Citation: Štuhec, S.; Planjšek, P.; Ptak,
M.; ˇCoh, M.; Mackala, K. Application
of the Laser Linear
Distance-Speed-Acceleration
Measurement System and Sport
Kinematic Analysis Software. Sensors
2022, 22, 5876. https://doi.org/
10.3390/s22155876
Received: 29 June 2022
Accepted: 3 August 2022
Published: 5 August 2022
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sensors
Article
Application of the Laser Linear Distance-Speed-Acceleration
Measurement System and Sport Kinematic Analysis Software
Stanko Štuhec 1, Peter Planjšek 2, Mariusz Ptak 3
, Milan ˇCoh 1 and Krzysztof Mackala 4,*
1
Faculty of Sport, University of Ljubljana, Gortanova 22, 1000 Ljubljana, Slovenia
2
Ljubljana School of Business, Management and Informatics, Tržaška cesta 42, 1000 Ljubljana, Slovenia
3
Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Lukasiewicza 7/9,
50-371 Wrocław, Poland
4
Faculty of Physical Education and Sport, Wroclaw University of Health and Sport Science, Paderewskiego 35,
51-612 Wrocław, Poland
*
Correspondence: krzysztof.mackala@awf.wroc.pl; Tel.: +48-347-3147
Abstract: The industrial development of technology, with appropriate adaptation, enables us to
discover possibilities in sport training control. Therefore, we have developed a new approach
to linear running analysis. This study aims to determine the measurement possibilities using an
LDM301A laser system in obtaining basic kinematic parameters. The second goal is the application
of specialized computer programs based on appropriate algorithms to calculate a vast number of
variables that can be used to adjust the training and the rivalry. It is a non-invasive, non-contact
measurement method. We can also determine the inﬂuence of both subjective and objective external
factors. In this way, we can also conduct training with real-time scientiﬁc feedback. This method is
easy to use and requires very little time to set up and use. The efﬁciency and running economy can
be calculated with various time, speed, acceleration, and length indexes. Calculating the symmetries
between the left and right leg in velocity, stride lengths, support phase times, ﬂight phase times, and
step frequency are possible. Using the laser measurement method and detailed kinematic analysis
may constitute a new chapter in measuring speed. However, it still has to compete with classic
photocell measurement methods. This is mainly due to their high frequency of measurement used,
despite some reservations about the scale of measurement errors.
Keywords: speed; sprints; laser system; step kinematics; run measurements; biomechanics
1. Introduction
Training control based on new technologies and technological–methodological solu-
tions are essential in sport. These procedures aim to determine the relevant and objective
parameters of the athlete’s current speed preparation. Without data on biomotor, mor-
phological, physiological, biochemical, psychological, and sociological characteristics, it
is unmanageable to plan, program, and model a modern training process [1–5]. Based on
the measured variables, we can choose the most effective methods and means for planning
training and, thereby, improving sport results.
The athlete’s motor skill development and special conditioning preparation interact
in the sport training process [6,7]. This relationship is dynamic and always different
depending on the phases of the training process and the biological development of the
athlete [1,7,8]. Given that automated stereotypes and the level of motor abilities are
changing, the training process must be monitored, controlled, and ﬁnally, corrected. The
speed of the sprint changes in the individual phases of the run, thus each stage deserves
special treatment, both in terms of training control and training itself [9]. Notably, the sprint
is considered as running up to 100 m. The advantage of our new training control method is
the help of a laser measurement, which is very useful because, in many sport disciplines,
the ability to accelerate well from a static position and quickly develop the highest running
Sensors 2022, 22, 5876. https://doi.org/10.3390/s22155876
https://www.mdpi.com/journal/sensors
Sensors 2022, 22, 5876
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speed is the key to improving performance in both teams and individual sports. Running
velocity is the composition of stride frequency and stride length; in studies where the same
subjects ran at different speeds, both stride rate and stride length are highly correlated with
increasing running speed [1,10].
Current information, biocybernetic, and visual technologies solve the most demanding
movement problems in the diagnostics of the sport training process [11–14]. The conceptual-
ized modern measurement allows for an objective analysis of the movement structures, the
selection, and the application of the most suitable training control methods for individual
modelling of athletic training [15]. Locomotor speed is undoubtedly one essential biomotor
ability that improves sport performance. It occurs in various sport disciplines such as
running, sprinting, or jumping. In recent decades, the main focus has been on diagnostic
capabilities that allow monitoring changes in the kinematic values of sprint parameters.
This applies to both the competition and the monitoring of the training process. It is only
possible due to the application of appropriate measurement methods (fully automatic tim-
ing systems, photocells, Optojump System, video-recording via high-speed camera, global
positioning systems—GPS, or laser system) and proper software, enabling an insightful
analysis. This last methodology was used in the most critical athletic competitions in the
world (World Championships, Olympic Games). The LDM (laser distance measurement)
device appears to be an entirely new chapter in developing speed training control methods.
The most desirable information about a moving athlete can be obtained from the
description of the linear speed—the movement of SG (center of gravity). Such a reference is
essential when the direction of the human body with all segments is studied as a maximal
speed of a single point [16]. With limitations, an LDM can also be used to set realistic
conditions for acceleration—deceleration drills [17]. Results in research indicate that a low-
cost and accessible laser system can be used to accurately determine walking and running
speed [18]. Therefore, the aims of this study are twofold. First, to present the usefulness of
data acquisition from the LDM 301A (ASTECH GmbH, Rostock, Germany) device during
the maximum sprint. The second goal was to determine the analytical capabilities of a new
software, and consequently, to evaluate the usefulness of this software for analysis of speed
kinematic proﬁles.
2. Materials and Methods
2.1. Participants
The measurement of the 100 m sprint with the LDM 301A (ASTECH GmbH,
Rostock, Germany) system involved one national-level Slovenian sprinter (age 22.4 years,
body height 177.6 cm, and body weight 74.9 kg; best result 10.39 s/100 m). Before the
experiment, the subject had six years of active training and competition in sprinting (60, 100,
and 200 m). The laser measurement occurred at the beginning of the competition period, at
the Faculty of the Sport, University of Ljubljana stadium. It was a sunny day, and the wind
was +1.2 m/s. Before the study, approval by the Human Ethics Committee of the University
of Ljubljana was obtained for this experiment. The participant was notiﬁed about the risks
associated with participating in this experiment, the purpose of the investigation, and the
measuring procedures. He signed an informed consent document before any testing.
2.2. Description of the Laser Distance Measurement Device
A laser distance measurement device is completely non-invasive, which in practice
means that an athlete can run in competition conditions without any sensors on the body.
The LDM301A with an invisible beam (Figure 1) is a Class 1 laser device based on the
norm IEC 60825-1:2003. The connection to a computer was established via a particular base
station using the RS232 to USB port converter. The pilot laser with a visible redpoint is a
Class 2 laser device based on the norm IEC 60825-1:2007.
The average angle of the laser beam spread to 1.7 mrad. The divergence of the laser
was 1.7 mrad × 0.08 mrad (rectangle). The receiver divergence was 2.9 mrad (circle). The
size and shape of the laser beam on the reﬂecting surface increased with distance. From ten
Sensors 2022, 22, 5876
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to one hundred meters, the surface of the laser beam increased by a factor of 12.9 (from
1548 mm2 to 19,900 mm2). The measurement precision of the device was ±20 mm in a
mode measurement frequency of 2 kHz and measurement value output of 100 Hz. The
LDM tool created a text ﬁle containing the measurement data (ﬁrst row, time; second row,
distance). A schematic representation of the ofﬁcial measurement compared to a laser
distance measurement used in the study is depicted in Figure 2.
Sensors 2022, 22, x FOR PEER REVIEW 
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base station using the RS232 to USB port converter. The pilot laser with a visible redpoint 
is a Class 2 laser device based on the norm IEC 60825-1:2007. 
 
