysis and
artificial intelligence algorithms to assist in making decisions with better
accuracy in offside situations, with respect to traditional video arbitration
systems (VAR), is subordinated. This new technology not only makes decisions
faster, but also tries to correct as much as possible the human error that has
plagued refereeing.
However, as automated competitions increase, a
critical analysis of the appropriateness and integrity of sports governance is
prompted. Stakeholders must address the question of the appropriateness of
machine versus human decision making and whether changes in refereeing may
eventually alter the natural competitive nature of the various disciplines.
While AI may offer a number of important improvements in the accuracy of
refereeing practice, its application should be approached with great care to
ensure that its principles are matched and respected in practice.
In health
The intersection of artificial intelligence and
athlete health is a rapidly evolving discipline that brings to bear both the
potential advantages, as well as the limitations that are present in
professional sport. While it is clear that physical exercise is a health
benefit, it is recognized that high-performance athletes are often faced with
high-demanding subjections that are risky to health, particularly injury.
Artificial intelligence technologies have increasingly become a fundamental
tool in this field because they provide accurate information about the
athlete's health while delivering timely information of value for performance
tuning that, in turn, protects the athlete's health.
Machine learning algorithms can perform a scan of
extensive training data to identify patterns that indicate when an athlete
should experience injury relative to their trajectory. This prompts coaches to
adjust training regimens, employing pre-exercise performance analysis in order
to detect or prevent injury to the athlete or to supplement the diagnosis in
the event that injury cannot be avoided, with artificial intelligence being
used as an imaging technology technique to facilitate diagnosis, for example.
Emerging technologies that are revolutionizing sports
Innovative technologies linked to artificial
intelligence are generating in the field of sports practice a radical change
insofar as they transform all training and competition methodologies, but also
the way in which sports data are managed and processed and their analysis.
Through complex algorithms and intelligent systems, athletic performance can be
improved, injuries can be prevented and/or a personalized strategy can be
carried out simultaneously, which in turn represents a major change in the
usual functioning of a sport. This convergence between technology and sports
gives rise to new ways of interacting with each other and with devices, which
represents a turning point in the history of current sports practices.
Figure 1. Chronology
of emerging technologies in sport.
Source: Caballero & Nuñez (2022)
Among the emerging trends identified by Caballero
& Nuñez (2022), the following represent some of the most relevant
milestones in the combination of artificial intelligence and sport. On the one
hand, the analysis of massive data (Big Data) makes it possible to evaluate the
performance of athletes with levels of precision never seen before, which
allows decisions to be made. On the other hand, personalized training systems
based on machine learning adapt to the individual characteristics of each
athlete in their routines and strategies. Likewise, the use of intelligent
sensors and wearable devices provides real-time information on certain
physiological and biomechanical parameters that are related to improving injury
prevention.
Finally, augmented reality and virtual simulation
present new ways of preparation and experience for athletes and fans with the
inclusion of artificial intelligence in the creation of immersive and highly
interactive scenarios.
The combination of these trends will redefine the
future of sport from multiple perspectives. Figure 1 presents the timeline of
emerging technologies applicable to physical activity and sports; there it can
be seen, according to Caballero & Nuñez (2022), from its birth, with video
analysis in 2000, to the inclusion of advances in blockchain in 2024.
Cases of artificial intelligence technologies and models applied to
physical activity and sports.
Athletics
Artificial intelligence (AI) has made a strong entry
into the world of athletics, transforming the way in which athletes'
performance is measured and analyzed. One of the most important applications is
that which makes use of inertial measurement units (IMUs), which consist of
sensors placed on the athlete's body that capture real-time data on
acceleration, angular velocity and orientation. This data is further processed
by AI-based algorithms that allow for a highly accurate and rigorous assessment
of the athlete's biomechanics, exposing technical aspects that can be improved
to maximize performance in disciplines such as sprinting, race walking, jumping
and throwing. Perceptible feedback through visual, auditory or kinesthetic
signals constitutes a mechanism for almost instantaneous adjustment of
technique and, in this sense, represents a step forward, without a doubt, in
the process of personalization of training.
In addition, AI can be used as a component of GPS
tracking systems and physiological measurement systems for endurance sports,
such as long-distance running. Deport-Tec (2024) and KGW News (2021) agree that
one of the automation systems that Word Atletics is using is the 3D Athletics
Tracking System, a system that continuously reports on parameters such as
incorporating postural angles, heart rate, speed, distance covered, which are
then processed by intelligent models to adjust the load and duration of
training, thus preventing overtraining and making the information a method of
minimizing the risk of injury by keeping athletes within safe exertion zones.
At the same time, fatigue can be anticipated and, based on this foresight, the
timing of a recovery period can be indicated, thus contributing to more
efficient long-term performance management.
