POINT OF INTEREST AWARENESS USING INDOOR

POSITIONING WITH A MOBILE PHONE

Paulo Pombinho, Ana Paula Afonso and Maria Beatriz Carmo

Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal

Keywords: Mobile Devices, Point of Interest, Mixed Environments, Indoor Positioning, Step Detection Algorithm.

Abstract: Although location based applications have been gaining popularity, most positioning devices do not work

when in an indoor environment, hindering the development of both mixed and indoor location based

applications. In this paper we present a point of interest aware application that shows information to the user

that is dependent on his indoor location. To be able to detect the user’s position, we propose a technique,

based on the detection of footsteps, and the direction in which they were taken, to be able to calculate the

position of the user inside a building. To improve the accuracy of the system, we use information about the

buildings floor plan to create a graph that can be used to correct invalid movements.

1 INTRODUCTION

Global positioning devices (GPS) are becoming

progressively more common in new mobile devices

and, for this reason, the real time information about

the location of users has become widely used in an

extensive range of location based applications, as

diverse as car navigation, city guides or geocaching

applications.

Indoor location based services can be used to

show relevant information about the users’ location

and help them navigate in an unknown building, or

track users with special needs, children, or inmates

inside a prison compound. The indoor location could

also be used to allow emergency services to explore

unknown areas in an easier and more efficient way

(Pahlavan, Li and Mäkelä, 2002; Jensen, Kruse and

Wendholt, 2009).

Despite being reliable and precise while in the

open, GPS devices need to be able to view a large

portion of the sky to be able to correctly calculate

the device’s position. This limitation has the

consequence of rendering the GPS useless while

indoors. Furthermore, alternative positioning

systems like the ones that use GSM or mobile phone

tracking do not have high enough accuracy to be

able to correctly identify a position inside a building.

Consequently, these restrictions hinder the

development of location based applications that also

focus on indoor use (Beauregard and Haas, 2006).

Our research is mainly focused on location based

services in mixed environments and the study of

visual adaptations to different contexts. We have

previously explored outdoor location based

applications, namely a point of interest visualization

application (Carmo, Afonso, Pombinho and Vaz,

2008) where information is presented over a map of

the region the user is currently at, and a location and

orientation based application that shows information

about points of interest that lie in the direction the

user is aiming the device at (Pombinho, Carmo,

Afonso and Aguiar). Hereafter, to allow the

exploration of indoor locations and enable us to

proceed our research in mixed environments, we

need to be able to obtain the position of the user

while inside a building.

Our goal is to develop an application that

enables the exploration of buildings unknown to the

user and also allow him to obtain information about

the different points of interest that may exist, inside

the building, near the position the user is currently

at. To achieve this, we first have to be able to

correctly estimate the user’s indoor position. To

avoid the limitations of positioning systems like the

GPS that, as referred, do not work indoors, or the

use of expensive and complex physical

infrastructures installed in each building, we present

an algorithm that allows the position of users inside

a building to be inferred from the movement done by

the user. To achieve this goal we use a mobile

device with an integrated accelerometer to detect

Pombinho P., Afonso A. and Carmo M..

POINT OF INTEREST AWARENESS USING INDOOR POSITIONING WITH A MOBILE PHONE.

DOI: 10.5220/0003351700050014

In Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS-2011), pages 5-14

ISBN: 978-989-8425-48-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

when the users takes a footstep, and a digital

compass to determine the direction of each footstep.

Using this information and the knowledge of the

buildings floor plan we can calculate with an

acceptable accuracy the indoor location of the user.

In the next section we will describe the current

indoor positioning technologies and techniques. In

section 3 our proposed positioning algorithms and

techniques are explained. In section 4, we present

the developed point of interest location aware

application prototype. In section 5, we present the

user experiments that were carried out. Finally, in

section 6 we conclude and point out future work.

