An Experimental Study on a Pedestrian Tracking Device

An Experimental Study on a Pedestrian Tracking
Device
Sailesh Kumar G1
Electrical Engg. Dept.
Shiv Nadar University
Noida, 201314, India
Shyam Sundar S1
Electrical Engg. Dept.
Shiv Nadar University
Noida, 201314, India
Amit K Gupta
GT Silicon Pvt Ltd
Kanpur, 208016, India
amitg@gt-silicon.com
Peter Händel
Dept. of Signal Processing
KTH Royal Institute of Technology
Stockholm, Sweden
Abstract—The implemented navigational algorithm of an in-
ertial navigation system (INS), along with the hardware con-
figuration, decides its tracking performance. Besides, operating
conditions also influence its tracking performance. The aim of
this study is to demonstrate robust performance of a multiple
Inertial Measurement Units (IMUs) based foot-mounted INS,
The Osmium MIMU22BTP, under varying operating conditions.
The device, which performs zero-velocity-update (ZUPT) aided
navigation, is subjected to different conditions which could
potentially influence gait of its wearer, its hardware configuration
etc. The gait-influencing factors chosen for study are shoe type,
walking surface, path profile and walking speed. Besides, the
tracking performance of the device is also studied for different
number of on-board IMUs and the ambient temperature. The
tracking performance of MIMU22BTP is reported for all these
factors and benchmarked using identified performance metrics.
We observe very robust tracking performance of MIMU22BTP.
The average relative errors are less than 3 to 4% under all the
conditions, with respect to drift, distance and height, indicating
a potential for a variety of location based services based on foot
mounted inertial sensing and dead reckoning.
I. INTRODUCTION
The advancements in MEMS technology have paved the
way for low cost sensors which can be integrated into an
Inertial Navigation System (INS). These sensors are also less
power craving and occupy far lesser size, than before. INSs
are self-contained systems that can operate in harsh radio
environments without pre-installed infrastructure and prior
knowledge of the environment. This opens up applications in
emergency response situations where the system has to be self-
sufficient at least for a short period of time [1], [2], [3].
Conventional INSs calculate the change in position through
dead reckoning. In pedestrian navigation with foot-mounted
INSs, this means measuring the length of the steps and
the direction of the pedestrian given the initial position and
heading is known. They calculate double integration of ac-
celeration measurements and single integration of gyroscope
measurements which are noisy and therefore suffer from huge
accumulation of position and heading errors with time. This
1Sailesh Kumar G and Shyam Sundar S carried out the presented work as
part of their internship program at GT Silicon Pvt Ltd, Kanpur.
Fig. 1: The Osmium MIMU22BTP : A foot-mounted INS for ZUPT-aided
pedestrian navigation. Operating on a rechargeable Li-ion battery, it is capable
of transmitting data over Bluetooth and USB interfaces, both. The device
utilizes multi-IMU (MIMU) approach which enables data fusion from on-
board redundant inertial sensors and hence improves the tracking performance.
is because the new estimates of position and heading rely on
previous estimates of the same [1].
Zero-velocity-update (ZUPT) is one of the popular methods
in literature to minimise the errors encountered in position and
heading estimates for foot-mounted INSs. There is a standstill
phase in walking, when the foot comes in complete contact
with ground. The instantaneous velocity of the foot, for that
moment becomes zero. This essentially means that the foot-
mounted sensor is stationary at standstill position and therefore
any non-zero estimated velocity or rotation is interpreted as
error and is used to reset the estimated velocity to zero
and correct the system's internal errors. ZUPT allows the
foot-mounted INSs to correct errors at every step occurrence
(typically every second with normal gait). Without ZUPT the
drift error would have grown cubic with time. It increases
the accuracy of conventional INS thus making it suitable
for a large number of promising applications in the area of
foot-mounted indoor navigation, for example tracking in GPS
denied environments [4], [5].
Tracking performance of a foot-mounted pedestrian navi-
gation device depends upon number of factors which could
influence the way hardware and the embedded algorithm
operate. Some of the factors which could influence the hard-
ware operation are different number of on-board IMUs and
the ambiance temperature. Similarly the operating conditions
Copyright ©IEEE2015

which could influence the ZUPT based navigational algorithm,
are shoes, wearers of the device, walking surface, walking
speed and the path profile. These factors typically influence
gait of a person and hence play important role in step detection
for a ZUPT-aided INS.
In this paper, we present an experimental study on the
ZUPT-aided foot-mounted pedestrian navigation device Os-
mium MIMU22BTP, shown in Fig. 1, to demonstrate influence
of various factors on its performance.
