Journal of Global Positioning Systems (2004)
Vol. 3, No. 1-2: 273-279
A Step, Stride and Heading Determination for the Pedestrian
Navigation System
Jeong Won Kim
GNSS Technology Research Center, Chungnam National University, Korea
e-mail: [e-mail] Tel: + 82-42-821-7709 ; Fax: [phone]
Han Jin Jang
GNSS Technology Research Center, Chungnam National University, Korea
e-mail: [e-mail] Tel: + 82-42-821-7709 ; Fax: [phone]
Dong-Hwan Hwang
GNSS Technology Research Center, Chungnam National University, Korea
e-mail: [e-mail] Tel: + 82-42-821-5670 ; Fax: [phone]
Chansik Park
School of Electrical and Computer Engineering, Chungbuk National University, Korea
e-mail: [e-mail] Tel: + 82-43-261-3259 ; Fax: [phone]
Received: 15 Nov 2004 / Accepted: 3 Feb 2005
Abstract. Recently, several simple and cost-effective
pedestrian navigation systems (PNS) have been
introduced. These systems utilized accelerometers and
gyros in order to determine step, stride and heading. The
performance of the PNS depends on not only the
accuracy of the sensors but also the measurement
processing methods. In most PNS, a vertical impact is
measured to detect a step. A step is counted when the
measured vertical impact is larger than the given
threshold. The numbers of steps are miscounted
sometimes since the vertical impacts are not correctly
measured due to inclination of the foot. Because the
stride is not constant and changes with speed, the step
length parameter must be determined continuously during
the walk in order to get the accurate travelled distance.
Also, to get the accurate heading, it is required to
overcome drawbacks of low grade gyro and magnetic
compass. This paper proposes new step, stride and
heading determination methods for the pedestrian
navigation system: A new reliable step determination
method based on pattern recognition is proposed from the
analysis of the vertical and horizontal acceleration of the
foot during one step of the walking. A simple and robust
stride determination method is also obtained by analysing
the relationship between stride, step period and
acceleration. Furthermore, a new integration method of
gyroscope and magnetic compass gives a reliable
heading. The walking test is preformed using the
implemented system consists of a 1-axis accelerometer, a
1-axis gyroscope, a magnetic compass and 16-bit
microprocessor. The results of walking test confirmed the
proposed method.
Key words: Pedestrian navigation system, Step
detection, Stride determination, Heading determination
1 Introduction
Pedestrian navigation system(PNS) provides velocity and
position of a person and can be applied to many other
areas such as E-911 service, location based services
(LBS), tourism, rescue, military infantry, medical studies,
leisure, and navigation for the blind. In PNS, it is
necessary to locate the position of the user in any time
and any environment. Even GPS is useful personal
navigation system, its availability is significantly reduced
when a signal is blocked. Also ultra wide band (UWB)
and radio frequency identification (RFID) techniques are
introduced for personal navigation, but these systems

274 Journal of Global Positioning Systems
require dense infrastructure. For these reasons, a self-
contained navigation system based on a dead reckoning
(DR) principle is of interest (B. Merminod et al, 2002).
To locate the position of the PNS user, distance and
heading from a known origin have to be measured with
an acceptable level of accuracy. In PNS, an accelerometer
is used to count the number of steps, which is combined
with the stride to obtain the travelled distance. In
addition, a magnetic compass or gyroscope is used as a
heading sensor.
The stride and step are important parameters for PNS
dead reckoning algorithm. Many methods have been
suggested to detect a step. One such method is to detect
the peaks of vertical acceleration, which correspond to
the step occurrences because the vertical acceleration is
generated by vertical impact when the foot hits the
ground. If the vertical impact is larger than given
threshold, it is considered as a step. Since the pattern of
impact signal depends on type of movement (going up or
down stairs, crawling, running etc.) and type of ground
over which the person walks (hard or soft surface, sand),
the determination of threshold is not so easy for reliable
step detection (Ladetto and Merminod, 2002). This paper
proposes reliable step detection method based on pattern
recognition. From the analysis of the vertical and
horizontal acceleration of the foot during one step of the
walking, the signal pattern of walking behaviours is
obtained.
