

Citation: Chahal, A.K.; Lim, J.Z.; Pan,
J.-W.; Kong, P.W. Inter-Unit
Consistency and Validity of 10-Hz
GNSS Units in Straight-Line Sprint
Running. Sensors 2022, 22, 1888.
https://doi.org/10.3390/s22051888
Academic Editor: Mario
Munoz-Organero
Received: 19 January 2022
Accepted: 25 February 2022
Published: 28 February 2022
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sensors
Article
Inter-Unit Consistency and Validity of 10-Hz GNSS Units in
Straight-Line Sprint Running
Amandeep Kaur Chahal, Jolene Ziyuan Lim, Jing-Wen Pan
and Pui Wah Kong *
Physical Education and Sports Science Academic Group, National Institute of Education,
Nanyang Technological University, Singapore 637616, Singapore; amandeep001@e.ntu.edu.sg (A.K.C.);
nie20.lzj@e.ntu.edu.sg (J.Z.L.); nie173748@e.ntu.edu.sg (J.-W.P.)
* Correspondence: puiwah.kong@nie.edu.sg
Abstract: The present study aimed to investigate the inter-unit consistency and validity of multiple
10-Hz Catapult Global Navigation Satellite System (GNSS) units in measuring straight-line sprint
distances and speeds. A total of 13 participants performed one 45.72-m linear sprint at maximum
effort while wearing all eight GNSS units at once. Total run distance and peak speed recorded using
GNSS units during the sprint duration were extracted for analysis. Sprint time and peak speed were
also obtained from video recordings as reference values. Inter-unit consistency was assessed using
intraclass correlation coefﬁcients (ICC) and standard errors of measurements (SEM). For a validity
test, one-sample t-tests were performed to compare each GNSS unit’s distance with the known
distance. Additionally, Wilcoxon signed-rank tests were performed to compare each unit’s peak
speed with the reference peak speed measured using video analysis. Results showed poor inter-unit
consistency for both distance (ICC = 0.131; SEM = 8.8 m) and speed (ICC = 0.323; SEM 1.3 m/s)
measurements. For validity, most units recorded a total distance (44.50 m to 52.69 m) greater than the
known distance of 45.72 m and a lower peak speed (7.25 (0.51) m/s) than the video-based reference
values (7.78 (0.90) m/s). The present ﬁndings demonstrate that there exist variations in distance and
speed measurements among different units of the same GNSS system during straight-line sprint
running. Practitioners should be aware of the window of errors associated with GNSS measurements
and interpret the results with caution. When making comparisons over a season, players should wear
the same unit every time if logistically possible.
Keywords: Global Navigation Satellite System; reliability; distance; speed; video; movement analysis
1. Introduction
In sports, the movement characteristics of players during competitions and training
are of interest for in-game analyses. Traditionally, player activity data were manually
collected on pen and paper, which was extremely labor-intensive and time-consuming [1,2].
With technological advancements in time-motion analysis, more convenient methods, such
as video analysis, have been regularly used to track player movements during competitions
and training. However, video analysis can be troublesome to set up and requires extensive
manual analysis after data collection. The development of Global Navigation Satellite
System (GNSS) and Global Positioning System (GPS) units, which are light, small, and
portable, allows for simultaneous movement patterns analyses of multiple players [3,4].
Since then, the use of GNSS/GPS for athlete tracking has become widespread in various
sports, such as soccer, rugby, and ﬁeld hockey [3,5–7] due to the ease of data collection and
quality of analysis provided by these systems [8–11]. Additionally, GNSS/GPS units are
the conventional technology used for the assessment of external training load variables
in team sports due to their ability to give real-time feedback. This is essential, given the
limited amount of time to process data and carry out post-session analysis [3,12,13].
GPS is a navigation system based on connections to satellites that allows locations
of users to be triangulated through signals sent out by the satellites and received by the
Sensors 2022, 22, 1888. https://doi.org/10.3390/s22051888
https://www.mdpi.com/journal/sensors
Sensors 2022, 22, 1888
2 of 10
units [14]. The accuracy of data is dependent on the conﬁguration of the satellites in relation
to the receiver and how evenly spaced they are, known as the dilution of precision (DOP).