2 INDOOR POSITIONING

TECHNOLOGIES

There are already some works that explore indoor

positioning mechanisms. Most of these works can be

divided in three main categories: techniques that use

physical infrastructures installed on the buildings,

explicitly for indoor positioning; techniques that use

existing Wi-Fi networks; and the techniques that use

sensors installed in the mobile device or the user

himself.

2.1 Infrastructures Installed for Indoor

Positioning

There are several diverse approaches that use

transmitters of some kind, installed on the buildings,

and corresponding receivers, carried by the user.

Hiyama et al. (2005) propose a museum guiding

systems using infrared (IR) transmitters installed at

each 1 meter interval on the ceiling of the Japanese

National Science Museum in Tokyo, and an IR

receiver embedded in a monaural headphone

connected to a museum guiding device. The infrared

transmitters cover overlapping areas, allowing the

receiver to recognize that the user is standing

between two specific transmitters.

Lim and Zhang (2006) propose a similar indoor

positioning method but, instead of IR transmitters,

use Radio Frequency Identification (RFID) tags

distributed around an area, enabling a user carrying

an RFID reader to get, at one time, more than one

RFID, and estimate his physical location. From the

experiments made, the authors conclude that this

approach is capable of an estimation error of only 1

meter.

Ghiani, Paternó, Santoro and Spano (2009)

implemented a mobile guide in the Carrara Marble

Museum and in the Calci Museum of Natural

History, where active RFID Beacon tags are placed

near the museum exhibits. The RFID reader carried

by the user allows the application to identify which

beacons are visible and, by analyzing their RSSI

(Received Signal Strength Indication) calculate

which of these the nearest one is.

Xu et al. (2009) propose a two step tracking

procedure. In the first step, a beacon-correlation

algorithm uses RSSI information from all the

detected beacon nodes to identify a wider area where

the user is. In the next step, a shadowing-grid

localization algorithm, using only the RSSI

information from the beacons present in the

identified area, is used to identify the accurate

location of the user.

Ikeda et al. (2008) propose the use of a wireless

sensor network using low power VHF radio to

provide indoor positioning services. This system was

implemented on the Yokohama Landmark Plaza in

Japan and uses a Smartphone that integrates a node

that receives beacon signals and processes them to

detect the user’s position and trajectories.

The PhoneGuide system (Bruns, Brombach,

Zeidler and Bimber, 2007) is a museum guidance

system that uses Bluetooth emitters in each exhibit,

which are identified by the user’s receiver. This does

not allow sufficient accuracy to differentiate

individual objects located within the signal range.

However it does allow the system to know which

exhibits are currently visible and, with this

information, to use the pictures captured by the

camera and compare them to the stored pictures of

the currently visible objects, to be able to recognize

to which one the user is looking at.

Rainer Mautz (2009) studied some recent indoor

positioning systems that are capable of achieving

cm-level accuracy. In his paper, he explores: the

iGPS system (Krautschneider, 2006) that uses a

transmitter of rotating fan-shaped infrared laser

planes that is capable of a mm-level accuracy; the

Locata System (Barnes, Rizos, Kanli and Pahwa,

2005) which is a positioning system that uses radio

signals transmitted in the 2.4 GHz band, to be able

to penetrate the inside of buildings, by transceivers

placed within a range of several hundred meters up

to several kilometres and with a sub cm precision;

and the Cricket (Priyantha, 2006), Active Bat (Hazas

and Hopper, 2006), and Dolphin system (Fukuju,

Minami, Morikawa and Aoyama, 2003), which are

ultrasonic positioning systems that have beacons

emitting both ultrasonic pulses and radio frequency

messages, and receivers that can use the difference

in the arrival of both the signals to calculate de

PECCS 2011 - International Conference on Pervasive and Embedded Computing and Communication Systems

distance from each beacon and triangulate their

position.