The stride of the walker in PNS is a scale factor in a dead
reckoning algorithm. Unlike a scale factor of an odometer
in a car navigation system, the stride in PNS is a time-
varying parameter (Mar and Leu, 1996). The
predetermined stride cannot be used effectively for the
distance measurement because the strides of the walker
are different according to the human parameters. The
stride depends on several factors such as walking
velocity, step frequency and height of walker etc. As the
stride is not a constant and can change with speed, the
step length parameter must be determined continuously
during the walk to increase the precision. It is suggested
that the stride could be estimated online based on a linear
relationship between the measured step frequency and the
stride (Levi and Judd, 1996). A real-time step calibration
algorithm using a Kalman filter with GPS positioning
measurement was also proposed (Jirawimut et al,2003).
In this paper, we analyse a relationship between stride,
step period and acceleration to obtain simple and robust
method of stride determination. A real time online
estimation is possible by using only 1-aixis
accelerometer.
The combination of gyroscope and magnetic compass has
already been applied in car navigation (Mar and Leu,
1996) and it might be a very useful heading sensor for
pedestrian navigation system. However low cost sensor
has important drawbacks: A low cost gyro has large bias
and drift error. The magnetic disturbances can be induced
fatal compass error. Moreover the error is occurred by an
oscillation of human body in a walking behaviour. In this
paper, a gyro and a magnetic compass are integrated
using Kalman filter for reliable heading of pedestrian.
To evaluate the performance of the proposed methods,
actual walking test in the indoor environment is
conducted. The equipment of walking test is implemented
using a 1-aixs accelerometer, a 1-axis gyroscope and a
magnetic compass. It consists of two parts: a sensor
module and a navigation computer module. The sensor
module is attached on the ankle. The step number and
stride is computed using the output of the accelerometer
on the sensor module. And walking direction is obtained
from the gyro and magnetic compass module. The
experiments show the very promising results: less than
1% step detection error, less than 5% travelled distance
error and less than 5% heading error.
2 Step detection
2.1 Step behaviour analysis of pedestrian
A cycle of human walking is composed of two phases:
standing and walking phase. The step detection means a
recognition of walking phase. The walking phase is
divided into a swing phase and a heel-touch-down phase.
Each phase is shown in figure 1.
Ground
Swing phase
1st Swing phase2nd Swing phaseHeel-touch-down phase
Fig. 1 A walking behaviour
In the 1st swing phase, the foot is located on behind of
gravity centre of human body. And the foot is located on
front of gravity centre of human body in the 2nd swing
phase. The foot accelerated during swing phase. The
acceleration is composed of vertical and horizontal
components as shown in figure 2, where a, h ,
g
means horizontal acceleration, vertical acceleration and
gravity force, respectively.
Figure 3 and 4 show motion of leg in the 1st swing phase
and the 2nd swing phase respectively.

Kim et al: A Step, Stride and Heading Determination for the Pedestrian Navigation System 275
h
g
a
Ground
Fig. 2 Leg of walker
a
gh −
a
gh −
θ
θ
θ
θ
θ cosa
θ sin)( gh −
θθ sin)(cos gha −−
θθ cos)(sin gha −+
θ cos)( gh−
θ sina
Acc eleration
of
Vertical direction
Acceleration
of
Horizontal direction
directionH
tA− )(
directionV
tA− )(
Fig. 3 1st Swing phase
directionH
tA− )(
directionV
tA− )(
θ
θ
θ cos)( gh−
θ sina
a
gh −
θ
θ
θ cosa
a
θ sin)( gh −
θθ sincos)(agh −−
θθ cossin)( agh +−
Acceleration
of
Vertical direction
Acceleration
of
Horizontal direction
Fig. 4 2nd Swing phase
The horizontal direction acceleration and vertical
direction acceleration during the swing phase is denoted
in equation 1, where )(t
θ
is inclination angle of the leg
at time t.
