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Real-Life Application of a Wearable Device towards Injury Prevention in Tennis: A Single-Case Study

Authors:

Abstract

The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.
Citation: Kramberger, I.; Filipˇciˇc, A.;
Germiˇc, A.; Kos, M. Real-Life
Application of a Wearable Device
towards Injury Prevention in Tennis:
A Single-Case Study. Sensors 2022,22,
4436. https://doi.org/10.3390/
s22124436
Academic Editor: Pui Wah
(Veni) Kong
Received: 28 April 2022
Accepted: 9 June 2022
Published: 11 June 2022
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4.0/).
sensors
Article
Real-Life Application of a Wearable Device towards Injury
Prevention in Tennis: A Single-Case Study
Iztok Kramberger 1, * , Aleš Filipˇciˇc 2, Aleš Germiˇc 2and Marko Kos 1
1Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 046,
2000 Maribor, Slovenia; marko.kos@um.si
2Faculty of Sport, University of Ljubljana, Gortanova 22, 1000 Ljubljana, Slovenia;
ales.filipcic@fsp.uni-lj.si (A.F.); ag6608@student.uni-lj.si (A.G.)
*Correspondence: iztok.kramberger@um.si; Tel.: +386-2-220-7178
Abstract:
The purpose of this article is to present the use of a previously validated wearable sensor
device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and
analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators,
based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device,
designed to be worn on a wrist, that can detect and record movement and biometric information,
where the basic signal processing is performed directly on the device, while the more complex
signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of
the wearable device were validated previously, showing acceptability for monitoring workload and
recovery during tennis practice and matches. This study is one of the first attempts to monitor the
daily workload and recovery of tennis players under real conditions. Based on these data, we can
instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s
training load, increases the effectiveness of training, enables an individual approach, and reduces the
possibility of overuse or injuries. This study is a practical example of the use of modern technology
in the return of injured athletes to normal training and competition. This information will help tennis
coaches and players to objectify their workloads during training and competitions, as this is usually
only an intuitive assessment.
Keywords:
tennis; training; data-based coaching; shot recognition; wearable device; workload; recovery
1. Introduction
The role of data in sports has increased significantly in recent years. The data-driven
approach is the subject of scientific research and constant development. Professional, junior,
and recreational athletes collect, monitor, and analyze data to gain information about their
performance or the possibility of improvement [
1
]. With the increasing availability of data
in tennis tournaments, it is possible to study player performance from tactical, technical,
mental, and physical perspectives [2].
Most commercially available micro-engineered devices contain microsensors such
as accelerometers, gyroscopes, magnetometers, and global positioning systems (GPS).
Some available inertial measurement units (IMUs), such as microelectromechanical sensors
(MEMS), contain one or a combination of these sensors to capture and analyze the move-
ments of athletes during many sport activities [
3
]. Because of the IMU’s small size and
minimum weight, it is especially appropriate for swing-based sports such as racket sports,
volleyball, or golf, where any additional weight on the arms would most likely disturb
the player and have some influence on the player’s performance [
4
]. This gives rise to the
potential for athletes to be observed outside of a laboratory setting and in their natural
training or competitive environment [5].
These devices, commonly referred to as wearable sensors, provide detailed real-time
motion analysis of athletes during competition and training and offer an alternative to
Sensors 2022,22, 4436. https://doi.org/10.3390/s22124436 https://www.mdpi.com/journal/sensors
Sensors 2022,22, 4436 2 of 21
labor-intensive video coding and testing in a laboratory setting [
3
,
5
]. The wearable device
is worn on the wrist or chest and can collect data on the athlete’s kinematics, heart rate,
workload, skin temperature, and sleep patterns. It can also monitor movements of the body
or body segments [
3
,
5
,
6
], distinguish between amateur and recreational tennis players [
7
],
and recognize tennis stroke types [8] using an inertial measurement unit.
Wearable sensor systems are an emerging tool for assessing athletic activity and can
be used to quantify the athlete’s external workload in racket sports [
9
12
]. Training load
can be described as external and/or internal, depending on whether we are referring to
measurable aspects that occur internally or externally to the athlete. The organization,
quality, and quantity of exercise determine the external load, which is defined as the
physical work prescribed in the training plan [
13
]. In tennis, wearable devices attempt to
quantify or estimate gross motion to provide a measure of the external load. The relevance
of these metrics to training specifications is twofold. First, athletes must be exposed to
sufficient load to achieve the desired performance improvements. Second, the training load
(acute or chronic) must not be so high as to increase the risk of injury. In this pursuit of
optimal training load, monitoring tools are critical [10].
The use of wearable devices in tennis has expanded over the past decade in research,
testing and screening, and daily monitoring of tennis players. Genevois et al. [
14
] used
relatively simple tools to provide coaches with practical information to calculate and
optimize training load, paying particular attention to the rate of the perceived exertion
(RPE) method for a session, calculation of the monotonicity index, and calculation of the
acute/chronic workload ratio. The RPE scale measures the perceived intensity level of a
physical activity.
Giménez-Egido et al. [
11
] used wearable devices to assess the optimization of teaching
and learning using a comprehensive approach. Based on the data collected, coaches were
able to focus on quantity, variability, and creativity in training, assessing health parameters,
improving cognitive motivation, and analyzing individual technical-tactical patterns to
correct errors or training deficits.
Filipcic et al. [
15
] conducted a single-case study using a wearable device that provided
good insight into the entire training and competition process and could serve as a basis for
determining workload indicators for female professional tennis players.
Modern tennis players are faced with crowded schedules that force them to use differ-
ent recovery strategies. Therefore, recovery must be fine-tuned with accurate quantification
of its effects, especially with respect to training-induced fatigue. A periodized approach
to recovery is a first step toward interventions based on the interactions between train-
ing load, training content, and perceived recovery [
16
]. Heart rate (HR), recovery rate
(HRR), and heart rate variability (HRV) after exercise are commonly used to measure the
cardiovascular parasympathetic function noninvasively [
17
] and the influence of training
status, different types of training, gender, and age on heart rate variability (HRV) indices in
athletes. The predictability of HRV is also considered in overtraining, athletic status, and
athletic performance. The cardiac autonomic imbalance observed in overtrained athletes
implies changes in HRV and, therefore, suggests that heart rate variability may provide
useful parameters for detecting overtraining in athletes [18].
The aim of this study is to develop a model for data-driven monitoring of specific
tennis loads during training and matches using a personal wearable tracker and mobile
app. Targeting specific tennis loads will also allow monitoring of the athlete’s response to
training load and the quality of the recovery process using morning HRV measurements.
Using numerous performance indicators and features, coaches and players can monitor,
analyze, and plan the workload, content, and efficiency of tennis practice and matches. The
analysis is carried out in real time on a daily basis and is based on objective and measurable
data. The standard values were determined and set based on scientific studies that analyzed
workload during official tennis matches and tennis training and on the practical experience
of tennis coaches.
Sensors 2022,22, 4436 3 of 21
2. Materials and Methods
A professional female tennis player (age: 21 years; height: 167 cm; weight: 64.5 kg;
years played: 16; WTA ranking position at the time of the study: 505) agreed to be observed
for six months. At the start of observation, she had no severe muscle, joint, or bone
injuries. The athlete was informed of the benefits and risks of the study before signing an
institutionally approved informed consent form to participate in the study.
The tennis player did not practice or compete for eight months due to lower back
pain. The results of the injury examination (MRI, ultrasound, physiotherapy tests) did
not reveal the true cause of the pain. Therefore, we decided to monitor the workload
with a personal wearable tracker and a mobile app (the Tennis Armbeep System—TAS) to
prevent a possible overload and, thus, a recurrence of the pain. The process of monitoring,
analysis, and planning was based on the athlete’s individual daily measurements during
the preparation, pre-competition, and competition phases of an annual season.