At the tactical level, AI aids in talent detection and
proper training scheduling. From large volumes of historical and current data,
AI can identify patterns of results and athletic potential, helping coaches and
federations to make decisions. In Spain, this technology has been implemented
in projects in different athletics disciplines, proving that artificial intelligence
can transform sport through the combined use of intelligent sensors,
information processing and real-time feedback. AI will not only improve
individual performance, but will also allow the general evolution of athletics
to reach other types of technological practices.
Basketball
Artificial intelligence (AI) has changed the ways
basketball is played by integrating complex systems for analyzing player and
team performance. One of the most notable applications has been the real-time
tracking of player movements with the use of cameras and sensors that capture
precise data on the position, speed, and on-court movements of ball-handlers.
This information is converted into a data set that is processed by AI
algorithms and allows coaches and analysts a more detailed and comprehensive
picture with which they can make more accurate tactical decisions, stabilize
lineups and make tactical adjustments during a match. According to the website
Noeliagorod (2023), companies such as Second Spectrum have developed
technologies that allow them to track every single action taken on the pitch,
thus facilitating an understanding of the game that was not possible before.
In addition, we can establish that AI is also used for
the training of technical aspects of players, whether mechanical or not. Tools
such as ShotTracker analyze, by means of sensors on the ball, on the players or
another form of sensor, the shape of a shot, the accuracy of a shot, shot
selection, etc., generating immediate feedback that serves to correct and
perfect the mechanics of the shot. Figure 2 shows the "visible"
behavior of this platform, which can be expanded with the parallel metrics of
the teams or player being worked with. This form of analysis, in combination
with AI for the operation of predictive models, increases the ability to
anticipate the performance that can achieve as the training of players
progresses and the design of efficient training to enhance strengths and
minimize weaknesses.
Figure 2. Example of ShotTracker firing technology
Source: Noeliagorod (2023)
The website StatsPerform (2025), with more than 40
years of collecting and archiving sports data, also applies artificial
intelligence to detect opponents' patterns of play, developing game plans and
tactics that exploit their weaknesses and nullify their strengths.
On the other hand, AI also accompanies the integral
management of the player and the sports career. Applications such as QB-BCA.AI
are intelligent assistants that monitor the evolution of the athlete from the
beginning, recommending exercises, habits and strategies that enhance
performance and prevent injuries. At the same time, computer vision
technologies, such as Ultralytics YOLO11, enable real-time analysis of matches
in which infractions, offsides or fouls can be identified, thus favoring fairer
and more effective refereeing. The combination of these advances means
transforming the game of basketball not only on the court, but also making it
much more enriching for spectators, as well as improving decision making in the
different instances of professional basketball.
Nor can we ignore progress in the field of robotics.
Lázaro (2024) points out, in the digital portal Inspenet, how a robot produced
by Toyota, named CUE, has managed to set a Guinness record by performing the
farthest basketball throw made by a humanoid robot, nothing more and nothing
less than at a distance of 24.55 meters. The feat was accomplished in September
2024 in Nagakute, Japan. This is a remarkable breakthrough in robot mechanics
when it comes to being able to execute precise and technical movements in
highly demanding sports. CUE makes use of artificial intelligence and makes
real-time adjustments to its posture, strength and arm position, learning with
each throw. Toyota's intention is for this robot to make more complex movements
in the future, such as running or feinting, getting closer and closer to human
movements. This breakthrough not only demonstrates that machines can replicate
complex, high-precision movements, but also opens the door to new forms of
training and sports analysis.
Baseball
The irruption of artificial intelligence (AI) on the
baseball field is bringing about a sincere and notorious transformation that
reveals, on the one hand, better decision making by umpires, while, on the
other hand, it favors the work done by players. A clear example of this
transformation is the Automated Ball and Strike System (ABS). This system was
introduced in development leagues such as the Atlantic League and the Pioneer
League and was subsequently tested in the 2023 and 2025 Spring Trainings. As
explained in Nestoraltuve.com (2025), ABS makes use of a high-speed camera
system and complex computer vision algorithms; this technology is capable of
tracking the path of all pitches with such accuracy that it can easily
determine whether it is a ball or a strike. The implementation of its hybrid
version, which gives teams the right to request immediate reviews of unclear
plays, has proven to be effective in this regard and has led to a reduction in
on-field disputes, increasing umpiring accuracy from 88% to 90%. This
technological innovation is so important that Major League Baseball (MLB) is
considering its full implementation for the 2026 regular season, a move that
will undoubtedly remain a key episode in the modernization of this sport with a
long and deep tradition.
However, the projection that AI captures in baseball
is not limited to umpiring, it has also asserted itself as a basic tool that
accompanies predictive analytics and the formulation of game strategies.
