=Paper=
{{Paper
|id=Vol-2498/short22
|storemode=property
|title=A comparison of human body wearable sensor positions for UWB-based indoor localization
|pdfUrl=https://ceur-ws.org/Vol-2498/short22.pdf
|volume=Vol-2498
|authors=Timothy Otim,Luis Díez,Alfonso Bahillo,Peio Lopez-Iturri,Francisco Falcone
|dblpUrl=https://dblp.org/rec/conf/ipin/OtimDBLF19
}}
==A comparison of human body wearable sensor positions for UWB-based indoor localization==
https://ceur-ws.org/Vol-2498/short22.pdf
A Comparison of Human Body Wearable Sensor Positions
for UWB-based Indoor Localization
Timothy Otim1 , Luis E. Díez1 , Alfonso Bahillo1 , Peio Lopez-Iturri2 , and Francisco Falcone2
Faculty of Engineering, University of Deusto, Avda. Universidades 24, 48007, Bilbao, Spain
1
{otim.timothy, luis.enrique.diez, alfonso.bahillo}@deusto.es
2
Department of Electric, Electronic and Communication Engineering, Public University of Navarra,
Campus de Arrosadia, 31006, Pamplona, Spain
{peio.lopez, francisco.falcone}@unavarra.es
Abstract. Over the years, several UWB localization systems have already been proposed
for accurate position estimation of pedestrians. Most of these systems already proposed
for pedestrian localization have only been individually evaluated for a particular wearable
sensor position. In this paper, we compare the positioning performance of seven body
wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, fore-head, and hand using
an Extended Kalman Filter (EKF) based localization algorithm in an indoor environment.
The conclusion drawn is that the fore-head is the best, and the chest is the worst body
sensor location for tracking a pedestrian. While the fore-head position is able to set an
error lower than 0.35 m (90th percentile), the chest is able to set 4 m. The fore-head’s
performance is followed by the hand, ankle, wrist, thigh, arm, and chest in that order.
Keywords: UWB · Localization · Body Wearable Sensors · Human body shadowing.
1 Introduction
Wearable connected devices hold a great deal of promise, and therefore their market is expected
to reach $ 70 billion in 2025 [1]. The major sectors in this market are consumer electronics,
defences, and healthcare. Some of the popular wearable sensors that are widely used include:
gyroscope and accelerometer sensors for navigation purposes and proximity sensors to detect if a
defined subject or obstacle is present nearby. Therefore, one of the key application of wearables
is in tracking of pedestrians [2].
Indoor localization of pedestrians is still an open problem though many different approaches
have been proposed using diverse technologies to obtain a similar performance to the Global
Navigation Satelite System (GNSS) outdoors. For instance, Wi-Fi, Ultrawideband (UWB), or
Ultrasound, and advanced processing techniques, such as Kalman and Particle Filters, have been
proposed to cope with the deterioration in performance due to combined effects of pathloss
and multipath fading. Among the most accurate localization solutions are those that rely on
ultrasound or UWB radio signals but both their performances deteriorate in non-line-of sight
(NLOS). UWB signals can achieve decimeter-level errors in indoor positioning and has gained
significant interest in research as a promising candidate for industry solutions. In recent years,
there has been a great deal of interest in its use for positioning [3–5].
However, in tracking context of pedestrians using UWB technology, an important factor which
has been often overlooked but has significant effect on the positioning error is the influence caused
by the human body itself. The effects of human body shadowing are additional propagation losses
or biases in time of flight (TOF) measurements when the body blocks the line-of-sight (LOS)
between a wearable sensor and an anchor. These effects, which generally depend on: i) body
2 T. Otim et al.
wearable sensor position, and ii) relative heading angle are currently, not adequately accounted
for and will decrease the accuracy of localization systems.
In recent years, a few works have focused on the ranging and positioning error introduced
by the human body due to the relative heading angle, leaving aside the impact of body wear-
able sensor positions [6–10]. Therefore, knowledge of a body wearable sensor position that will
provide good accuracy results is important. In this paper, we present a novel comparison of
UWB positioning error results for seven body wearable sensor locations namely, fore-head, hand,
chest, wrist, arm, thigh and ankle using an Extended Kalman Filter (EKF)-based localization
algorithm. The aforementioned body wearable positions are chosen because they are the most
popular in the market according to Vandrico database [11].
The rest of this paper is organized as follows: experimental setup and the method adopted
to collect the experimental data is described in Section II. The UWB positioning performance is
analyzed in sections III for different body wearable sensor positions. In section IV, the presenta-
tion and discussion of results is made. Finally, in the last section, we give some conclusions and
future work.
