UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

Pedestrian Dead Reckoning
Based on Human Activity Sensing
Knowledge
Yuya Murata
Kei Hiroi
Graduate School ofInstitute of Innovation forEngineering, Nagoya University Future Society, Nagoya
1 Furo-cho, Chikusa-ku,
University
Nagoya, Aichi, 464-8603 Japan 1 Furo-cho, Chikusa-ku,
murata@ucl.nuee.nagoyaNagoya, Aichi, 464-8603 Japanu.ac.jpk.hiroi@ucl.nuee.nagoya-u.ac.jp

Nobuo Kawaguchi
Katsuhiko KajiInstitute of Innovation for
Graduate School ofEngineering, Nagoya University Future Society, Nagoya
University
1 Furo-cho, Chikusa-ku,
Nagoya, Aichi, 464-8603 Japan 1 Furo-cho, Chikusa-ku,
Nagoya, Aichi, 464-8603 Japankaji@nuee.nagoya-u.ac.jp
kawaguti@nagoya-u.jp

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797

Abstract
This research addresses improvement of the accuracy ofpedestrian dead reckoning (PDR), which is one eﬀective
technique to estimate indoor positions using smartphone
sensors. Even though various techniques using step
lengths and their number have been previously proposed
for PDR, insuﬃcient accuracy is gotten from smartphone
sensors. In this research, we define human activity sensing
knowledge and propose improvements to PDR accuracy
based on it. Human activity sensing knowledge consists offour kinds of information: pedestrian, environmental,
activity, and terminal. Previous studies separately used
these kinds of information; however, no study has
systematically arranged them for use in PDR. We
improved PDR accuracy by adjusting the step length inpassages and on stairs and revised activity recognition
error with human activity sensing knowledge. To
investigate the eﬀectiveness of that strategy, we used
HASC-IPSC, which is an indoor pedestrian sensing corpus.
After our investigation, activity recognition accuracy
improved from 71.2% to 91.4%, and the distanceestimation error was reduced from approximately 27 m toapproximately 7 m using human activity sensing
knowledge.

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

Author Keywords

accumulates errors because it estimates the current
position by displacement from the immediately previous
one.

pedestrian dead reckoning; activity recognition;
smartphone; sensor

ACM Classification Keywords
H.1.2 [User/Machine Systems]: Human information
processing.

General Terms
Documentation, Standardization

Introduction
Pedestrian dead reckoning (PDR) with acceleration andgyro sensors is one eﬀective technique to estimate theindoor positions of people. PDR uses steps, step lengths,
and walking directions to estimate current positions by
the displacement from the immediately previous position.
Even though various techniques have been proposed for it,
their performances using smartphone sensors remain
inadequate.

To revise the accumulative error of PDR, techniques using
RFID tags and map matching have been proposed. Anzai[2] reduced errors by map matching in corners. Positions
are revised when the system determines by geomagnetismand gyro sensors that a pedestrian turned or changed her
walking direction.
Korogi [12] proposed an RFID positioning system withmap matching to revise pedestrian positions. When an
RFID reader detects a tag with a specific ID, it notifies
the user of the positional information and the detection
time by a network to revise the position. In addition,
movement through floors indoors is limited to stairs andelevators or escalators. Therefore, with a map, we canrevise the pedestrian position when the system detects anelevator or escalator.

In this research, we define human activity sensing
knowledge and propose improvements to PDR accuracy
based on it. Human activity sensing knowledge consists offour kinds of information: pedestrian, environmental,
activity, and terminal. Previous studies used suchinformation separately [9][7]; no study has systematicallyintegrated it for PDR. We improved PDR accuracy byadjusting the step lengths in passages and on stairs andrevising the activity recognition error with human activity
sensing knowledge.

Previous studies realized position estimation with highprecision using RFID tags and map matching in PDR. But
these techniques need maintenance, management, andelaborate prior investigation infrastructure and design. If
PDR precision is high, this cost can be cut by reducing
the amount of infrastructure or the labor of preliminary
surveys. Highly precise position estimation with lower costis necessary to improve PDR accuracy.

