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Artificial Intelligence in Sports on the Example of Weight Training


Abstract and Figures

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10- 12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice.
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©Journal of Sports Science and Medicine (2013) 12, 27-37
Received: 29 June 2012 / Accepted: 02 November 2012 / Published (online): 01 March 2013
Artificial Intelligence in Sports on the Example of Weight Training
Hristo Novatchkov and Arnold Baca
University of Vienna, Centre for Sport Science and University Sports, Auf der Schmelz 6A, 1150 Vienna, Austria
The overall goal of the present study was to illustrate the poten-
tial of artificial intelligence (AI) techniques in sports on the
example of weight training. The research focused in particular
on the implementation of pattern recognition methods for the
evaluation of performed exercises on training machines. The
data acquisition was carried out using way and cable force sen-
sors attached to various weight machines, thereby enabling the
measurement of essential displacement and force determinants
during training. On the basis of the gathered data, it was conse-
quently possible to deduce other significant characteristics like
time periods or movement velocities. These parameters were
applied for the development of intelligent methods adapted from
conventional machine learning concepts, allowing an automatic
assessment of the exercise technique and providing individuals
with appropriate feedback. In practice, the implementation of
such techniques could be crucial for the investigation of the
quality of the execution, the assistance of athletes but also
coaches, the training optimization and for prevention purposes.
For the current study, the data was based on measurements from
15 rather inexperienced participants, performing 3-5 sets of 10-
12 repetitions on a leg press machine. The initially preprocessed
data was used for the extraction of significant features, on which
supervised modeling methods were applied. Professional train-
ers were involved in the assessment and classification processes
by analyzing the video recorded executions. The so far obtained
modeling results showed good performance and prediction
outcomes, indicating the feasibility and potency of AI tech-
niques in assessing performances on weight training equipment
automatically and providing sportsmen with prompt advice.
Key words: Artificial intelligence, machine learning, pattern
recognition, weight training, feedback.
The design and implementation of innovative systems on
the basis of state-of-the-art information and communica-
tion technologies in combination with sophisticated proc-
essing methods are getting increasingly important for the
instant collection, transfer, storage as well as analysis of
sensor data in sports. Moreover, the integration of ma-
chine-aided intelligence into the development of modern
sport information systems enables a prompt and automatic
evaluation of sport-specific parameter values, thereby
allowing the establishment of computer-based feedback
and intervention routines (Baca et al., 2009; 2012).
In general, artificial intelligence (AI) is derived
from imitating human actions and abilities such as think-
ing and learning. It involves the idea of designing so-
called intelligent agents or machines that are similarly
able to acquire, simulate and employ knowledge, analyti-
cal capabilities and professional skills for the overall
purpose of problem solving (Poole et al., 1998). While AI
techniques experienced a boom with the rise of expert
systems in the 1980s, such methods are meanwhile main-
ly applied for rather specific and isolated research topics.
Chess and particularly the first win of a computer against
a world champion (Deep Blue vs. Garry Kasparov) in
1997 (Newborn, 1997; Campbell, 2002) is an example
illustrating the high potential of AI. It has to be consid-
ered, however, that such achievements are strongly re-
lated to the constant increase of computer power – a main
feature and benefit of today’s information technology
Weight training fundamentals and research goals
The current paper focuses on the implementation of AI
routines for the automatic evaluation of exercises in
weight training. Weight training is commonly described
as a specific type of strength training where lifting
weights causes an overload of a certain muscle or muscle
group to trigger adaptive reactions of the organism (su-
percompensation). Also known as resistance training,
weight training is nowadays among the most popular
stabilizing, invigorating or even prime sport activities at
professional and amateur level. Positive effects of this
type of training include the overall strengthening as well
as the improvement of the physical condition, fitness and
performance levels. Thus, not only athletes but also non-
professional sportsmen can benefit from resistance train-
ing. Other known advantages of weight training include
prevention (e.g. in case of back pain or osteoporosis),
maintenance of muscular functional abilities, fat loss and
promotion of a healthy cardiovascular system (Winett and
Carpinelli, 2001). Therefore, weight training is often
recommended by specialists and professional organiza-
tions (Westcott, 2009), as it decreases the risk of injuries
and serves as effective prophylaxis compared to other
Nowadays, plenty of diverse weight training ma-
chines exist, aiming at a controlled and hence less risky
execution. Particularly inexperienced individuals initially
prefer exercising on such equipment, due to their conven-
ience, easier use and supporting purpose in comparison to
free weights and in order to get used to the movement.
However, it is still important that experts like fitness
coaches assist and provide advice to beginners, inexperi-
enced and elderly people for the purpose of adjusting and
correcting the execution, preventing health and injury
risks as well as adapting and improving the overall train-
ing. Today’s weight training machines usually include
directions on how to use the equipment, visually illustrat-
Research article
Artificial intelligence in sports
ing and describing the proper technique (for example:
performing each repetition in a slow and constant man-
ner). Also in the literature, it is reported that the flexion
and extension phases should be executed smoothly and
completely with preferable time durations of 2-3 seconds
(e.g. Evans, 1999). Similarly, the velocity (in terms of a
constant and consistent movement) plays a crucial role for
a correct and low-impact execution (Rana et al., 2008).
Figure 1 represents an example of a typical begin-
ner’s mistake. It shows the measured parameters (cable
force and weight displacement) on an incline bench press
machine, illustrating inconstant and incorrect characteris-
tics with noticeable force fluctuations at the turning point
of a single repetition. The visible oscillation in the cable
force graph was most probably caused by a sudden re-
lease of stress and a consecutive loading, which is a
common error among beginners.