2 Experimental Setup
Throughout this paper, several TREK1000 development kits manufactured by Decawave were
used. According to [12], TREK1000 development kits are the best UWB commercial products for
ranging. The nodes are fully compliant with the IEEE 802.15.4-2011 UWB standard and make
it possible to achieve ranging measurements using two-way ranging measurements at a rate of
3.57 Hz. For the purpose of these measurement campaigns, 1 TREK1000 node was configured as
a wearable sensor and 4 nodes were configured as anchors and installed at fixed positions in the
Lab. The nodes were made to work with a 110 kb/s data rate and in the channel 2 (3990 MHz).
The experiments were carried out inside the Luis Mercader Lab at the department of Electric,
Electronic and Communication engineering at the Public University of Navarra in Spain. The
Lab had the following dimensions: 6 m wide, 13 m long, and 4 m high, and contained a number
of computers, monitors, chairs, desks, closets and working people. The floor and ceiling were
made of concrete. The floor plan of lab environment where the tests were performed is showed
in Fig. 1.
The floor plan shows detailed anchor positions and a path with 26 ground-truth points with
each point approximately 1 m from the other. There are several interfering objects such as
pieces of furniture, metallic cabinets and desktop computers. The origin of the reference system
is defined at the top left corner. The UWB anchors were mounted on tripods in the positions
indicated in Table 1.
Table 1: Coordinates of UWB Anchors, where n is the anchor identity number defined as n = 0,
1, 2, 3
Anchor (n) X (cm) Y (cm) Z (cm)
Anchor 0 1240 571 170
Anchor 1 1240 70 173
Anchor 2 548 33 172
Anchor 3 68 21 172
3
Anchor 2 Anchor 1
5 4 3 2
6
19 20 21 22
7 START 1 18 23
26 25 24
17
8 9 10 11 12 END
13 14 15 16
Anchor 3 Anchor 0
Fig. 1: Map of the room and the installation in Luis Mercader Lab. A path with 26 ground-truth
points (marked with crosses) was selected. Also shown are the 4 anchors at the corners of the
Lab.
A male subject, 1.80 m height and 77 kg mass was considered for the measurements. The
wearable sensors were mounted on the subject with the help of Velcro straps at the right-ankle,
right-thigh, fore-head, right-hand, right-arm, chest, and right- wrist as seen in Fig. 2.The heights
at which the wearable sensors were mounted are showed in Table 2.
Table 2: Height (H) in centimetres at which the wearable sensors are mounted
Ankle Thigh Fore-head Hand Arm Chest Wrist
H 15 70 177 120 130 130 90
Using the installed ground marks, firstly, the wearable sensor was mounted on a tripod and
moved along the path starting from test point 1 and ending at test point 26 [see – Fig. 1] as a
reference for further comparisons.
Similar to the work in [13], for each wearable sensor position, the subject was made to walk
the same path, and ranges were recorded continuously without stopping as the subject moved
from the start to end. At each ground-truth point, the subject stood still for approximately 10
s before moving to the next.
3 Positioning Performance
Because the main application of ranging is positioning, in this section we compare the positioning
performance. As explained in the previous section, the infrastructure of the experiments consists
of one UWB node mounted on seven body locations and four UWB nodes in fixed and known
positions acting as anchors. In order to achieve the positioning performance, a localization al-
gorithm based on the EKF is implemented. We use range measurements from the continuous
scenario. To minimise the effects of NLOS, the measured ranges with a very large error were
assumed to be under NLOS conditions. And so, they were rejected in the update of the state
vector estimation.
4 T. Otim et al.
(a) Chest (b) Arm (c) Ankle (d) Wrist (e) Thigh (f) Fore-head (g) Hand
Fig. 2: Wearable sensors mounted at different positions on the body. At the hand, the sensor is
about 20 cm from the chest since this is a usual place for texting or looking at the screen of a
smart phone when locating your position in a real world scenario.
Similar to [14] and [15], the EKF will consist of a discrete time white noise acceleration
driven model as the dynamic model and the ranges between the wearable sensor at different body
locations and the anchors as the measurements. The dynamic model is modeled as a constant
position model driven by acceleration noise instead of a constant velocity model. The position
model driven by acceleration noise is equivalent to having a constant velocity = 0. We chose this
model because the subject was still most of the time.
The state vector xk is defined by the 3-D position (p) and velocity (v) estimates as indicated
in (1) as :
T
xk|k = xk = (pk v k ) (1)
Therefore, xk is defined as :
xk|k−1 = F k xk−1|k−1 + wk (2)
where F k is the state transition matrix given as :
100
F k = 0 1 0 (3)
001
wk is the process noise, modeled as a white noise acceleration with covariance matrix Qk :
σax ∆T 2 /2
2
0 0
Qk = 0 2
σay ∆T 2 /2 0 (4)
2 2
0 0 σaz ∆T /2
where ∆T is equal to the time difference between timestamps k and k − 1 and σax = 100
cm, σay = 100 cm , and σaz = 10 cm are the uncertainty that model the acceleration driving
noise of the dynamic model in the x, y, and z directions. The state vector xk is initialized with
the coordinates of the first ground marks. Their initial standard deviations were set to 20 cm
for the x and y coordinates, and 0.5 cm for the z coordinate. These parameters were set from
preliminary measurements to characterize the UWB nodes and the motion of the subject.
