doc

1. Pedestrian Dead Reckoning for Mobile Phones through Walking and
Running Mode Recognition
Noriaki Kakiuchi1 and Shunsuke Kamijo1
Abstract— In this paper, we propose a novel model of stride
length estimation for pedestrian dead reckoning (PDR) that
allows a PDR system to switch its estimation method according
to whether the pedestrian is walking or running. Then, we
study the application of the mode switching to a PDR/GPS/map-
matching integrated positioning system for mobile phones. The
experimental results show that this mode switching makes
stride length estimation more adaptive, and improves the total
accuracy of positioning.
I. INTRODUCTION
Inertial sensor-based pedestrian dead reckoning (PDR)
is an attractive positioning method for pedestrians. The
primary means of positioning is global positioning system
(GPS) receivers, but the resulting positioning accuracy is
very significantly degraded in urban canyon environments.
To provide a positioning solution in these environments,
many approaches for PDR have been studied. Most of
them, however, assume the pedestrian walks at a normal
pace and do not support a running motion. Therefore, we
consider applying differing activity modes, that is, whether
the pedestrian is walking, running, or stationary for PDR. In
everyday life, it is often the case that one casually begins to
run on a street when a pedestrian crossing signal is turning
red or one is about to miss a train. In this paper, we propose
a novel stride length estimation model that is applicable to
the running mode, because when the pedestrian is running
instead of walking the conventional model no longer adapts
well.
PDR systems have been developed that use dedicated
sensor units mounted on the body (e.g., on a waistband
[1], shoe [2], or helmet [3]). These approaches rely on an
assumption about the sensor’s orientation and/or a calibration
technique called zero velocity update. In [4] and [5], PDR
methods were proposed that use a mobile device that is
carried in the pedestrian’s trouser pocket or hand, respec-
tively, and therefore, has no fixed orientation. Since most
smartphones and other recent mobile devices are equipped
with various sensors, PDR can now be deployed to these
everyday devices. Our PDR method also uses a mobile device
placed freely in the user’s trouser front pocket, where people
most commonly carry their mobile phones (especially males)
[6]. In other words, our method does not depend on a priori
knowledge of the device’s orientation. From the viewpoint
of user-acceptable usage, it is not realistic to require users to
keep their phones in a rigidly fixed position and orientation.
1N. Kakiuchi and S. Kamijo are with the Institute of Industrial
Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo,
Japan kakiuchi at kmj.iis.u-tokyo.ac.jp, kamijo
at iis.u-tokyo.ac.jp
In terms of absolute positioning, GPS or other positioning
means must be combined with PDR, because PDR needs
one reference point as a starting point, and additional points
for parameter calibration. Wi-Fi or RFID-based positioning
is a widely accepted approach that takes advantage of the
communication modules of mobile phones [7], [8]. However,
in practice, the use of these techniques is limited in restricted
areas or indoor situations. They require a number of Wi-Fi
access points or RFID tags with the known absolute location,
or a fingerprinting database of existing ones. For positioning
that does not require this additional infrastructure, [9] and
[10] proposed PDR/GPS integrated methods based on a
Kalman filter, the results of which are reasonably accurate.
In addition, map matching of a pedestrian trajectory is also
a popular approach for calibrated positioning systems. The
matching methods applied in PDR vary according to the type
of map used. Some maps describe “walls” through which
pedestrians cannot pass, and other maps describe “paths”
along which pedestrians are supposed to move. In [11],
building plan information and particle filter-based indoor
PDR were combined, thus, eliminating particles trying to
move across walls. In [8], a dynamic time warping algorithm
was applied to map matching between pedestrian path maps
and outdoor PDR results.
In this paper, we also describe an outdoor positioning
system where GPS data and map matching are integrated
with PDR. We show that the mode switching mechanism
in PDR improves the accuracy of the integrated positioning
system.