Technological platforms are available that have the function of managing a
large amount of data that can range from those we have in the historical
database to those we receive in real time (speed in the throw, ball spin,
location of a player, etc.), with the purpose of making predictive models that
help us to improve elements of the game such as adjusting the position of the
defensive alignment, the choice of the pitch that the pitcher will execute, the
execution of the offensive tactic that we are going to develop, etc. These types
of systems, as Toolify.ai (2024) expresses, allow the technical staff to
anticipate the opponent's game patterns and, consequently, to modify their
planning, using probability calculations to increase the possibility of
achieving a good outcome of the game. At the same time, immersive simulation
tools using virtual reality technology have the added advantage of being able
to reproduce complex game scenarios and thus provide deeper and safer cognitive
training for players.
On the other hand, health and injury prevention in
athletes also benefit considerably from technology. Wearable devices or
biometric sensors that are discreetly incorporated into players' sports
equipment allow uninterrupted monitoring of the most relevant variables:
accumulated muscle fatigue, total physical workload, etc., which have a direct
impact on the probability of injury. AI is in charge of analyzing this constant
stream of data, which makes it possible to predict the risk of injury earlier.
The anticipated analytical capacity to be acquired
translates practically into the possibility of fine-tuning and fine-tuning
training routines, even including tailor-made recovery programs for each
athlete. Moreover, such functionality not only predicts that players' careers
can be extended over time, but that their performance can also be improved by
incorporating a balance between required effort and recovery-oriented rest; the
Dodgers and the Yankees, for example, are some elite teams that are already
making active use of these technologies in their day-to-day work, combining
very specific physiological data with machine learning procedures to support
their conclusions regarding strategic management and training decisions.
Of course, the arrival of AI in a sport with such a
deep historical significance as baseball has logically reopened a controversy
between the defense of the traditional and the innovative proposals in search
of a new model of sport. There are some sectors more anchored to a purist
vision that consider that the elevation of automation could to some extent lead
to a loss of the very human essence so characteristic of baseball. They
attribute to elements such as the subjectivity of the umpires' decisions or the
unpredictability of the games themselves, aspects that were historically
factors that explain baseball's idiosyncratic appeal. But MLB seems to have
opted for a transition that seeks coexistence between technology and sufficient
human intervention, understanding the delicate balance that has to exist
between improved accuracy and deeply rooted traditions. This hybrid model could
be seen as a first step towards a sport that is perceived to be fairer and more
asymmetrical, but without losing sight of the condition of maintaining a fan
base that is very friendly to the more classic aspects of baseball. Well, the
intensity of the strategic confluence that exists between AI, Big Data and
emerging technologies is undoubtedly taking baseball into a new era, an era in
which innovation becomes a fact of life within the field that pertains to
sports. To conclude, the question remains: will baseball preserve its soul
through algorithms and predictive metrics?
Soccer
Historical situations of strong and enduring
controversy, such as Diego Maradona's mythical goal known as the "hand of
God", decisive for Argentina to advance to the quarterfinals of the 1986
World Cup in Mexico, or the disputed "ghost goal" attributed to Geoff
Hurst, which ended up favoring England in the final of the 1966 World Cup, have
led to significant technological investment in the sport, the one that ended up
going in England's favor in the 1966 World Cup final, have led to a significant
technological investment in the sport, marking the development and evolution of
a soccer that today is notably different from the one practiced just a few
decades ago. The demand for greater objectivity in refereeing decisions was a
fact observed a posteriori, after those episodes, which served as a starting
point for the technological process we are currently experiencing.
In the context of today's soccer, the implementation
of advanced technologies has drastically changed the on-field experience and
the strategic management of the game. According to the article on the
Ndmarketingdigital (2025) website, artificial intelligence, above all, is the
driving force behind this revolution. AI exerts its influence on multiple
fronts: from customizing training plans and improving individual and team
practice, to collecting and analyzing data to prevent injuries, to influencing
advertising, the way matches are broadcast or even optimizing sports
infrastructures. Likewise, AI is used to better evaluate players' skills and
create performance reports, also to facilitate the detection of other talents
and predictions about the possible outcome of matches. There is no denying that
all these reforms are radically transforming the way soccer is played, the way
athletes are prepared and the way refereeing is managed in the most followed
sport in the world.
In the refereeing field, one of the systems whose
implementation has given more visibility and controversy has been the VAR
(Video Assistant Referee or video assistant referee) as we currently understand
it. A system that is understood as a support for the referee himself on the
field of play, where conflicting or dubious actions are reviewed with various
shots and instant replays. It has proven to be effective in correcting human
errors of great magnitude, helping to make decisions fairer and promoting
transparency in the context of matches. The use of VAR has played a fundamental
role in ensuring that the rules of the game are applied from as many objective
facts as possible. However, as is often the case with innovations of such
caliber, it has also brought with it a debate about its influence on the
fluidity and dynamism that characterize traditional soccer.