This paper is organized into the following sections. Section
I presents brief introduction to the device under test. Section II
outlines the experimental design and the performance metrics
which are used for benchmarking the performance of the
device. Experimental results are present in Section III. In
Section IV conclusion of the study is outlined.
II. DEVICE UNDER TEST
The Osmium MIMU22BTP is a foot-mounted navigation
device which implements ZUPT-aided inertial navigation al-
gorithm [6]. Osmium MIMU22BTP is based on an open-
source platform OpenShoe, first presented in [7] – a de-
vice initially targeting cooperative localization by dual foot-
mounted inertial sensors and inter-agent radio-frequency based
ranging [8], [9]. The recent version presented in [6] contains
multiple IMUs each of which consists of MEMS sensors
accelerometers, gyroscopes and magnetometers. This “wisdom
of the crowd approach” not only reduces the errors by using
multiple inertial sensors but also provides sensor redundancy.
In simple words, MIMU22BTP detects step of its wearer,
computes relative coordinates and heading of each detected
step with respect to the previous one and transmit it over
Bluetooth interface to the application platform for construction
of the tracked path. Presence of IMU arrays in MIMU22BTP
enables advanced motion sensing by using sensor fusion
and array signal processing methods. The presence of on-
board microcontroller with floating point processing capability
simplifies the output data interface and hence very low rate of
transmission (∼ 1Hz) is achieved [6]. The device is supported
by open source embedded code in C which implements ZUPT-
aided navigation.
The accelerometers in these systems measure the linear
acceleration when the system-in-motion is subjected to a force.
The gyroscopes measure the angular rotation of the system
in terms of roll, pitch and yaw. Since these devices can
measure in a particular orthogonal axis of motion, there are
three accelerometers and three gyroscopes in a single IMU
to measure acceleration and angular velocity respectively in
all the three axes. Magnetometer is not used for navigation in
Osmium MIMU22BT because the device is targeted towards
indoor navigation and the presence of ferrous objects in the
tracking path such as the computers, electric wires around
the building may cause magnetic interference. Calibration is
required to be performed to compensate the errors which
occur due to the fabrication process. Calibration under static
conditions is carried out by placing the device inside a
twenty faced polyhedron (icosahedron) different orientations.
Fig. 2: The foot-mounted Osmium MIMU22BTP. The experiments are
performed with a single device mounted on the shoe front.
Fig. 3: The data recording Android application DaRe. MIMU22BTP com-
municates with DaRe via Bluetooth. DaRe constructs the estimated data path
and records step coordinates, step number, time stamp of each step and other
useful information.
Inter-IMU misalignment, gain, bias and sensitivity axis non-
orthogonality of the accelerometers are then estimated by
placing the icosahedron in different positions [10].
The MIMU22BTP comes equipped with four 9-axis IMUs,
32-bits floating point microcontroller, Bluetooth and USB con-
nector for data communication and an on-board Li-ion battery
power management circuitry. This configuration makes it a
robust embedded system for possible wearable applications,
tracking and motion detection needs. The tracking device
is also equipped with an on-board pressure sensor, a flash
memory and JTAG programming capability. The device can
be attached to the shoe as shown in Fig. 2 and with the
help of an application on a processing platform, the user can
find coordinates of the estimated path along with the distance
covered. Android based data recording application DaRe is
one such application which receives pedestrian dead reckoning
data via Bluetooth and constructs the estimated path as shown
in Fig. 3. Prior to mounting, the device is switched on and
connected to DaRe via Bluetooth. When the step is detected,
i.e when the foot-mounted device experiences zero velocity,

TABLE I: Summary of the test tracks. Three different tracks are used for
experiments.
Rectangular Field Straight Path 1 Straight Path 2
Perimeter: 129 m Length: 26 m Length: 100 m
Closed loop path Sharp 180° turns Sharp 180° turn
3 laps 2 laps 1 lap
Total distance: 387m Total distance: 104 m Total Distance: 200m
Fig. 4: Bird eye view of the rectangular (left) and straight (right) tracks.
Arrows show the walking directions.
the dead reckoning updates are sent from the device to DaRe.
III. EXPERIMENTS
A. Design of experiment
The experiments were carried out using a marked track in
the path profiles given in Tab. I. Pictures of the test sites
are shown in Figs. 4-5. The mounting point on the foot (or
shoe) was carefully chosen so that the device was firmly
mounted on the shoe. The device was allowed to reach a steady
temperature before the tests were carried out by allowing a
warm-up time of two to three minutes. The device wearer's gait
and certain conditions potentially affect the path estimation.