Based on such abnormalities, it can be concluded
that determinants like force, displacement, velocity and
duration are essential for the analysis of the quality of the
technique. In particular, these features may be applied for
the automatic evaluation of weight training exercises with
the help of sophisticated modeling methods. Moreover,
such routines could be integrated in an automated coach-
ing system, allowing real-time analysis of the quality of
the movement and returning prompt feedback informa-
tion. The notifications for the mistake in Figure 1 might
for instance alert on the occurred fluctuation point and
provide directions for error correction.
In the area of weight training, only few ap-
proaches integrating computer-based evaluation routines
have been presented in the literature so far. Although
Ariel (1984) suggested first ideas for the design of intelli-
gent weight training machines on the basis of AI methods
already in 1984, no effective realizations have been con-
structed up to now. The author proposed the implementa-
tion of a feedback-based system integrating factors like
duration, displacement and force characteristics of the
movement, thereby suggesting the most suitable exercise.
A more recent study (Chang et al., 2007) concentrated on
the recognition of various free-weight exercises by apply-
ing specific classifiers and models to measured accelera-
tion characteristics. The purpose of the developed meth-
ods, though, was to determine which but not how the
exercise was executed.
The research objectives of the present paper were
to confirm and demonstrate the high capability and poten-
tial of AI and, in particular, of machine learning methods
in the field of sports by the practical and still not well-
investigated example of weight training. A specific aim
included the assessment of measured way, force and fur-
ther derived characteristics on the basis of common pat-
tern recognition methodologies including automatic clas-
sification algorithms. First results involving rather basic
data analysis provided thereby a basis for the improve-
ment, optimization and extension of the developed ma-
chine learning routines (Novatchkov and Baca, 2012).
The current research, however, focused on the implemen-
tation of more enhanced, exact and in-depth modeling
techniques and outcomes by applying multi-scale feature
spaces and further classification types.
Based on the analysis conducted so far, the ulti-
mate goal is to integrate machine-aided techniques into a
mobile coaching system (Baca et al., 2010), providing
athletes with automated and instant evaluation and feed-
back notifications. This system integration would enable a
real-time data transfer – for instance via an Internet en-
abled portable device such as a handheld or tablet PC – to
a server component (see also section “Data acquisition
and equipment” in the chapter “Methods”), where the
measured parameters would be analyzed and assessed by
the developed models. Crucial notifications could be then
sent to the exercising person, giving feedback on the
quality of the execution as well as providing appropriate
Figure 1. Filtered cable force and weight displacement data of an execution by an inexperienced individual on a sen-
sor-equipped incline bench press machine collected at 200 Hz with a load of 30 kg. The red circles label the turning
point of the repetition, indicating the discrepancy of the measured channels with appearing force fluctuations.
Novatchkov and Baca
advices. This information could be presented to the per-
forming individual via a mobile device, indicating oc-
curred mistakes, suggesting corrective measures and in
this way reducing the risk of injuries. In an alternative
design of the mobile coaching system, the portable de-
vices could be replaced by an in-built computer device
including a screen, used for the instant transfer of the
measured information to the server component and the
prompt display of feedback alerts.
In the following, the overall method including rel-
evant AI fundamentals, main application fields as well as
the underlying study design and applied procedure are
presented. The rest of the article includes current results,
an outlook and final conclusions.
AI techniques in sports
Practical concepts for the realization of AI-based method-
ologies for sport science disciplines like biomechanics or
kinesiology have been already discussed and reviewed
earlier (e.g. Lapham and Bartlett, 1995; Bartlett 2006). A
commonly used technique involves the development of
methods on the basis of AI for the assessment of different
sport-related data measurements or game analysis. The
so-called TESSY (tennis simulation system) framework,
for instance, is one of the first knowledge-based decision-
making implementations, aiming at the supervision, proc-
essing and interpretation of results and tactical behavior
as well as the subsequent transformation of conclusions
into tennis practice (Lames et al., 1990). Other, more
recent, approaches also suggest the implementation of
expert systems integrating fuzzy logic procedures for
diverse purposes like the evaluation of the fast bowling
technique in cricket (Bartlett, 2006; Curtis, 2010) or for
the identification of sport talents (Papić et al., 2009).
Ratiu et al. (2010) provide an overview on the overall
application of AI in sports biomechanics, giving examples
of diagnostic tools for the evaluation of movements in
different sports.
Specific investigations, on the other hand, focus
on the design of machine learning methods for the cluster-
ing, classification, pattern recognition and prediction of
sport-specific data such as movement sequences. Today,
particularly performance analyses by means of self-
learning algorithms like artificial neural networks (ANNs)
are increasingly discussed as promising application areas
in the mathematics and computer science related sports
literature and fields of activities (Perl, 2004a; 2004b;
McCullagh, 2010). Successful implementations include
also analytical studies for different movement evaluations
in sports such as golf (Ghasemzadeh et al., 2009), base-
ball (Ghasemzadeh et al., 2011), and soccer or basketball
(Lamb et al., 2010; Bartlett and Lamb, 2011). As another
example, Silva et al. (2007) present predictive solutions
for the dynamic system modeling and talent identification
in swimming. Furthermore, in (Baca and Kornfeind,
2012) a self organizing map is trained for the purpose of
clustering stability of the aiming process of elite biathlon
But also other classifiers like the k-nearest
neighbor (k-NN) algorithm or Support Vector Machines
(SVMs) are commonly applied modeling tools, providing
good opportunities for the analysis and recognition of
sport-specific data patterns. Acikkar et al. (2009), for
instance, use SVMs in their approach in order to predict
the aerobic fitness of athletes. A number of further studies
are related to running, aiming either at the in-built classi-
fication of track inclination and speed parameters
(Eskofier et al., 2010) or the identification of differentia-
tion of kinematic characteristics (Fischer at al., 2011).
Design and procedure
Based on the above described methodologies, the current
study was built on a typical machine learning approach
including the following distinct and successive phases:
data acquisition, preprocessing, feature extraction and
classification. Figure 2 shows in detail the connections,
purpose and significance of each step.