II. PROPOSED MODEL
In this section, we describe the proposed stride length
model in detail. First, we offer a hypothetical equation of
stride length estimation, and then we verify the equation and
determine the parameter of the equation empirically.
In PDR algorithms, the motion of a human body is
modeled, and its positions are estimated incrementally from a
given starting point. For inertial sensor-based PDR, methods
based on step detection are standard, because simple double
integration of acceleration produces a rapid error accumu-
lation due to sensor drifts. Thus, PDR needs a method to
determine the stride length, because inertial sensors cannot
directly observe it. Stride length varies from person to
person, and even an individual takes different stride lengths
depending on his/her speed and other factors. In studies on
PDR, to date various models have been built that describe
the relation between stride length and other observable
quantities.
Proceedings of the 16th International IEEE Annual Conference on
Intelligent Transportation Systems (ITSC 2013), The Hague, The
Netherlands, October 6-9, 2013
MoB9.5
978-1-4799-2914-613/$31.00 ©2013 IEEE 261

2. A. Stride Length Model for Running Mode
We propose a system that uses different models to estimate
the stride length in different activity modes. The estimation
equations for the walking and running modes are Eq. 1 and
Eq. 2, respectively. Both models use continuous acceleration
measurement sampled at some rate.
l = Kw(av,max − av,min)
1
4 (1)
l = Kr
i ah,i
N
p
(2)
where l is the stride length, av,max − av,min is the peak-
to-peak value of vertical acceleration av during each stride,
and ah is a horizontal acceleration sample. N is the number
of samples sufficiently larger than the running motion cycle,
and Kw and Kr are coefficients calibrated for individuals.
Eq. 1 was proposed in [12] and is widely used in many
subsequent PDR systems. This model is popular because it
estimates the walking stride length without any knowledge
about the user’s body. However, from our experience of
actual measurement we found that the av,max −av,min value
has no increasing or decreasing trend along the increase in
the stride length when the user is running. To estimate the
stride length properly in both activity modes, we hypothesize
Eq. 2 fitted to the running motion.
B. Model Verification
To verify the equation and determine the parameter p, we
collected acceleration samples using an Android smartphone
device (data collection is detailed in Section V). We asked
eight subjects to run on a paved course at different speeds.
Using the data acquired from each subject, we plotted the
stride length of every step against the i ah,i
N value. This
value generally showed an increasing trend when the stride
length increased. Fitting the power trend lines to the plots
resulted in the exponents being distributing around 0.7. We
therefore determined p = 0.7.
III. PEDESTRIAN DEAD RECKONING
In this section, we construct a PDR system in which the
model switching described above is integrated. Our PDR
system consists of two sensors and four modules, as shown in
Fig. 1. The system’s awareness of whether the user is walking
or running affects the stride length estimation module.
At every measurement of the acceleration and magnetic
field, the first module estimates the orientation of the device
and transforms the accelerometer readings into E-N-V (East-
North-Vertex) global coordinates. Steps are detected from
the vertical acceleration sequence. At each step, the moving
direction and the stride length of the user are estimated
from the three components of acceleration. The position is
then incremented from the previous step according to this
direction and distance.
It should be noted that the step detection component
detects the steps of the left or right leg, depending on in
which trouser pocket the device is located. This means that
the stride length refers to the distance between two step
points made by the same foot, and that the position is actually
incremented at every two steps.
Device
Orient
ation
Estima
tion
Step
Detection
Direction
Estimation
Stride Length
Estimation
Displacement
step by stepAcceler
ometer
Magnet
ometer
Walking mode
Running mode
Fig. 1. PDR system. The stride length models are switched for different
activity mode.
A. Device Orientation Estimation
The accelerometer and magnetometer readings are ex-
pressed in an XYZ local coordinate system fixed on the
device1
. Estimating the orientation of the device is equivalent
to estimating the east, north, and vertical direction expressed
in the XYZ coordinates.
Vertical direction ev is estimated from acceleration a.