The miniature wearable device (Armbeep; version 2.0, Biometrika, Maribor, Slovenia)
was designed with low weight and small form in mind. The device is worn on the player ’s
dominant arm, above the ulnar head. The device measures 39 mm
×
33 mm
×
12 mm
and weighs only 12 g and, therefore, does not influence the player’s feeling for the racket
and/or the player’s performance. Because it is lightweight, it can also detect vibrations
from the racket very efficiently. The vibrations are a good indicator of whether a player
is hitting the ball with the racket in the sweet spot (the optimal spot on the racket head).
The housing is made from plastic material, which is resistant to moisture and sweat. The
housing is also waterproof, so that the printed circuit board (PCB) and the sensors inside
do not become contaminated and oxidized. This is especially important for battery contacts.
The device can also be placed and worn under a sweatband.
The wearable device can monitor and record movement and biometric information of a
player during a sports activity. The device was recently upgraded to version 2.0, where the
pulse sensing circuit and IMU were replaced, and its connectivity was also upgraded. The
main communication channel is now via Bluetooth, which enables the device to connect to
smart mobile devices (phones, tablets) wirelessly. The IMU was replaced with a unit with a
larger detection range. The device also grew minimally in size and weight. A graphical
representation and size comparison are depicted in Figure 1. The device is placed laterally
on the ventral surface of the forearm on the dominant arm. This position is favorable
for effective arm movement detection, racket vibration monitoring, and also for reliable
pulse and blood oxidation measurements. The device incorporates a 6-DOF MEMS IMU
(DOF—degrees of freedom, MEMS—microelectromechanical system) for monitoring arm
accelerations and movement. With its selectable accelerometer range of up to
±
30 g, it is
suitable for high-impact sport applications. Such a high accelerometer range is needed
for accurate tennis stroke monitoring; otherwise, some clipping can occur. The gyroscope
supports angular velocity measurements up to
±
4000 dps. The initial sensitivity of both
the accelerometer and gyro is factory-calibrated, so no additional calibration is needed. The
MEMS IMU offers communication over a 7 MHz SPI bus, which is useful for reading the
internal 4 kB FIFO buffer in burst modes. The IMU also has a programmable data rate. Rates
are supported from 4 Hz up to 4.5 kHz for the accelerometer and 9 kHz for the gyro. We
used a data rate of 1.25 kHz, because we are convinced that this is enough for detecting and
monitoring racket vibrations and string oscillations. Previous work also confirms this [
19
].
Besides the movement, the device is also able to monitor biometric information during
sports activity. The device is equipped with a circuit for pulse rate measurement using
the reflective photoplethysmography (PPG) method. The device supports measurement
with two different light sources (green and yellow) for more robust pulse detection and
estimation. The pulse rate (PR) value and pulse rate variability (PRV) are measured and
stored during minimum or no wrist movement. The moments when the player’s hand is
not moving are detected with the IMU. More reliable readings are obtained in this way. The
device is powered with a 155 mAh lithium-polymer (LiPo) battery with a nominal voltage
Sensors 2022,22, 4436 4 of 21
of 3.7 V. With one charge, the device can operate for around 6 h in active mode, which is
enough for typical tennis training sessions and matches.
Sensors 2022, 22, x FOR PEER REVIEW 4 of 21
moments when the player’s hand is not moving are detected with the IMU. More reliable
readings are obtained in this way. The device is powered with a 155 mAh lithium-polymer
(LiPo) battery with a nominal voltage of 3.7 V. With one charge, the device can operate for
around 6 h in active mode, which is enough for typical tennis training sessions and
matches.
With the Armbeep wearable device, data were collected throughout the session. The
IMU collected the accelerometer and gyroscope data for each stroke, and the PPG circuit
collected the HR and HRV. Acceleration values were measured in all three axes, and only
movements with amplitude values greater than 1 G at the point of impact were detected
as a stroke. Hadžić et al. [9] reported that, based on the results, the Armbeep sensor can
be considered a valid and reliable tool for measuring the actual number of strokes and
monitoring hitting load during tennis practice and matches.
Figure 1. Armbeep device placement and size comparison.
2.1. Data Acquisition and Processing
During the six-month training and competition period, the athlete recorded all tennis
practice and matches in the singles and doubles categories as well as the morning HRV
measurements. The athlete performed a 3–5 min HRV measurement each morning before
any other activity. She transferred the data to her personal profile via the mobile app.
Based on the HRV data evaluated with the scoring algorithm, the system assesses the level
of fatigue, with three values displayed on the calendar (green, yellow, orange). Based on
the displayed values in the TAS, the coach and the athlete perform the planned daily train-
ing schedule. The athlete also measured all planned daily tennis activities with a monitor-
ing device on her wrist. After each training session, she transferred the collected data via
the mobile app to her personal profile in the TAS. The process is depicted in Figure 2.
Figure 2. Display of the athlete’s daily activities and use of the smart wearable sensor device.
For the study purposes, the data in the athlete’s personal profile were exported in
CSV format for further processing.
The data were collected over a period of six months (from March until November).
In this time interval, the observed athlete went through three training periods: prepara-
tion, pre-competition, and competition. In the preparation period, the athlete had 47 train-
ing sessions and five matches and was able to carry out 50 HRV measurements. In the pre-
competition period, the athlete had 34 training sessions and nine matches and completed
43 HRV measurements. In the competition period, the athlete had 58 training sessions and
Figure 1. Armbeep device placement and size comparison.
With the Armbeep wearable device, data were collected throughout the session. The
IMU collected the accelerometer and gyroscope data for each stroke, and the PPG circuit
collected the HR and HRV. Acceleration values were measured in all three axes, and only
movements with amplitude values greater than 1 G at the point of impact were detected
as a stroke. Hadži´c et al. [
9
] reported that, based on the results, the Armbeep sensor can
be considered a valid and reliable tool for measuring the actual number of strokes and
monitoring hitting load during tennis practice and matches.
2.1. Data Acquisition and Processing
During the six-month training and competition period, the athlete recorded all tennis
practice and matches in the singles and doubles categories as well as the morning HRV
measurements. The athlete performed a 3–5 min HRV measurement each morning before
any other activity. She transferred the data to her personal profile via the mobile app. Based
on the HRV data evaluated with the scoring algorithm, the system assesses the level of
fatigue, with three values displayed on the calendar (green, yellow, orange). Based on the
displayed values in the TAS, the coach and the athlete perform the planned daily training
schedule. The athlete also measured all planned daily tennis activities with a monitoring
device on her wrist. After each training session, she transferred the collected data via the
mobile app to her personal profile in the TAS. The process is depicted in Figure 2.
Sensors 2022, 22, x FOR PEER REVIEW 4 of 21
moments when the player’s hand is not moving are detected with the IMU. More reliable
readings are obtained in this way. The device is powered with a 155 mAh lithium-polymer
(LiPo) battery with a nominal voltage of 3.7 V. With one charge, the device can operate for
around 6 h in active mode, which is enough for typical tennis training sessions and
matches.
With the Armbeep wearable device, data were collected throughout the session. The
IMU collected the accelerometer and gyroscope data for each stroke, and the PPG circuit
collected the HR and HRV. Acceleration values were measured in all three axes, and only
movements with amplitude values greater than 1 G at the point of impact were detected
as a stroke. Hadžić et al. [9] reported that, based on the results, the Armbeep sensor can
be considered a valid and reliable tool for measuring the actual number of strokes and
monitoring hitting load during tennis practice and matches.
Figure 1. Armbeep device placement and size comparison.
2.1. Data Acquisition and Processing
During the six-month training and competition period, the athlete recorded all tennis
practice and matches in the singles and doubles categories as well as the morning HRV
measurements. The athlete performed a 3–5 min HRV measurement each morning before
any other activity. She transferred the data to her personal profile via the mobile app.
Based on the HRV data evaluated with the scoring algorithm, the system assesses the level
of fatigue, with three values displayed on the calendar (green, yellow, orange). Based on
the displayed values in the TAS, the coach and the athlete perform the planned daily train-
ing schedule. The athlete also measured all planned daily tennis activities with a monitor-
ing device on her wrist. After each training session, she transferred the collected data via
the mobile app to her personal profile in the TAS. The process is depicted in Figure 2.