Figure 2. Applied machine learning approach.
In the following, all stages are described individu-
ally, giving an overview on used equipment, participants,
applied data processing as well as analysis and modeling
Data acquisition and equipment
The data acquisition procedure was based on sensors
attached to various exercise equipment, allowing the
collection of crucial characteristics during the workout.
For the current study, a weight leg press machine was
equipped with a load cell (PW10A or PW12C3, Hottinger
Baldwin) and a rotary encoder (DP18, Altmann). In this
way, significant force and displacement parameters could
be measured directly and thereupon used for the detection
of single repetitions and extraction of further determinants
such as time periods, velocity, acceleration or power. The
data was acquired at a sampling of 100 Hz for each chan-
nel. In addition, a special sensor construction (NEON,
Spantec) with an integrated microSD card served as
collection point of the measured values. As the sensor
platform supports wireless sensor transmission on the
basis of the so-called ANT+ (Dynastream) protocol, one
future aim is to immediately forward the gathered
information to a handheld PC such as already available
smartphones with in-built ANT technology or a more
powerful laptop including an USB reception hardware.
Figure 3 illustrates the university sport’s hall with the
Artificial intelligence in sports
Figure 3. Sensor-equipped weight training machines and attached force (load cell) and way (rotary encoder)
sensors to a leg press machine.
installed weight machines and embedded sensors.
The current study examined executions by 15 individuals
with a rather inexperienced background in weight train-
ing, performing 3-5 sets of 10-12 repetitions on a leg
press machine. Descriptive details regarding the partici-
pants are shown in Table 1.
Table 1. General biographical characteristics of the study
Men (n) 8
Women (n) 7
Mean age (±SD) [years] 24.6 (2.7)
Mean height (±SD) [m] 1.73 (.10)
Mean body mass (±SD) [kg] 63.6 (13.8)
All subjects gave written informed consent for the
study procedures, which were reviewed and approved by
the research Ethics Committee of the Medical University
of Vienna.
Table 2 gives an in-depth description of the sub-
ject’s biographical characteristics, experience level and
used load for each of the performed sets. The participants
in this study were primarily inexperienced and slightly
experienced individuals, since the overall goals were to
assess the quality of the movement focusing on beginners
and to identify significant characteristics regarding the
performances of unskilled people. The initial intention
was neither to investigate the maximum load and force
potentials of the participants nor to examine the condition
and improvement in performance, power and muscle gain.
The actual research idea was rather to evaluate and clas-
sify the performed exercises according to crucial criteria
such as duration, constancy and completeness and not to
analyze the effects of different exercise methods. There-
fore, commonly recommended exercise methods with a
reasonable amount of sets, repetitions and loads for basic
resistance training were chosen to be followed (Garber et
al., 2011). The variable selection was chosen in accor-
dance with the literature-based importance of the men-
tioned criteria as well as today’s effective possibilities to
instantly measure, derive and assess significant parame-
ters by integrating modern sensors into the equipment.
Finally, another major aspect refers to the applicability of
the measured data to AI-based modeling techniques for
automatic classification purposes.
Data preprocessing
Once the raw sensor output was collected, the subsequent
step included the preprocessing of the acquired
Table 2. Detailed biographical characteristics of the participants, experience level and used load for each set.
Number Age Sex
(kg) Experience
Set 1
Set 2
Set 3
Set 4
Set 5
1 22 f 1.62 49 Yes 30 40 40 50 60
2 30 m 1.72 66 Yes 110 120 110 100
3 22 f 1.63 46 Yes 40 50 60 70
4 27 f 1.68 55 Yes 50 70 80 90
5 21 m 1.80 73 Yes 120 100 90 90
6 21 f 1.80 63 Yes 60 60 60 60
7 23 m 1.73 80 No 40 60 60 80
8 24 m 1.76 72 No 40 60 90 120
9 27 m 1.78 71 No 40 60 60 70
10 24 m 1.72 79 Yes 50 100 110 120
11 25 m 1.90 72 No 50 70 100 120 140
12 31 f 1.62 43 No 30 30 30 30
13 27 m 1.93 85 No 40 60 70 80 100
14 25 f 1.64 51 No 40 40 40
15 20 f 1.62 49 No 40 40 40
Novatchkov and Baca
measurements. This procedure was necessary for the
preparation of the data without any loss of significant
information, the improvement of its quality and, conse-
quently, the final outcome and performance of the applied
machine learning method on the refined training set. In
particular, this comprised the processes of cleaning and
filtering the measured parameters. The cleaning routine
involved procedures such as detecting, correcting and
removing unreliable, incorrect and irrelevant data. Filter-
ing, on the other hand, had the goal of smoothing the time
series by the reduction of the effect of noise. A detailed
survey regarding the most useful preprocessing methods
is described by Kotsiantis et al. (2006). In addition, also
the segmentation of the gathered data was closely con-
nected with the preparation step, as it was essential in
forming the basis for the fragmentation (particularly into
single repetitions).
As it was necessary to preprocess the time series
before analyzing and classifying them, after several tries,
the measured displacement values were refined by a
strong low-pass filter (with a passband frequency of 0.1π
radians/sample, stopband frequency of 0.3π radi-
ans/sample, 10 dB of allowable passband ripple and a
stopband attenuation of 20 dB) in order to simplify the
process of segmenting the data. On the other hand, the
force input was essential for identifying possibly occur-
ring fluctuations (relevant for the classification of the
data) and was therefore smoothed by applying an average
digital low-pass Butterworth filter with normalized cutoff
frequency of 0.02 radians/sample, which is equivalent to 1
Hz. Figure 4 shows the measured and subsequently fil-
tered force and displacement data of a rather well-
performed and, in comparison, a poor execution (sta-
ble/complete vs. instable/incomplete movement range and
constant vs. inconstant time and force characteristics) of
the same participant.