The force holding the device against the gravity is always
influencing the measured acceleration. Therefore, we assume
severe low-pass filtering of acceleration eliminates the in-
fluence of dynamic pushes, and gives the vertical direction
(opposite to the gravity g) at the time of the ith measurement:
−ˆgi = (1 − α)ai + α(−ˆgi−1) (α ≥ 0.9) (3)
ev,i = normalize(−ˆgi) (4)
The north direction en is estimated from magnetic field m
and ev. The ith magnetometer readings are ignored when the
magnitude is outside the thresholds, thus implying external
excessive magnetic disturbance. We take the horizontal com-
ponent and rotate it around the vertical axis by the magnetic
declination value of the location (by a rotation matrix Rdec)
ˆmi =
mi if mlow < mi < mhigh
ˆmi−1 else
(5)
en,i = normalize(Rdec( ˆmi − ˆmi · ev)) (6)
Finally, ev, en, and the east direction ee(= en × ev)
comprise a matrix, which projects the raw accelerometer
readings a = (ax, ay, az)T
onto the global coordinates
a⋆
= (ae, an, av)T
:
a⋆
= (ee en ev)T
a (7)
1In the Android document, the X and Y axis are defined as left-to-
right and bottom-to-top direction, respectively, of the display in portrait
orientation. The Z axis is defined as the direction in which the display is
facing.
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3. B. Step Detection
During periodical walking/running motions, the sequence
of vertical acceleration av displays one explicit peak and dip
per one foot impact, irrespectively of how loosely the pocket
is attached. After smoothing the sequence with a moving
average to remove noise, we detect the dips of vertical
acceleration as steps (Fig. 2). To avoid false detections
resulting from shallow dips, we introduce a dip threshold.
We also ignore a detection if a deeper dip exists within a
time window around the detected dip.
C. Moving Direction Estimation
The direction in which the user is moving is estimated
in two steps. We first estimate the antero-posterior axis and
then find its forward side.
The first step is based on principal component analysis
(PCA), following the method proposed in [4]. The back
and forth movement of the leg produces a large variance of
horizontal acceleration in a direction parallel to the antero-
posterior axis. Therefore, PCA is applied in the sequence
of east and north components of acceleration (ae and an).
Specifically, we first smooth the two sequences with a
moving average, and then apply eigenvalue decomposition of
the covariance matrix to them. The resulting first eigenvector
gives an estimation of the direction that is parallel to the
antero-posterior axis. The length of the time window of PCA
is set sufficiently longer than the walking motion cycle.
Subsequently, the forward direction is determined from
the antero-posterior acceleration, namely, the horizontal ac-
celeration projected onto the first eigenvector. We detect the
peak and dip from the antero-posterior acceleration after
the detected vertical dip. The cyclic patterns of vertical and
forward acceleration have a temporal relation such that a
vertical dip is closely followed by a forward peak [14], [5]
(Fig. 2). Therefore, the eigenvector is determined as the
forward direction if the peak is earlier, but the direction
opposite to the eigenvector is determined as the forward
direction if the dip is earlier.
5
10
15
20
oothedacceleration[m/s^2]
Detected steps
-10
-5
0
1 11 21 31 41 51 61 71 81 91
Vertical/forwardsmo
Samples (at 32Hz)
Vertical
Forward
Forward acceleration peaks
Fig. 2. Vertical and forward smoothed acceleration.
If the calibration parameter of the moving direction has
been fed back from the calibration component (described in
Section IV), finally, the parameter θ′
is added to the estimated
direction.
D. Stride Length Estimation
As proposed in the previous section, we estimate the stride
length using two different models. In the walking mode, we
derive the stride length l from the peak-to-peak value of
vertical acceleration av during each stride:
l = Kw(av,max − av,min)
1
4 .
In the running mode, however, we derive l from the average
of magnitude of horizontal acceleration ah = a2
e + a2
n in
N samples:
l = Kr
i ah,i
N
0.7
.