Triangulation of position is the most accurate when one satellite is directly overhead the
receiver while the rest of the satellites are evenly spaced around the horizon (DOP = 1).
It has generally been suggested that GPS units require at least 4 satellites for data to be
considered accurate. In addition, satellites that are more evenly spaced are considered
stronger than when satellites are close together [14]. GPS units are usually combined with
microsensors such as accelerometers that are capable of recording movements in three
planes, allowing the intensity of body load (also known as player load in some systems) to
be measured. In addition, the inclusion of gyroscopes and magnetometers in these units
allows for directional orientation and rotational velocity to also be measured [15]. Sampling
rates of GPS units may range from 1 to 15 Hz, indicating the multiple speeds at which the
GPS units collect data. Existing studies have shown that higher sampling rates increase
the accuracy of performance indicators [16–18] recorded by the GPS units. For instance,
10-Hz GPS units are more accurate than those of lower sampling rates in measuring total
distance covered during both linear activities and sport-speciﬁc circuits and measuring
peak speed [19]. No additional accuracy has been found between 10-Hz and 15-Hz GPS
units [19], indicating that a sampling rate of 10 Hz could be sufﬁcient.
GNSS/GPS units provide a multitude of movement variables including distance,
speed, acceleration/deceleration, and metabolic power [3,20]. These movements can be
purposefully analyzed (external training load) to comprehend the positional demands
in sports, allowing practitioners to design programs that accurately emulate and equip
athletes for their speciﬁc sport [3]. Furthermore, the GNSS/GPS data have also been proven
useful in aiding practitioners to understand physiological and technical demands of their
players through the extraction of various external training load measures such as volume,
intensity, and frequency [21]. Such information can inform and guide coaches and sport
scientists to develop appropriate conditioning and recovery plans [22,23].
While GNSS/GPS units provide practical and useful feedback, environmental objects
such as surrounding tall buildings [24], atmospheric pressure [25], as well as the level of
satellite giving out the signals (with signals from lower satellites having to go through
more atmosphere) can result in obstruction of signals, leading to lower signal-to-noise
ratio and lower accuracy in measurements. Hence, it is important to establish the validity
and reliability of these units before applying them in sports [4,16,26]. Testing validity
provides an understanding of the differences between the measures recorded by the units
and standard measures. Reliability testing, on the other hand, tests reproducibility of values
when the same test is repeated by another unit. While studies have generally agreed that
GNSS/GPS devices can be reliable in straight-line running, there is a sizable inconsistency
in accuracy among the models of GNSS/GPS manufacturers [12,18,27]. Imparting the
validation of one system to another can be imprecise even if it is introduced by the same
manufacturer [28]. While Johnston and co-workers [29] used a different software to collect
and analyze GPS data collected by other brands of GPS units, the authors cautioned that
the mismatching in GPS models may have inﬂuenced the movement demand data. Hence,
it is vital to carry out an independent and thorough trial for each new GNSS/GPS device
(hardware) and its analysis tool (software).
Within the same system, high consistency between different GNSS/GPS units is critical
especially in team sports whereby each player wears an independent unit. Previous studies
examining the inter-unit reliability of GPS units have placed multiple units on solid objects
such as a golf cart and motorcycle [26], plastic sled [30], and a trundle wheel [31]. It should
be noted that the movement trajectories of solid objects may differ from those of the human
players who can freely move individual body segments in different directions at various
magnitudes. There are very few studies placing GPS units on human participants and these
studies typically compared among only two to four units each time [27,32]. To the best of
the authors’ knowledge, only one study has tested the inter-unit reliability of eight GPS
units on an individual [33]. Although their study found inter- and intra-receiver reliability
Sensors 2022, 22, 1888
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to be acceptable, the GPS units were sampled at 1 Hz which is far below the recommended
frequency of 10 Hz for accurate measurements [19]. Thus, there is a need to examine the
inter-unit consistency and validity of multiple GNSS/GPS units sampled at sufﬁciently
high frequency (i.e., at least 10 Hz) with the units placed on human participants and not
solid objects.