Figure 2. Display of the athlete’s daily activities and use of the smart wearable sensor device.
For the study purposes, the data in the athlete’s personal profile were exported in
CSV format for further processing.
The data were collected over a period of six months (from March until November).
In this time interval, the observed athlete went through three training periods: prepara-
tion, pre-competition, and competition. In the preparation period, the athlete had 47 train-
ing sessions and five matches and was able to carry out 50 HRV measurements. In the pre-
competition period, the athlete had 34 training sessions and nine matches and completed
43 HRV measurements. In the competition period, the athlete had 58 training sessions and
Figure 2. Display of the athlete’s daily activities and use of the smart wearable sensor device.
For the study purposes, the data in the athlete’s personal profile were exported in CSV
format for further processing.
The data were collected over a period of six months (from March until November). In
this time interval, the observed athlete went through three training periods: preparation,
pre-competition, and competition. In the preparation period, the athlete had 47 training
sessions and five matches and was able to carry out 50 HRV measurements. In the pre-
competition period, the athlete had 34 training sessions and nine matches and completed
43 HRV measurements. In the competition period, the athlete had 58 training sessions
and 35 tennis matches and was able to complete 66 HRV measurements. As mentioned
previously, the HRV measurements were performed in the morning before any activity.
Sensors 2022,22, 4436 5 of 21
2.2. Variables
The indicators used in this study, recorded by a smart wearable sensor device, were
divided into four groups: session time characteristics, physiological and movement indica-
tors, and tennis-specific performance indicators. These were divided into two subgroups:
shot and rally indicators. All of the variables, with descriptions, units, and the descriptive
statistics’ parameters, are listed in Table 1.
Table 1. Practice and match data for the observed athlete gathered in a period of six months.
Practice Match
Variable ID Description (Unit) Mean SD Mean SD
SessionTime Session time (s) 5805.67 1662.91 5247.24 1520.75
ActiveTime Active time (s) 1961.66 516.67 1252.65 538.12
ActiveTimePercentage Active time (%) 34.86 7.68 24.03 8.68
AvgRallyTime Average rally time (s) 17.79 5.11 11.81 4.33
AvgRestTime Average rest time (s) 34.69 17.04 37.86 7.06
AvgHR Average HR 129.78 9.29 135.19 9.63
MinHR Min HR 79.40 8.97 83.80 13.07
MaxHR Max HR 175.88 13.43 179.35 12.20
HighHR Time in high-HR zone (%) 5.94 8.71 8.49 10.91
ModerateHR Time in moderate-HR zone (%) 32.64 13.2 40.8 14.23
LowHR Time in low-HR zone (%) 61.37 18.1 50.63 20.38
TotalRecoveries Total recoveries after max or
submax HR value 3.01 3.05 67 5.09
Recovery20Count Number of recoveries after 20 s 3.01 3.05 8.67 5.09
Recovery60Count Number of recoveries after 60 s 0.82 1.31 1.51 1.26
Recovery120Count Number of recoveries after 120 s 0.00 0.00 0.00 0.00
Recovery20BPM HR value after 20 s 3.31 3.11 4.25 2.38
Recovery60BPM HR value after 60 s 15.93 10.95 17.54 8.43
Recovery120BPM HR value after 120 s 0.00 0.00 0.00 0.00
CardioLoad Cardio load index (algorithm) 55.75 43.77 73.76 66.02
Movement Movement index (Valencell data) 1.66 0.11 1.65 0.08
Sprinting
Number of values in sprinting (%)
13.46 1.56 13.80 2.12
Running Number of values in running (%) 46.05 7.21 39.88 8.80
Walking Number of values in walking (%) 33.66 5.99 43.63 9.10
Standing
Number of values in standing (%)
6.70 3.53 2.69 1.99
Shots Number of shots 780.40 203.69 542.86 205.67
ShotsOverhead Number of overheads 88.16 37.59 98.18 39.51
POverhead Percentage of overheads (%) 11.18 4.16 18.20 4.51
ShotsForehand Number of forehands 258.63 86.20 166.18 87.70
Pforehand Percentage of forehands (%) 32.56 5.74 29.14 7.16
ShotsBackhand Number of backhands 334.44 101.43 186.80 93.84
Pbackhand Percentage of backhands (%) 42.32 7.15 33.22 9.41
ShotsOther Number of other shots 99.17 34.42 91.69 64.10
pOther Percentage of other shots (%) 13.94 10.48 19.43 16.93
ShotsPerHour Shots per hour 492.54 88.26 371.00 99.45
ShotsPerRally Shots per rally 7.02 1.64 5.11 1.40
ShotsPerRallyLow Rallies with 1–2 shots (%) 28.96 10.33 38.96 9.65
ShotsPerRally
Moderate Rallies with 3–4 shots (%) 38.96 10.11 36.37 8.80
ShotsPerRallyHigh Rallies with 5+ shots (%) 42.40 13.88 24.65 12.28
ShotsPerRallyMin Shots per rally—minimum value
in session 2.00 0.00 2.00 0.00
ShotsPerRallyMax Shots per rally—maximum value
in session 50.47 21.53 23.12 23.05
RalliesTotal Rallies number 117.46 39.45 107.67 34.93
Sensors 2022,22, 4436 6 of 21
Table 1. Cont.
Practice Match
Variable ID Description (Unit) Mean SD Mean SD
Tempo Shots per minute 24.02 1.86 26.60 2.24
TempoLow Shots per minute
(1–10 shots per minute) (%) 0.00 0.00 0.00 0.00
TempoModerate Shots per minute
(11–19 shots per minute) (%) 16.59 7.54 11.69 7.28
TempoHigh Shots per minute
(20+ shots per minute) (%) 83.42 7.55 88.33 7.28
TempoMin Shots per minute minimum
value in session 14.47 1.77 15.36 1.98
TempoMax Shots per minute maximum
value in session 176.76 78.73 200.39 59.21
ShotsPower Shots acceleration (g) 13.40 1.79 14.72 2.40
ShotsPowerLow Shots acceleration (1–10 g) (%) 32.89 12.50 32.94 15.01
ShotsPowerModerate Shots acceleration (11–19 g) (%) 55.14 11.02 48.20 11.90
ShotsPowerHigh Shots acceleration (20+ g) (%) 11.87 5.52 18.69 5.57
HittingLoad Hitting load (algorithm) (%) 215.18 110.34 87.82 87.27
Note: numbers in bold represent higher average value for individual indicator for easier comparison between
practice and match sessions.
Basic information about the current activity is stored for each session during practice
or a match. A unique session ID is generated, and the unique athlete ID is also stored in the
header of the session file along with the session date and time. The time and date of when
the session is eventually uploaded to the cloud service are stored separately.
In Table 1, the mean values for information and performance indicators are presented,
and standard deviation (SD) is also presented, for better statistical insight (data scatter).
The first group of performance indicators is linked to average session duration, which
includes active and inactive moments of activity. Active time is defined as the sum of all
rally times in a practice session or match. A rally is when the athlete is exchanging shots
with the opponent, and it can be tracked with a wearable device as moments when tennis
shots are detected with a minimum pause (7 s). A new rally starts when the time between
two consecutive shots is longer than 7 s. The percentage of active time, average rally time,
and average rest time are also monitored.
The next group of performance indicators is heart rate (HR)-related. The average,
minimum, and maximum heart rates are monitored (AvgHR, MinHR, MaxHR), whereas
the percentages are also recorded in the high-, moderate-, and low-HR zones. HighHR
is defined as the percentage of a session time when the HR was in the 86–100% zone
of maximum individual HR. ModerateHR is defined as the percentage of a session time
when the HR was between 71% and 85% of the maximum individual HR, and LowHR is
defined as the percentage of a session time when the HR was in the 50–70% zone of the
maximum individual HR. Recovery performance indicators also fit in the HR performance
indicator group. Performance indicators Recovery20Count, Recovery60Count, and Recov-
ery120Count are defined as the percentage in HR decrease in a corresponding time window
(20, 60, and 120 s, respectively). Recovery 20 BPM, recovery 60 BPM, and recovery 120 BPM
are defined as the number of HR BPM (BPM—beats per minute) drops where the athlete’s
HR was in the corresponding time window after a HR drop from a high value.