Of course, this estimation module needs external infor-
mation as to whether the user is walking or running at
every step. It is assumed that the user’s activity mode is
automatically classified and updated at sufficient intervals
by other components (described in Section IV).
IV. INTEGRATED POSITIONING SYSTEM
In this section, we deal with the integration of
PDR/GPS/map-matching into one positioning system. An
overview of the integrated positioning system is shown in
Fig. 3. The system input is the continuous measurement data
of the GPS, accelerometer, and magnetometer, together with
map information. The final output is the position estimation
updated at every step.
First, from the acceleration and GPS speed values, the
activity mode is classified and updated periodically as system
state information. While the activity mode is “walking”
or “running”, the PDR modules estimate the step-by-step
displacement from the acceleration and magnetic field, and
increment the position from the previous point. GPS po-
sitions and map node information are used to correct the
estimated position repetitively. Some parameters in the PDR
algorithm are calibrated according to the correction result.
The trajectory estimated in single PDR modules contains
errors both in direction and distance. Direction errors are
caused by inaccuracy in the gravity estimation, magnetic
disturbance, and the difference between the device’s ac-
celeration direction and the true antero-posterior axis. The
coefficients of the stride length models have to be fitted to
the individual to reduce distance errors. For this reason, our
system performs repetitive position correction and update of
PDR parameters based on Kalman filter integration with GPS
and map node matching.
A. Activity Mode Classification
Activity mode classification, which precedes the perfor-
mance of the PDR modules, continuously classifies the user’s
state of motion into “walking,” “running,” and “stationary.”
This allows the stride length estimation part of the PDR sys-
tem to switch the estimation model according to whether the
user is walking or running. While the mode is “stationary”
the PDR modules stop updating the position to prevent stance
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4. GPS Accelerometer MagnetometerMap
Information
Activity Mode
Classification
Pedestrian
Dead
Reckoning
Device Hardware
Position Correction and Calibration
Estimated Position
Fig. 3. An overview of the integrated positioning system. The PDR
component refers to the four modules illustrated in Fig. 1.
changes and other occasional motions when the pedestrian is
standing from being misdetected as steps. The activity modes
are classified according to the speed and oscillation pattern
based on pattern recognition, following mainly the method
proposed in [13].
The GPS and accelerometer are the sources of the data
from which five features are extracted for classification.
The time window size of the feature extraction is set to
1 s, and the interval of classification update remains the
same. Four of the five features are derived from samples
of accelerometer values. The magnitude of acceleration is
calculated from three axis values at every measurement time:
amag = (ax)2 + (ay)2 + (az)2. Then, the variance and
DFT (discrete Fourier transform) energy coefficients of 1,
2, and 3 Hz of amags within the window are calculated
as the acceleration features. The fifth feature is the speed
value directly obtained from the GPS receiver. If no GPS
measurement occurs during the 1 s window, the classifier
handles the speed value as “missing.”
A decision tree classifier generated with the C4.5 training
algorithm (Weka J48 implementation) is used in our method.
Using 26691 samples labeled with the correct activity modes
(1966 “stationary,” 15609 “walking,” and 9116 “running.”
Data collection methods are given in detail in section V),
we evaluated the classifier’s performance by 10-fold cross
validation (Table I), resulting in a roughly 99% recall value
for all classes.
TABLE I
PERFORMANCE OF DECISION TREE-BASED ACTIVITY MODE CLASSIFIER
Classified as
Stationary Walking Running Recall value
Stationary 1959 7 0 99.6%
Walking 8 15426 175 98.8%
Running 0 120 8996 98.7%
B. Kalman Filter-based Integration with GPS
The Kalman filter is a popular algorithm in the field
of multi-sensor-based positioning. It recursively estimates
the state of a process using a model of time propagation
and observations containing some inaccuracy, alternating
between the prediction phase and the update phase. Our
system conducts position correction accompanying the GPS
measurements based on a Kalman filter framework, referring
to the method proposed in [9].