This study, therefore, aimed to investigate the inter-unit consistency and validity of
10-Hz Catapult GNSS (S5 OptimEye, Catapult Innovations, Melbourne, Australia) units
during straight-line sprint running. Eight GNSS units were analyzed using the Sprint
software developed by Catapult. It was hypothesized that all GNSS units, when placed on
human participants, would be consistent and accurate in measuring distances and peak
speeds during sprint running [18].
2. Materials and Methods
2.1. Participants
This study was approved by the Nanyang Technological University Institutional
Review Board (IRB-2020-09-033). Thirteen active participants (4 males, 9 females) were
recruited via convenient sampling [age 21.6 (1.6) years, height 170.6 (7.7) cm, body mass 63.1
(10.1) kg]. To be eligible for this study, participants must have been training with a sports
team at least twice a week and had minimally a year of experience in the speciﬁed sport.
Additionally, they were required to be injury-free and pain-free at the time of the study.
2.2. Equipment
The Catapult S5 OptimEye GNSS system was used in the present study. Accessing
GPS and Global Navigation Satellite System (GLONASS) satellite constellations, this
GNSS system ensures high-quality data even in challenging performance environments
(https://www.catapultsports.com (accessed on 1 October 2020)). Eight 10-Hz GNSS units
were worn at once on each participant during the test (Figure 1). The eight GNSS units
were placed near the mid-back area using a custom-made strap, with slightly different
positions. While the tightness of strap was adjusted to fit individual body sizes, the relative
positions of the GNSS units on the strap remained consistent across all participants.
 
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different directions at various magnitudes. There are very few studies placing GPS units 
on human participants and these studies typically compared among only two to four units 
each time [27,32]. To the best of the authors’ knowledge, only one study has tested the 
inter-unit reliability of eight GPS units on an individual [33]. Although their study found 
inter- and intra-receiver reliability to be acceptable, the GPS units were sampled at 1 Hz 
which is far below the recommended frequency of 10 Hz for accurate measurements [19]. 
Thus, there is a need to examine the inter-unit consistency and validity of multiple 
GNSS/GPS units sampled at sufficiently high frequency (i.e., at least 10 Hz) with the units 
placed on human participants and not solid objects. 
This study, therefore, aimed to investigate the inter-unit consistency and validity of 
10-Hz Catapult GNSS (S5 OptimEye, Catapult Innovations, Melbourne, Australia) units 
during straight-line sprint running. Eight GNSS units were analyzed using the Sprint 
software developed by Catapult. It was hypothesized that all GNSS units, when placed 
on human participants, would be consistent and accurate in measuring distances and peak 
speeds during sprint running [18]. 
2. Materials and Methods 
2.1. Participants 
This study was approved by the Nanyang Technological University Institutional 
Review Board (IRB-2020-09-033). Thirteen active participants (4 males, 9 females) were 
recruited via convenient sampling [age 21.6 (1.6) years, height 170.6 (7.7) cm, body mass 
63.1 (10.1) kg]. To be eligible for this study, participants must have been training with a 
sports team at least twice a week and had minimally a year of experience in the specified 
sport. Additionally, they were required to be injury-free and pain-free at the time of the 
study. 
2.2. Equipment 
The Catapult S5 OptimEye GNSS system was used in the present study. Accessing 
GPS and Global Navigation Satellite System (GLONASS) satellite constellations, this 
GNSS system ensures high-quality data even in challenging performance environments 
(https://www.catapultsports.com (accessed on 1 October 2020)). Eight 10-Hz GNSS units 
were worn at once on each participant during the test (Figure 1). The eight GNSS units 
were placed near the mid-back area using a custom-made strap, with slightly different 
positions. While the tightness of strap was adjusted to fit individual body sizes, the 
relative positions of the GNSS units on the strap remained consistent across all 
participants. 
 
Figure 1. Each participant wore eight GNSS units at once in the maximal sprint test. 