On the other hand, and beyond the systems designed to
improve refereeing, there is an innovation to take into account: the arrival of
"smart" balls. Equipped with high-frequency sensors, these balls are
expected to become a standard in future top-level international events. They
feature sensory and capture technology aimed at retaining and communicating
incredibly accurate, real-time data on their trajectory, speed and exact
position. The information generated is quickly sent to referee support systems
(for example, VAR), greatly increasing accuracy in cases where ball placement
is a determining factor: think of offside or checking whether or not the ball
has crossed the goal line. The aerodynamic and technological design of these
balls increases performance, allowing for better stability and greater
consistency of play.
Beyond refereeing, modern soccer has incorporated
various technological systems that specialize in a series of processes that are
more dedicated to the analysis and comprehensive review of players' physical
and tactical performance. Electronic performance tracking devices (EPTS) and
different motion sensors allow the collection of a lot of detailed information:
from total distance and maximum speed, to acceleration and other relevant
physical parameters of both training exercises and official matches; these data
are subjected to an analysis process with computer tools that provide coaches
and physical trainers with key information to customize their training plans,
fine-tune their tactical strategies and prevent, in the sense of properly
managing the workload of soccer players and, therefore, decrease the risk of
injuries. Artificial intelligence is also actively used to analyze these large
volumes of data and provide tactical analysis with predictions about future
performance, complex simulation of scenarios that require extensive analytical
work to individually use the strengths in the process of adapting
individualized training and the specific weaknesses of each footballer. For
their part, immersive technologies, such as virtual reality and augmented
reality, are booming as tools to simulate real game situations, working on
improving the decision-making capacity under the demands of competition, thus
increasing the preparation of soccer players in the digital era.
Artistic Gymnastics
Artistic gymnastics is a sport in which the evaluation
of performance depends, fundamentally, on the criteria of a group of referees.
Indeed, it is these professionals who evaluate the difficulty and quality of
each exercise, a circumstance that has historically given rise to numerous
controversies and complaints from gymnasts who consider that the score is
disproportionate to the effort made. For this reason, the International
Gymnastics Federation (FIG) has sought a solution that aims to increase the
reliability and transparency of scoring in artistic gymnastics. The FIG,
together with Fujitsu Limited, presented in 2019 an innovative system based on
artificial intelligence technology. This technological innovation uses highly
sophisticated 3D detection systems to capture, in a very precise way, the
gymnasts' execution and obtain a comprehensive analysis of it. This method is
called JUDGING SUPPORT SYSTEM (abbreviated to JSS) and is based on 3D sensors
coupled to cameras that continuously record the performance and generate and
project in parallel a three-dimensional graphic representation of the gymnast
performing the exercise in question, as shown in Figure 3. The artificial
intelligence of JSS can recognize the body posture, the relative position of
the apparatus used and the type of movement performed, but it can also identify
very reliably the technical element that is being executed.
Figure 3. Image of Fujitsu's support system using 3D sensing technology for
gymnastics evaluation
Source: Fujitsu Limited (2019).
The JSS is able to offer considerable benefits to the
sport. Mainly, it makes the function of a fundamental tool to support the
judges, since the system calculates in an objective way the difficulty value of
the elements executed by the athletes, to work as an excellent pillar that has
the function of reducing the risk of human error and, therefore, of erring in
the final scores and, in this way, narrowing the disputes around the final
sketch, but always without giving up the much needed function and judgment
offered by human judges.
In addition, the system generates an extremely
exhaustive numerical analysis with data that allows to know, with accuracy, the
body angles performed in the turns or specific acrobatic elements, which
contributes to a more objective and, moreover, quantifiable assessment of the performance. It must be accepted that
the recognition capacity is considerable; there is a database that already
identifies more than 800 elements in men's artistic gymnastics and more than
500 in women's artistic gymnastics. On the other hand, and no less important,
is the training assistance tool, which has become a fundamental pillar that
both gymnasts and their coaches can use as a JSS to rehearse the construction
of new elements, improve technique by correcting the errors detected
numerically and, ultimately, improve technical performance for competition.
Once tested on selected apparatus at the 2019 World
Championships, this new system was formally implemented and used at the Paris
2024 Olympic Games. The International Gymnastics Federation considers the JSS
as one of the most representative technical developments introduced in the
sport in the last couple of decades and, also, believes that it will have a
positive impact on both the fairness of refereeing and the overall improvement
of the quality of competitions and the training process. FIG President Morinari
Watanabe has publicly highlighted the importance of this technology, pointing
out the fundamental reasons behind its development and its potential to shape
the future of evaluation in artistic gymnastics, seeking that delicate balance between
technological precision and the inherent artistry of the performances, this is
what he said:
The people we should always keep in mind are the
gymnasts. They dedicate their youth to this sport, which gives them dreams and
sees them thrive. We cannot accept that all those years of hard work and all
those dreams can be destroyed by a single error in judgment. Scoring
controversies must be a thing of the past, and the technology that Fujitsu has
been developing will reinforce confidence in judging.