Therefore these variables have been chosen in the analysis
of the device. The device wearer's gait is affected by the
type of shoe, path profile, walking speed, walking surface
etc. The other conditions refer to varying temperatures and
varying number of on-board IMUs. Achieving right mounting
is an iterative process. The experiments were performed with
the device mounted on shoe front as shown in Fig. 2. For
every experiment, single device was attached to wearer's foot.
GPS, knowledge of the environment and any other kind of
pre-installed infrastructure were not used for navigation.
Details of the experiments conducted under different con-
ditions are presented in Tab. II and III with the total elapsed
distance and average speeds for each set of experiment.
B. Performance metrics
The performance metrics used to benchmark the perfor-
mance of the device are as follows: The drift error (Drifterror):
Drifterror =
1
N
N
i=1
xi,start − xi,end
2
+ yi,start − yi,end
2
di,act
(1)
where xi,start and yi,start are the estimated ith start point in
x axis and y axis of the user's reference frame, respectively.
Fig. 5: Satellite view of the rectangular test site. Path traversed is shown by
dots. Arrows show the walking direction.
TABLE II: Details of the tests conducted with varying operating conditions
which could influence gait.
S. No. Gait Influencing Factors
Experimental Details
Total
Dis-
tance
(km)
Average
Speed
(kmph)
1 Shoe and User
Running Shoe
(User#1)
7,27 4.41
Formal Shoe
(User#2)
7.97 4.51
2 Surface
Pavement 8.70 5.43
Grass 6.54 4.58
3 Speed
Slow 4.60 3.51
Medium 5.56 4.52
Fast 5.07 5.81
4 Path profile
Rectangular 8.00 4.16
Straight 7.13 4.28
Similarly, xi,stop and yi,stop are the estimated ith stop point
in x axis and y axis, respectively. The total number of test
cases is denoted by N and di,act denotes the actual distance
covered in ith test case. Drifterror in percentage provides the
magnitude of displacement between estimated start and stop
points, which are coinciding in reality, per 100 m distance
covered.
The distance error (Distanceerror):
Distanceerror =
1
N
N
i=1
di,meas − di,act
di,act
2
(2)
where di,est denotes the distance estimated by the Osmium
MIMU22BTP in ith test case. Distanceerror in percentage
provides the root-mean-square of distance estimation error per
100 m distance covered.
The height error (Heighterror):
Heighterror =
1
N
N
i=1
zi,start − zi,end
di,act
2
(3)
where zi,start and zi,end are the estimated ith start and end
points in the z axis respectively. Heighterror in percentage
provides the root-mean-square height estimation error per 100
m distance covered.
The experiments were conducted on plane surfaces, for all
the path profiles. Therefore only x-y coordinates are consid-
ered in computing distance and drift errors.

TABLE III: Details of the tests conducted with varying factors which could
influence hardware performance.
S. No. External Factors
Experimental Details
Total
Dis-
tance
(km)
Average
Speed
(kmph)
1 Temperature
25.7°C-34.0°C 21.20 4.67
34.1°C-37.7°C 17.90 4.66
2 Number of IMUs
4 2.00 4.45
2 2.97 4.59
1 3.00 4.56
Fig. 6: Rectangular path as estimated by MIMU22BTP. Dots correspond
to the detected steps. The coordinate axes indicate the distance covered in
meters.
IV. RESULTS & DISCUSSION
A. Experimental results
The quality of data depends on the mounting of the device
on the shoe. When the output data from the device is plotted,
the estimated rectangle and straight paths are observed as
shown in Figs. 6-7. Manual mounting of the device resulted
in the slight misalignment of the plotted path with the global
x-y axes.
The results with respect to shoe-type are presented in Tab.
IV, type of walking surface in Tab. V, walking speed in Tab.
VI, path profile in Tab. VII, number of enabled IMUs in the
MIMU22BTP in Tab. IX and ambient temperature in Tab. VIII.
TABLE IV: Performance versus shoe-type.
Performance Metric Formal Running
Drift Error (%) 0.98 1.20
Distance Error (%) 1.61 1.69
Height Error (%) 1.61 3.39
TABLE V: Performance versus type of walking surface.
Performance Metric Pavement Grass
Fig. 7: Straight path as estimated by MIMU22BTP. Dots correspond to the
detected steps. The coordinate axes indicate the distance covered in meters.
TABLE VI: Performance versus walking speed.
Performance Metric Slow Medium Fast
Drift Error (%) 1.16 1.09 1.01
Distance Error (%) 1.06 0.83 2.53
Height Error (%) 2.26 1.65 4.61
TABLE VII: Performance versus path profile.
Performance Metric Rectangle Straight
TABLE VIII: Performance versus ambient temperature.
Performance Metric 25.7°C-34°C 34.1°C-37.7°C
TABLE IX: Performance versus number of enabled IMUs.
