=Paper=
{{Paper
|id=Vol-3919/short17
|storemode=property
|title=Anchors' Placement for UWB-Based Indoor Localization System for Water Polo
|pdfUrl=https://ceur-ws.org/Vol-3919/short17.pdf
|volume=Vol-3919
|authors=Florent Cotton,Elizabeth Colin,Laurie Conteville,Katarzyna Wegrzyn-Wolska
|dblpUrl=https://dblp.org/rec/conf/ipin/CottonCCW24
}}
==Anchors' Placement for UWB-Based Indoor Localization System for Water Polo==
https://ceur-ws.org/Vol-3919/short17.pdf
Anchors’ Placement for UWB-Based Indoor Localization
System for Water Polo
Florent Cotton1,2 , Elizabeth Colin1 , Laurie Conteville1 and Katarzyna Wegrzyn-Wolska1,2
1
EFREI Research Lab, Efrei Paris Pantheon Assas Université, Villejuif, France
2
SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France
Abstract
The study of position, speed and trajectories in elite sports is widely used to analyse the performance and work
load of athletes. However, these techniques can also be used for strategic analysis. There are several methods for
acquiring this information, such as video, Inertial Measurement Unit (IMU) and Radio Frequency (RF) systems.
Based on RF technology, Ultra WideBand (UWB) provides high accuracy and is the most widely used in the world
of sports. The objective of this paper is to propose a layout for an indoor localization system around the water
polo field to obtain a position error of less than 30cm. With a particular focus on the attack/defence area (6x20m
around the goal), where most of the game actions take place. This paper evaluates the UWB system by examining
the position error according to the spacing of the transmitters. It then compares two different configurations, on
the floor and above the pool. The system deployed achieves an accuracy of 29cm when the transmitters are above
the pool and 30cm when the transmitters are on the floor in 90% of cases.
Keywords
Anchors configuration, Indoor localization, Water Polo, UWB, Dynamic accuracy
1. Introduction
In sports, it is becoming essential to analyse athletes performances and strategies in order to be able to
monitor their progress and their workloads.
In aquatic sports, particularly water polo, a variety of technologies are employed to analyse the
athlete performance, such as video, Artificial Intelligence (AI) and Inertial Measurement Units (IMU).
To study the water polo game, these technologies are employed to visualise information like shots,
speed, probability of goal or movement of the human pose. For instance, IMU is used to analyse ball
throws and shots [1], AI to track the movement of the human pose by analysing visible joint points [2].
Hochstein et al. [3], use cameras to observe the area of the Voronoi-cells to determine the probability of
scoring a goal. These methods are expensive and time-consuming to implement.
Concerning strategic analysis, the automatic detection of players in the pool by computer vision is
difficult to implement due to the reflections of light on the water and the phenomenon of shadowing.
The shadowing avoids direct view of players close to each other so they are not individually detected.
There are various methods (Yoon, mixture of Gaussian models, Cb and Cr components of the YCbCr
colour model) for subtracting the water and obtaining with difficult the position of the athletes [4].
In the context of water polo, radio frequency (RF) technologies, can be an alternative to measure
players position and deduce speed, acceleration, trajectory and also for strategic analysis. In their
surveys, Zafary et al. [5] and Farahsari et al. [6] examine the performance of various RF technologies,
including Ultra WideBand (UWB), Bluetooth, Wi-Fi, Radio Frequency IDentification (RFID) and ZigBee,
for indoor positioning applications with a focus on coverage, power consumption, and accuracy. UWB
is the most accurate in terms of position estimation, with a static accuracy of approximately ten
centimetres [6].
Proceedings of the Work-in-Progress Papers at the 14th International Conference on Indoor Positioning and Indoor Navigation
(IPIN-WiP 2024)
$ florent.cotton@efrei.fr (F. Cotton); elizabeth.colin@efrei.fr (E. Colin); laurie.conteville@efrei.fr (L. Conteville);
katarzyna.wegrzyn@efrei.fr (K. Wegrzyn-Wolska)
0000-0002-5992-8124 (E. Colin); 0000-0001-9587-0751 (L. Conteville); 0000-0002-9776-3842 (K. Wegrzyn-Wolska)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Figure 1: Water polo areas
Conteville et al.[7] proposes a method to assess the performance of positioning systems in high-level
sports and test there method with a UWB system. they obtain a accuracy of 10cm in static scenarios
and 20cm in dynamic conditions. Hodder et al. [8] use UWB to measure the inter-player distance and
compared their results with the position obtained by camera. The Root Mean Square Error (RMSE) is
20 +/- 5cm. Santoro et al. [9] present static and dynamic using UWB, in static they obtain an error
position lower than 20cm and in dynamic tests the error can reach 55cm. Alasiry et al. [10] make static
monitoring in basket using UWB and obtain an average position error in the Line Of Sight (LOS) of
16cm.