Figure 5, on the other hand, illustrates the acquired
signals of an inexperienced in relation to a slightly ex-
perienced female individual, both with similar biographi-
cal data and identical load set-up (subject 1, set 4 and
subject 15, set 1 from Table 2). Obviously, the execution
of the complete beginner is characterized by variable
properties including bigger force fluctuations and variable
displacement paths, compared to the rather smooth com-
pletion of the slightly experienced participant.
The last task of the preprocessing step involved the seg-
mentation of the data into single repetitions. This proce-
dure was accomplished on the basis of the filtered dis-
placement measurements by detecting peak regions. In
particular, the filtered force characteristics were parti-
tioned into individual cycles by identifying extrema and
turning points within the entire data sets. Since some of
the first and last repetitions appeared to be interrupted
(e.g. by correcting the feet position just after the initial
extension or abandoning the final flexion phase) causing,
for instance, “incorrect” time intervals, these sequences
were not included in the classification process. Further-
more, the time series were divided into single stages (ex-
tension, flexion and holding phases), allowing precise
data analyses in the subsequent transformation procedure.
Figure 4. Comparison of the time series of improper (a) and proper (b) executions of an entire set by the same subject.
Artificial intelligence in sports
Figure 5. Comparison of the time series of an inexperienced (a) and a slightly experienced (b)
female participant with similar biographical characteristics, using the same load.
Table 3. Gathered parameters, main criteria and exemplary
features derived for the applied pattern recognition proce-
Parameters Criteria Features
Cable force
Data transformation
In the following step, the initially cleaned, filtered and
segmented sensor measurements were applied to further
data analyses. This mainly included the identification and
deduction of relevant information, describing the gathered
time series. This feature extraction procedure aimed at the
data characterization by dimension reduction, detecting
and deriving crucial attributes (e.g. durations, amplitudes,
fluctuations or ranges) within the segmented periods. The
features were transformed into a vector representing the
data in feature space. A selected subset was thereby ap-
plied for the classification regarding various criteria in-
cluding time, completeness and constancy (see also Table
The preprocessing, data analysis and transforma-
tion stages were carried out using MATLAB® version
R2010b for Windows. The programming environment
includes the so-called Neural Network Toolbox™, pro-
viding functions for the design, realization, visualization
and simulation of various ANNs. The practical applica-
tion of the routines is described in the following sections.
Modeling and supervised classification (ANNs)
In the overall machine learning theory, the specific area of
ANNs is generally divided into the so-called supervised
and unsupervised learning methods depending on the
labeling of the data. While supervised techniques require
labeled input data, the goal of unsupervised procedures is
to find significant patterns from the given examples. A
typical ANN structure with input, hidden and output lay-
ers is illustrated in Figure 6.
For the current study, the use of supervised learn-
ing methods, mapping input objects to desired output
values, appeared to be a suitable modeling technique,
considering the inclusion of the measured time series and
the experts’ evaluations of the executions. These assess-
ments in respect to pre-defined indicators and specifica-
tions were carried out on the basis of video recordings
with the help of professional coaches. In particular, the
chosen evaluation process was based on the available
literature discussed earlier and common recommendations
stating that factors like time, velocity, constancy and
completeness are significant determinants for the execu-
tion and the quality of the movement.
The appraisements were furthermore used
for training and classification purposes by labeling the
Novatchkov and Baca
Figure 6. The typical design of an ANN with an input, hid-
den and output layer, respectively consisting of 3, 4 and 2
extracted feature information in respect to the evaluated
exercises. Consequently, with regard to the presented
weight training approach, the application of supervised
procedures aimed at the classification of the executions
into rather good and bad categories in terms of the men-
tioned factors. In this way, particularly inexperienced
individuals could benefit from the realization of automatic
algorithms by optimizing their technique and hence their
In the present research, the modeling of the meas-
ured signals included the design and realization of con-
ventional ANNs. More precisely, various multilayer pat-
tern recognition networks (special type of feedforward
networks) were set up for learning and classification pur-
poses. The first step included the assignment of the ex-
tracted information to chosen labels or classes, which
were thereupon applied together as training sets for the
development of data models. Thereby, the extracted fea-
tures were combined into input vectors characterizing
each repetition and thus defining the shape of the training
data and number of dimensions. The output, on the other
hand, consisted of the expert assessments, also represent-
ing the number of neurons and the respective layer size.
Traditionally, the mapping of the data was accomplished
by the insertion of hidden layers. The used training func-
tion was based on the Levenberg-Marquardt algorithm,
which is among the fastest techniques for feedforward
networks due to its high efficiency in minimizing a func-
tion by applying curve-fitting methods.
In order to enhance and evaluate the learning pro-
cess, the computed feature vectors were divided into 3
subsets. This fragmentation was particularly needed for
identifying a model fitting the seen set, representing the
actual training process. Afterwards the model was pruned
based on different techniques such as the estimation of
prediction error (also known as validation) and finally
evaluated by unseen data (often referred to as testing
stage). The division ratio was fixed to 70:15:15.
This split-up was furthermore important for im-
proving generalization, whereas the performances of the
created models were measured on the basis of the error
rate (where error rate is defined as the number of incor-
rectly classified instances). For these purposes, the so-
called early stopping method was applied, aborting the
learning process at the point of minimal validation set
error, where the networks usually generalize the best. In
this way, the performance was verified and controlled
after each iteration and an overfitting or overtraining
could be avoided. A practical example including the clas-
sification and performance outcome of the designed ANN
is presented in the following section.
Data segmentation and feature extraction
The implemented algorithm was able to segment all per-
formed sets (more than 60), detecting all executed repeti-
tions (over 750). The outcome of the segmentation proce-
dure on the example of continuous executions is illus-
trated in Figure 7.