Figure 1. Laser distance measurement system setup (left) and LDM301 device with pivot laser point 
and base station (right). 
The average angle of the laser beam spread to 1.7 mrad. The divergence of the laser 
was 1.7 mrad × 0.08 mrad (rectangle). The receiver divergence was 2.9 mrad (circle). The 
size and shape of the laser beam on the reflecting surface increased with distance. From 
ten to one hundred meters, the surface of the laser beam increased by a factor of 12.9 (from 
1548 mm2 to 19,900 mm2). The measurement precision of the device was ±20 mm in a mode 
measurement frequency of 2 kHz and measurement value output of 100 Hz. The LDM 
tool created a text file containing the measurement data (first row, time; second row, dis-
tance). A schematic representation of the official measurement compared to a laser dis-
tance measurement used in the study is depicted in Figure 2.  
 
Figure 2. A schematic representation of the official measurement compared to a laser distance meas-
urement used in the study. 
Start signal
First movement
Official line crossing
Reaction time
Running
Running
Lower back finish line crossing
Lower back start line crossing
Official time
Laser time
Figure 1. Laser distance measurement system setup (left) and LDM301 device with pivot laser point
and base station (right).
base station using the RS232 to USB port converter. The pilot laser with a visible redpoint 
is a Class 2 laser device based on the norm IEC 60825-1:2007. 
 