Kalkusch et al. (2002) present a mobile

augmented reality application where the user has a

helmet with a mounted camera that detects and

tracks well known markers, previously scattered

around the environment. This tracking allows the

system to identify the position and orientation of the

user.

2.2 Indoor Positioning using Existing

Wi-Fi Networks

Several systems have explored the use of Wi-Fi

wireless network access points.

Bahl and Padmanabhan (2000) developed the

RADAR system, which operates by identifying and

processing the signal strength information of

multiple base stations that are positioned in a way to

provide overlapping coverage, and triangulate the

position of the user with an accuracy of a few

meters.

Kitasuka, Nakanishi and Fukuda (2003) propose

the WiPS system. This system is similar to the

previous one, however, instead of only using

information from the wireless LAN access points,

the WiPS system also communicates with the

neighbouring WiPS terminals and uses information

obtained from them to be able to increase its

accuracy.

There are also commercial systems that explore

Wi-Fi Networks to provide indoor positioning. The

Ekahau Real Time Location System (RLTS)

(Ekahau, 2010) uses the existing Wi-Fi Networks to

provide sub room, room, floor or building level

accuracy, depending on the number of access points

available. Nokia are also currently developing an

experimental indoor positioning service at the

Kamppi Shopping Center in Helsinki, Finland, that

uses Wireless LAN (Nokia, 2009).

2.3 Infrastructure Free Indoor

Positioning

Kourogi et al. (2009) use an approach where sensors

are placed on the user’s waist, to detect walking

stance and velocity. This approach is then enhanced

by the use of surveillance cameras to estimate the

walking parameters.

Stéphane Beauregard (2008) focuses on the

indoor positioning of emergency first responders.

Shoe mounted sensors are used and a foot-inertial

pedestrian dead reckoning approach is proposed to

detect the displacement made by the foot in each

footstep and consequently the total displacement

made by the user. The placement of the sensor on

the shoes instead of other body areas allows better

results to be achieved when in the presence of

irregular walking patterns or sharp turns typical of

rescue missions.

Jiménez, Seco, Prieto and Guevara (2010) also

use shoe mounted sensors and developed the

INS-EKF-ZUPT framework to detect the position

and attitude of a person while walking. They also

employ a stance detection algorithm and several

methods for heading drift reduction.

Commercially, the Nike + iPod Pedometer

System (Nike, 2010) that has sensors placed in the

user’s shoes allows him to hear useful information

through his iPod about the exercise he is currently

doing.

Glanzer, Bernoulli, Wiessflecker and Walder

(2009) present an inertial pedestrian navigation

system that uses a low-cost micro-electro-

mechanical-system (MEMS) based Inertial

Measurement Unit consisting of a three-axis

accelerometer, a three-axis gyroscope, a three-axis

magnetometer, a temperature sensor, an A/D

converter and a microcontroller. From the data

obtained from these sensors, an integration of the

acceleration values is done to obtain changes in

position and attitude, and calculate the final position

of the user.

Köhler et al. (2007) have developed a prototype

for indoor positioning in small rooms, that uses a

compass, camera and grid projector. The system

projects a grid pattern into the environment to try

and determine the location of the planes

(corresponding to the walls) and intersections

(corresponding to the corners). If the system can

detect three orthogonal planes, for example two

walls and a ceiling, it can find its position and

orientation relative to that particular corner and, by

using a compass, find which particular corner it is

and consequently the relative position inside the

building.

Randell, Djiallis and Muller (2003) compare

different pedestrian dead reckoning techniques and

the use of different sensors that can be used to get

accurate measurements. They explored four different

case studies and were able to obtain accuracies of

the same order as a basic GPS receiver.

Ladstätter, Luley, Almer and Paletta (2010) use a

sensor placed in the pocket of the user to detect

shifts in acceleration and use the Weinberg

Expression to adjust the thresholds dynamically.