Figure 1. Each participant wore eight GNSS units at once in the maximal sprint test.
2.3. Experimental Protocol
This experiment involved one single visit to the ﬁeld hockey pitch at the National
Institute of Education, Nanyang Technological University, Singapore. GNSS data were
collected outdoors without high surrounding buildings to enhance satellite reception [34].
After sufﬁcient warm-up and putting on the strap with 8 GNSS units, participants were
asked to perform one straight-line sprint across the hockey pitch with maximum effort
(Figure 2). A sprint distance of 45.72 m was chosen as it is exactly half of the hockey pitch
Sensors 2022, 22, 1888
4 of 10
with a clear marked line. This distance also allowed sufﬁcient time for participants to reach
their peak speed. To help participants discern the start and end points, the sprint area was
marked out using colored cones. Participants were asked to run pass the 45.72 m end-line
before they could slow down. The sprint test was recorded using two video cameras at
60 Hz for subsequent determination of sprint times and peak speeds. To minimize the effect
of camera lens distortion, we used two smartphone cameras to cover the entire 45.72 m
range (Figure 2). The midline distance of 22.86 m was used to calibrate each camera (0 to
22.86 m, 22.86 to 45.72 m) as the midline can be clearly seen from both camera views.
 
This experiment involved one single visit to the field hockey pitch at the National 
Institute of Education, Nanyang Technological University, Singapore. GNSS data were 
collected outdoors without high surrounding buildings to enhance satellite reception [34]. 
After sufficient warm-up and putting on the strap with 8 GNSS units, participants were 
asked to perform one straight-line sprint across the hockey pitch with maximum effort 
(Figure 2). A sprint distance of 45.72 m was chosen as it is exactly half of the hockey pitch 
with a clear marked line. This distance also allowed sufficient time for participants to 
reach their peak speed. To help participants discern the start and end points, the sprint 
area was marked out using colored cones. Participants were asked to run pass the 45.72 
m end-line before they could slow down. The sprint test was recorded using two video 
cameras at 60 Hz for subsequent determination of sprint times and peak speeds. To 
minimize the effect of camera lens distortion, we used two smartphone cameras to cover 
the entire 45.72 m range (Figure 2). The midline distance of 22.86 m was used to calibrate 
each camera (0 to 22.86 m, 22.86 to 45.72 m) as the midline can be clearly seen from both 
camera views. 
 
Figure 2. Experimental set-up of the sprint test over half a field hockey pitch (45.72 m) with two 
smartphone cameras recording the performances (Camera 1: 0 to 22.86 m, Camera 2: 22.86 m to 45.72 
m). 
GNSS units were switched on at least 5 min before the units were strapped on the 
participants. After strapping on all units, participants were verbally briefed and then 
asked to familiarize themselves with the task. The GNSS units were switched on for more 
than 15 min to receive the complete almanac before the commencement of the test. 
Participants were also instructed to stay still for 30 s, before the start of the sprint. This 
was to enable subsequent determination of the start time for each trial when the speed 
increased sharply from zero. 
2.4. Data Processing 
The GNSS movement data were downloaded using the manufacturer’s software 
(Catapult Sprint Version 5.1.7, Melbourne, Australia) at the default ‘GPS rate’ of 10 Hz. 
Customized MATLAB codes were written to extract the relevant distance and speed time-
series data using MATLAB (R2021a, MathWorks, Natick, MA, USA). The start of the 
sprint was identified from a sharp and continuous increase in speed above a threshold of 
0.5 m/s. The duration each participant took to complete the 45.72 m distance was obtained 
based on the video recordings of the sprint. This sprint duration was then used to 
determine the end time of the sprint in the GNSS data. From the start to the end of the 
sprint, total distance traveled, and peak speeds were obtained from each of the 8 GNSS 
Figure 2. Experimental set-up of the sprint test over half a field hockey pitch (45.72 m) with two
smartphone cameras recording the performances (Camera 1: 0 to 22.86 m, Camera 2: 22.86 m to 45.72 m).