In line with the perspectives expressed by FIG
President Morinari Watanabe regarding the need to strengthen referee judgment
support systems through technology, a recent example that highlights the
inherent complexity of scoring in artistic gymnastics is the controversial
definition of the medals in the women's floor exercise final at the Paris 2024
Olympic Games. In that competition, Brazil's Rebeca Andrade won the gold medal,
followed by the United States' Simone Biles, with the silver medal. Controversy
arose over the third position, initially awarded to American Jordan Chiles,
while Romanian Ana Barbosu was placed fourth.
The controversy escalated when, after a review by the
High Court of Judges and Referees, an error was identified in Jordan Chiles'
score. This error caused the initial decision to be suspended, awarding the
bronze medal to Ana Barbosu eleven days after the competition. This event
sparked a great debate on the legitimacy of the appeals process and the
significant emotional cost that these situations produce to the athletes
involved. Moments like this can clearly illustrate how, despite the experience
and greater professionalism of the judges, human error can appear in relevant
parts and how technology could intervene as a possible solution.
The Judging Support System (JSS), in this sense, is an
innovative system, which shows the path of artificial intelligence as a support
for the field of sports disciplines as highly subjective as Gymnastics, because
it favors objectivity, transparency and, thus, accuracy in the evaluation of
performance. Events such as Paris 2024 clearly show the convenience of having
tools for the verification of the starting marks of gymnastic compositions,
this being a clear precedent that could lay the groundwork for the
implementation of similar technological support systems in other sports that
rely on human assessment evaluations.
Swimming
One of the most striking applications of artificial
intelligence (AI) in swimming are devices that integrate sensors in the aquatic
environment together with underwater cameras, which make it possible to obtain
highly nuanced information containing details of the swimmers' technique or
movement pattern. This information is then processed through AI algorithms that
contain procedures suitable for the analysis of essential biomechanical
aspects, such as the position adopted by the body in the water, the frequency
and good execution of the strokes or the optimization of each of the moments of
the swimming cycle. This detailed technical information is of great value for
coaches and athletes, since it facilitates the correct identification of the
susceptible areas to improve technique and maximize performance in this sport
(Turbo Swim, 2024).
In addition, artificial intelligence has become a
fundamental component of the platforms of the physical control systems,
sensorization and study of the training cycles that operate in athletes who
perform their training or competition sessions. These systems are responsible
for capturing data in real time: heart rate information, blood oxygen level
saturation or muscle fatigue that is accumulated by athletes who perform
continuous and repeated very intense training sessions. With the help of
predictive models, these systems make it possible to anticipate with maximum
precision the risk of overtraining or predisposition to injury, and enable
physical trainers and coaches to program the workload to promote recovery
processes and thus long-term performance.
A tangible example of these resources are smart
glasses, some of which even integrate augmented reality technology. A
paradigmatic model is that of the SwimAR brand, seen on the website Wwwhatsnew
(2018) (see Figure 4), smart glasses that are able to collect data on swimming
experience, to propitiate directionality indications useful in open water and
to display values of heart rate that allow to control the cardiovascular
training zone through this physiological indicator that specifies the intensity
of training.
Figure1 . Smart
swimming lenses.
Source: Computer Today (2023)
The ability to adapt exercise plans in a dynamically
tailored way based on the physiological response and progress of each swimmer
is undoubtedly a breakthrough in improving injury prevention and sustained
competitive performance training. And there are also specific tools that
function as data aggregation centers that obtain data from different smart
devices and generate personalized training routines that are dynamically
updated with the goals and improvement of each swimmer, thus optimizing the
quality of training and risk reduction.
On the other hand, artificial intelligence is also
increasingly present in the action of tactically analyzing swimming
competitions, thus informing from the use of technologies such as computer
vision or machine learning so that information on different metrics of each
event can be well controlled and recorded. While, for example, from the Nagi
Smartpool application, as indicated by CIDE (2025), it is possible to monitor,
among other parameters: the swimming speed, the distance swum and the remaining
distance, the count of the lengths that are being completed, sending all these
data in real time to electronic devices.