Figure 1. Laser distance measurement system setup (left) and LDM301 device with pivot laser point 
and base station (right). 
The average angle of the laser beam spread to 1.7 mrad. The divergence of the laser 
was 1.7 mrad × 0.08 mrad (rectangle). The receiver divergence was 2.9 mrad (circle). The 
size and shape of the laser beam on the reflecting surface increased with distance. From 
ten to one hundred meters, the surface of the laser beam increased by a factor of 12.9 (from 
1548 mm2 to 19,900 mm2). The measurement precision of the device was ±20 mm in a mode 
measurement frequency of 2 kHz and measurement value output of 100 Hz. The LDM 
tool created a text file containing the measurement data (first row, time; second row, dis-
tance). A schematic representation of the official measurement compared to a laser dis-
tance measurement used in the study is depicted in Figure 2.  
 
Figure 2. A schematic representation of the official measurement compared to a laser distance meas-
urement used in the study. 
Start signal
First movement
Official line crossing
Reaction time
Running
Running
Lower back finish line crossing
Lower back start line crossing
Official time
Laser time
Figure 2. A schematic representation of the ofﬁcial measurement compared to a laser distance
measurement used in the study.
2.3. Measurement Method
Before starting the measurement, we needed to calibrate the track with a particular
calibrating device. The calibration was performed by placing a rectangular bar (height
1.5 m, width 0.03 m, and depth 0.03 m) in an exact vertical position on the starting line and
measuring the distance to the bar with the laser. The laser must be in a precise horizontal
position and at the exact height of the lumbar spine of the measured person (L1 in Figure 3).
Sensors 2022, 22, 5876
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This distance represents the basis for the measurement. When the sprinter passed the
measured calibration distance, the sprinter entered the measurement zone (L2 in Figure 3).
The measurement lasted as long as the sprinter’s lower back was in the measurement
zone (L2).
 
2.3. Measurement Method  
Before starting the measurement, we needed to calibrate the track with a particular 
calibrating device. The calibration was performed by placing a rectangular bar (height 1.5 
m, width 0 .03 m, and depth 0.03 m) in an exact vertical position on the starting line and 
measuring the distance to the bar with the laser. The laser must be in a precise horizontal 
position and at the exact height of the lumbar spine of the measured person (L1 in Figure 
3). This distance represents the basis for the measurement. When the sprinter passed the 
measured calibration distance, the sprinter entered the measurement zone (L2 in Figure 
3). The measurement lasted as long as the sprinter’s lower back was in the measurement 
zone (L2). 
 