GNSS units were switched on at least 5 min before the units were strapped on the
participants. After strapping on all units, participants were verbally briefed and then asked
to familiarize themselves with the task. The GNSS units were switched on for more than
15 min to receive the complete almanac before the commencement of the test. Participants
were also instructed to stay still for 30 s, before the start of the sprint. This was to enable
subsequent determination of the start time for each trial when the speed increased sharply
from zero.
2.4. Data Processing
The GNSS movement data were downloaded using the manufacturer’s software
(Catapult Sprint Version 5.1.7, Melbourne, Australia) at the default ‘GPS rate’ of 10 Hz.
Customized MATLAB codes were written to extract the relevant distance and speed time-
series data using MATLAB (R2021a, MathWorks, Natick, MA, USA). The start of the sprint
was identiﬁed from a sharp and continuous increase in speed above a threshold of 0.5 m/s.
The duration each participant took to complete the 45.72 m distance was obtained based on
the video recordings of the sprint. This sprint duration was then used to determine the end
time of the sprint in the GNSS data. From the start to the end of the sprint, total distance
traveled, and peak speeds were obtained from each of the 8 GNSS units. Raw GNSS data
were used without further down sampling, ﬁltering, or smoothing procedures. Due to
transmission and technical errors, it was not possible to obtain complete data sets from all
8 GNSS units throughout all trials. Among the 13 participants, 7 had complete data set and
6 had missing data from either 1 or 2 GNSS units.
For validity analysis, a reference value of the gold standard was needed. In the present
study, the total distance was 45.72 m, which was the known size of half of a standard
ﬁeld hockey pitch. This distance was also conﬁrmed by experimental measurement using
a trundle wheel. To calculate the speed from position data, manual digitization of the
player’s center of the head was performed through the sprint duration using the software
Kinovea (version 0.9.3, Kinovea, Bordeaux, France, available for download at: http://
Sensors 2022, 22, 1888
5 of 10
www.kinovea.org (accessed on 15 April 2021)). The present study used video analysis
as the gold standard for kinematics, which is aligned with previous work evaluating the
accuracy of 10 Hz GPS system [12]. Kinovea has been demonstrated as a reliable and
accurate tool for video-based angular and linear measurements via digitization of x- and
y-axis coordinates [35]. While an optimal angle of 90◦ was recommended, an accepted level
accuracy was also established when the camera was placed within an angle range of 45◦ to
90◦ [35].
Figure 3 illustrates examples of the speed-time data measured using one GNSS unit and
video analysis. The raw speed data from videos were low-passed filter at 10 Hz to remove
the noise associated with manual digitization. The peak value of the filtered speed data
during the entire sprint duration was then identified. This video-based peak speed was used
as a reference value in the subsequent validity analysis of GNSS units. The mean (SD) of the
raw and filtered peak speeds were 7.82 (0.81) m/s and 7.78 (0.90) m/s, respectively.
 
present study, the total distance was 45.72 m, which was the known size of half of a 
standard field hockey pitch. This distance was also confirmed by experimental 
measurement using a trundle wheel. To calculate the speed from position data, manual 
digitization of the player’s center of the head was performed through the sprint duration 
using the software Kinovea (version 0.9.3, Kinovea, Bordeaux, France, available for 
download at: http://www.kinovea.org (accessed on 15 April 2021)). The present study 
used video analysis as the gold standard for kinematics, which is aligned with previous 
work evaluating the accuracy of 10 Hz GPS system [12]. Kinovea has been demonstrated 
as a reliable and accurate tool for video-based angular and linear measurements via 
digitization of x- and y-axis coordinates [35]. While an optimal angle of 90° was 
recommended, an accepted level accuracy was also established when the camera was 
placed within an angle range of 45° to 90° [35]. 
Figure 3 illustrates examples of the speed-time data measured using one GNSS unit 
and video analysis. The raw speed data from videos were low-passed filter at 10 Hz to 
remove the noise associated with manual digitization. The peak value of the filtered speed 
data during the entire sprint duration was then identified. This video-based peak speed 
was used as a reference value in the subsequent validity analysis of GNSS units. The mean 
(SD) of the raw and filtered peak speeds were 7.82 (0.81) m/s and 7.78 (0.90) m/s, 
respectively. 