The next group of performance indicators is cardio load indicators. The first one
is the CardioLoad index, which is calculated for the last seven days of activity with the
following formula:
CL =sum o f last week sessions
sum av erage o f l ast f our we eks (1)
where CL stands for CardioLoad, and the rest are the last seven days’ sum and 28 days’
session sum divided by 4 (one week average). The individual cardio load index is deter-
Sensors 2022,22, 4436 7 of 21
mined based on %VO
2
(body oxygen consumption) measurements, supported by the new
Armbeep 2.0 device. The equation is:
%VO2=VO2
VO2ma x
=0.002(current HR)20.13(current HR)+2.3
0.002(max HR)20.13(max HR)+2.3 (2)
where current HR is the HR in a given moment, and the max HR is the maximum HR
in the current session. The partial in-session cardio load index is then determined from
the table values (cardio load EPOC). The values are presented in Table 2. For individual
sessions, the partial cardio load index values are summed up. An individual session is
divided into five time zones (0–5 min, 5–10 min, 10–30 min, etc.). For a weekly cardio load
estimation, individual sessions’ cardio load values are summed up for a one-week period.
The optimum value of a CL index is between
25% decrease and +25% increase compared
to a four-week average value. Values that are out of this bound can lead to an increase
in risk of overuse types of injuries and, worse, gain of endurance types, due to a lack of
regeneration [20].
Table 2. Values of CL (cardio load) in dependence of %VO2and activity session duration.
Cardio Load EPOC Value (CLe)
%VO25 Min 10 Min 30 Min 60 Min
100% 0.2167 0.2167 0.2167 0.1517
90% 0.1500 0.1433 0.1517 0.0833
80% 0.1000 0.0833 0.0833 0.0683
70% 0.0667 0.0500 0.0417 0.0289
60% 0.0417 0.0250 0.0192 0.0094
50% 0.0333 0.0067 0.0067 0.0028
40% 0.0233 0.0100 0.0008 0.0006
30% 0.0167 0.0067 0.0008 0.0006
Note: Excess post-exercise oxygen consumption (EPOC) is a noninvasive method used to estimate the anaerobic
energy production that occurs during exercise.
The next type of performance indicator is movement. These performance indicators
are calculated once per second according to the Valencell propriety algorithms embedded
in their module, which is a part of the Armbeep 2.0’s hardware (Biometrika d.o.o, Maribor,
Slovenia) and is responsible for movement tracking. Different movements around the
tennis court are tracked and classified, such as running, sprinting, walking, and standing
(no or little movement). Movement is detected and labeled every second. Values are
presented in percentages.
The following are the shot type performance indicators. Shots such as forehand,
backhand, and overhead type shots (serve, smash) are detected and classified. Performance
indicator “shots” is the sum of all shots in one session, and the average for a timespan of
six months is presented in Table 1(like all other indicators). Shots labeled as “ShotsOther”
are all of the detected shots that are not classified as any other shot type.
The next type of indicator is in close relation to the previous ones, and is also about
shots, but more the frequency of shots than the type. Performance indicators are calculated
such as shots per hour and shots per rally. The minimum and maximum number of shots
per rally are also monitored, and the total number of rallies is also determined. The number
of shots per rally is presented in three categories: low, moderate, and high. All are presented
in percentages.
The next category is the tempo of shots, where six different performance indicators
are monitored. Tempo is presented as the number of shots per minute (frequency of shots
per minute), whereas TempoMin and TempoMax are indicators for the minimum and
maximum number of tempos per session. TempoLow is defined as the percentage of shots
per minute in the range of 1 to 10; TempoModerate is defined as the percentage of shots
Sensors 2022,22, 4436 8 of 21
per minute in the range of 11 to 19; and TempoHigh is defined as the percentage of shots
per minute exceeding 20 shots per minute.
The last presented category of performance indicator regards shot power and hitting
load. The ShotsPower indicator is the value of the average power of shots measured on
the wrist in g. The sum of all shots’ accelerations is divided by the number of shots. The
average is calculated for each individual session. The power of shots is then also monitored
in three ranges. The ShotsPowerLow is defined as the percentage of shots in the range of 1
to 10 g; ShotsPowerModerate is defined as the percentage of shots in the range of 11 to 19 g;
and ShotsPowerHigh is defined as the percentage of shots with wrist acceleration over 20 g.
The HittingLoad (HL) performance indicator is, in a sense, an armload index, related to the
number and power of shots in one hour of play. It is calculated according to the equation:
HL =
SP ATh hAT
ST AT
3600 i
10 (3)
where SP stands for the sum of power for all shots in one hour; ATh stands for active time
per hour; AT stands for active time; and ST stands for session time. The HL values in Table 1
show that the hitting load is higher for practice sessions than for match sessions. This is
due to the fact that the duration of training sessions is longer, and, therefore, the values for
hitting load are higher [10].
3. Results
In Table 1, the descriptive statistics were reported as mean
±
standard deviation for all
numerical variables, along with the names, descriptions, and units. The table includes all
variables that we used to monitor and determine the daily, weekly, or monthly workload of
athletes for training and competition.
Time variables (SessionTime, ActiveTime, ActiveTimePercentage, AvgRallyTime) are
expected to be higher during training; this is consistent with the basic principles of
sports training, which recommend exceeding (overreaching) the competitive load during
training [21]
. The physiological indicators expressed by different heart rate values (AvgRest-
Time, AvgHR, MinHR, MaxHR, HighHR, ModerateHR) are higher during matches. Tennis
players are exposed to both physiological (movement and execution of tennis shots) and
psychological stress during the match. As Smekal et al. [
22
] note, one of the most important
factors affecting the heart rate indicators is the length of the rallies (AvgRallyTime).
The indicators that determine the decrease in maximum heart rate after physical
activity (TotalRecoveries, Recovery20Count, Recovery60Count, Recovery20BPM, Recov-
ery60BPM) are higher during games, because the timing and duration of rallies and rest
periods are determined by the rules. Therefore, we did not record any event during the
matches and training sessions that would determine the drop-in heart rate after 120 s of
rest (Recovery120BPM, Recovery120Count).
Cardio load (CardioLoad) was calculated based on a developed algorithm and deter-
mines the weekly cardio load of athletes based on the total physiological load. Cardio load
depends on the distribution of recovery and rest periods and the content, intensity, fre-
quency, and volume of daily training when considering the total weekly cardio load. Due to
the aforementioned influence of psychological stress and high-intensity and long-duration
activities during competitions, the cardio load is higher during matches.
With TAS, we were (for the first time) able to collect data on the athlete’s movement
(movement, sprinting, running, walking, standing) during training and competition. There
are no differences between matches and training in terms of the total movement index or
the percentage of the player’s movement in other speed classes.
During an annual training season, we collected data on the average number of shots
per session and shots per hour that had higher values during practice (Shots, ShotsPer-
Hour). Based on our shot detection and recognition algorithm, we categorized shots into
four groups: overheads, forehands, backhands, and other shots. It was expected that both
Sensors 2022,22, 4436 9 of 21
the total number and percentage of baseline shots would be higher during the practice
(ShotsForehand, PForehand, ShotsBackhand, PBackhand, ShotsOther), while the values of
serves (ShotsOverhead, POverhead) would be higher during matches. The conclusion is
consistent with the findings that coaches pay too little attention to the serve and the routine
of the serve [23].
Just as the duration of rallies, the number of shots (ShotsPerRally) is higher in practice
sessions. Since rallies in a match always start with a serve or return of serve, this has a
significant impact on the higher intensity of the movements and, thus, on the shortening
of the duration. Researchers [
24
,
25
] found that up to 70% of rallies in matches end within
four shots of both players. Other performance indicators (ShotsPerRallyLow, ShotsPerRal-
lyModerate, ShotsPerRallyHigh, ShotsPerRallyMin, ShotsPerRallyMax) that determine the
number of shots in rallies indicate higher values during training, resulting from training
sessions or parts of training sessions that target specific warm-up exercises or repetitive
game situations. There are no significant differences in the number of rallies (RalliesTotal)
between the two types of session observed.