)(sin)(cos)()(
)(cos)(sin)()(
tatghtA
tatghtA
directionV
directionH
θθ
θθ
−−=
+−=
−
− (1)
In many researches, a step is declared when the measured
directionH
tA − )( or directionV tA − )( is larger than the
threshold. However since the )(t
θ
depend on
characteristics of walking which is different from each
person, it is hard to determine the exact value of threshold
of directionH
tA − )(or directionV tA − )(. The step number is
miscounted when wrongly predetermined threshold is
applied. By using the signal pattern of acceleration, this
problem can be solved. Typical signal pattern of
acceleration is obtained from the computer simulation.
We adopted common assumptions that a typical
inclination of leg was within the limit of 30 degree ~ 50
degree and a, h have a range of 0.8 ~ 2.3g and 0.6 ~
2.0g. The pattern of acceleration signal in figure 5 is
obtained from 625 times simulations.
Fig. 5 Pattern of horizontal and vertical acceleration signal
Figure 5 shows the typical pattern of acceleration signal
on the swing phase. The acceleration of horizontal
direction has 1 positive peak and 1 negative peak in
swing phase while the acceleration of vertical direction
has 1 negative peak only.
The heel-touch-down phase follows the swing phase. A
heel-touch-down is impact motion which hits the ground.
In heel-touch-down phase, a heel hits the ground at first.
And then a sole of foot and toe contact with the ground.
When the foot hits the ground, the ground repulses the
foot. At this time, impact force acts on the foot. The
figure 6 shows the heel-touch-down phase.
Im p a ct
force
Ground
Repulsive
Power
Fig. 6 Heel-touch-down phase
Ground Repulsive PowerImpact force
Vertical AxisHorizontal Axis
Heel-touch-downHeel-touch-down
Fig. 7 The typical pattern of signal in heel-touch-down phase

276 Journal of Global Positioning Systems
Figure 7 shows typical repulsive and impact force
patterns during the heel-touch-down phase.
By combining the swing phase and heel-touch-down
phase in the figures 5 and 7, we obtain the signal pattern
of one walking cycle. Figure 8 and 9 show entire signal
pattern of the walking phase.
Tim e
First
Swing
Phase
Acceleration
Second
Swing
Phase
Heel
Touch
Do wn
Fig. 8 Vertical acceleration signal pattern in walking phase
Heel
Touch
Down
Time
Second
Swing
Phase
Acceleration
First
Swing
Phase
Fig. 9 Horizontal acceleration signal pattern walking phase
It is expected intuitively that the period of heel-touch-
down phase is much shorter than the period of swing
phase. The figure 10 shows a real horizontal acceleration
signal in one step. It coincides with the signal pattern
model in figure 9.
Second
Swing
Phase
Heel
Touch
Down
First
Swing
Phase
Fig. 10 Real horizontal acceleration signal
2.2 Step detection method
To discriminate one cycle of walking behaviour, the
signal pattern of swing phase and heel-touch-down phase
in figure 8 and 9 is adopted. The accelerometer measures
the signal which is caused by walking behaviour. The
step number is counted when all three phases (1st swing,
2nd swing and heel-touch-down phase) are detected. This
method reduces step misdetection probability and
increase reliability. Recognizing swing and heel-touch-
down pattern using sequential multi-threshold gives a
robust and reliable step detection. Also the method can
reduce misdetection probability of non-walking
behaviour such as sitting, turning, kicking and jumping
etc. The detail detection algorithm is given in figure 11.
START
Input
acceleration
Input >
1st
Upper Threshold
Input >
1st
Lower Threshold
Input >
2nd
Upper Threshold
Input >
2nd
Lower Threshold
Step
Detection
END
Yes
Yes
Yes
Yes
No
No
No
No
Fig. 11 Flow chart of step detection
3 Stride determination
Because the stride is not a constant value and changes
with speed, the stride parameter must be determined
continuously during the walk to increase its precision.
The stride relates on walking speed, walking frequency
and acceleration magnitude. In typical human walking
behaviour, a period of one step becomes shorter, a stride
becomes larger and the vertical impact becomes bigger as
the walking speed increases. The relation between stride,
period of one step and acceleration is established thru the

Kim et al: A Step, Stride and Heading Determination for the Pedestrian Navigation System 277
actual walking test. Figure 12 show test result of two type
strides: 60 cm and 80 cm stride.