As shown, the determined peaks (visualized by as-
terisk markers) were used to detect two subsequent repeti-
tions including the durations of the reversals and the con-
centric and eccentric actions. In addition, due to further
data transformation needs and simplification purposes, the
data was not only divided into single repetitions but inter-
nally also into extension and flexion phases as well as
holding times in between both movements. This division
was accomplished by taking into consideration the meas-
ured time series and particularly the changes of the dis-
placement values. Moreover, when looking at the force
characteristics, similar patterns can be recognized, based
on which the application of supervised machine learning
techniques appears to be a suitable classification method.
In particular, various specifications including time, force,
velocity, consistency, range and completeness factors
were defined for feature extraction and selection purposes
(see also Table 3).
Figure 8 shows the classification results of the applied
pattern recognition network in respect to the overall exe-
cution based on the specified constancy, time and com-
pleteness criteria, considering duration, force and velocity
dependent features. The layer sizes of the networks were
fixed as follows: 7 (input), 10 (hidden) and 3 (output).
The selected labels are illustrated in Table 4.
Table 4. Definition of labels for classification purposes re-
garding the overall stability of the executions.
Label/class Definition
Stable execution
Instable eccentric stage
Instable concentric stage
Sub-chart (a) reflects the classification outcome in
form of a confusion matrix, indicating the correlations of
the labels. Apparently, the agreement is quite high for all
3 labels, demonstrating the high potential of the applied
pattern recognition method. Sub-diagram (b), on the other
hand, highlights the performance development of the
ANN throughout the training stage, illustrating also the
Artificial intelligence in sports
Figure 7. Illustration of the segmentation process for an improper (a) and proper (b) execution applied on the
same data as in Figure 4. The displacement data was segmented into single repetitions based on peak detection
techniques (marked by asterisk). The red and green lines indicate the starting and ending points in time of the
holding phases after the concentric and eccentric actions.
application of the used stopping methodology. Thereby,
the best performance was reached at epoch 70 (with a
maximum failing rate fixed to 5). As the displayed train-
ing, validation and test curves represent quite similar
characteristics with likewise and constant slopes, the
performance outcome appears to be an adequate founda-
tion for the developed techniques and further applications.
Nowadays, due to the progress of information and com-
munication technologies including simplified and conven-
ient implementations of wireless sensor networks for data
acquisition and mobile devices for processing purposes,
the integration of intelligent methods becomes increas-
ingly important for the automatic analysis of measured
parameters and the realization of prompt intervention
AI concepts appear to be particularly suitable for
the design of effective evaluation and feedback frame-
works in sport. After the initial boom in the 1970s and
1980s, the use of AI techniques is meanwhile limited to
rather specific application fields including also sport, as
their application gets essential for the assessment of sports
data. Recent examples include the development of mobile
monitoring systems integrating classification algorithms
for the real-time analysis and feedback generation in
sports like, for instance, running (Kugler et al., 2011) or
golf (Eskofier et al., 2011). Similarly, the current study
investigated the application of AI methods in combination
with novel measuring instruments in the field of weight
Today, due to the advances in measuring tech-
nologies, effective hardware implementations exist that
enable the integration of modern sensors into the fitness
equipment itself. For example, it is possible to attach load
cells or rotary encoders directly to weight training ma-
chines, allowing the measurement of relevant force and
displacement characteristics. The gathered data can there-
by be used for the implementation of sophisticated rou-
tines by means of machine learning techniques, automati-
cally analyzing the exercises. Particularly supervised
ANNs appear to be promising classifiers, as they offer
effective techniques for mapping input data into already
labeled output information.
In light of the current popularity of fitness studios
and the broader usage of weight training machines, also
the accuracy and correctness of the performances and
executions on the offered equipment has become crucial
(particularly for inexperienced or elderly individuals).
Practically, the quality of the movement plays a signifi-
cant role, as it contributes to the efficiency and value of
the workout. Therefore, a particular focus of the illus-
trated approach was to provide automatic analysis on the
technique as well as appropriate interventions and sugges-
tions. The development and integration of such models
Novatchkov and Baca
Figure 8. Classification results illustrating the outcome of the confusion matrix (a) and performance
curves (b) for the trained ANN in respect to characteristics regarding the constancy of the executions.
and routines thus might enable new facilities for the sup-
port of sportsmen and injury prevention.
The target user group includes in the first instance
inexperienced and elderly individuals, who can benefit
from the modern sensor equipment and measurements in
combination with the realization of the developed assess-
ment routines. Their systematic integration in intelligent
weight training machines would allow an automatic anal-
ysis of the quality of the execution on the basis of the pre-
defined criteria, thereby providing appropriate feedback
during or just after the performed exercise.
The most suitable field of application of such de-
velopments including the intended design of automated
resistance training equipment can be seen in the increas-
ing amount of available regular but also exclusive fitness
clubs. In these facilities the personal and individual care,
assistance and mentoring of the members play major
factors. Sportsmen can improve their technique on the
Artificial intelligence in sports
basis of the automated evaluation routines and the return
of instant notifications regarding occurred mistakes, by
receiving appropriate corrective advices and improvement
suggestions. Based on this feedback information also the
risk of injuries can be reduced, which is another big future
objective of the approach.
At the same time, the application of the developed
approach would bring valuable advantages to professional
sportspeople. In this context, the aim of monitoring veloc-
ity characteristics and action forces of exercising move-
ments might be to determine the contraction force speci-
ficity. Well-trained athletes need to optimize their training
to their sport-specific requirements and to improve their
functional ability more specifically. In order to achieve
the desired adaptations it is therefore advisable to choose
exercises that specifically meet the force-velocity needs
of the sport. Hence, the presented approach would allow
professionals but also their coaches to analyze in detail
the athletes’ executions and improve their performances
by looking in real time at the measured force and dis-
placement time series or also calculated acceleration,
velocity and power properties. Consequently, the possibil-
ity of immediate control and comparison of the results
could lead to a considerable training enhancement for
elite sportsmen.