An important group of performance indicators measures the number of shots per
minute (Tempo, TempoLow, TempoModerate, TempoHigh, TempoMin, TempoMax). This
means that the number of shots per minute determines the expected number of shots in
most rallies (especially those shorter than 1 min). Since rallies in the game start with the
serve and continue with the return of serve, the values for shots per minute (tempo or rally
speed) are higher in matches than in practice. In practice, rallies begin with the baseline
shots, serve, or return of serve. For this reason, the values for the shots per minute are
lower in practice. Tempo is an important factor in tennis, because it includes cognition,
perception, anticipation, reaction, accuracy, speed of movement, agility, and the technical
competence of players [
26
]. In our opinion, all of these can also have a decisive influence
on the outcome of a match, namely, the ability to play at a higher tempo than an opponent.
The power of the shot is measured in a 300 ms time window in the zone of the tennis
shot. The IMU contains data 150 ms before and 150 ms after the point of impact. The
different values of the impact power (ShotsPower, ShotsPowerLow, ShotsPowerModer-
ate, ShotsPowerHigh) determine the ability to accelerate the racket through the impact
zone directly and the technical efficiency of the shot indirectly, both from a motor and
biomechanical point of view [911,27].
Hitting load (HittingLoad, HL) is calculated based on an algorithm and determines
the total load on the dominant limb. Hitting load indicates the number and power of shots
in one hour of play. Individual analysis of a single tennis player allows monitoring of
hitting load during practice and matches. Large deviations from the daily hitting load can
increase the risk of injury [15].
Based on the performance indicators presented in Table 3, we obtained feedback on
the efficiency of regeneration and the current state of the athlete based on the athlete’s
morning measurements. Based on the HRV index value and other related indicators, we
derived or adjusted the planned daily training load.
Table 3.
Morning measurement indicators with names, description of indicators, units, and results of
descriptive statistics.
Variable ID Description Mean SD
MinBPM Min HR—morning measurement 49.67 9.48
MaxBPM Max HR—morning measurement 85.09 16.97
AvgBPM Average HR—morning measurement 65.31 14.30
SDNN Standard deviation of the NN (R-R) intervals 106.31 31.68
RMSSD Reflects the integrity of vagus nerve-mediated
autonomic control of the heart 122.91 42.01
pNN50 The proportion of NN50 div. by the total
number of NNs 60.17 19.75
Sensors 2022,22, 4436 10 of 21
Table 3. Cont.
Variable ID Description Mean SD
HRVScore Variation in the time interval
between heartbeats 94.68 8.80
hrv HRV index (algorithm) 0.05 3.30
The variables in Table 3are common HR and HRV variables (MinBPM, MaxBPM,
AvgBPM, SDNN, RMSSD, pNN50, HRVScore), except the hrv index, which is calculated
according to the procedure below. First, we calculate an intermediate value hrv
0
by the
following formula:
hrv0=SDNN
maxSD NN +RMSSD
maxRMSSD +pN N50
maxp NN50 +H RVScore
maxHRVScore (4)
where SDNN stands for standard deviation of NN (R-R) intervals; max SDNN stands
for absolute maximum deviation of SDNN;RMSSD stands for the root mean square of
successive heartbeat interval differences; max RMSSD stands for the absolute maximum
deviation of RMSSD;pNN50 stands for a proportion of the number of successive NN
intervals that differ by more than 50 ms; max pNN50 stands for the absolute maximum
deviation of pNN50;HRVScore is the variation of the time between heartbeats; and the
max HRVScore is the absolute maximum deviation of the HRVScore. The next step is to
determine the absolute maximum deviation of hrv0:
maxdev_hrv0=M AXABShrv0_dev;[1 : n] (5)
where hrv
0
_dev is the deviation of hrv
0
, and n is the consecutive hrv
0
value measurement
index. The final value of the hrv index is calculated according to the following formula:
hrv =10 hrv0
maxdev_hrv0(6)
where hrv
0
is the intermediate value of HRV, and maxdev_hrv
0
is the absolute maximum
deviation of hrv
0
. Formula (6) gives us a range of values for hrv from
10 to 10. Values from
7 to 8 coincide with low risk of injury, whereas other values indicate a greater possibility
of injuries [28].
In the figures, performance indicators were compared by session type (practice, match)
and by training period (preparation, pre-competition, competition). Information in the
figures is presented in box-plot form. Circles and asterisks represent outliers. In all
three observed training periods (Figure 3), the session duration was higher in training
than in matches. The duration of matches was shortest in the preparation phase, which is
consistent with the theoretical recommendations that athletes should pay the most attention
to preparation rather than matches in this phase [
29
]. In the pre-competition phase, the
number of matches increased, including unofficial sparring matches, and corresponded
to the number of training sessions. In the competition period, the volume of training
sessions remained at the same level compared to the previous period, while the volume of
competitions decreased.
In Figure 4, the percentage of active time compared to total training time does not
differ in the three periods, which is consistent with the values recommended by experts for
female tennis players [
15
]. In the competitions, the values of the performance indicators
from the period decreased, and in the competition period, they correspond to the values
measured in many studies [3033].
Sensors 2022,22, 4436 11 of 21
Sensors 2022, 22, x FOR PEER REVIEW 11 of 21
Figure 3. Comparison of practice (a) and match (b) session time in the preparation (1), pre-competi-
tion (2), and competition periods (3).
In Figure 4, the percentage of active time compared to total training time does not
differ in the three periods, which is consistent with the values recommended by experts
for female tennis players [15]. In the competitions, the values of the performance indica-
tors from the period decreased, and in the competition period, they correspond to the
values measured in many studies [30–33].
Figure 4. Comparison of percentage of active time in practice (a) and match (b) in the preparation
(1), pre-competition (2), and competition periods (3).
The difference in average heart rate (Figure 5) between training and competition was
greatest during the competitive phase. During this period, the athlete completed the most
official matches. According to the findings of Mendez-Villanueva et al. [34] and Smekal et
al. [22], the high intensity of the play and the mental factors increase the level of physical
and physiological demands.
Figure 3.
Comparison of practice (a) and match (b) session time in the preparation (1),
pre-competition (2), and competition periods (3).
Sensors 2022, 22, x FOR PEER REVIEW 11 of 21
Figure 3. Comparison of practice (a) and match (b) session time in the preparation (1), pre-competi-
tion (2), and competition periods (3).
In Figure 4, the percentage of active time compared to total training time does not
differ in the three periods, which is consistent with the values recommended by experts
for female tennis players [15]. In the competitions, the values of the performance indica-
tors from the period decreased, and in the competition period, they correspond to the
values measured in many studies [30–33].
Figure 4. Comparison of percentage of active time in practice (a) and match (b) in the preparation
(1), pre-competition (2), and competition periods (3).
The difference in average heart rate (Figure 5) between training and competition was
greatest during the competitive phase. During this period, the athlete completed the most
official matches. According to the findings of Mendez-Villanueva et al. [34] and Smekal et
al. [22], the high intensity of the play and the mental factors increase the level of physical
and physiological demands.
Figure 4.
Comparison of percentage of active time in practice (a) and match (b) in the preparation (1),
pre-competition (2), and competition periods (3).
The difference in average heart rate (Figure 5) between training and competition
was greatest during the competitive phase. During this period, the athlete completed
the most official matches. According to the findings of Mendez-Villanueva et al. [
34
] and
Smekal et al. [22]
, the high intensity of the play and the mental factors increase the level of
physical and physiological demands.
Sensors 2022,22, 4436 12 of 21
Sensors 2022, 22, x FOR PEER REVIEW 12 of 21
Figure 5. Comparison of average heart rate in practice (a) and match (b) in the preparation (1), pre-
competition (2), and competition periods (3).