Figure 3. Diagram showing a sprint measurement protocol using a laser distance measurement. 
Legend: A—laser position, B—starting line, C—distance to the lower back of the runner, D—finish 
line, L1—calibration distance, L2—measuring distance, L3—the actual measuring distance of 
lower back to the laser measurement (L2 is calculated as the difference between L3 and L1). 
Due to various factors, some errors were made in the raw measurement. The most 
common errors occurred due to a loss of laser contact with the lower back due to the 
movement of the sprinter left to right, interruptions of the laser beam by hand, low-reflec-
tion material of the shirt, and other interruptions to the laser signal. 
2.4. Data Processing  
For raw distance-time data capture, we used the original software LDMTool from 
ASTECH. A simple user interface displayed all available parameters of the currently con-
nected sensor and all measured values, as well as the state of the different output signals. 
There were four main groups of direct communication: device parameters, device data, a 
graphical display of a distance-time diagram, and a log window. 
The raw displacement data obtained with the LDM device were captured with a fre-
quency of 100 Hz. From the change in displacement, the speed sprint (vh) was calculated 
in every hundredth of a second of running. The curve was smoothed with a moving av-
erage filter over a 0.1 s interval (n = 10, smoothing frequency m = 10) to eliminate any 
within-step velocity fluctuations. The polynomial startpoint was identified from where 
the raw displacement values increased and remained more significant than 2 SD above 
the mean noisy pre-start signal level. The endpoint was 50 data points after the displace-
ment exceeded 60 m.  
2.5. Software for Kinematic Laser Distance Linear Running Analysis 
At the University of Ljubljana, Faculty of Sports, Institute of Sports, we developed an 
entirely new approach to kinematic measurements of linear sprint running in the Biome-
chanical Laboratory. The development of laser meters, which allows us to perform non-
L1
L2
L3
A
B
C
D
Figure 3. Diagram showing a sprint measurement protocol using a laser distance measurement.
Legend: A—laser position, B—starting line, C—distance to the lower back of the runner, D—ﬁnish
line, L1—calibration distance, L2—measuring distance, L3—the actual measuring distance of lower
back to the laser measurement (L2 is calculated as the difference between L3 and L1).
Due to various factors, some errors were made in the raw measurement. The most
common errors occurred due to a loss of laser contact with the lower back due to the
movement of the sprinter left to right, interruptions of the laser beam by hand, low-
reﬂection material of the shirt, and other interruptions to the laser signal.
2.4. Data Processing
For raw distance-time data capture, we used the original software LDMTool from
ASTECH. A simple user interface displayed all available parameters of the currently
connected sensor and all measured values, as well as the state of the different output
signals. There were four main groups of direct communication: device parameters, device
data, a graphical display of a distance-time diagram, and a log window.
The raw displacement data obtained with the LDM device were captured with a
frequency of 100 Hz. From the change in displacement, the speed sprint (vh) was calculated
in every hundredth of a second of running. The curve was smoothed with a moving
average ﬁlter over a 0.1 s interval (n = 10, smoothing frequency m = 10) to eliminate any
within-step velocity ﬂuctuations. The polynomial startpoint was identiﬁed from where the
raw displacement values increased and remained more signiﬁcant than 2 SD above the
mean noisy pre-start signal level. The endpoint was 50 data points after the displacement
exceeded 60 m.
2.5. Software for Kinematic Laser Distance Linear Running Analysis
At the University of Ljubljana, Faculty of Sports, Institute of Sports, we developed
an entirely new approach to kinematic measurements of linear sprint running in the
Biomechanical Laboratory. The development of laser meters, which allows us to perform
non-contact measurements of horizontal position, speed, and acceleration, combined with
our program, has enabled us to quickly and accurately diagnose running techniques
and tactics. With entirely new algorithms and the already mentioned essential variables,
we additionally calculate the support time, ﬂight time, step time, and frequency within
individual steps. Advanced functions allow us to calculate the symmetries between the left
and right foot for each step and step phase. The horizontal running speed of the sprinter
ﬂuctuates from the maximum speed at the last contact of the push-off leg with the ground,
Sensors 2022, 22, 5876
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and then decreases due to air resistance and the force of gravity in the ﬂight phase until the
opposite leg touches the ground, where it starts to increase again due to the action of the
muscles in the push-off phase until the last contact time when maximum running speed is
again reached on the opposite push-off leg. The time of the support phase is deﬁned from
the moment when the slope of the speed reduction curve changes and starts to increase
again until the moment of maximum speed. The time of the left step lasts from the ﬁrst
contact of the left foot with the ground through the push-off time (the time until the last
contact with the ground) and until the ﬁrst contact of the right foot with the ground. The
time of the right step lasts from the ﬁrst contact of the right foot with the ground through
the push-off time (the time until the last contact with the ground) and until the ﬁrst contact
of the left foot with the ground. The ﬂight time of the left step lasts from the last contact of
the left foot with the ground to the ﬁrst contact of the right foot with the ground. The ﬂight
time of the right step lasts from the last contact of the right foot with the ground to the
ﬁrst contact of the left foot with the ground. When we have certain time phases of the left
and right steps, we also calculate the step frequency, where we can deﬁne the beginning
and end of the left or right step in different ways (the last contact of the left foot with the
ground, through the ﬂight phase from the left to the right foot and until the last contact of
the right foot with the ground or from the ﬁrst contact of the left foot with the ground to
the last contact of the left foot with the ground, through the ﬂight phase from the left to
the right foot and until the ﬁrst contact of the right foot with the ground). In the following,
we calculated the new shape of the separate curve for path, velocity, and acceleration by
prescribing the best match of the curve shape according to the wavelength, amplitude, and
direction of inclination of the average section of each variable. The ﬁrst derivative of path
change over time is velocity, and the second derivative of it is acceleration.
The software was based on functionality divided into twelve modules: (1) data editing:
data preview, data preparation, adding missing data, data cut, error data elimination,
converting data to sprint format, and data save; (2) measurement description: place,
date, wind, ofﬁcial time, reaction time, measured distance, name, birth date, country,
height, weight, left leg length, right leg length, sport discipline, dominant hand, dominant
leg, ﬁrst leg on start, moving or static start, static high or low start, notes, calibration
distance, and maximum speed tolerance; (3) data smoothing: the type and rate of the
smoothing used for each variable; (4) data calculation: times, speed, acceleration, steps,
zones, phases, sections, frequencies, symmetry index, efﬁciency index, effectiveness index,
and performance index; (5) single analysis: analyze a single measurement; (6) databases:
database organization; (7) multiple analysis: analyze and compare multiple measurements;
(8) statistical: descriptive statistical data analysis; (9) artiﬁcial intelligence: with the use of
machine learning and artiﬁcial intelligence, we developed a unique code to look for new
ways of optimizing and improving the efﬁciency and effectiveness of running; (10) export:
exported all raw and calculated data in txt, CSV, or xlsx data format; (11) report: graphical
and numerical report (diagrams and tables); (12) print: print selected report options.
3. Results
Regarding the maximum running speed, both timing and distance data are essential.
Based on this, we can adjust the training in real time so that the athlete can check on which
section of the track he reached the maximum speed on each sprint. Therefore, raw data
obtained during the measurement is a critical issue. It was ﬁrst processed with the raw
data editing module. The next phase is the data cut module. In this phase, we removed
unwanted data before movement started and unwanted data after the lower back crossed
the ﬁnish line. We recommend keeping data for one second before point B and after point
D in Figure 3. The measured raw data and ﬁlter data are depicted in Figure 4.
Further, we can deﬁne the speed zones, which always appear in a 100 m race, no matter
the performance level. It is possible to do this via smoothed speed data. The diagram shows
the three primary phases of the 100 m run: red—ascending speed, blue—maintenance
of the maximum speed, and green—descending speed (Figure 5). However, in this case,
Sensors 2022, 22, 5876
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the maximum speed phase is deﬁned by a tolerance of 2% from the maximum speed. An
essential element of these two graphs is that the course of the variability of the maximum
speed is presented in relation to the time of exercise and the change in distance. Both charts
overlap and are the same. Maintaining a maximum speed is one of the most important
indexes in sprint performance.
Sensors 2022, 22, x FOR PEER REVIEW 
6 of 14 
 