The measurement model has the form:
z k = h(xk|k ) + nk (5)
where z k is the measurements vector, h is the measurement non-linear function, and nk is the
measurement noise with covariance matrix Rk .
5
The standard deviation (SD) of the measurement model was set according to Table 3. The
values were obtained from a set of measurements to characterize the UWB nodes for each wearable
sensor position.
Using UWB ranges from the 4 anchors to update the estimate of the state xk|k , the measure-
ments take on the following form:
zn,k = hn (xk|k ) =
q
= (px − ax,n )2 + (py − ay,n )2 + (pz − az,n )2 (6)
where zn,k is the measured range between the nth anchor at the position ax,n , ay,n , az,n , and
wearable sensor with current position estimates at px , py and pz .
Table 3: The uncertainty in centimetres of the measurement model for each wearable sensor
position
Ankle Thigh Fore-head Hand Arm Chest Wrist
SD 50 60 10 30 50 130 20
Because the output of the EKF filter provides a continuous estimation of the position, it
is necessary to detect when the subject feet are still so that the ground truth positions are
estimated. Therefore, we performed the following tasks for each of the body wearable positions.
1. Smoothing: where a moving average filter is applied to each position component. The length
of the window was empirically set to 4 s.
2. Filtering: in order to reduce the number of outliers in the position estimation, a moving
variance with window length of 4 s was also set.
3. To determine the ground truth position, the k-means clustering is applied to the filtered
estimates. The ground truth positions are extorted from the obtained centroids.
4 Results and Discussion
To compare the performance of the different wearable sensor positions, the absolute value of the
difference between the real ground truth positions and the values estimated by the EKF are used
as the error metric. To better see the performance results, the main statistics are presented in
the Table 4. In Table 4, we differentiate the performance results according to the sensors that
worn on the the frontal plane (fore-head, chest and hand) and the side plane (ankle, wrist, thigh,
and arm) of the user.
Looking at Table 4, the first observation is the existence of a clear relationship between the
localization performance and the body wearable sensor position. Overall, it can be observed that
the fore-head and the chest position give the best and worst possible localization performance,
respectively. In absolute terms, 90 % of the estimates were below the error of 0.35 m and 4.04 m
for the fore-head and chest, respectively.
The performance of the chest is heavily influenced by NLOS conditions due to human body
shadowing. Though all the wearable positions are influenced by human body shadowing effect,
this effect is more pronounced at the chest because the large size of the chest allows a lot more
power to get absorbed by the body, which explicitly translates in to extremely large errors.
Therefore, the chest position can be used if it is possible to install enough anchors to minimize
the risk of NLOS.
6 T. Otim et al.
Table 4: Body wearable positions with their estimation errors (in meters). P90 is the 90th per-
centile.
Body Plane Body Location Mean Median P90 SD
Fore-head 0.20 0.21 0.35 0.11
Front Hand 0.35 0.26 0.62 0.33
Chest 2.46 2.55 4.04 1.66
Ankle 0.50 0.36 0.97 0.36
Wrist 0.62 0.52 1.14 0.48
Side
Thigh 0.68 0.57 1.46 0.45
Arm 1.36 1.26 2.47 0.77
After the fore-head, the hand position obtains the second lowest errors as 90 % of the estimates
were below the error of 0.62 m. The hand performs better than the chest (though in both cases
the wearable is at the center of the chest) because the space of 20 cm [see – Fig. 2g] allows
for creeping wave propagation during NLOS. Among the wearable positions where the sensor is
placed on side plane of the user, the ankle gives the best performance as 90 % of the estimates
were below the error of 0.97 m. Following the performance of the ankle position is the wrist,
thigh, and arm locations with 90th percentile of 1.14 m, 1.46 m, 2.47 m, respectively. Similar
to the ranging performance, the differences in the position errors among these locations can be
attributed to the differences in the height and size of the limb on which the sensor is attached.
5 Conclusion
We have presented an experimental comparison of seven body wearable sensor positions available
for tracking the position of a pedestrian. The comparison has been done in an indoor environment
with LOS and NLOS conditions. Several NLOS have been created by the body of the user which
acts as an obstacle of the direct signal path between the wearable sensor and the access point.
While the performance of forehead has been superior, the performance results for the chest
position has been observed to be lower than the other wearable sensor positions.
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