In the literature, UWB is the most relevant and widely used RF system for sports applications due to
its static position accuracy of about 10cm [6]. UWB technology has already demonstrated its efficiency
for sports applications, but not yet in water polo. This is a challenging application because in addition
to the effects of multi-paths, typical of indoor environments, there are the effects of water. The aims of
this study is to determine feasibility of UWB technology to obtain a dynamic position in a swimming
pool and the optimal configuration of an UWB system for locating polo players in a specific area namely
attack/defense, in order to guarantee a position error less than 30cm. Polo player analysis is done for
performance analysis and strategic analysis purposes.
The paper is organized into five sections. Firstly, we describe in section 2 the water polo playing
areas and the UWB technology used to measure the position. In section 3, we study the static accuracy
of the deployed UWB system. In section 4, we compare the accuracy of dynamic measurements for two
different device configurations based on the observations made in section 3. Finally, we conclude with
a discussion and give perspectives in section 5.
2. Environment and Measurement Equipment
2.1. Water Polo Environment and Playing Areas
Water polo is an aquatic sport played in an Olympic-sized pool (50x25m), where two teams of seven
players compete (six players and a goalkeeper). The experiments presented were carried out in an
Olympic indoor swimming pool of the Institut National du Sport, de l’Expertise et de la Performance
(INSEP).The dimensions of the water polo field are 30x20m for men and 25x20m for women. We consider
in this study the largest area. There are three playing zones described in Figure 1 :
• Transition area : Located in the center of the field between the 6 meter lines, with a length of
18m long. Players cross this area to reach the cages.
• The attack/defense area : Located between the goal and the 6 meter area. It is an area of high
concentration because players from both teams (6 attackers and 6 defenders) can be there.
• The 2 meter area : this zone surrounds the goal and only attackers with a ball can be there.
Table 1
Data samples
Perimeter [m*m] 2x26 4x26 6x26 8x26 10x26 12x26 14x26 16x26 18x26 20x26 22x26
Number of acquired data 202 194 147 157 167 186 173 144 165 160 158
Percentage of valid data [%] 100 100 100 100 100 99.46 91.33 0 90.10 0 1.90
Mean acquisition time [ms] 148 152 204 191 179 161 170 207 180 184 189
Figure 2: Experimental layout in static
2.2. Measurement Equipment
In this paper, the experiments are carried out using UWB modules, specifically the development kit
MDEK1001 from Qorvo/Decawave [11]. The UWB modules can be configured to behave as an "anchor"
for fixed nodes or as a "tag" for mobile nodes within the system.
Distance estimation is based on Time of Flight (ToF) measurement thanks to the Two-Way-Ranging
(TWR) technique. The position of the tag is calculated by multilateration.
During the experiment, four anchors and one tag were employed, to find the best position for the
minimum number of anchors, thereby limiting the size of the system and overall cost. The anchors
are placed at a height of one metre around the pool. The tag acquisition frequency is set to 10Hz. For
static experiments, the tag is placed on a plexiglas and plastic tripod, 30cm above the water surface. For
dynamic experiments, the tag is placed under a swimmer’s cap to obtain the position of the player. When
the tag is under water, the signal is absorbed by the water. The UWB system operates at frequencies
from 6 GHz to 10 GHz, at this frequencies, signals are strongly attenuated.
3. Protocol and Evaluation of the System for Static Measurements
This study aims to determine the distance between the anchors to guarantee a low position error (<
30cm). This precision is required specially in the attack/defense area. In this section we consider static
measurements.
3.1. Experimental Protocol
In this test four anchors are deployed and one tag is used as shown in Figure 2. The goal is to measure
the tag position error when widening the perimeter made by the four anchors, which is the minimum
numbers of anchors for positioning. The anchors are placed 50cm from the edge of the pool, thus,
the initial surface is 2x26m. The anchors are moved apart by steps of 1m until they no longer receive
the transmitted signal. The tag is placed in the center of the pool (25m, 12.5m) which corresponds to
the center of the surface circumscribed by the anchors. The acquisition frequency is 10Hz and the
acquisition time is 30s.
Figure 3: Position error relative to the tag-anchor distance
Figure 4: CDF of position error relative to tag-anchor distance
3.2. Static Results and Analysis
On the one hand, Figure 3 shows the mean position error vs. the tag-anchor distance. The biggest area
(before loosing the tag signal) is 22x26m and the tag-anchor distance is 17.03m.
On the other hand, the maximum perimeter with an average error below 30cm is 14x26m, this
corresponds to a tag-anchor distance of 14.76m, reaching an error of 29.2cm with a standard deviation
of 10.9cm. Finally, the lowest mean error is 3.9cm with a standard deviation of 2.5cm for a tag-anchor
distance of 13.34m (perimeter of 6x26m).