 
data editing module. The next phase was to cut the module. In this phase, we removed 
unwanted data before movement started and unwanted data after the lower back crossed 
the finish line. We recommend keeping data for one second before point B and after point 
D in Figure 3. The measured raw data and filter data are depicted in Figure 4.  
 
Figure 4. Measured raw distance-time data (blue line), calculated raw speed (thin green line), and 
averaged speed (bold green line). 
Further, we can define the speed zones, which always appear in a 100 m race, no 
matter the performance level. It is possible to do this via smoothed speed data. The dia-
gram shows the three primary phases of the 100 m run: red—ascending speed, blue—
maintenance of the maximum speed, and green—descending speed (Figure 5). However, 
in this case, the maximum speed phase is defined by a tolerance of 2% from the maximum 
speed. An essential element of these two graphs is that the course of the variability of the 
maximum speed is presented in relation to the time of exercise and the change in distance. 
Both charts overlap and are the same. Maintaining a maximum speed is one of the most 
important indexes in sprint performance.  
0
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speed (m/s)
distance (m)
time (ms)
distance
speed (raw)
speed (smooth)
Figure 4. Measured raw distance-time data (blue line), calculated raw speed (thin green line), and
averaged speed (bold green line).
This part of the analysis of the collected data is based on smaller, 10 m long segments
of the 100 m distance, which allows studying the nature of the maximum speed in more
detail. Such an analysis differs signiﬁcantly from the overall approach because the course
of individual running parameters is more distinct and is even due to smaller differences in
the values of the discussed variables. Figure 6 shows how the sprinter’s speed increases
proportionally concerning the average and maximum zone speeds. In this way, it can be
found at what distance and when the sprinter reached a certain speed.
In addition, our analysis of the speed of the object’s movement allows us to determine
the type of movement, whether the runner is in an acceleration or deceleration phase.
Consequently, the acceleration value expresses the speed of changing the position of a
given object or the direction of its movement. More precisely, linear acceleration can be
deﬁned as the change of speed over time (1):
ai = v
t = (vi + 1) − (vi − 1)
(ti + 1) − (ti − 1)
(1)
where: a = acceleration, v = speed, t = time, and i = calculation point. The calculated
acceleration is one of the important variables used to determine sprint performance. We
have to remember that the greater initial acceleration and longer positive acceleration
deﬁne better performance. In Figure 7, we can observe the runner’s speed and acceleration
in time. The peak velocity is calculated from the smoothed speed curve.
Sensors 2022, 22, 5876
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Sensors 2022, 22, x FOR PEER REVIEW 
7 of 14 
 
 
 