The values for cardio load (Figure 6) show that the load increases gradually from the
preparation phase to the pre-competition phase, both in the practice sessions and in the
competitions. In the competition phase, the physiological load is reduced in training so
that the tennis player can regenerate properly and prepare for the match optimally.
Figure 6. Comparison of practice (a) and match (b) cardio load in the preparation (1), pre-competi-
tion (2), and competition periods (3).
The speed of movements in practice and in a match has not been measured in previ-
ous studies. Values that measure the percentage of movements accurately in four speed
zones provide information about the demands that occur during a match. Figure 7 shows
the percentage of time in the sprinting zone, in which the athlete was moving at a speed
greater than 16 km/h. Tennis coaches can determine the movement load more accurately
during practice using reference values. The analysis of the speed of movement has a sig-
nificant impact on the determination of the movement loads of athletes and, consequently,
with appropriate dosage, on the reduction in injuries of the lower extremities.
Figure 5.
Comparison of average heart rate in practice (a) and match (b) in the preparation (1),
pre-competition (2), and competition periods (3).
The values for cardio load (Figure 6) show that the load increases gradually from the
preparation phase to the pre-competition phase, both in the practice sessions and in the
competitions. In the competition phase, the physiological load is reduced in training so
that the tennis player can regenerate properly and prepare for the match optimally.
Sensors 2022, 22, x FOR PEER REVIEW 12 of 21
Figure 5. Comparison of average heart rate in practice (a) and match (b) in the preparation (1), pre-
competition (2), and competition periods (3).
The values for cardio load (Figure 6) show that the load increases gradually from the
preparation phase to the pre-competition phase, both in the practice sessions and in the
competitions. In the competition phase, the physiological load is reduced in training so
that the tennis player can regenerate properly and prepare for the match optimally.
Figure 6. Comparison of practice (a) and match (b) cardio load in the preparation (1), pre-competi-
tion (2), and competition periods (3).
The speed of movements in practice and in a match has not been measured in previ-
ous studies. Values that measure the percentage of movements accurately in four speed
zones provide information about the demands that occur during a match. Figure 7 shows
the percentage of time in the sprinting zone, in which the athlete was moving at a speed
greater than 16 km/h. Tennis coaches can determine the movement load more accurately
during practice using reference values. The analysis of the speed of movement has a sig-
nificant impact on the determination of the movement loads of athletes and, consequently,
with appropriate dosage, on the reduction in injuries of the lower extremities.
Figure 6.
Comparison of practice (a) and match (b) cardio load in the preparation (1),
pre-competition (2)
,
and competition periods (3).
The speed of movements in practice and in a match has not been measured in previous
studies. Values that measure the percentage of movements accurately in four speed zones
provide information about the demands that occur during a match. Figure 7shows the
percentage of time in the sprinting zone, in which the athlete was moving at a speed greater
than 16 km/h. Tennis coaches can determine the movement load more accurately during
practice using reference values. The analysis of the speed of movement has a significant
impact on the determination of the movement loads of athletes and, consequently, with
appropriate dosage, on the reduction in injuries of the lower extremities.
Sensors 2022,22, 4436 13 of 21
Sensors 2022, 22, x FOR PEER REVIEW 13 of 21
Figure 7. Comparison of the percentage of time in the sprinting speed zone; percentage in practice
(a) and match (b) in the preparation (1), pre-competition (2), and competition periods (3).
The number of rallies in a session (Figure 8) helps tennis, fitness, and conditioning
coaches to determine the number of repetitions of individual exercises objectively and,
thus, to control the workload systematically during the preparation period.
Figure 8. Comparison of the number of rallies in practice (a) and match (b) in the preparation (1),
pre-competition (2), and competition periods (3).
Shots per hour (Figure 9) is a relative variable that allows you to compare specific
tennis loads at different stages of training and competition. In training, the values are in
line with the studies conducted so far and range between 400 and 600 shots [15,35,36]. The
number of shots per hour during matches is usually lower (pre-competition, competition).
Important factors influencing the number of shots per hour in competition are the surface,
the playing style of the player and the opponent, and the level of play.
0.20
0.18
0.16
0.14
0.12
0.10
0.08
Sprinting (%)
Figure 7.
Comparison of the percentage of time in the sprinting speed zone; percentage in practice (a)
and match (b) in the preparation (1), pre-competition (2), and competition periods (3).
The number of rallies in a session (Figure 8) helps tennis, fitness, and conditioning
coaches to determine the number of repetitions of individual exercises objectively and,
thus, to control the workload systematically during the preparation period.
Sensors 2022, 22, x FOR PEER REVIEW 13 of 21
Figure 7. Comparison of the percentage of time in the sprinting speed zone; percentage in practice
(a) and match (b) in the preparation (1), pre-competition (2), and competition periods (3).
The number of rallies in a session (Figure 8) helps tennis, fitness, and conditioning
coaches to determine the number of repetitions of individual exercises objectively and,
thus, to control the workload systematically during the preparation period.
Figure 8. Comparison of the number of rallies in practice (a) and match (b) in the preparation (1),
pre-competition (2), and competition periods (3).
Shots per hour (Figure 9) is a relative variable that allows you to compare specific
tennis loads at different stages of training and competition. In training, the values are in
line with the studies conducted so far and range between 400 and 600 shots [15,35,36]. The
number of shots per hour during matches is usually lower (pre-competition, competition).
Important factors influencing the number of shots per hour in competition are the surface,
the playing style of the player and the opponent, and the level of play.
0.20
0.18
0.16
0.14
0.12
0.10
0.08
Sprinting (%)
Figure 8.
Comparison of the number of rallies in practice (a) and match (b) in the preparation (1),
pre-competition (2), and competition periods (3).
Shots per hour (Figure 9) is a relative variable that allows you to compare specific
tennis loads at different stages of training and competition. In training, the values are in
line with the studies conducted so far and range between 400 and 600 shots [
15
,
35
,
36
]. The
number of shots per hour during matches is usually lower (pre-competition, competition).
Important factors influencing the number of shots per hour in competition are the surface,
the playing style of the player and the opponent, and the level of play.
The percentage of serves (Figure 10) is 20% or more in official matches [
37
,
38
]. In
training, it is important to be as close as possible to the competition level, so a high
percentage of rallies begin with a serve. Although we spent half of our training time
serving and continuing the rally, we did not come close to the competition values. It is
important for tennis coaches to create match-like conditions in practice.
Sensors 2022,22, 4436 14 of 21
Sensors 2022, 22, x FOR PEER REVIEW 14 of 21
Figure 9. Comparison of shots per hour in practice (a) and match (b) in the preparation (1), pre-
competition (2), and competition periods (3).
The percentage of serves (Figure 10) is 20% or more in official matches [37,38]. In
training, it is important to be as close as possible to the competition level, so a high per-
centage of rallies begin with a serve. Although we spent half of our training time serving
and continuing the rally, we did not come close to the competition values. It is important
for tennis coaches to create match-like conditions in practice.
Figure 10. Comparison of overhead percentage in practice (a) and match (b) in the preparation (1),
pre-competition (2), and competition periods (3).
With the exception of the preparation phase, the tempo (Figure 11) is higher during
matches. All rallies in matches start with a serve, which tennis players execute faster than
other shots, which, in turn, leads to a higher number of shots per minute [2,36]. This also
affects the percentage of rallies where the tempo is higher than 21 shots per minute (Figure
12).
Figure 9.
Comparison of shots per hour in practice (a) and match (b) in the preparation (1),
pre-competition (2), and competition periods (3).
Figure 10.
Comparison of overhead percentage in practice (a) and match (b) in the preparation (1),
pre-competition (2), and competition periods (3).
With the exception of the preparation phase, the tempo (Figure 11) is higher during
matches. All rallies in matches start with a serve, which tennis players execute faster than
other shots, which, in turn, leads to a higher number of shots per minute [
2
,
36
]. This
also affects the percentage of rallies where the tempo is higher than 21 shots per minute
(Figure 12).