Related Work on PDR

Such factors as psychological conditions, the clothing
being worn, and weather conditions influence pedestrians
and their walk environments [13]. Among these factors,
pedestrian, environmental, and activity information are

Traditional PDR generally estimates the number of steps,
their length, and the walking direction from acceleration,gyro, and geomagnetism data [3]. However, this technique

798

Definition of Human Activity Sensing Knowledge

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easy to use at low cost and are useful to improve PDR
accuracy. In addition, terminal information using
pedestrian observations is also easy to use at lower cost to
improve PDR accuracy because the sensor precision is
diﬀerent. Therefore, in this research, we arrange thisinformation as pedestrian, environmental, activity, andterminal information and define them as human activity
sensing knowledge.

over 67, it decreases to 40% of the walker’s height [13].
As the walking speed increases, the influence of age
becomes bigger. Gender and age information are not usedvery much in PDR, although both are useful for precise
improvement. The existence of obstacles and gait
diﬀerences also aﬀect step length and their number. But
using them is diﬃcult because they vary greatly amongpeople.

We also improve PDR accuracy by adjusting the steps inpassages and on stairs to correct the activity recognition
error by human activity sensing knowledge.

Therefore, we concentrate on such physical information as
gender and age and the walker’s health condition.

We obtain such information by two methods: theinformation input by users and the information estimatedby the system. The information input by users is easy to
get without maps or documents; the information
estimated by the system is obtained by machine learningfrom sensor data.
Pedestrian Information
We define the physical information and the healthcondition of walkers as pedestrian information. The
number of steps and their lengths are aﬀected by manyfactors about the walkers, including age, gender, andpsychological conditions [13]. For setting the step lengths
in PDR, the walker’s height is often used. Even thoughgender and age also influence the step length [13], we
don’t consider them. Usually, there is no diﬀerence of step
length between genders. However, when walking speed
exceeds 130 m/min, the step length is influenced bygender. The step length is also changed by the walker’s
age. The ratio of the step length by height changes byage. Walking patterns begin to change for people in their50s, and step length and pace become shorter in their late
60s. The step length is 45% of the walker’s height whenpeople are younger than 65 years, although when they are799

Environmental Information
We define the outline of the environment and its buildings
as environmental information. Walking is influenced by
the corridor situation and the blockage percentage [13].
Step lengths on stairs or on slopes are diﬀerent from thesteps in passages. If we have a floor map or detailedbuilding information, we can reduce the error by map
matching. Map matching is available at low cost if we use
a floor map as an image [11]. Such information as thecongestion degree is also easy to use and is probably
useful for PDR precision improvement. Since easily
available information about pedestrians does not taketime to update, it is available at low cost. In this study,
environmental information includes data that are assumed
to be identical in the same building, such as the ceilingheight and the width of a single step on stairs. But it
doesn’t include data that vary according to places such as
the distance of passages.
Activity Information
We define a kind of action of pedestrians and theircharacteristics as activity information. In this definition,
we only focus on activities when they can walk normally.
Pedestrians perform various actions, including stopping,walking, and going up or down stairs. All actions have

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

characteristics. We can determine which movements thewalker does based on such characteristics. The frequenciesof the diﬀerences among walking, running, and going up
and down stairs are observed. A technique was proposedthat used a power spectrum and a peak appearing in afrequency band [7]. EE The diﬀerence between going up
stairs and walking is observed in the patterns of the gyro
sensor data. The diﬀerence between going down stairs
and walking is observed in the diﬀerences of the peakvalue of the vertical acceleration and the acceleration of
the running direction [11]. Some studies used an
atmospheric pressure sensor to detect going up and down
stairs as well as the number of floors [4]. But few
smartphones are equipped with atmospheric pressure
sensors. Precision is crucial for the information provided
by this identification. If the identification is correct, we
can improve PDR precision using activity information.
But if the identification is not correct, the PDR precisionworsens. When a walker moves, we suppose that she
navigates directly without encountering any obstacles. In
addition, since we assume that she takes the same action
if the walking environment doesnft change, we can use
such information to improve PDR precision.
Terminal Information
We define the kind and the maintenance position of thesensor terminals as terminal information. Such sensor
terminals as smartphones or private terminals are used in
PDR. They are equipped with the sensors of diﬀerent
manufacturers, so we assumed that their sensor precision
is diﬀerent. Iokura pointed out that the sampling intervalsof acceleration sensors are diﬀerent in each version of anAndroid OS, even if the models are identical [6]. Haba
identified a delay in the acquisition of wireless LAN
electric wave information that is diﬀerent in all available
models [5]. Since such sensor terminals aﬀect PDR

800

precision, they must be considered for PDR. In addition,
the sensor data greatly diﬀer from every position of asensor terminal whose position is important to PDR.
Some studies estimate the sensor terminal’s position [10].
The figures show the sensor data from the following
positions: hand-held (Fig. 1(a)), a pants’ pocket (Fig.
1(b)), and rear middle of the waist (Fig. 1(c)). A
low-pass filter is applied to these data.
In the case of the hand-held and the rear middle of thewaist positions, a rhythm is seen every wavelength. Whencarried in a pocket, the rhythm is seen every two
wavelengths because the foot movement aﬀects the sensor
data. In step detection, the norm of acceleration sensor
data is used in many cases. Therefore, we must use anappropriate detection method for the terminal’s
maintenance site. Various studies estimate sensor terminal
positions with sensor data [10]. If the estimation iscorrect, it is available for precision improvement in PDR.
However, wrong estimation increases error.