Figure 5. Sprinting phases: the diagram of speed in relation to time (above) and distance (below), 
acceleration phase (red line), maximum speed phase (blue line), and descending speed phase (green 
line). 
This part of the analysis of the collected data is based on smaller, 10 m long segments 
of the 100 m distance, which allows studying the nature of the maximum speed in more 
detail. Such an analysis differs significantly from the overall approach because the course 
of individual running parameters is more distinct and is even due to smaller differences 
in the values of the discussed variables. Figure 6 shows how the sprinter’s speed increases 
proportionally concerning the average and maximum zone speeds. In this way, it can be 
found at what distance and when the sprinter reached a certain speed. 
Figure 5. Sprinting phases: the diagram of speed in relation to time (above) and distance (be-
low), acceleration phase (red line), maximum speed phase (blue line), and descending speed
phase (green line).
Sensors 2022, 22, x FOR PEER REVIEW 
8 of 14 
 
Figure 6. Cont.
Sensors 2022, 22, 5876
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Figure 6. Speed-time (above) and speed-distance (below) with 10 m sections (blue lines) and point 
of maximum speed (green line). 
In addition, our analysis of the speed of the object’s movement allows us to determine 
the type of movement, whether the runner is in an acceleration or deceleration phase. 
Consequently, the acceleration value expresses the speed of changing the position of a 
given object or the direction of its movement. More precisely, linear acceleration can be 
defined as the change of speed over time (1): 
𝑎𝑖 = 𝑣
𝑡 = (𝑣𝑖 + 1) − (𝑣𝑖 − 1)
(𝑡𝑖 + 1) − (𝑡𝑖 − 1) 
(1) 
where: a = acceleration, v = speed, t = time, and i = calculation point. The calculated accel-
eration is one of the important variables used to determine sprint performance. We have 
to remember that the greater initial acceleration and longer positive acceleration define 
better performance. In Figure 7, we can observe the runner’s speed and acceleration in 
time. The peak velocity is calculated from the smoothed speed curve. 
Figure 6. Speed-time (above) and speed-distance (below) with 10 m sections (blue lines) and point of
maximum speed (green line).
2, x FOR PEER REVIEW 
9 of 14 
 
Figure 7. Speed and acceleration (raw and smoothed data). 
The primary criterion determining the effectiveness of a maximum speed is the 
length and frequency of steps and their mutual relations. High values of both these pa-
rameters have a decisive influence on the maximum speed value; they are also an indica-
tor of the correct running technique. Using our program, we can accurately determine the 
length of each step. The module for automatically determining the length of the steps di-
vides the entire run into individual steps based on the fluctuation of the speed of the run 
between the phase of the support phase and the flight phase. By smoothing, we eliminate 
the wrong calculated step length (Figure 8).  
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speed (m/s) and acceleration (m/s2)
time (ms)
speed-raw
speed-smooth
acceleration-raw
acceleration-smooth
    
    
    
Figure 7. Speed and acceleration (raw and smoothed data).
The primary criterion determining the effectiveness of a maximum speed is the length
and frequency of steps and their mutual relations. High values of both these parameters
have a decisive inﬂuence on the maximum speed value; they are also an indicator of the
correct running technique. Using our program, we can accurately determine the length of
each step. The module for automatically determining the length of the steps divides the
entire run into individual steps based on the ﬂuctuation of the speed of the run between
the phase of the support phase and the ﬂight phase. By smoothing, we eliminate the wrong
calculated step length (Figure 8).
Sensors 2022, 22, 5876
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parameters have a decisive influence on the maximum speed value; they are also an 
indicator of the correct running technique. Using our program, we can accurately 
determine the length of each step. The module for automatically determining the length 
of the steps divides the entire run into individual steps based on the fluctuation of the 
speed of the run between the phase of the support phase and the flight phase. By 
smoothing, we eliminate the wrong calculated step length (Figure 8).  
 
Figure 8. Function determining step length (thin line—raw and bold line—smooth length). 
Figure 8. Function determining step length (thin line—raw and bold line—smooth length).
By the same principle, the frequency of the steps and the time are also calculated
(Figures 9 and 10). Length and frequency of steps are the basis for optimizing and adapting
these two variables to achieve a higher average running speed.
FOR PEER REVIEW 
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By the same principle, the frequency of the steps and the tie are also calculated 
(Figures 9 and 10). Length and frequency of steps are the basis for optimizing and 
adapting these two variables to achieve a higher average running speed. 
 
Figure 9. The function of determining step frequency (thin line—raw and bold line—smooth 
frequency). 
Figure 9. The function of determining step frequency (thin line—raw and bold line—smooth frequency).
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Figure 9. The function of determining step frequency (thin line—raw and bold line—smooth 
frequency). 
 