These innovations make it possible to have an
"intelligent" aquatic environment through the use of sensors that,
together with intelligent devices, configure an ideal environment to enhance
training, ensure conditions and improve the performance of swimmers. This
undoubtedly gives rise to a revolution towards the mutation of sport management
models and towards technical improvement in swimming; after all, the ability to
apply artificial intelligence, in a recurrent way, is forcing aquatic sport to
change towards a sport that is more science-assisted, more efficient in its
processes and more personalized in the development of the swimmers.
Field tennis
The so-called artificial intelligence (AI) is taking
the field tennis to the current transformations derived from the adoption of
advanced technologies that are associated with a comprehensive analysis in real
time of the tennis players. One of the most relevant applications of artificial
intelligence in the practice of tennis is the existence of some algorithms that
manage to process large amounts of data that are recorded by different sensors
and cameras installed strategically. This information is essential to keep
track of the stroke, the position of the player on the court and also the
trajectory of the ball during training and competitions.
These systems, therefore, are not only able to
identify highly complex patterns of play, but also establish highly
personalized strategic recommendations capable of enabling tennis players to
anticipate their opponents' shot sequencing while, in parallel, also optimizing
the execution of their own shot sequencing, which makes AI also act directly in
contributing to the planning and execution of a more efficient training that
facilitates much more the way in which tennis players can make decisions on the
court.
Meanwhile, smart rackets have also been one of the
technologies that have been gaining relevance in the field of tennis training.
These work from the implementation of sensors inside the racquet that can
collect accurate information on aspects such as the type of stroke made, the
force of the blow on the ball or the point of contact itself. In addition,
these devices are often connected to mobile applications so that both players
and coaches can help perform a more comprehensive technical analysis off the
court that warns about technical aspects that need to be reviewed to help
identify where they require more attention to perfect the techniques of the
game. The growing acceptance by sports authorities of these tools has facilitated
their integration into professional training activities and they are very
useful for generating detailed and tailored analysis of the progress of each
tennis player, or for making objective comparisons of the progress that each
player's technical aspects may have over time.
In addition, the combination of sensors and AI systems
allows for improved analysis and the generation of training programs and
results based on the specific needs of the player. A fundamental element for
the technological modernization of tennis is undoubtedly the Hawk-Eye system.
Inventa S.A. (2023) describes that the also called "Hawk-Eye", is a
system that uses several high-speed cameras as sensors to track the movement of
the ball in order to know if it has touched inside or outside the regulatory
limits. This system, based on triangulation principles and real-time image
processing, has revolutionized refereeing by providing faster and more
objective decisions, reducing controversies in matches.
Hawk-Eye not only fulfills an umpiring (match)
function, it also provides other data that can be used in statistical or
strategic analysis by players and coaches, and thus enters a technological
ecosystem that enhances both sports fairness and performance. Indeed, all these
innovations supported by artificial intelligence represent an evolution for
field tennis, improving the competitive standard and, at the same time, the
training and competition experience. The ability to monitor, analyze and
predict on-court behavior allows players to gain a competitive advantage, while
coaches have access to more fine-tuned tools for designing highly personalized
programs. The integration of intelligent sensors, data analysis and computer
vision systems will undoubtedly continue to further the development of tennis
in an era of increasing technology and efficiency.
Beyond tennis itself, Hawk-Eye has found application
in sports as diverse as cricket, soccer, volleyball and badminton, as can be
seen on the Hawk-Eye Innovations Ltd (2025) website. In each of the sports the
system is tailored to meet the specific needs of the discipline in order to
follow the game or analyze it. Its ability to provide objective, real-time
information has changed the way matches are refereed and has improved the experience
of athletes and the public at matches.
Fitness
The incorporation of artificial intelligence (AI) into
fitness technology systems is profoundly revolutionizing the way we train,
monitor health and achieve fitness goals. AI-based systems use advanced algorithms
to process large volumes of user data, such as performance, body composition
and pulse rate, with the goal of providing individualized training
recommendations and plans that change with individual progress.
This power of continuous readjustment translates into
an obvious advantage over traditional systems, as it allows, dynamically, to
adapt the exercise program over time and increase motivation for the user. In
this regard, some of the most obvious applications pointed out by the website
Unite.ai (2025) are the Fitbod and FitnessAI platforms, which make use of AI to
provide specific training routines dynamically, using the person's performance
and recovery to adjust sets, reps and weights; they also include large
libraries on exercises, bring in demonstration videos and intelligent recovery
tracking, so that the whole experience is satisfying, complete and safe from
beginners to experienced athletes.
Interconnection with wearable devices and health
applications also allows for further data collection in real time, which
enriches and increases the possibilities for personalizing training sessions.