Performance Metric 4 IMUs 2 IMUs 1 IMU
Drift Error (%) 1.30 2.04 2.05
Distance Error (%) 0.84 1.85 1.51
Height Error (%) 1.76 1.82 1.54
B. Discussion
For all the experiments, we have observed that the drift and
distance errors are within 3%. Under all the conditions, errors
in height estimation are higher as compared to the errors (drift
and distance) in walking plane x-y. This is due to the reason
that fabrication process is more controlled for the sensors used
for x and y motion than for z motion. The height error and drift
error are almost independent of each other, with a coefficient
of correlation around 0.06.
From the results in Tab. IV, one may note that a formal
shoe performs better tracking compared to a running shoe.

This can be explained by the fact that running shoes have
flexible structure and are elastic in nature, which will influence
the inertial navigation device’s ability to detect standstill.
Conversely, formal shoes have rigid structure which makes
it easier for the device to detect standstill events.
Walking surface with grass gives somewhat better tracking
performance than pavement with respect to drift and distance
measurement as indicated in Tab. V. Walk on pavement turns
out to be somewhat better in terms of height error.
In the ZUPT-aided device, errors are corrected only when
a step is detected. Since the velocity thresholds are optimized
for giving best performance for a normal walking speed (that
is, around 4 - 5 kmph), certain steps go undetected by the
device at higher speed. In spite of that, the drift error is
almost the same for all walking speeds, as indicated by the
results presented in Tab. VI. If the MIMU22BTP is mounted
properly onto the shoe, it delivers high quality data upto 5
kmph. Though data starts deteriorating beyond that, it should
be fine for tracking upto 5.5 to 6 kmph, as demonstrated.
Walk on the rectangular path has resulted in somewhat better
tracking performance than on straight line walk as indicated
in Tab. VII. The 100 m straight line walk consisted of sharp
u-turns, whereas rectangular path consisted of round edges.
Another interesting observation is seen when experiments
were conducted at different ambient temperatures (See Table
VIII). The scale 34.1°C-37.7°C indicates data collected during
afternoon of the April month (at Kanpur, India) for which the
drift and height error are higher, though within acceptable
limits, compared to the 25.7°C-34.0°C scale which repre-
sents data collected in early morning, forenoon and evening.
Tracking performance deteriorates a bit at higher ambient
temperature.
A clear difference in performance is seen when the number
of IMUs are reduced from four to two or one (See Table
IX). Even though, the height error is comparable for all the
three cases, the distance and drift errors are higher in case
the number of IMUs is two or one. This demonstrates trade-
off in performance for reduced cost as the number of IMUs
are reduced. This maybe interesting to note that number of
IMUs are changed without making any change in the algorithm
and without changing any important parameters which were
initially fine tuned for four IMUs. We hope to achieve results
somewhat better than reported, by fine tuning the parameters
for one and two IMUs configuration.
V. CONCLUSION
The Osmium MIMU22BTP was tested under various con-
ditions (type of shoe, walking surface, walking speed, path
profile, ambient temperature and number of on-board inertial
sensors) which influence the tracking performance of a foot-
mounted inertial navigation device. Errors in drift, distance
and height measurement were chosen to benchmark the per-
formance. Experiments were conducted on plane surfaces.
For every experiment, single device was attached to wearer's
foot. GPS, environmental information and any other kind pre-
installed infrastructure were not used for tracking.
Results obtained from the experiments ascertain the robust
performance of the navigation device under different operating
conditions. Very small variation in errors is observed for all
the considered cases. Drift and distance errors are always
within 3% irrespective of type of shoe, nature of walking
surface, wearer's walking speed, type of path and ambient
temperature. Whereas height error is within 4% for most of the
cases. This means that Osmium MIMU22BTP is capable of
delivering more than 96% tracking accuracy. There is hardly
any correlation observed between drift and height errors.
This is also experimentally demonstrated that the tracking
performance improves by increasing the number of on-board
inertial sensors which is a highlighting feature of Osmium
MIMU22BTP.
In very simple words, one may infer from the presented
experimental study that the multiple-IMU based foot-mounted
navigation device Osmium MIMU22BTP is capable of locat-
ing a pedestrian who has walked for 100 m on a plane surface,
in a circle of radius 3 m. This performance expectation is
without any aid of GPS data, environmental information or
any other pre-installed infrastructure.
VI. ACKNOWLEDGEMENT
The authors acknowledge GT Silicon Pvt Ltd for providing
logistical support to carry out the study. They also acknowl-
edge Swedish Governmental Agency for Innovation Systems
for supporting work of Peter Händel.
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