Figure 10. The function of determining step time from take-off (thin line—raw and bold line—
smooth values). 
In the descriptive statistical data processing module, it is possible to calculate mean, 
median, mode, minimum, maximum, standard deviation, skewness, and kurtosis. We can 
also calculate the linear correlation between the selected variables.  
Figure 10. The function of determining step time from take-off (thin line—raw and bold line—
smooth values).
In the descriptive statistical data processing module, it is possible to calculate mean,
median, mode, minimum, maximum, standard deviation, skewness, and kurtosis. We can
also calculate the linear correlation between the selected variables.
4. Discussion
The aims of this study are twofold. First, assess the usefulness of a laser measuring
system to record the time of a sprint run with maximum intensity. Secondly, propose an
innovative computer software for the sprint kinematic analysis, based on the logarithm of
the extraction of motion parameters, including information on the change in the course of
the maximum speed in a straight line. The conclusion is that the time measurement made
by using the LDM laser system is useful to provide data for a statistical analysis of the
course of maximum speed changes during a sprint.
Exploring diagnostic problems related to sprint performance assessment, speciﬁcally
the impact and magnitude of various external conditions, technologies, and monitoring,
is one of the essential factors in the training process. In this case, the focus is on the
diagnostic possibilities related to monitoring changes in the value of the linear running
speed during competition and speed training. It seems to be a key element in improving
sprint performance.
The cinematographic method (Omega’s fully automatic timing system, Fribourg,
Switzerland or Sony DCR-PC105E, Japan) is one of the primary methods of studying
and evaluating the movement technique [11]. The main advantage of this method is the
lack of direct interference in the athlete’s motor task performance, which allows for the
full and free use of acquired technical skills. In recent years, several technologies have
appeared to measure a run at maximum speed in a non-invasive way, both in a straight
line and in curvature. There is an advanced technology that provides positional data
with high spatiotemporal resolution [19]. The data can be collected with a radio-based
position detection system (RedFIR, Fraunhofer Institute, Germany). The second option
is to apply inertial measurement units to capture multidimensional accelerometers and
gyroscope data to measure the kinematic parameters of a system. During the last decade,
GPS with integrated accelerometers was extensively applied in various team sports to
measure running velocity during training sessions and games [19–22]. One time-effective
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method for obtaining speed-time curves is applying a laser distance measurement (LDM)
device [12,13]. The most recognizable and used at international athletic competitions
(2008 World Championships and Olympic Games) is the LAVEG laser speed gun (LAVEG
Sport, Jenoptik, Germany). The system is placed behind the starting line during the
measurement, and the beam laser must be aimed (tracking the competitor) at his pelvis
throughout the measurement [23]. The LAVEG system measures the positional information
of an athlete at 100 Hz. Therefore, training with immediate feedback using a laser device
can be of great help when we try to improve running performance.
An essential component of all these measurement methods is the validity and relia-
bility of measurement [24,25]. Velocity data obtained from sprint trials were previously
assessed but were limited, by comparison, to linear velocities at the hip over a distance.
According to Haugen et al. [11], Omega’s fully automatic timing system demonstrated
that the measurement method was valid to the instrument’s precision (±0.01 s), which is
a very accurate value. The RedFIR (a radio-based position detection system) can provide
precise in-ﬁeld performance data based on reference systems [14,26]. Regardless of the
level of validity and reliability, one crucial thing is that all these methodologies of time
measurement systems allow obtaining the data that can be subjected to multidirectional
analysis. It applies to velocity proﬁles—calculation of acceleration, instantaneous speed,
split time, and parameters—that determine the length and frequency of the step.
The more precise this diagnostic, the better are the technology used, especially re-
garding the software for kinematic analysis. A proper kinematic analysis software of a
sprint run should be based on a reliable acquisition of the primary structural properties of
the movement, its characteristic quantities (numerical values), and the relations between
them. Often, the identical movements of a sprinter’s lower limbs differ in many respects.
This is mainly not due to different methods of recording a sprint run, but to the computer
software applications used for detail analysis. A detailed description of the software opera-
tion can be found in the Materials and Methods section. However, this software aims to
estimate the kinematic parameters (i.e., position, speed, acceleration, and possibly basic
running step parameters) of the moving sprinter based on noisy measurements collected
by the laser beam sensor. It is possible thanks to the use of a special tracking algorithm. It
must consider the deterministic model of the dynamics of changes in the target’s position
(e.g., model of acceleration or increasing speed). Such activity enables the estimation of the
batch processing method (raw data from the running time) to obtain the target kinematic