One of the most relevant innovations is the application of 3D sensors and
artificial vision in devices such as Peloton Guide and Tempo, which make it possible
to ensure user tracking during the training itself. These devices ensure
feedback and information regarding the user's technique and attitude, improving
not only the adequacy of the exercise but also the prevention of injuries,
correcting, in situ, its execution, automatically. Another use of AI allows the
creation of tools for the evaluation of daily readiness (training
readiness) to adjust the intensity of the training load based on the quality of sleep or
fatigue, optimizing performance and recovery.
Another application of AI put into practice in fitness
is the one established by the digital portal iidca.net (2023), which describes
how AI has also allowed the democratization of access to virtual personal
training, available 24 hours a day through chatbots and/or intelligent
assistants, which can motivate, answer questions and adapt exercise plans under
the action of the user's comments and results. Thus, this continuous
availability of the application and the personalization of the system favored
AI technology systems to become a viable alternative to traditional gyms, which
must adapt to an increasingly digital and user-focused industry.
In short, AI technology systems have revolutionized
the fitness industry by offering individuals personalized workouts,
contextualized health monitoring, and immediate feedback, all of which are
grounded in real-time interpretation of accurate data. Although there are
challenges such as minimizing discomfort and some ethical concerns, the
positive impact of AI on the personalization, effectiveness and accessibility
of fitness is undeniable.
DISCUSSION
Artificial intelligence (AI) has established itself as
a tool that can transform and streamline processes in many areas, such as
sports. In this study, we inquired about the applications and benefits that AI
provides when it comes to personalizing sports training, especially those
ranging from performance measurement, the important prediction of injuries or
improving the health of athletes. AI, which academically we can analyze as the
method or set of techniques with which we can simulate, with computers, the
processes that humans have developed in cognitive terms, definition generated
from iidca.net (2023), has made it clear that it is able to provide much
efficiency, quality and speed to sports training.
From this perspective, Sanabria et al. (2024) state
that artificial intelligence, at the sport level, adjusts errors, supports
decision-making, optimizes training strategies and can help prevent injuries.
This capacity is supported through processes of analysis of a lot of
information that comes from the use of sensors, video, wearables, etc., and
allows the analysis of biomechanical, physiological or tactical aspects of
athletes. A concrete example among the many applications that are launched
every day in sports is Intel's 3D Athlete Tracking System (3DAT), which
provides specific metrics to improve athletic performance by analyzing speed
and body position. To this end, technology, objective assessments and
individual training designs are offered. All in all, the use of AI in sport
also presents significant ethical and moral opportunities.
One of the most relevant aspects is to ensure equity
and equality of opportunity, both for the athletes themselves regardless of
access to technology. Thus, if AI becomes an exclusive element of elite
athletes or teams with great resources, then existing inequalities will be
maximized and an even greater gap may open up between the haves and have-nots.
As highlighted by Carrio (2024), it is critical to critically analyze the
implications of technology automation for the equity and ethics of sport.
Another important ethical issue has to do with the
transparency and applicability of AI algorithms. Athletes and coaches must
understand how such algorithms work; what data is used to make decisions that
affect their performance and career. The potential lack of transparency in AI
systems can cause hesitation in sport, slow down the adoption of AI and
ultimately discard its contribution to the improvement of every dimension of
sport; i.e., the life of sport. In this sense, it is necessary to ensure the
irreproachable defense and protection of the privacy of sensitive data
generated by athletes, but also ensuring that the use of such data is subject
to good ethical and responsible practices.
Irrefutably, artificial intelligence is changing
sports refereeing, a transformation that was intensely observed during the 2022
FIFA World Cup which, during its course, had a semi-automated offside detection
system or SOAT. The model under study is based on advanced data analysis and AI
algorithms to enhance the efficiency and accuracy of refereeing decisions
around this particular soccer rule (Carrio, 2024), finding its justification,
moreover, in that implementing this type of technology highlights the need to
continue reflecting on the implications that automation can have on the
fundamental principles of fairness and integrity in sport.
In the context of athlete health, AI can be useful, as
it provides valuable and objective information on different health metrics and
gives feedback at the right time. On the one hand, it improves performance and,
on the other hand, it contributes to maintaining the athlete's fitness. Machine
learning algorithms can analyze complex patterns in training data that could
show a high risk of injury and thus serve as a foundation for coaches to change
and adapt training plans in a preventive and personalized way.
Technological innovations in sport through AI have
modernized training and competition methodologies, management methodology and
data analysis methodology. According to Caballero and Nuñez (2022), the
functionalities that are ratified as trends are massive data analysis, training
personalization, intelligent sensors and augmented reality. In the field of
athletics, the use of AI is presented through inertial measurement units
(IMUs), devices that instantly collect large volumes of data on acceleration,
angular velocity and orientation, favoring biomechanical assessment and
personalized training (Deport-Tec, 2024; KGW News, 2021). AI in basketball also
enables full, real-time, and highly accurate player tracking, providing both
coaches and analysts with a wealth of information for tactical game
decision-making.