The state x is the position in the east and north coordinates
relative to the starting point. Whenever the stride length lk
and the moving direction θk are obtained in the PDR system,
the position after the kth step and its covariance matrix are
“predicted” as



˜xk = ˆxk−1 + lk
cos θk
sin θk
˜Pk = Ak



Pk−1 O
σ2
l,k 0
O 0 (lk tan σθ)2


 AT
k
(8)
where Ak =
1 0 cos θk − sin θk
0 1 sin θk cos θk
, ˆx0 =
0
0
and
P0 =
0 0
0 0
. σ2
l,k is proportional to the kth stride length,
whereas σθ is set to a constant value.
The observation z is the position obtained by the GPS
and transformed from latitude and longitude to the relative
coordinates. (Thus, the update phase is skipped while the
interval of GPS measurement: ˆxk = ˜xk, Pk = ˜Pk.) The
update phase is:
Kk = ˜Pk
˜Pk + α
σ2
G 0
0 σ2
G
−1
(9)
ˆxk = ˜xk + Kk(zk − ˜xk)
Pk = (I − Kk)˜Pk.
(10)
For σ2
G, which represents error variance, we use the square
of the Accuracy2
value acquired through the Android API
that handles GPS facilities. α is a constant value.
C. Map Node Matching
Our system uses the Walking Space Network Data pub-
lished by the Japanese Ministry of Land, Infrastructure,
Transport and Tourism as the map data. This network map
describes outdoor pedestrian paths (sidewalks, crosswalks,
pedestrian paths in parks etc.) with nodes and links. Each
node has its unique ID, latitude and longitude, and the IDs
of connected links. Each link has its unique ID, length, the
IDs of two bounding nodes, and type of path.
The position is corrected using map information when a
significant shift of direction (hereafter we call it turning)
is detected from the PDR trajectory. It is assumed that
pedestrians turn at the corner of buildings or at the side
2In the Android document, this value is defined as the length of the radius
of the 68% confidence circle of the position measurement.
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5. Fixed point
Detected Turning
PDR trajectory
Network
Map
Matched
node
Current PDR
Position
Corrected
Position
fx
kx~
kxˆ
DRL
CL
θ∆ Matched
Node
Detected
TurningFixed
point
Fig. 4. Position correction with map information.
of crosswalks, etc., represented by map nodes. We define
a turning as the kth step that fulfills the conditions below,
where ¯θ− is the average direction of k-9 to k-5th steps and
¯θ+ is that of k+1 to k+5th steps.
• |¯θ+ − ¯θ−| is larger than the threshold;
• The k-1th step is not a turning.
Thus, a turning is detected with a delay of five steps.
For the detected turning, next we search matching map
node candidates that fulfill the conditions below, where ˜xk
is the position of the turning step, and θk+5 is the current
PDR moving direction. A correction is not performed if no
node fulfills the conditions.
• The distance from ˜xk is smaller than the distance
threshold;
• A link stretches from the node in a direction φ such
that |φ − θk+5| is smaller than the angle threshold.
Among the candidates, the position of the node nearest to
˜xk is taken as the matched node ˆxk.
The matched node is then used for parameter calibration of
the PDR module (Fig. 4). The point called the “fixed point” is
initially the starting point of the positioning, which is given
to the system manually, and updated every time the map
node matching process has been performed. xf denotes the
current “fixed point”. ∆θ is the angle between vectors
−−−→
xf ˜xk
and
−−−→
xf ˆxk, and LDR, LC are the length of these vectors. The
PDR parameters θ′
, Kw, and Kr are revised at the time of
the nth calibration:
θ′
n = θ′
n−1 + ∆θ (11)
Kw/r, n =
1
n
LC
LDR
Kw/r, n−1 +
n − 1
n
Kw/r, n−1 (12)
noting that Kw and Kr are revised only when 80% or more
of the trajectory from xf to ˜x is constituted of walking steps
and running steps, respectively.