In all of the observed periods, the percentage of shots with high force (more than 20 g)
was higher during matches, which is due to the structure of training, where a certain part
also consists of warm-up, cool-down, and technical exercises and where tennis players do
not perform kicks with high speed (Figure 13).
Sensors 2022,22, 4436 15 of 21
Sensors 2022, 22, x FOR PEER REVIEW 15 of 21
Figure 11. Comparison of tempo in practice (a) and match (b) in the preparation (1), pre-competition
(2), and competition periods (3).
Figure 12. Comparison of high tempo percentage in practice (a) and match (b) in the preparation
(1), pre-competition (2), and competition periods (3).
In all of the observed periods, the percentage of shots with high force (more than 20
g) was higher during matches, which is due to the structure of training, where a certain
part also consists of warm-up, cool-down, and technical exercises and where tennis play-
ers do not perform kicks with high speed (Figure 13).
1.0
0.8
0.6
0.4
Tempo high (%)
Figure 11.
Comparison of tempo in practice (a) and match (b) in the preparation (1), pre-competition
(2), and competition periods (3).
Sensors 2022, 22, x FOR PEER REVIEW 15 of 21
Figure 11. Comparison of tempo in practice (a) and match (b) in the preparation (1), pre-competition
(2), and competition periods (3).
Figure 12. Comparison of high tempo percentage in practice (a) and match (b) in the preparation
(1), pre-competition (2), and competition periods (3).
In all of the observed periods, the percentage of shots with high force (more than 20
g) was higher during matches, which is due to the structure of training, where a certain
part also consists of warm-up, cool-down, and technical exercises and where tennis play-
ers do not perform kicks with high speed (Figure 13).
1.0
0.8
0.6
0.4
Tempo high (%)
Figure 12.
Comparison of high tempo percentage in practice (a) and match (b) in the preparation (1),
pre-competition (2), and competition periods (3).
Hitting load (Figure 14) is reduced significantly only during the competitive period,
when the tennis player participates only in official matches limited by the rules of tennis.
While in the preparation and pre-competition periods, the duration of matches according to
the training schedule was also longer, and, therefore, the values for hitting load were higher.
Sensors 2022,22, 4436 16 of 21
Sensors 2022, 22, x FOR PEER REVIEW 16 of 21
Figure 13. Comparison of high shot acceleration (20 g or more) percentage in practice (a) and match
(b) in the preparation (1), pre-competition (2), and competition periods (3).
Hitting load (Figure 14) is reduced significantly only during the competitive period,
when the tennis player participates only in official matches limited by the rules of tennis.
While in the preparation and pre-competition periods, the duration of matches according
to the training schedule was also longer, and, therefore, the values for hitting load were
higher.
Figure 14. Comparison of hitting load in practice (a) and match (b) in the preparation (1), pre-com-
petition (2), and competition periods (3).
3.1. Practical Use
The Tennis Armbeep System allows the collection of data that can be exported
through the online platform for later statistical analysis. The other part of the system is
the mobile app. The mobile app is a user interface designed to display data to users (ath-
letes, coaches, parents). Each user has their own profile, with personal information that is
important for data analysis (age, gender, body weight and height, dominant limb, back-
hand type, and racket type and weight). The mobile app displays all of the important data
for two types of sessions: practice and match, which are set manually by the user after the
session data are transferred to the user’s personal profile via Bluetooth.
0.4
0.3
0.2
0.1
0.0
Shot Acceleration (20 + g) (%)
Figure 13.
Comparison of high shot acceleration (20 g or more) percentage in practice (a) and match
(b) in the preparation (1), pre-competition (2), and competition periods (3).
Sensors 2022, 22, x FOR PEER REVIEW 16 of 21
Figure 13. Comparison of high shot acceleration (20 g or more) percentage in practice (a) and match
(b) in the preparation (1), pre-competition (2), and competition periods (3).
Hitting load (Figure 14) is reduced significantly only during the competitive period,
when the tennis player participates only in official matches limited by the rules of tennis.
While in the preparation and pre-competition periods, the duration of matches according
to the training schedule was also longer, and, therefore, the values for hitting load were
higher.
Figure 14. Comparison of hitting load in practice (a) and match (b) in the preparation (1), pre-com-
petition (2), and competition periods (3).
3.1. Practical Use
The Tennis Armbeep System allows the collection of data that can be exported
through the online platform for later statistical analysis. The other part of the system is
the mobile app. The mobile app is a user interface designed to display data to users (ath-
letes, coaches, parents). Each user has their own profile, with personal information that is
important for data analysis (age, gender, body weight and height, dominant limb, back-
hand type, and racket type and weight). The mobile app displays all of the important data
for two types of sessions: practice and match, which are set manually by the user after the
session data are transferred to the user’s personal profile via Bluetooth.
0.4
0.3
0.2
0.1
0.0
Shot Acceleration (20 + g) (%)
Figure 14.
Comparison of hitting load in practice (a) and match (b) in the preparation (1),
pre-competition (2), and competition periods (3).
3.1. Practical Use
The Tennis Armbeep System allows the collection of data that can be exported through
the online platform for later statistical analysis. The other part of the system is the mobile
app. The mobile app is a user interface designed to display data to users (athletes, coaches,
parents). Each user has their own profile, with personal information that is important for
data analysis (age, gender, body weight and height, dominant limb, backhand type, and
racket type and weight). The mobile app displays all of the important data for two types of
sessions: practice and match, which are set manually by the user after the session data are
transferred to the user’s personal profile via Bluetooth.
The first screen (Figure 15a) at the top contains personal information about the user
(first and last name) and the current date, which you can expand by clicking on it to display
the “Calendar”. This is followed by information about the name and the type of session
(practice or match). Below that, you will find information about the session start time,
duration, active time in minutes and percentages, and average rally and rest time. After
Sensors 2022,22, 4436 17 of 21
the time information come three important groups of performance indicators: (1) heart
rate,
(2) movement
, (3) shots. At the bottom of the first screen, the user can obtain quick
feedback on the basic functions of the session by pressing “Tips”.
Sensors 2022, 22, x FOR PEER REVIEW 17 of 21
The first screen (Figure 15a) at the top contains personal information about the user
(first and last name) and the current date, which you can expand by clicking on it to dis-
play the “Calendar. This is followed by information about the name and the type of ses-
sion (practice or match). Below that, you will find information about the session start time,
duration, active time in minutes and percentages, and average rally and rest time. After
the time information come three important groups of performance indicators: (1) heart
rate, (2) movement, (3) shots. At the bottom of the first screen, the user can obtain quick
feedback on the basic functions of the session by pressing “Tips”.
On the second screen (Figure 15b), the basic performance indicators are divided into
sub-indicators that provide the user with a detailed insight into the content, workload,
and quality of the performed activities. The tennis-specific group of performance indica-
tors is divided into shots per session, shots per hour, shots per rally, shots per minute
(tempo), and shot power. In terms of content, shots per session also provides extremely
important information on the percentage of three types of shot: overhead, forehand, and
backhand.
For a more accurate determination of workload, we developed two proprietary algo-
rithms. The first is “cardio-load”, which defines the athlete’s total physiological load dur-
ing tennis activity, while “hitting load” determines the total mechanical load on the skel-
etal system, especially the shoulder girdle and the dominant upper extremities. Important
physiological performance indicators are the number of recoveries after 20, 60, and 120 s,
which provide information on how often an athlete competed in the highest zone HR,
followed by rest and recovery periods and the decreases in heart rate values. Motion data
can be monitored by the user using a movement index that calculates the average speed
of movement during practice or a match. The tennis-specific variables—shots, shot power,
and tempo—provide information about the athlete’s technical competence and efficiency.
Figure 15. Presentation of the mobile device application graphical user interface—main window (a).
The application shows many performance indicators clearly for intuitive user interpretation (b).
The third screen (Figure 16a) displays trends for each performance indicator during
each session, week, month, and year. The trends allow the user to monitor the values of
the indicators over time and to adjust the workload in specific training periods.
Figure 15.
Presentation of the mobile device application graphical user interface—main window (
a
).