 
Figure 3. Representative raw speed-time from one participant measured using Kinovea video 
analysis and one GNSS unit. 
2.5. Statistical Analyses 
Statistical analyses were carried out on JASP (version 0.14.1, JASP Team 2020) and SPSS 
(version 26.0, IBM Corp., Armonk, NY, USA). Data are expressed as mean (standard 
deviation). An alpha level of p < 0.05 was set as the level of significance. Inter-unit 
consistency was assessed using intraclass correlation coefficients (ICC). ICC was 
interpreted as slight (<0.20), fair (0.21–0.40), moderate (0.41–60), substantial (0.61–0.80), or 
Figure 3. Representative raw speed-time from one participant measured using Kinovea video analysis
and one GNSS unit.
2.5. Statistical Analyses
Statistical analyses were carried out on JASP (version 0.14.1, JASP Team 2020) and
SPSS (version 26.0, IBM Corp., Armonk, NY, USA). Data are expressed as mean (standard
deviation). An alpha level of p < 0.05 was set as the level of signiﬁcance. Inter-unit
consistency was assessed using intraclass correlation coefﬁcients (ICC). ICC was interpreted
as slight (<0.20), fair (0.21–0.40), moderate (0.41–60), substantial (0.61–0.80), or almost perfect
reliability (>0.80) [36,37]. Standard error of measurement (SEM) was calculated from the
ICC results using the formula: SEM = SD × √(1 − ICC).
For the validity assessment, one-sample t-tests were performed to compare the distance
measured using each GNSS unit with the known distance of 45.72 m. Effect sizes were
indicated by Cohen’s d and interpreted as small (0.2 ≤ d < 0.5), medium (0.5 ≤ d < 0.8),
or large (d ≥ 0.8). Since the speed data were not normally distributed, non-parametric
statistical tests were employed. Specially, Wilcoxon signed-rank tests were used to compare
each GNSS unit’s peak speed with the reference speed measured using video analysis.
Effect size (r) for the Wilcoxon signed-rank tests was calculated from the Z-value and
interpreted as small (0.1 ≤ |r| < 0.3), medium (0.3 ≤ |r| < 0.5), or large (|r| ≥ 0.5).
Sensors 2022, 22, 1888
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3. Results
3.1. Inter-Unit Consistency
The results of ICC analysis showed slight reliability for the total sprint distance and
fair reliability for peak speed (Table 1). These results indicate that the 8 tested GNSS units
are not sufﬁciently consistent among themselves.
Table 1. Reliability statistical outputs to assess inter-unit consistency.
GNSS Variables
ICC
95% Conﬁdence Intervals
SEM
Total distance
0.131
[−0.024, 0.556]
8.8 m
Peak speed
0.323
[0.101, 0.736]
1.3 m/s
Note. ICC denotes intraclass correlation coefﬁcients; SEM denotes standard error of measurement.
3.2. Validity
Most GNSS units recorded a total distance greater than the known distance of 45.72 m
(Table 2). While statistical signiﬁcance was only found in two units, the effect sizes of the
differences were large across all units. These results indicate that GNSS units, although
belonging to the same system, do not always measure distance with the same degree
of accuracy.
Table 2. Validity of GNSS distance measurements against known distance of 45.72 m.
GNSS Units
Mean (SD)
p-Value
Effect Size (d)
Unit 1 (n = 13)
49.77 (5.92)
0.030 *
8.41
Large
Unit 2 (n = 13)
46.69 (10.62)
0.747
4.49
Large
Unit 3 (n = 10)
44.50 (8.55)
0.663
5.29
Large
Unit 4 (n = 13)
52.23 (10.11)
0.039 *
5.17
Large
Unit 5 (n = 11)
52.00 (10.13)
0.067
5.13
Large
Unit 6 (n = 12)
50.83 (8.57)
0.063
5.93
Large
Unit 7 (n = 12)
47.50 (8.06)
0.460
5.89
Large
Unit 8 (n = 13)
52.69 (12.18)
0.061
4.33
Large
Note. * Signiﬁcant differences detected using one-sample t-tests (p < 0.05).