The restrictions of the presented approach are that, for the
moment, the implemented data analysis and feedback
methods are narrowed down to the mentioned criteria
such as time, constancy, velocity and completeness. In
particular, it is not possible to observe other pre-
conditions like, for example, the correct sitting position or
placement of the feet, which would be important parame-
ters for the application of the implemented models in
conjunction with the sensor-equipped leg press machine.
Furthermore, the developed routines are not able to di-
rectly monitor the posture of the body throughout the
movement including, for instance, the knee or upper body
and particularly the lower back motions. Such factors,
however, might be for example detected and assessed on
the basis of the integration of other measuring devices
such as goniometers, torsiometers or pressure sensors.
The future work of the presented research will concentrate
on the adaptation of the developed models for different
sensor-equipped machines including lat biceps curl, lat
pull-down and shoulder press machine. At the same time,
another particular aim will focus on the development of
further models and solutions for the computer-based as-
sessment of weight training data. One promising approach
would, for instance, involve the implementation of fuzzy
logic concepts, which are also commonly applied in the
area of AI. As part of the probabilistic logic, such meth-
ods might be suitable for the realization of other effective
possibilities for the automatic evaluation of weight train-
ing exercises.
It is intended to include the implemented proce-
dures into the already mentioned mobile coaching system
by integrating the ANT technology as well as computer
devices into the measurement process. Thus, a bidirec-
tional data transfer between the weight training machines
and the implemented evaluation routines on the server
could be accomplished. This system integration would
contribute significantly to the idea of instant data acquisi-
tion, automated analysis and prompt feedback routines.
Computer-based feedback frameworks involving sophisti-
cated assessment techniques become increasingly essen-
tial for the instant analysis and appropriate intervention
during workouts. The present study suggests a novel
evaluation approach integrating AI methods for the ma-
chine-aided appraisement of weight training exercises.
The implementation involved the use of modern sensor
technologies attached to the training equipment, allowing
an effective acquisition and collection of sport-specific
data. The gathered parameter values were applied for the
automatic analysis of the performed exercises. The mod-
eling of the data was based on supervised learning proce-
dures integrating ANNs. The pre-processed sensor input
was used for the classification and autonomous appraisal
of the executions. The developed techniques showed good
results and performance outcome, raising promise for
their practical application in integrated feedback systems.
Further research, optimizations and hardware realizations
would then allow the intended implementation of a sup-
portive coaching system that provides professional and
hobby athletes as well as coaches with prompt assessment
and feedback tools.
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Key points
Artificial intelligence is a promising field for sport-
related analysis.
Implementations integrating pattern recognition
techniques enable the automatic evaluation of data
Artificial neural networks applied for the analysis of
weight training data show good performance and
high classification rates.
Research Assistant, University of Vienna,
Centre for Sport Science and University
Sports, Vienna, Austria
Research interests
Machine learning, mobile and pervasive
computing in sports
Arnold BACA
Full Professor and Head of Centre, University of
Vienna, Centre for Sport Science and University
Sports, Vienna, Austria
Dipl.-Ing. Dr.
Research interests
Motion analysis, feedback systems, mobile coach-
ing, pervasive computing in sports
Hristo Novatchkov
University of Vienna, Centre for Sport Science and Uni-
versity Sports, Auf der Schmelz 6A, 1150 Vienna, Austria
... Continuous real-time data before, during and after training as well as during the event can be analysed to modify and improve athletic performance. For example, Novatchkov et al. described the use of sensors to monitor force, displacement, velocity and duration variables during resistance training sessions, and pointed out ways to improve the technique of weight-lifters [13]. Sensors are most useful in identifying patterns in complex movements. ...
... Classification of studies reporting sensors' use in the different phases of sports[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. ...
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Wearable technologies are small electronic and mobile devices with wireless communication capabilities that can be worn on the body as a part of devices, accessories or clothes. Sensors incorporated within wearable devices enable the collection of a broad spectrum of data that can be processed and analysed by artificial intelligence (AI) systems. In this narrative review, we performed a literature search of the MEDLINE, Embase and Scopus databases. We included any original studies that used sensors to collect data for a sporting event and subsequently used an AI-based system to process the data with diagnostic, treatment or monitoring intents. The included studies show the use of AI in various sports including basketball, baseball and motor racing to improve athletic performance. We classified the studies according to the stage of an event, including pre-event training to guide performance and predict the possibility of injuries; during events to optimise performance and inform strategies; and in diagnosing injuries after an event. Based on the included studies, AI techniques to process data from sensors can detect patterns in physiological variables as well as positional and kinematic data to inform how athletes can improve their performance. Although AI has promising applications in sports medicine, there are several challenges that can hinder their adoption. We have also identified avenues for future work that can provide solutions to overcome these challenges.
... Second, the significance of our current research lies in identifying the effect of AI services on the promotion of sport consumers' purchase intentions. Previous AI services studies in the sport industry have focused on athlete performance analysis [62], forecasting injuries [69], and training [70]. Deviating from professional sport athletes' applications, our research focused on sport consumers and how they perceive the usability of AI services and their attitudes toward the technology, as well as how those perception and attitudes influence their purchase intentions. ...
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Artificial intelligence (AI) has recently been introduced as a new way of analyzing and predicting sport consumer behavior. The goal of this study was to investigate the relationships among the perceived usefulness, perceived ease of use, the importance of exercise, attitudes towards use, and the behavioral intention to use AI services based on the technology adoption model. The authors recruited 408 participants who participated in an experiment designed to provide a deeper understanding of AI fitness services. After screening, the collected data were screened through assumption tests, and we conducted a confirmatory factor analysis and structural equation modeling to analyze research hypotheses. The results indicated that three types of consumer evaluations (i.e., perceived usefulness, perceived ease of use, and importance of exercise) positively influence their attitudes toward AI fitness services. In addition, the positive attitudes regarding AI services positively influenced the intention to use AI services. The results of this research contribute to our knowledge of the consumers’ attitudes and behaviors toward AI services in the sport industry based on the technology acceptance model. Furthermore, this study provided the empirical evidence critically needed to increase our understanding of AI in the sport industry and offered new insights into how sport facility managers can predict their consumers’ intention to use AI services.