(a) Hand-held

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Experiment content
We performed activity recognition and distance estimation
with route data from HASC-IPSC. The activity
recognition was performed with the route data of 107
walkers. We performed the distance estimation with thedata of five walkers from the same route. In thisexperiment, we got correct information about the positionof the terminal and the corners. In the activity recognitionand distance estimation, we used the HASC Tool [1].
(b) Pocket

Figure 2: Route (2D)(c) Waist: rear

Figure 1: Sensor data of each position

Evaluation
To confirm the accuracy improvement, we performed
distance estimation and activity recognition with thehuman activity sensing knowledge. We investigated theeﬀectiveness of our proposed technique using HASC-IPSC[8], which is an indoor sensing corpus. HASC-IPSC
contains the data of 107 men and women whose ages
range from their 20s to their 60s. The data include route
data in buildings and such basic activities such as staying
and walking.

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Figure 3: Route (3D)UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

Human activity sensing knowledge
In this experiment, we used the following human action
sensing knowledge.
Pedestrian information
We adjusted the step length by the walker’s height
and age. We assumed that the step length is 45%of the walker’s height of people who are younger
than 65. When they are over 65, we assumed that
their step length is 40% of their height.
Environmental information
There are 12 steps on the stairs in this building. We
assume that one step length is 0.3 m, and one stepheight is 0.15 m. The stairs have a landing, and thesize of the landing is approximately 2 m In addition,
the walker always turns before entering the stairs,
which are perpendicularly placed in the entrance.
Activity information
The actions from a corner to the next corner are thesame because the stairs and the passages are divided
by a corner in this experiment’s route. The walkers
moved linearly from a corner to the next corner.
Terminal information
We used Nexus 4 (OS: Android 4.2) made by LG
Electronics Incorporated and put it at the rear
middle of the waist of the walkers. We used one
wavelength for the step detection.
The table shows the pedestrian information used in our
experiment.

802

Table 1: Pedestrian information

Person 1207
Person 1208
Person 1217
Person 1234
Person 1296

Height [cm]
178
171
168
174
147

Age
20
20
30
50
60

Gender
Male
Male
Male
Male
FemaleActivity Recognition
We performed activity recognition by the route data of all
the walkers. Since few terminals have atmosphericpressure sensors, we only used acceleration and angularvelocity sensors. The kind of action was estimated bymachine learning using the characteristics. For them, weused the maximum, the minimum, the mean, and thevariance of the three axial gyro sensor data and the normof the acceleration sensor data. We used a J48 decision
tree of Weka in machine learning and the human action
sensing knowledge for the activity recognition. For theactivity information, we assumed that the walker takes thesame action from one corner to the next one. We
calculated the total time for each action from one cornerto the next and decided the longest time action as thesection’s action. Based on the environmental information,
there is a landing on the building’s stairs. We regarded
the short section between the stairs as a landing. We
improved the precision of our activity recognition using
human activity sensing knowledge. For the learning data,
we used the basic action data of all the walkers included
in HASC-IPSC and show the activity recognition result inTable 2.

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acceleration sensor data. We show the detection state in
Fig. 4.

Table 2: Activity recognition rate (%)

Walk
Up stairs
Down stairs

Walk88.8 (55.0)
1.6 (7.6)
7.4 (15.2)

Up stairs
4.6 (22.3)
93.5 (81.2)
0.7 (5.5)

Down stairs
6.6 (22.7)
4.9 (11.2)
91.9 (79.3)

The number in the parentheses expresses the activity
identification rate when we did not use the human activity
sensing knowledge. With the activity and environmental
information in the human activity sensing knowledge, we
improved the activity recognition rate. Without thehuman activity sensing knowledge, some results identified
walking as going up stairs. Between the stairs, there is alanding that is shorter than the stairs. Therefore, they
probably aﬀected the recognition result.
Distance Estimation
Using the activity recognition results, we performed
distance estimation. When the walker travels along theroute, she does her usual walk and goes up and down one
step of the stairs. We show the route in Figs. 2 and 3.
The black line shows the passages, and the red line shows
the stairs. The route’s full length is 74.9 m with four
stairs. This route goes from the first to the third floors.
We calculated the distance by multiplying the steps by
their length. We adjusted the step length using thepedestrian and environmental information. When theestimated activity is walking, we adjusted the step length
using the pedestrian information. When the estimatedactivity is going up or down stairs, we adjusted the step
length using the environmental information.
For step detection, we applied a threshold to the norm of803