After the calibration, the current position ˜xk+5 is cor-
rected. Vector
−−−→
xf ˜xk is rotated around xf by ∆θ. Then, LC
LDR
is multiplied by the length. The endpoint of the resulting
vector is the corrected current position ˆxk+5. In addition, the
matched node becomes the newest “fixed point.” The Kalman
filter state covariance matrix of the turning step Pk is set to
0 0
0 0
. From that point, Pk+1 to Pk+5 are recalculated.
V. EXPERIMENTS AND RESULTS
To evaluate the contribution of our new stride length
model, we conducted two experiments. In the first exper-
iment, we compared the performance of our stride length
estimation method and a conventional method, which was
based on a single estimation model (Eq. 1). In the second
experiment, we tested whether the introduction of the new
model influences the total accuracy of the integrated posi-
tioning system.
A. Data Collection
GPS and sensor measurement data for both training the
activity mode classifier and the experiments were collected
using an actual smartphone device. In this study, we used
a Pantech IS11PT MIRACH smartphone, which contains
an assisted-GPS receiver, triaxial accelerometer, and triaxial
magnetometer. A subject carried this device in the trouser
pocket and walked or ran several designated courses. During
the movement, the device recorded GPS positions, accelera-
tion, and magnetic field. The GPS position was sampled at
1 Hz, but an arbitrary rate was simulated later by selecting
recorded samples at intervals. The acceleration and magnetic
field were sampled at 32 Hz, which is a sufficient rate for
both activity mode classification and PDR. The subjects were
eight persons (four males and four females) between the ages
of 20 and 25.
B. Experiment 1
The eight subjects walked/ran on an outdoor paved
straight-line course. The subjects were asked to make records
six times for both activity modes by moving at three dif-
ferent speeds (fast, normal, and slow) while carrying the
smartphone in a pocket on the right and left side. As the
ground truth of each stride length, we used the course length
measured in advance divided by the number of steps counted
by the subjects. From the total 12 records of each subject, we
evaluated the estimation errors of stride length as follows:
For the conventional method ... The stride length was
plotted against the (av,max − av,min)
1
4 value of each step
for the records of both activity modes. Then, the coefficient
Kw was determined by fitting the Eq. 1 to the plots with the
least square method. The RMSE (root-mean-square errors)
of the estimated stride length using Kw was calculated for
all steps.
For the proposed method ... The records of the walking
mode were processed as described above. For the running
mode, the stride length was plotted against the i ah,i
N
0.7
value, and the coefficient Kr was determined by fitting the
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6. 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.0 1.0 2.0 3.0
Stridelength[m]
(av,max - av,min)^1/4
Walk Run
(a) Conventional method
1.4
1.5
1.6
1.7
1.8
1.9
2.0
1.6 1.8 2.0 2.2 2.4
Stridelength[m]
(av,max - av,min)^1/4
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
2.0 3.0 4.0 5.0
Stridelength[m]
(∑ ah,i / N)^0.7
(b) Proposed method
Fig. 5. An example of the plots of stride length and the fitting line through (0, 0).
Eq. 2 to the plots. The RMSE was calculated for all steps,
too, by
RMSE =
j(lj − ˆlw,j)2 + k(lk − ˆlr,k)2
Nw + Nr
(13)
where ˆl is the estimated stride length in the respective model,
and Nw, Nr are the numbers of walking and running steps.
An example of this regression analysis is shown in Fig.
5. Fig. 5(a) shows that the data points of the running mode
do not fit the linear relation, which the data points of the
walking mode do. In contrast, Fig. 5(b) shows that the new
variable in the running mode changes more proportionally to
the stride length.