The Cumulative Distribution Function (CDF) of the position error in Figure 4 indicates that for a
tag-anchor distance of 13.34m (6x26m perimeter), the error is less than 7cm at 90%. Whereas for a
tag-anchor distance of 14.76m (14x26m perimeter) the error is less than 43cm at 90%. Since the perimeter
of 14x26m (tag-anchor distance : 14.76m) has an error greater than 30cm, we consider the perimeter
of 12x26m (tag-anchor distance : 14.32m) the maximum perimeter with an error bellow 30cm. The
perimeter made by a tag-anchor distance of 14.32m (perimeter - 12x26m) has an error less than 20cm at
90%.
The results obtained in Table 1 show that the acquisition time varies and fluctuates between 148ms
and 207ms. The table also shows the percentage of valid data. This information corresponds to the
number of positions received minus the “NaN” (Not a Number) positions when the tag receives signals
from less than three anchors. Up to a perimeter of 10x26m (tag-anchor distance 13.93m), all data
received is valid. Once the perimeter exceeds 12x26m, the valid data decreases up to 1.90% for perimeter
22x26m (tag-anchor distance 17.03m).
Static analysis of the position error according to the the tag-anchor distance shows the best accuracy
for perimeter 6x26m (tag-anchor distance 13.34m) with an average error of 3.9cm. Under these conditions,
the CDF at 90% is less than 7cm error. In addition, all the data acquired within this perimeter is valid.
This area corresponds to the dimensions of the “attack/defense area”, which needs the higher accuracy.
Figure 5: Experimental protocol diagram for dynamic tests
Table 2
Mean error and standard deviation along the trajectory with the "anchors on the ground" configuration
Segment 1 2 3 4
Mean Error [cm] 10 19 20 19
Standard deviation [cm] 5 15 22 15
Static position error analysis needs to be completed with a study in dynamic conditions to characterize
the UWB system in the context of water polo.
4. Dynamic Validation of Anchors’ Position
The attack/defense area is an area of high concentration of players due to the presence of 6 attackers
and 6 defenders. The presence of a high number of athletes in a narrow area could bring an error in the
system’s accuracy. This is due in particular to the shadowing between players. In order to validate the
perimeter of 6x26m to obtain a position accuracy better than 30cm and minimise the errors that can
occur due to shadowing, we test in dynamic situations two layouts of the anchors : around the pool on
the ground and above the pool. This second layout avoids shadowing and have direct paths between
the anchors and the tag.
4.1. Dynamic Experimental Protocol
For these tests, the tag is placed under a swimmer’s cap. The trajectory followed by the swimmer is
marked out at the bottom of the pool. It delimits a 5x17.5m rectangle inside the perimeter made by the
anchors. The dynamic evaluation of the device is carried out by calculating the position error relative
to the trajectory. As shown in Figure 5 , in the first test, four anchors are placed on the ground around
the pool with tripods at 1m height. Anchors make a rectangle of 6x26m. And in the second test, four
anchors are hung to a cable above the attack/defense area at a height of 3m and make a rectangle of
6x20m.
4.2. Results and Analysis of a Test with Anchors on the Ground (6x26m)
The positions measured along the trajectory are shown in Figure 6 and results can be broken down into
four segments, each corresponding to one side of the trajectory perimeter. We observe that there are
fewer measurements along segment 3 than for the other segments, only 27 positions versus 117 for
the first segment, 208 for the second and 167 for the fourth. Moreover, the perimeter trajectory is well
followed except at 17m along segment 2. The results presented in the Table 2 show that the mean error
with respect to the trajectory is 10cm to 20cm, with a standard deviation of 5cm and 22cm respectively.
In Figure 7 the CDF at 90% shows that the error is bellow 16cm, 19cm, 30cm and 33cm for segments
1, 2, 3 and 4 respectively. The accuracy error of segments 1, 2, 3 is compliant to the required precision.
Figure 6: Dynamic position measurements along the trajectory for "anchors on the ground" configuration
Figure 7: CDF of position errors along the the trajectory with the "anchors on the ground" configuration
Figure 8: Global CDF for the "anchors on the ground" configuration
The accuracy error of the segment 4 is 3cm higher than the required precision. This first observation
shows us that it is possible to obtain an error of less than 30cm in dynamics and in a swimming pool.
The global CDF is the CDF of all errors along the four segments. The global CDF at 90% is 31cm, see
Figure 8.
The time acquisition of this dynamic experiment is 101s and the acquisition frequency is 10Hz. The
Table 3 shows that 783 samples are acquired instead of 1010. In addition, 66.28% of these data are valid.
The average acquisition time is 0.13s instead of 0.1s.