... Across all three phases, athletes may benefit from a wide variety of research that often stems from adjacent disciplines. This includes many forms of statistical analysis (Albert et al., 2017;Giblin et al., 2016;Lord et al., 2020;Perin et al., 2018;Sidhu, 2011), artificial intelligence in the analysis of sports performance (Araújo et al., 2021;Novatchkov and Baca, 2013), tactics Note. This co-occurrence map for author keywords was created using the bibliometric software VOSviewer (van Eck and Waltman, 2010). ...
Rapid technological progress and digitalization have considerably changed the role of technology in sports in the past two decades. As the human limits of performance have been reached in many disciplines, reaching future limits will increasingly depend on technology. While this represents progress in how athletes train and compete, similar developments await sports managers in the way they lead sports organizations and sports consumers in the way they consume and engage with sports. Using the SportsTech Matrix (i.e., a framework to capture how different types of technologies provide solutions to different user groups in sports), we examine how technology will impact sports in the future. We present a Delphi-based prospective study with quantitative and qualitative assessments from 92 subject matter experts for six future projections and 35 non-Delphi prospective survey items. We find that, by 2030, technology will significantly impact all three user groups in sports: athletes, consumers, and managers. For athletes, experts anticipate technology to play a major role for sporting performance improvements. For consumers, the consumption of sports content will continue to change significantly. For management, new types of manager profiles in terms of backgrounds and skill sets would be desirable. We discuss two possible future scenarios: (1) a probable future and (2) a game changer. Our findings should provide relevant insights for decision-makers and other stakeholders in sports and raise promising directions for future research.
... Wang and Yang [12] carried out research on the assessment of sports injury based on neural network. Novatchkov and other scholars [13] analyzed the training plan and training feedback of artificial intelligence strength training in the fitness process. National fitness is an effective means to prevent cardiovascular diseases. ...
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This paper studies the construction and development strategy of intelligent sports system in the context of Chinese National Fitness Program with methods of literature review and model construction. The research shows that there are four dilemmas in the implementation of intelligent sports in national fitness: data security, market monopoly, legal supervision, and product iteration. However, there are also three promoting factors in this regard, including policy guarantee, market demand, and industrial upgrading. Following the principles of scientificity, effectiveness, public welfare, and collaboration, this paper designs a system for intelligent sports in national fitness. The construction of the national fitness intelligent sports system mainly consists of four modules, including basic framework construction, function design, content design, and operation analysis. With the systematic analysis of the status quo of intelligent sports application in national fitness, this paper puts forward intelligent sports development strategies in the implementation of national fitness from four aspects: optimizing the top-level design of government, speeding up industrial transformation and upgrading, constructing market supervision mechanism, and establishing a talent training system.
This book gathers selected high-impact articles from the 3rd International Conference on Data Science, Machine Learning & Applications 2021. It highlights the latest developments in the areas of artificial intelligence, machine learning, soft computing, human–computer interaction and various data science and machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise.
The aim of this work was to develop an AI-controlled fitness trainer to tend to each user’s needs. It includes an AI-based voice assistant that acts as a virtual fitness trainer to guide the user in performing a certain routine of exercises, which was implemented through the use of NLP to recognize the user’s voice for commands to activate the trainer and body pose recognition to monitor the user’s postures for the workouts in real-time. This work combined two different features and collectively helped the user to workout in a more efficient way. The trainer can currently perform these operations over a set of 10 workouts—7 for muscle workouts and 3 for cardio. Once the workout session is complete, a bar plot consisting of all the exercises performed during that session is constructed and stored on the user’s device. The study below goes into further detail on the major insinuations for future fitness coach design and assessment.
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The aim of this study was to analyse trainers’ appraisal of body posture adjustment -specifically joint alignment and flexion/extension of the legs- in athletes performing a leg press exercise. Ten strength trainers observed 15 video records of 15 athletes with no musculoskeletal problems. Kinematic analysis was based on motion capture data from athletes, combined with data from the semantic differential that trainers used to appraise joint alignment and flexion/extension. Multiple analysis of the two kinds of data showed that trainers’ appraisals did not always coincide with what the kinematic parameters indicated regarding the athletes’ posture adjustment while performing the leg press exercise. Keywords: Leg press; posture adjustment appraisal; flexion parameters; flexion adjustment appraisal.
In this paper, 1,538 papers retrieved with the keywords “sports artificial intelligence (AI)” on the Web of Science database since 2007 were taken as the data source, and the Cite Space V software was used to visualize and analyze them. A visual knowledge graph was used to streamline the countries, institutions and authors conducting sports AI research, discipline distribution, research hotspots and development trends in the past 15 years. Subsequently, its development direction and research progress were discussed. Sports AI was widely distributed, with the US, China and the UK leading the way. The most prolific authors and teams in research on sports AI were concentrated in American universities. Their main research direction is to develop and improve smart wearable devices based on machine learning and deep learning technologies for different groups of people. Research on sports AI involved multiple disciplines, which mainly applied and referred to research methodologies and theories on engineering, computer science and sports science. It could be seen from the frequency and centrality of keywords that in the current field of sports AI, machine learning is the main direction, artificial neural networks is the main algorithm, and practical and empirical research based on data mining is the focus. The research hotspots were divided into three major clusters: physical health promotion, sports injury prevention and control, and athletic performance enhancement. How to introduce intelligent technology into sports for a perfect integration still has an arduous and long way to go. Future development requires joint efforts and participation of scientific researchers, professionals and common people.