The evaluation results are summarized in Fig. 6. The
errors are originally small for female subjects because their
stride length changed modestly when they changed speed.
However, in general the errors were reduced by 24 to 73%
as compared to the conventional method, except for one
subject. These results validated our method for estimating
the stride length accurately even if a pedestrian trajectory
contains running steps in any proportion.
0
0.1
0.2
0.3
0.4
0.5
0.6
A B C D F G H I
RMSE[m]
Subjects
Conventional
method
Proposed
method
Fig. 6. Comparison of stride length estimation methods in RMSEs. Subjects
A to D are males and F to I are females.
C. Experiment 2
Subject A walked and ran on the outdoor course at
Shimbashi, Tokyo shown in Fig. 7. While moving from point
A to point B, he ran on the two segments drawn in red
and walked on the other segments drawn in blue. To the
recorded data, we applied our integrated positioning system
using the different methods of stride length estimation.
The conventional method produces shorter estimates of the
running stride length compared to the true length. Thus, the
resulting shortness of the running distance is expected to
cause a wrong matching between the trajectory and the map
nodes.
Segment 1
Segment 2
Fig. 7. The course of the second experiment. Segment 1 is 67 m long and
has a 10 m crosswalk at the last. Segment 2 is 62 m long and, similarly,
has a 6 m crosswalk at the last. The length of the whole course is about
260 m. (Map data copyright 2013 Google, ZENRIN.)
We set the parameters of the PDR system Kw, Kr, and
θ′
to the optimum values initially, on the supposition that
the PDR system was already calibrated for the subject.
Kw and Kr, for the proposed method and the conventional
method, were set to the values that were derived in the first
experiment. θ′
was adjusted so that the walking direction
at the beginning fits the first line of the course correctly.
We eliminated the positioning error that comes from the
inaccuracy of the direction estimation, because the aim of
this experiment is to validate the improvement of the distance
estimation.
Fig. 8 shows the resulted trajectories and the matched map
nodes. It is found that the system with the conventional
method matched the first turning point with the wrong
corner before the crosswalk. In contrast, the system with the
proposed method estimated the distance of the movement
correctly, and thus, matched the turning point with the right
corner after the crosswalk. These results validated that the
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7. Trajectory (Conventional)
Trajectory (Proposed)
Matched Map Nodes (Conventional)
Matched Map Nodes (Proposed)
Fig. 8. The results of the second experiment. The sequences of positioning
are connected with lines. There are several jumps that refer to the position
correction with GPS or map matching. (Map data copyright 2013 Google,
ZENRIN.)
introduction of the new stride length model improves the
total accuracy of the integrated positioning system in some
cases of the actual urban situations.
In this experiment, the simulated GPS sampling rate was
one sample per 30 s. It is found from the trajectories that
the first and second GPS samples were almost ignored due to
their large Accuracy values, but the third and fourth samples
significantly contributed to the position correction. Overall,
the average error of the integrated positioning system was
reduced compared to that of all recorded GPS samples.
VI. CONCLUSION AND FUTURE WORK
We proposed a novel model of stride length estimation
fitted to the running motion. It allows a PDR system to
switch its estimation method according to whether the pedes-
trian is walking or running. The first experimental results
showed that our method estimates the stride length more
adaptively and accurately. We also dealt with the integration
of PDR/GPS/map-matching into one positioning system for
mobile phones. The second experimental results showed that
the proposed model improves the accuracy of the integrated
positioning system. To further confirm the feasibility and
the performance of our positioning system, experiments with
more subjects, in other courses, and without pre-calibrated
parameters will be conducted.
As a direction for future research, we are planning to
improve the map matching method in turn to achieve a
higher level of accuracy. If a person stops walking and
remains “stationary” for a period beside a crossroad, then
it is considered that he/she will probably cross the road to
the opposite sidewalk. Such knowledge about travel patterns
is worth investigating in order to make the map matching
algorithm more sophisticated and robust.