Compared with the reference speed data obtained from video analysis, Unit 4 mea-
sured signiﬁcantly higher peak speed (p = 0.010, large effect size, Table 3). No signiﬁcant
differences were identiﬁed between other GNSS units and video analysis, with data of
4 units approaching statistical signiﬁcance (Units 1, 3, 7, 8). In general, most GNSS units
measured a lower peak speed (7.25 (0.51) m/s) than the video-based value (7.78 (0.90) m/s)
and the effect sizes of the differences were medium to large.
Table 3. Validity of GNSS peak speed measurements against video analysis.
GNSS Units
Mean (SD)
p-Value
Effect Size (r)
Unit 1 (n = 13)
7.04 (1.15)
0.057
0.604
Large
Unit 2 (n = 13)
6.89 (1.98)
0.127
0.495
Medium
Unit 3 (n = 10)
6.92 (1.03)
0.064
0.673
Large
Unit 4 (n = 13)
7.11 (0.89)
0.010 *
0.780
Large
Unit 5 (n = 11)
7.37 (1.55)
0.416
0.303
Medium
Unit 6 (n = 12)
8.40 (2.53)
0.970
0.026
Negligible
Unit 7 (n = 12)
6.86 (1.34)
0.064
0.615
Large
Unit 8 (n = 13)
7.37 (1.05)
0.057
0.604
Large
Note. * Signiﬁcant differences detected using Wilcoxon signed-rank tests (p < 0.05). Group mean (SD) of video-
based peak speed was 7.78 (0.90) m/s.
4. Discussion
The aim of the study was to investigate the inter-unit reliability and validity of multiple
10-Hz Catapult GNSS units during straight-line sprint running. Inter-unit consistency was
Sensors 2022, 22, 1888
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assessed among eight GNSS units worn on each participant, and validity was tested by
comparing total distance and peak speed against criterion-referenced values. The most
prevailing outcomes were that despite all GNSS units belonging to the same system, low
inter-unit reliability and varied accuracies in distance and speed measurements were found
during fast speed running.
4.1. Distance
We originally expect that all GNSS units, when placed on the participant, would
be consistent and accurate in measuring total distance traveled during 45.72-m sprint.
However, there was only slight reliability for inter-unit consistency among the eight GNSS
units and two out of eight units (Units 1 and 4, Table 2) had signiﬁcantly different values
from the criterion distance. In addition, seven out of the eight GNSS units overestimated the
values during the straight-line sprint. These results in the present study are somewhat in
congruence with previous research which reported moderate errors when measuring total
distance over very high-speed running (>5.56 m/s) [17]. Additionally, overestimation of the
total distance measured using GNSS units has also been found when the sprinting distances
were set as 15 m and 30 m [34,35]. The reliability and accuracy may also be affected by rapid
changes in speed during the acceleration phase of the sprint. A previous study revealed
that distance measures over the post-acceleration phase of 20–40 m were more accurate
than the acceleration phase of 0–20 m in a 40-m linear acceleration run [16], suggesting that
smaller variations in speed may facilitate more accurate measures in distance. In the present
study, participants started from a stationary position and were asked to sprint as fast as
they could using maximal effort. Hence, phases with great variations in speed could have
resulted in inconsistent and less accurate total distances measurement across different units.
It is also possible that some participants did not sprint in a perfectly straight line hence
covering a longer distance than the reference value of 45.72 m. Although the deviation
from a straight line can be expected to be quite small, this could partly explain why seven
out of eight GNSS units recorded a longer total distance than the reference value based on
the distance between standard marked lines on the ﬁeld. Finally, the GNSS units could
miss data owing to the poor satellite connection [19]. This may have caused measurement
errors in certain GNSS units, leading to inconsistency among the different units.