It should be noted that all technologies in the field
of sport are based fundamentally on the constructivist field of sport
biomechanics. The biomechanical principles that study both human movement and
those (gravitational) forces that affect the human body provide both the
theoretical and practical basis for the development of sensors, algorithms and
data analysis systems that today underpin AI in sport.
Finally, it is worth asking what impact the emergence
of AI has on the nature of sport; that is, can AI be used in sport to its
fullest extent? Can this be to the detriment of a fundamental aspect of sport
such as creativity, improvisation, etc. and of the competitive spirit itself?
Or, to put it another way, would an over-reliance on the use of AI in sport
lead, irremediably, to a homogenization of playing styles and the consequent
loss of the very nature of sport itself?
These are questions of great complexity and,
therefore, require further reflection by all actors involved in the debate. As
Caballero & Núñez (2022) conclude, we need the right balance that allows us
to take advantage of all the benefits of AI and find the right place for it in
sport without losing sight of the most fundamental aspects of sport.
From a detailed analysis of the information available
in the technical and scientific literature, it is clear that artificial
intelligence (AI) is emerging as a fundamental driver of change in the
transformation of modern sports training. The impressive capacity for the
interpretation and development of large volumes of data from sources such as
body sensors, super high-speed cameras and wearable devices allows for
extremely accurate assessments covering fundamental aspects of athletes, such
as: the biomechanics of their movements, their physiological responses and the
application of game tactics. This capability, which is truly enriching to read,
allows for unprecedented customization of training programs and, therefore, the
possibility of proactively preventing injuries.
Logically, the impact of AI goes beyond the mere
objective of being able to offer an optimization of individual performance by
analyzing and improving skills, but it is also understood that this impact is
located in all those areas that are no less relevant than refereeing or the
health care of the athlete. In the same sense of refereeing, the use of systems
that rely on AI, such as SOAT, shows that such technology increases the
accuracy when making decisions for the competitive dispute. In terms of sports
health, on the other hand, machine learning algorithms make it possible to
detect complex patterns in athlete data that could foreshadow a high
possibility of injury in a short period of time, leading technical teams to
preemptively modify training regimens.
Innovative technologies, aided by the development of
artificial intelligence, are radically changing both training and competition
procedures as well as the management and analysis of information in the field
of sport. Big Data, the application of hyper-personalized training systems, the
introduction of intelligent sensors and the growing use of augmented reality
technology are, without a doubt, the trends that show what the future of high
performance sport will be like. Some cases of their implementation in sports
such as athletics and basketball turn these strategies into best practices that
make it possible to evaluate, based on resources such as inertial measurement
units (IMUs) and real-time tracking systems, in a highly accurate way the
biomechanics of the athletes and the tactical strategy formulated in the sport.
In short, AI is presented not only as a tool to take the personalization of
sports training to levels hardly imaginable, but also as a way to favor the
path of continuous improvement for high competition.
But it should not be forgotten that this rapid
technological transformation is neither far from nor exempt from major ethical
and moral dilemmas that deserve such consideration. The massive collection of
biometric data, although very useful for improving performance and health, also
raises serious questions about privacy, information security and, above all,
the informed consent of athletes (Carrio, 2024). It is thus important that
athletes are not only aware, but also participants in the management, control
and use of their data; thus, the issue of equity in access to these emerging
technologies is also added as an important problem.
Finally, there is a serious risk of further widening
the gap between resource-rich and resource-constrained sport organizations. In
turn, this could lead to inequality between these organizations, which could
jeopardize the integrity and fairness that must be observed within the context
of competitive sport, according to Seed-Sport
Finally, it also sets out a necessary reflection on
how an increasing reliance on artificial intelligence could have effects on the
very essence of sports practice. Although the automation of processes and the
search for extreme performance optimization offer benefits, there is also the
concern that they limit spontaneity, the ability to create and all that highly
human dimension that is in charge of the very practice of sport, which, after
all, is the driving force behind sporting competition. So, the path, possibly,
is the search for a balance between the use of technology and human experience;
a balance to enhance performance from science, but without leaving aside the
human experience itself: the emotion that gives life and meaning to sport
(Caballero & Nuñez, 2022; Carrio, 2024).
Artificial intelligence offers the possibility of
accessing a fantastic opportunity to raise performance standards in sport,
provided that its use is in line with sound ethical principles and with a
holistic vision that not only contemplates sporting performance, but
above all, deals with the dignity and human development of the sport being
exhibited.
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*Doctorante en
Ciencias de la Motricidad Humana, Docente de la Universidad Nacional
Experimental del Yaracuy, UNEY, Venezuela.
Lzerpa@uney.edu.ve, https://orcid.org/0009-0003-6798-4411