Fig. 12 The acceleration signal of 60 cm and 80 cm stride
The tester walks with the fixed stride using ground
marks. In the figure, the relation between the acceleration
and stride is clearly shown. The tables 1 and 2 show
relation between acceleration and one step time in this
test. The longer stride induces the bigger acceleration.
However a difference of one step time is hard to apply
stride determination because of small difference in
measurements.
Tab. 1 The mean of acceleration absolute value
Stride Mean value (g)
60cm 0.2882
80cm 0.5549
Tab. 1 The period of one step
Stride Mean of time (sec.)
60cm 0.675
80cm 0.662
Equation 2 is the experimental equation obtained from
several walking tests, where means the measured
acceleration and represents the number of sample in one
cycle of walking. The equation represents the relation
between measured acceleration and stride. It is used for
online estimation of the stride.
31
98.0)( N
A
mStride
N
k
k ∑
=
×= (2)
4 Heading determination
The gyroscope and magnetic compass is widely used to
determine heading. The characteristics of two sensors are
summarized in Table 3, where advantage of one sensor is
disadvantage of the other.
Tab. 3 Comparison between compass and gyroscope
Advantage Disadvantage
Magnetic
compass
-absolute azimuth
-long term stable accuracy
unpredictable external
disturbances
Gyro
scope
-no external disturbances
-short term accuracy
relative azimuth drift
From table 3, an optimal and reliable system might be
expected by integrating the gyroscopes with the magnetic
compass. In the integrated system, the gyroscope can
correct the magnetic disturbances, at the same time the
compass can determine and compensate the bias of the
gyros and the initial orientation. The combination of
gyroscope and magnetic compass has already been
applied in the car navigation system. The integration
method of the gyroscope and the magnetic compass used
in this paper is given in Figure 13.
Gyroscope
Magnetic
compass
Kalman
Filter
Compass
rate
Disturbance
detection
Gyro bias
∫
Angular rateHeading
Heading
Initial Heading
Heading error
Fig. 13 Scheme for an integration of gyroscope and magnetic compass
When the pedestrian is walking, the influence of
magnetic disturbance sources changes unpredictably,
creating a error in the compass heading. This error
degrades the performance of integration system. The
impact of error can be reduced by detecting the
disturbance. The error can be observed via the angular
rate of compass heading:
t
ttt kcompasskcompass
compass ∆
−
∆
+
=)()(
ψ
ψ
ω (3)
where
ω
is angular rate,
ψ
is heading and t
∆
is the
time interval. The disturbance can be detected when a
difference of compass angular rate compass ω
and
gyroscope angular rate gyro ω is larger than given
threshold. The compass measurement is ignored. The
states of Kalman filter are heading error and sensor error
(gyro bias).

278 Journal of Global Positioning Systems
5 Experiments
In order to evaluate the performance of the proposed
method, the actual walking test is done. The tester is a
male aged 26 with 175cm height. The experiments are
done at the 4th floor hallway of the engineering building,
Chungnam National University, Daejeon, Korea. In the
experiments, walking distance determination and heading
determination are carried out separately.
5.1 Experimental setup
Figure 14 shows the experimental equipments.
Sensor Module
Navigation Computer
Data Acquisition
Bluetooth
16-bit
Microcontroller
MEMORY
POWER
Communication
Compass
Ac celermeter
Gyro
Fig. 14 Experimental equipment
The experimental equipments consist of the sensing
module, the navigation computer and a data acquisition
system (notebook computer). The body-worn sensing
module consists of a 16-bit microcontroller, a MEMS
accelerometer (ADXL105, Analog device Inc.), a
gyroscope (MEMS DMU, Crossbow Inc.), a low-cost
digital magnetic compass sensor (CMPS03, ROBOT
Electronics Inc.) and other electrical parts (RS-232
converter, DC-DC converter, 9V battery, Bluetooth
modem). The sensor module is attached on the ankle with
horizontal direction as shown figure 14.
5.2 Experiment of walking distance determination
To evaluate performance of the step detection and stride
determination algorithm, the tester was asked to walk for
pre-determined path (74.2m and 145.6m straight path).
Figure 15 shows the output of accelerometer.