4.2. Peak Speed
This study hypothesized that multiple GNSS units of the same system would be
consistent and accurate in measuring peak speed during a maximal effort sprint. The
results demonstrated fair reliability among the eight GNSS units and that seven out of the
eight units generally measured lower peak speeds than that video-based reference values
(Table 3). The results are not in line with previous ﬁndings which suggested conﬁdence in
10-Hz GNSS units being able to accurately measure consistent speeds and velocities [18].
The discrepancy in the peak speeds measured can be attributed to the compromises when
measuring instantaneous velocities during great decelerations [38] and accelerations [28].
Hence, rapid changes in speed during the acceleration phase of the 45.72-m sprint in the
present study could affect the accuracy of the GNSS units when measuring peak speeds.
Higher accuracy and inter-unit reliability may be expected if GNSS units are applied to
measure speed during a stable phase with small decelerations or accelerations.
Compared with the video analysis which was used as the golden standard for the
speed measurement, only one GNSS unit displayed statistically signiﬁcant result (Unit 4,
Table 3). It is worth noting that the effect sizes of the differences were medium to large
across all units regardless of statistical signiﬁcance. As the GNSS units tend to register
lower peak speeds (7.25 (0.51) m/s) than video-based reference values (7.78 (0.90) m/s),
such differences cannot be disregarded. Sport practitioners should keep in mind that GNSS
readings may slightly underestimate peak speeds during high-speed running and interpret
the results with consideration of the error window (SEM = 1.3 m/s).
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4.3. Limitations
There were a few limitations to the current study. Firstly, six participants had miss-
ing data due to either faulty units or the poor connection to the satellites. The current
sample size of 13 participants was smaller than expected since the experiment was halted
prematurely due to the COVID-19 pandemic. A larger sample size may have brought
about more reliable results, which was unfortunately not possible due to time constraints.
Secondly, environmental factors (e.g., presence of clouds) during the experiment may have
occurred and affected the results. Thirdly, we acknowledge that the use of smartphone
cameras can reduce the accuracy of data collected due to optical effects, such as lens dis-
tortion and parallax error. For fast sprint movements, the relatively low frame rate of
60 Hz could have also compromised accuracy of speed and time data collected. Lastly,
the current study investigated only two variables of linear sprints (total distance and peak
speed). In the future, researchers should expand to other variables and movement types
concerning the utilizations of GNSS units in sports such as change in direction, acceleration,
and deceleration.
5. Conclusions
In team sports, high consistency between different GNSS units is critical as coaches
compare the movement characteristics across players in a game or training. This study
revealed that there exist variations in distance and speed measurements among eight
GNSS units worn by participants at the same time. In general, GNSS units may lead to
an overestimation of total distance and underestimation of peak speed during high-speed
sprint running. Practitioners should be aware of the window of errors associated with
GNSS measurements and interpret the results with caution. This is especially important for
data collected during sport competitions or training which involve movement demands
at high speeds. When making comparisons over a season, players should wear the same
GNSS unit every time if logistically possible.
Despite some limitations, the use of GNSS/GPS technology is still widespread, and
it offers practical insights to players’ movements characteristics and playing demands.
In view of the rapid advancement in technology, it may be possible to improve current
GNSS/GPS systems so as to enhance their inter-unit consistency and measurement accura-
cies across different movement types including high-speed sprinting.
Author Contributions: Conceptualization, J.Z.L. and P.W.K.; methodology, J.Z.L., A.K.C. and P.W.K.;
validation, A.K.C.; formal analysis, A.K.C., J.-W.P. and P.W.K.; writing—original draft preparation,
A.K.C., J.Z.L. and J.-W.P.; writing—review and editing, A.K.C., J.Z.L., J.-W.P. and P.W.K.; supervision,
P.W.K. All authors have read and agreed to the published version of the manuscript.
Funding: We wish to acknowledge the funding support for this project from Nanyang Technological
University under the URECA Undergraduate Research Programme.
Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Nanyang Technological University Institutional Review
Board (IRB-2020-09-033).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Acknowledgments: The authors would like to acknowledge all participants for taking their time to
participate in this study.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running.
Chahal, Amandeep Kaur,Lim, Jolene Ziyuan,Pan, Jing-Wen,Kong, Pui Wah