We also aim to support other device placements for the
PDR method. In a chest pocket or in a hand bag, for example,
different acceleration patterns are delivered to the device. We
need to address the extraction of dead reckoning information
from these patterns, as well as a device placement recognition
technique.
REFERENCES
[1] Z. Sun, X. Mao, W. Tian and X. Zhang, “Activity Classification and
Dead Reckoning for Pedestrian Navigation with Wearable Sensors,”
IOP Measurement Science and Technology, vol. 20, pp. 15203-15212,
2009.
[2] A. R. Jimenez, F. Seco, C. Prieto and J. Guevara, “A Comparison
of Pedestrian Dead-Reckoning Algorithms using a Low-Cost MEMS
IMU,” IEEE 6th International Symposium on Intelligent Signal Pro-
cessing, pp. 37-42, 2009.
[3] S. Beauregard, “A Helmet-Mounted Pedestrian Dead Reckoning Sys-
tem,” IEEE 3rd International Forum on Applied Wearable Computing,
pp. 1-11, 2006.
[4] U. Steinhoff and B. Schiele, “Dead Reckoning from the Pocket -
An Experimental Study,” IEEE 2010 International Conference on
Pervasive Computing and Communications, pp. 162-170, 2010.
[5] D. Kamisaka, S. Muramatsu, T. Iwamoto and H. Yokoyama, “Design
and Implementation of Pedestrian Dead Reckoning System on a
Mobile Phone,” IEICE Transactions on Information and Systems, vol.
E94-D, no. 6, pp. 1137-1146, 2011.
[6] F. Ichikawa, J. Chipchase and R. Grignani, “Where’s The Phone?
A Study of Mobile Phone Location in Public Spaces,” IEEE 2nd
International Conference on Mobile Technology, Applications and
Systems, pp. 1-8, 2005.
[7] J. Seitz, J. Jahn, J. G. Boronat, T. Vaupel, S. Meyer and J. Thielecke,
“A Hidden Markov Model for Urban Navigation Based on Finger-
printing and Pedestrian Dead Reckoning,” IEEE 13th Conference on
Information Fusion, pp. 1-8, 2010.
[8] Y. Wakuda, S. Asano, N. Koshizuka and K. Sakamura, “An Adaptive
Map-Matching Based on Dynamic Time Warping for Pedestrian Posi-
tioning using Network Map,” IEEE/ION 2012 Position, Location and
Navigation Symposium, pp. 590-597, 2012.
[9] M. Kourogi and T. Kurata, “Indoor/Outdoor Pedestrian Naviga-
tion with an Embedded GPS/Self-contained Sensor System,” IEICE
Technical Report, vol. 106, no. 73(PRMU2006-33), pp.75-80, 2006.
(Japanese)
[10] D. Gusenbauer, C. Isert and J. Krosche, “Self-Contained Indoor Po-
sitioning on Off-The-Shelf Mobile Devices,” IEEE 2010 International
Conference on Indoor Positioning and Indoor Navigation, pp. 1-9,
2010.
[11] S. Beauregard, Widyawan and M. Klepal, “Indoor PDR Performance
Enhancement using Minimal Map Information and Particle Filters,”
IEEE/ION 2008 Position, Location and Navigation Symposium, pp.
141-147, 2008.
[12] H. Weinberg “Using the ADXL202 in Pedometer and Personal Navi-
gation Applications,” Analog Devices, Inc AN-602 Application Note,
2002.
[13] S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen and M. Srivastava,
“Using mobile phones to determine transportation modes,” ACM
Transactions on Sensor Networks, vol. 6, no. 2, pp. 1-27, 2010.
[14] M. Kourogi and T. Kurata, “Personal Positioning based on Walking
Locomotion Analysis with Self-Contained Sensors and a Wearable
Camera,” IEEE/ACM 2nd International Symposium on Mixed and
Augmented Reality, pp. 103-112, 2003.
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