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The recording of kinematic or physiological signals is an important part of any biomechanical study. Systems for the recording of such signals are widely available. However, they are often expensive, complicated to setup and can only be used in a lab environment. Therefore they cannot be used for outdoor recording or long-term monitoring of athletes performing outdoor sports. This is especially a problem for the assessment of habitual outdoor runners, where recording in a more natural environment would be preferred. To overcome these limitations, we have developed a lightweight mobile recording system for sport applications using off-the-shelf hardware. The system consists of SHIMMER TM sensor nodes and a standard Android TM smartphone running a mobile recording app. It enables simultaneous recording of physiological and kinematic data, as well as location information during outdoor sports. We give an overview of the recording framework and additionally present an automatic labeling algorithm based on the location of the athlet. We evaluate the usability and packet loss of the system in a pilot study, recording data from different sensors. Additionally a longer outdoor run was recorded to demonstrate the practical use for future studies. Finally, we outline how collected data could be used for data mining and real-time feedback applications.
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The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports
Full-text available
The present paper proposes pattern recognition techniques for the evaluation of exercises performed on weight training machines equipped with a load cell and rotary encoder for the measurement of essential force and weight displacement characteristics during training. The latter parameters can be used for the implementation of intelligent modeling methods like artificial neural networks in order to assess the exercise technique automatically and provide the athlete with appropriate feedback. First results of the developed classifiers indicate good performance values and high classification rates, demonstrating a significant potential of machine learning routines for the autonomous evaluation of performances on weight machines.
Conference Paper
Sensor and computing technologies provide people with information on their performance and load when doing sports. In order to automatically give advices on how to continue exercising and/or to adjust the sports equipment during the physical activity, intelligent devices are required. These devices rely on models for recognition and classification of patterns in the motion currently performed. Different methods and models, such as Neural Networks, Hidden Markov models or Support Vector Machines have proven to be applicable for this purpose. Pros and cons of the different approaches are discussed. Practical applications are presented and experiences reported.
Data Mining techniques have been applied successfully in many scientific, industrial and business domains. The area of professional sport is well known for the vast amounts of data collected for each player, training session, team, game and season, however the effective use of this data continues to be limited. Many sporting organisations have begun to realise that there is a wealth of untapped knowledge contained in their data and there is an increasing interest in techniques to utilize the data. The aim of this study is to investigate the potential of neural networks (NNs) to assist in the data mining process for the talent identification problem. Neural networks use a supervised learning approach, learning from training examples, adjusting weights to reduce the error between the correct result and the result produced by the network. They endeavour to determine a general relationship between the inputs and outputs provided. Once trained, neural networks can be used to predict outputs based on input data alone. The neural network approach will be applied to the selection of players in the annual Australian Football League (AFL) National Draft. Results from this study suggest that neural networks have the potential to assist recruiting managers in the talent identification process.
This article reviews developments in the use of Artificial Intelligence (AI) in sports biomechanics over the last decade. It outlines possible uses of Expert Systems as diagnostic tools for evaluating faults in sports movements ('techniques') and presents some example knowledge rules for such an expert system. It then compares the analysis of sports techniques, in which Expert Systems have found little place to date, with gait analysis, in which they are routinely used. Consideration is then given to the use of Artificial Neural Networks (ANNs) in sports biomechanics, focusing on Kohonen self-organizing maps, which have been the most widely used in technique analysis, and multi-layer networks, which have been far more widely used in biomechanics in general. Examples of the use of ANNs in sports biomechanics are presented for javelin and discus throwing, shot putting and football kicking. I also present an example of the use of Evolutionary Computation in movement optimization in the soccer throw in, which predicted an optimal technique close to that in the coaching literature. After briefly overviewing the use of AI in both sports science and biomechanics in general, the article concludes with some speculations about future uses of AI in sports biomechanics. Key PointsExpert Systems remain almost unused in sports biomechanics, unlike in the similar discipline of gait analysis.Artificial Neural Networks, particularly Kohonen Maps, have been used, although their full value remains unclear.Other AI applications, including Evolutionary Computation, have received little attention.
This paper reviews developments in the use of Artificial Intelligence (AI) in sports biomechanics. It outlines possible uses of Expert Systems as diagnostic tools for evaluating faults in sports movements (techniques) and presents some example knowledge rules for such an expert system. It then compares the analysis of sports techniques, in which Expert Systems have found little place to date, with gait analysis, in which they are routinely used. Consideration is then given to the use of Artificial Neural Networks (ANNs) in sports biomechanics, focusing on Kohonen self-organizing maps, which have been the most widely used in technique analysis, and multi-layer networks, which have been far more widely used in biomechanics in general. Examples of the use of ANNs in sports biomechanics are presented for javelin and discus throwing, shot putting and football kicking. I also present an example of the use of Evolutionary Computation in movement optimization in the soccer throw in, which predicted an optimal technique close to that in the coaching literature. After briefly over viewing the use of AI in both sports science and biomechanics in general, the article concludes with some speculations about future uses of AI in sports biomechanics.
LEARNING OBJECTIVES: • To provide information on the role of strength training in reversing the degenerative processes of muscle loss, metabolic slowdown, and fat gain. To examine the effects of resistance exercise on obesity and related chronic health problems. To present physiological and psychological benefits attained through application of the ACSM strength training guidelines.
Cricket has been a unifying force for the countries of the West Indies. However, from being the world champions the West Indies are now just struggling contenders. As the world becomes more technologically dependent, there have been numerous systems to enhance performances in various sports. In this paper a system is outlined that aids in the execution of cricket batting strokes. From an analysis of batting strokes, a mathematical classification of two batting strokes, using fuzzy set theory, is carried out. From the information provided by this classification a batting training system is suggested. This system captures the motion of a batter whilst playing a stroke. This is then compared to known strokes and feedback is provided which outlines how well the stroke was executed.