The application shows many performance indicators clearly for intuitive user interpretation (b).
On the second screen (Figure 15b), the basic performance indicators are divided into
sub-indicators that provide the user with a detailed insight into the content, workload, and
quality of the performed activities. The tennis-specific group of performance indicators is
divided into shots per session, shots per hour, shots per rally, shots per minute (tempo),
and shot power. In terms of content, shots per session also provides extremely important
information on the percentage of three types of shot: overhead, forehand, and backhand.
For a more accurate determination of workload, we developed two proprietary algo-
rithms. The first is “cardio-load”, which defines the athlete’s total physiological load during
tennis activity, while “hitting load” determines the total mechanical load on the skeletal
system, especially the shoulder girdle and the dominant upper extremities. Important
physiological performance indicators are the number of recoveries after 20, 60, and 120 s,
which provide information on how often an athlete competed in the highest zone HR,
followed by rest and recovery periods and the decreases in heart rate values. Motion data
can be monitored by the user using a movement index that calculates the average speed of
movement during practice or a match. The tennis-specific variables—shots, shot power,
and tempo—provide information about the athlete’s technical competence and efficiency.
The third screen (Figure 16a) displays trends for each performance indicator during
each session, week, month, and year. The trends allow the user to monitor the values of the
indicators over time and to adjust the workload in specific training periods.
Sensors 2022,22, 4436 18 of 21
Sensors 2022, 22, x FOR PEER REVIEW 18 of 21
Figure 16. Each performance indicator can be monitored by the user from time perspectives (session,
week, month, year) (a). Connection allows constant monitoring of a tennis player’s data by tennis
or fitness and conditioning coaches, parents, or other invited staff members (b).
In addition to the performance indicators, the mobile app also contains functionali-
ties that allow interaction between users. The connections are intended for interaction and
data exchange between users. The "Summary” provides a common overview of all activ-
ities performed in different time periods. The Armbeep indicators provide the user with
accurate information about the meaning and evaluation of the performance indicators.
The “Calendar displays all of the saved activities (training sessions, matches, morning
HRV measurements) that the user has performed, and the Tennis Armbeep System also
provides a training diary for the athlete.
The “Calendar is also used for one of the most important functionalities—daily
planning (Figure 16b), which gives the coach and the athlete insight into the athlete’s re-
covery level based on the morning HRV measurements. These are listed in Table 3 and
form the basis for calculating the HRV index (algorithm), which is sensitive to changes in
training or match load, disease states, and, in women, the menstrual cycle. In the case of
our athlete, we also found changes in the HRV index during fitness and conditioning
training to improve muscular strength and power. Based on the HRV index, the calendar
displays three heart colors (green, orange, red) that describe the quality of recovery and
the athlete’s condition. Based on the athlete’s recovery level, the coach and athlete adjust
the planned daily activities, both in terms of content and workload. Daily planning based
on the measured data objectifies the degree of recovery and, together with the subjective
assessment of the athlete, enables the optimal determination of the daily training load and
planning of future activities. This means greater efficiency of the training activities per-
formed, a reduction in the risk of injury and overload, and, ultimately, greater satisfaction
for the athlete.
3.2. Limitations
Figure 16.
Each performance indicator can be monitored by the user from time perspectives (session,
week, month, year) (
a
). Connection allows constant monitoring of a tennis player ’s data by tennis or
fitness and conditioning coaches, parents, or other invited staff members (b).
In addition to the performance indicators, the mobile app also contains functionalities
that allow interaction between users. The connections are intended for interaction and data
exchange between users. The “Summary” provides a common overview of all activities
performed in different time periods. The Armbeep indicators provide the user with accu-
rate information about the meaning and evaluation of the performance indicators. The
“Calendar” displays all of the saved activities (training sessions, matches, morning HRV
measurements) that the user has performed, and the Tennis Armbeep System also provides
a training diary for the athlete.
The “Calendar” is also used for one of the most important functionalities—daily plan-
ning (Figure 16b), which gives the coach and the athlete insight into the athlete’s recovery
level based on the morning HRV measurements. These are listed in Table 3and form the
basis for calculating the HRV index (algorithm), which is sensitive to changes in training or
match load, disease states, and, in women, the menstrual cycle. In the case of our athlete, we
also found changes in the HRV index during fitness and conditioning training to improve
muscular strength and power. Based on the HRV index, the calendar displays three heart
colors (green, orange, red) that describe the quality of recovery and the athlete’s condi-
tion. Based on the athlete’s recovery level, the coach and athlete adjust the planned daily
activities, both in terms of content and workload. Daily planning based on the measured
data objectifies the degree of recovery and, together with the subjective assessment of the
athlete, enables the optimal determination of the daily training load and planning of future
activities. This means greater efficiency of the training activities performed, a reduction in
the risk of injury and overload, and, ultimately, greater satisfaction for the athlete.
3.2. Limitations
To show the utility of the system, we included only one female athlete, which is a
limitation, especially when applying the collected data to other age and quality categories of
athlete. Another limitation is also the training and competition schedule, which was set in a
Sensors 2022,22, 4436 19 of 21
very conventional way, that is, with very low training loads during the preparation period
and the scheduling of the first official competitions four months after the start of training.
Finally, we were limited in the conduct of the study by the number of measurement devices
and athletes that were eligible for the study.
4. Conclusions
The presented Tennis Armbeep System is an updated version of the previous device,
where the data were transferred via USB and were displayed only on a personal computer;
the device did not contain such accurate data on the heart rate and movement of athletes,
and the data were intended for experts and researchers. The current system is much more
user-friendly, both in terms of transmission and in terms of display and interpretation of
data as well as in terms of functionalities for data-based communication between users.
The system enables continuous collection of relevant data from the point of view
of various performance indicators as well as analysis and planning based on personal
and objective data, which experts can compare with the recommended values confirmed
by research. The subjective evaluation of training load, which is still common in tennis,
replaces modern data-based coaching.
An important value of the system is the possibility of daily comparison of training
loads and objective evaluation of the athlete’s recovery performance. Proper dosage of
workload reduces the possibility of overload and injuries significantly [39].
The performance indicators presented in the system have been presented and tested
in several studies and cover an important part of the areas that define the workload of
tennis players. For most indicators, there are reference values that allow comparison of the
athlete’s personal values with the recommended values in training and competition for
both genders and different age and skill groups. Users of the system are also supported
by comparable standard values for individual performance indicators. Data acquisition
and analysis enable the transformation of data into information and later into knowledge.
Thus, the system enables a continuous learning process for the athlete and all of the
experts involved.
Let us conclude by highlighting one of the objectives of the study, namely, the use of
the system to monitor workload to prevent possible overuse of the athlete and, thus, the
recurrence of pain. During the study, the tennis player had no pain and was able to train
and compete without interruption. However, this is often a privilege rather than a constant
practice in elite sports. This increases the rationality, efficiency, and systematic nature of
the process of sports coaching in tennis.
Author Contributions:
Conceptualization, I.K., M.K. and A.F.; methodology, I.K., M.K. and A.F.; re-
sources, A.F. and I.K.; data acquisition, A.F. and A.G.; data curation, A.F. and A.G.;
writing—original
draft preparation, I.K., A.F., A.G. and M.K.; writing—review and editing, A.F. and M.K.; literature
review, A.F.; visualization, A.F. and M.K.; supervision, I.K. All authors have read and agreed to the
published version of the manuscript.
Funding:
This study was funded by the Slovenian Research Agency (grant numbers P5-014 and
P2-0069).
Institutional Review Board Statement:
This study was conducted in accordance with the guide-
lines of the Declaration of Helsinki and approved by the Ethics Committee of the Faculty of Sport,
University of Ljubljana (No. 20-2022).
Informed Consent Statement: Informed consent was obtained from the subject involved in the study.
Data Availability Statement: Not applicable.
Acknowledgments: The authors thank the athlete for her participation in the study.
Conflicts of Interest: The authors declare no conflict of interest.
Sensors 2022,22, 4436 20 of 21
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