4.3. Results and Analysis of a Test with Anchors Above the Pool (6x20m)
The positions measured along the trajectory marked out at the bottom of the pool are shown in Figure
9. We observe that there are less measurements between 2m and 8m on segment 4 than on the other
segments. We also observe that the trajectory is well followed except in the middle of the segment
Table 3
Data samples for "anchors on the ground" configuration
anchors on the ground (6x26m perimeter)
Number of acquired samples 783
Percentage of valid data [%] 66.28
Mean acquisition time [ms] 133
Figure 9: Dynamic position measurements along the trajectory with "anchors above the pool" configuration
Figure 10: CDF of position errors along the trajectory with "anchors above the pool" configuration
4 where we observe that measurements can be offset by up to 4m on the X axis. Indeed, according
to the values presented in the Table 4, the maximum mean error with respect to the trajectory of the
segment 4 is 35cm, and the standard deviation is 53cm. The accuracy of the other three segments with
respect to the trajectory is better, the mean error is 7cm to 15cm and the standard deviation 7cm and
5cm respectively. Looking at the fourth segments, we can conclude that the errors in the centre of
segment 4 are due to the environment and can only occur there.
Table 4
Mean error and standard deviation for "anchors above the pool" configuration
Segment 1 2 3 4
Mean Error [cm] 7 11 15 35
Standard deviation [cm] 7 6 5 53
Figure 10 shows that the CDF at 90% is 18cm, 19cm, 24cm and 70cm for segments 1, 2, 3 and 4
respectively. The accuracy error of segments 1, 2, 3 is compliant to the required precision unlike the
accuracy error of the segment 4. This result are due to the aberrant measurements observed in the
middle of this trajectory see Figure 9.
However, the global CDF at 90%, which is the CDF of errors across the four segments is 29cm, see
Figure 11: Global CDF for "anchors above the pool" configuration
Table 5
Data samples for "anchors above the pool" configuration
Anchors above the pool (6x20m perimeter)
Number of acquired samples 1418
Percentage of valid data [%] 88.58
Mean acquisition time [ms] 129
Table 6
Table of comparison for the anchor on the ground and above the pool
Anchors perimeter [m] 6x26 6x20
Mean acquisition time [ms] 133 129
Percentage of valid data [%] 66.28 88.58
Global mean error [cm] 17 17
Global error in 90% of cases [cm] 31 29
Figure 11.
To complete the analysis of the dynamic measurements obtained for the perimeter 6x20m above the
pool, the time acquisition is 180.2s and the acquisition frequency is 10Hz. The Table 5 shows that 1418
samples are acquired instead of 1802. In addition, 88.58% of these data are valid. The average acquisition
time is 0.129s instead of the 0.1s.
4.4. Validation and Comparison of Results for Anchors on the Ground and Above the
Pool
In this section we compare the results obtained with the two anchors layout : on the ground at 1m
height and above the pool hung at 3m height with perimeters 6x26m and 6x20m respectively, to define
the more efficient layout in the attack/defense area. We compare the results in terms of mean acquisition
time, percentage of valid data and positioning accuracy.
Even if the four values (acquisition time, valid data, mean error and CDF at 90%) are almost the
same for the both perimeters, Table 6 shows that there are no significant differences between the two
configurations, except in terms of the percentage of valid data : 88.58% and 66.28% for the 6x20m and
6x26m perimeter respectively. The analysis of the global CDF at 90% shows a good accuracy in : 29cm
for the 6x20m perimeter and 31cm for the 6x26m perimeter. The configuration above the pool offers no
degradation in accuracy, and has the advantage of avoiding possible shadowing between the players.
Conclusion
The attack/defense zone (6x20m) is the area requiring the highest localization accuracy (error <30cm).
In order to achieve this level of accuracy, a study of the anchors’ layout is required. An initial study in
static conditions determines the width of the perimeter made by the minimum number of anchors, i.e.
four anchors. For a perimeter of 6x26m, we obtained a mean error of 3.9cm and a standard deviation of
2.5cm. A study in dynamic conditions allows us to validate the deployment of the system established
statically and prevent shadowing by observing two configurations: around and above the pool (6x26m
and 6x20m). The position error relative to the trajectory varies between 7cm and 35cm, with respective
standard deviations of 7cm and 53cm for a 6x20m deployment above the attack/defense area. This
position error gives an overall mean error of 17cm, with 90% of cases having an error of less than 29cm.
The results show that there is no significant differences between the two configurations. Furthermore,
this study confirms the usability of UWB technology for measurements in water polo, with particular
relevance for dynamic measurements, and provides good accuracy for applications in elite sports.
In future works we will improve accuracy by filtering and carrying out measurements in water polo
game conditions (with 12 players) to observe the impact of a large number of tags on the system’s
accuracy.
Acknowledgments
We are grateful to Mathias MERCADAL and Robin PLA, from French Swimming Federation, for their
water polo advices, and to Magali RATIER and the INSEP for giving us the opportunity to make our
measurements in the Olympic pool.
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