Figure 4: Step detection

We set high and low thresholds. When the norm of theacceleration sensor data is under the low threshold within
about one second after the norm of the acceleration
sensor data exceeds the high threshold, we count it as one
step. In this experiment, we set the high threshold to 1.1
G and the low threshold to 0.95 G. 1G shows the size ofgravity’s acceleration.
Table 3: Distance estimation in stairs

Person 1207
Person 1208
Person 1217
Person 1234
Person 1296

Mean error [m]Without knowledge With knowledge
6.1
0.8
6.2
0.9
4.9
0.8
4.0
1.5
4.4
0.5

Improvement rate [%]
86
86
84
63
87

We show the result of walking on stairs in Table 3 of the
distance estimation with the environmental information.
The number shows the mean error in four times ofwalking up the stairs. We show the error for the route’s
full length in Table 4. This expresses the error of thehorizontal movement distance.

UBICOMP '14 ADJUNCT, SEPTEMBER 13 - 17, 2014, SEATTLE, WA, USA

By the environmental information in the human activity
sensing knowledge, we can reduce the error for all thewalkers. The error on the stairs also greatly influenced the
distance estimation. Using this distance estimation
technique, we converted a walker’s position during theroute into coordinates. We show the mean error in Table
5 when we compared it with the correct position every 0.1
seconds. The correct position was derived from thecorrect data included in HASC-IPSC.

The colored part in the graph is the stairs. Although we
adjusted the step length, errors increased in the stairs
because the cumulative error negatively influenced theaccuracy. In addition, the result of the activity recognitionof Person 1296 was partly wrong. The step length varies
based on places. Diﬀerences in recognition increased theerror.

Although we used the human activity sensing knowledge,
some errors have not been improved yet. Some estimation
accuracy worsened because the route’s stairs are half-turnstairs. The half-turn stairs refuted the error reduction in
the stairs using the knowledge. The figure shows the error
with the correct position every 0.1 seconds when a walker
moves in the route.
Table 4: Error for complete route length

Person 1207
Person 1208
Person 1217
Person 1234
Person 1296

error [m]Without knowledge With knowledge
28.4
4.3
42.0
19.5
22.6
0.7
16.7
5.6
23.0
4.6

Improvement rate [%]
85
54
97
66
80

Table 5: Position estimation precision

Person 1207
Person 1208
Person 1217
Person 1234
Person 1296

Mean error [m]Without knowledge With knowledge
3.9
3.8
4.9
5.5
6.5
6.2
2.8
2.9
2.3
3.8

Figure 5: Errors during route

Improvement rate [%]
3
-12
5
-4
-65

804

Conclusion
Summary
We defined human activity sensing knowledge to improve
PDR accuracy and proposed obtaining activity recognitionand distance estimation based on it. Human activity
sensing knowledge consists of four kinds of information:

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pedestrian, environmental, activity, and terminal. We
evaluated it using route data from HASC-IPSC, tested it,
and confirmed the eﬀectiveness of our proposed techniquefor activity recognition and distance estimation. With
human activity sensing knowledge, the accuracy of activity
recognition improved from 71.2% to 91.4%, and the
distance estimation error fell from approximately 27 m toapproximately 7 m. Although we used human activity
sensing knowledge, some errors failed to improve because
the route had multistage half-turn stairs. In addition,
cumulative error influenced it and worsened the precision.
Therefore, improvement of the position estimation
precision when walking through corridors is necessary.
Future work includes the following.
• Improvement of human action sensing knowledge
We defined human activity sensing knowledge inthis study and explained how to use it. However,
because it is thought that there are more
applications which are useful for precise PDR
improvement, further examination is necessary
about the usage of human activity sensing
knowledge.
• Adjustment of step length
Height and age are used for setting the step length.
A step length estimated by that information is thesame as the normal step length. However, since step
length is changed by walking speed andenvironment, we must adjust it based on them.
• Improvement of positioning accuracy based on
corner estimation
In our evaluations, we used correct information at
the maintenance position of a corner and a805

terminal. However, we must estimate them for realenvironments.

Acknowledgement
Part of this research was supported by the Strategic
Information and Communication RD Promotion Program(SCOPE).

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