parameters of the sprint. Therefore, estimation of target kinematic parameters based on
LDM measurements is not a template due to the linear nature of these measurements about
the target kinematic parameters. Additionally, this software represents closed solutions and,
thus, requires the repetition of numerical search algorithms to obtain accurate kinematic
data. This makes this software highly effective.
The main task of training in a sprint should be to raise the athlete’s movement potential
to the highest possible level to achieve maximum sport results [4,6]. This is mainly related
to conducting comprehensive activities to achieve optimal running efﬁciency, maintaining
the highest possible values of the maximum running speed over the entire distance [12].
The more signiﬁcant the correlation between the length and frequency of running steps,
the better.
The primary criterion determining the effectiveness of speed for a whole sprint run
and the level of speed preparation of a competitor is the length and frequency of steps and
their mutual relations [5,27]. High values of both these parameters have a decisive inﬂuence
on the maximum speed value; they are also an indicator of the correct running technique.
The research conducted by Luhtanen and Komi [10] and Mero and Komi [8] showed that
the values of these parameters change with increasing speed, which has a linear course until
the competitor develops a speed of about 7 m/s. As this value increases to about 9 m/s, the
increment in the stride length is small, and the frequency increment is signiﬁcant. This is
even more evident when the competitors reach speeds of 11–12 m/s, which is only possible
due to a substantial increase in pace [1]. In addition to the length of steps, the frequency
Sensors 2022, 22, 5876
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of steps is the primary parameter that allows developing the maximum speed of the run,
thus improving its efﬁciency. The more signiﬁcant the correlation between the length and
frequency of running steps, the better. However, this somewhat contradicts Deleclus’s
manuscript [28], which found a linear relationship between the length of the running
step and the speed developed, saying that there is no signiﬁcant correlation between the
frequency of steps and the speed. The author also believes that in short runs, the pace of
the run reaches its maximum value at the beginning of the sprint (after a few steps) and
does not change signiﬁcantly.
On the other hand, the stride length increases almost the entire distance, which is an
essential factor in developing maximum speed. The relationship between stride length
and speed achieved in a 100 m run for men was determined based on linear regression
analysis and was V = 0.79 + (3.89 Lk). The change in stride length can explain almost 85%
of the variation in maximum running speed. In such an argument, it is essential to indicate
whether the analysis considers only the distribution of the length of individual steps over
the entire distance or the average value calculated for a 10 m section. The distribution curve
shows a much greater variability (dispersion) between personal values for each competitor
than the average value of the results obtained in the individual ten sections.
5. Conclusions
A runner’s distance, time, speed, and acceleration vary from step to step and can be
divided into several phases and sections. Each of these phases has a particular inﬂuence
on the ﬁnal result. With LDM301A, the measured and calculated variables can be closely
monitored at all stages, and each phase can be analyzed and optimized during the training
process. Determining the right goals based on previous tests is crucial for the optimal
planning of the athlete’s development. Once a coach has an insight into each phase, they
can use the information to decide how to adapt the training process.
We will continue to develop the functions of the software in the future. One of the
priorities is the variability of the take-off speed of the consistent steps and the take-off
speed for the left and the right leg. Calculating acceleration within the steps can also
give us information about the ratio of positive acceleration (propulsion phase—the second
part of the support phase) to deceleration (ﬂight phase and braking in the ﬁrst part of the
support phase). This way, a large amount of measurement data will give us a new insight
into running optimization. We will also determine the new index between the maximum
speed zone, step length, step frequency, and step time. Some of our data indicate a high
probability of added value when planning sprint training with this approach.
Author Contributions: Conceptualization, S.Š. and M. ˇC.; methodology, S.Š., M. ˇC. and P.P.; software,
S.Š. and P.P.; validation, S.Š., P.P. and M.P.; formal analysis, S.Š., M. ˇC., M.P. and K.M.; investigation,
S.Š., M. ˇC., P.P. and K.M., resources, M. ˇC.; data curation, M. ˇC. and S.Š., writing—original draft
preparation, S.Š., M. ˇC. and K.M., writing—review and editing, K.M., M. ˇC. and M.P.; visualization,
S.Š. and K.M.; supervision, K.M. and M. ˇC. All authors have read and agreed to the published version
of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Human Ethics Committee of the University of Ljubljana.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: Open access publishing of this article was supported by the Slovenian Research
Agency (grant number P5-0147). The authors thank the athlete for his participation in the study.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Application of the Laser Linear Distance-Speed-Acceleration Measurement System and Sport Kinematic Analysis Software.
Štuhec, Stanko,Planjšek, Peter,Ptak, Mariusz,Čoh, Milan,Mackala, Krzysztof