The true step number of first test is 100 steps and second
test 200 steps. The stride is determined using equation 2.
The figure 16 and 17 show the strides of left leg.
The mean of estimated stride is obtained as 76.1 cm and
75.9 cm respectively. Table 4 shows result of walking test
in detail.
Fig. 15 The output signal of accelerometer
Fig. 16 Estimated stride in 1st test
Fig. 17 Estimated stride in 2nd test
In the 1st test, the proposed method count step number
without loss, while the 2 step detection is lost in 2nd test.
The 2 step loss is happened in the last 199th and 200th
step where the walking pattern is abruptly changing. The
walking distance error is obtained 2.5m, 6.1m
respectively. The travelled distance with less than 5%
error is obtained. These results verify that the proposed
method can measure accurate step numbers and distance.

Kim et al: A Step, Stride and Heading Determination for the Pedestrian Navigation System 279
Tab. 4 The measured walking distance
Actual walking behavior
Measured
step
number
Measured
walking
distance
Step
number 100 step
1st test
Walking
distance 74.2m
100 step 76.728 m
Step
number 200 step
2nd test
Walking
distance 145.6m
198 step 151.674 m
5.3 Experiment of heading determination
For heading determination test, the tester walks a straight
path of north direction. Figure 18 is result of heading
determination test.
Fig. 18 Estimated heading by gyro and by integration
In the figure, the heading of stand-alone gyro shows
oscillatory errors due to the body motion. Kalman filter in
the integrated system reduces these errors. The
experiments show that the heading of pedestrian can be
determined with accuracy of 5 degree.
6 Conclusions and outlook
This paper proposes methods to estimate the PNS DR
parameters: step, stride and heading. For accurate step
detection, we analyse the vertical and horizontal
acceleration of the foot during one step of the walking.
With this analysis, a new step determination based on the
pattern recognition is proposed and the step number can
be counted accurately. The relationship between stride
and acceleration is derived from actual test. An efficient
stride determination method where the stride can be
estimated online, so that the user does not need to specify
his/her stride, is proposed. The integration scheme of the
gyro and magnetic compass is proposed for error
compensation of gyro and disturbance rejection of
magnetic compass. The experiments using the actual
walking tests in indoor shows that the proposed method
gives less than 1% step, 5% travelled distance and 5%
heading errors. It is expected that the proposed PNS will
be very useful navigation system for pedestrian
navigation.
References
Gabaglio, V., (2003): GPS/INS Integration for Pedestrian
Navigation Ph. D. dissertation. Institute of Geomatics of
the Swiss Federal Institute of Technologye in Lausanne.
Mar, J., (1996): Simulations of the positioning accuracy of
integrated vehicular navigation systems. In: J.-H.
Leu(Eds.): Proc. Inst. Elect. Eng. Radar, Sonar Navigation,
vol. 143, Apr., 121–128.
Ladetto, Q., (2002): In Step with INS. In: B. Merminod
(Eds.):GPS WORLD magazine, 30-38
Quentin Ladetto (2000): On foot navigation: continuous step
calibration using both complementary recursive
prediction and adaptive Kalman filtering. Proceedings of
ION GPS 2000, 1735~1740.
Jirawimut, R., (2003): A Method for Dead Reckoning
Parameter Correction in Pedestrian Navigation System.
In: P. Ptasinski; V. Garaj; F. Cecelja; W.Balachandran
(Eds.):IEEE Transactions on Instrumentation and
Measurement , Vol. 52, No.1.
Levi, R. W., (1996): Dead Reckoning Navigational System
using Accelerometer to Measure Foot Impacts. In: T.
Judd,(Eds.): United States Patent No. 5,583,776
Lee, S.-W., (2001): Recognition of Walking Behaviours for
Pedestrian Navigation. In: K. Mase,(Eds.): Proc. 2001
IEEE Int’l Conf. Control Applications (CCA 01), IEEE
Control Systems Soc., Piscataway, N.J., 1152–1155.
Gabaglio, V., (1999): Real-time calibration of length of steps
with GPS and accelerometers. In: B. Merminod
(Eds.):Proceeding of GNSS 99, 599–605.