They have determined that the accuracy of the

technique is reliable for walking speed higher than 1

m/s, but has too many false positives when used at

POINT OF INTEREST AWARENESS USING INDOOR POSITIONING WITH A MOBILE PHONE

slow speeds.

Very recently, some preliminary works have

started to appear that focus only on the use of

sensors integrated in the mobile devices. Serra,

Carboni and Marotto (2010) present the early

developments of an indoor navigation system that

uses the device’s accelerometer, compass, camera

and internet connectivity. Despite using only the

sensor of the device, the proposed application uses

datamatrix drawn in maps scattered in the buildings

to obtain the buildings map and the starting position.

Some experiments have also been made recently,

to do an early evaluation of the accuracy of these

types of techniques. Dekel and Schiller (2010)

present an ongoing series of tests that explore indoor

navigation with un-augmented smart phones. They

were able to obtain an accuracy of more than 90%

on the detection of the distance travelled.

3 INDOOR POSITIONING

Despite providing some solutions for the indoor

positioning problem, the works presented in the

previous section are all either based on the existence

of a physical infrastructure in each building, or the

need of external sensors that are placed, for

example, on the user’s shoes or waist. These sensors

are a potential limitation to the natural movement of

the user or the practicability of the system.

Furthermore, some of the presented systems require

very expensive equipments and others, although

using relatively cheap beacons, require the

installation of a large number of them to be able to

obtain good accuracy.

Since our aim is to develop mixed environment

(indoor and outdoor) adaptive visualization

applications, our goal is to develop an approach that

does not need external sensors, beyond those

integrated in the mobile device, and that can be used

in buildings with no physical infrastructure installed.

Furthermore, since the user is holding the mobile

device in his hand, we want an approach that does

not hinder the users hand movements, allowing him

to retain some of the freedom of movement they

usually have when using a mobile device.

In the next subsections we will describe our

proposed approach. It comprises step detection and

the identification of the user’s position in a floor

plane graph.

3.1 Step Detection Algorithm

We use a 3-axis accelerometer integrated in the

mobile phone to capture, in real time, the

accelerations that the device is being subjected to.

When in a standing / resting stance, the only

acceleration that is present in the mobile device is

gravity. If we consider the mobile device to be

perpendicular to the floor plane, the X and Z axis

would be measuring no acceleration, and the vertical

Y-axis would be measuring the gravity, with a value

of approximately -9.8 m.s

-2

. Since the users can

freely tilt and rotate the mobile device, we have no

prior knowledge of which accelerometer axis is, at a

given point, measuring the vertical acceleration. For

this reason, despite potentially adding some noise,

we have chosen to analyze the resulting acceleration

vector norm, instead of analyzing each axis

separately.

Figure 1: Shifts in acceleration vector norm while

walking, represented in a blue thin line. The red thicker

line shows a single step pattern.

When walking, the user will, not only, apply a

forward acceleration but also, with a greater

magnitude, alternatively apply a vertical upward

acceleration followed by a vertical downward one.

Figure 1 shows an example of the acceleration

changes due to the user walking three steps.

We have defined four parameters that are used to

detect a footstep:

 a peak amplitude λp that represents the

minimum positive shift in acceleration caused

by a footstep,

 a trough amplitude λt that represents the

minimum negative shift in acceleration caused

by a footstep,

 a minimum time interval Δtmin that needs to

go by before completing the footstep pattern,

 a maximum time interval Δtmax that cannot

be exceeded for the footstep pattern to be

detected.

All of these parameters can be changed inside

the application in real time. The description of the

step detection algorithm is as follows:

PECCS 2011 - International Conference on Pervasive and Embedded Computing and Communication Systems

Step 1: calculate acceleration norm: a

Step 2: if(a > λp)

start timer t

start pattern detection

Step 3: while(t < Δtmin)

if(pattern detected)

discard footstep

stop pattern detection

Step 4: while(t > Δtmin AND t < Δtmax)

if(pattern detected AND a < λt)

footstep detected

Step 5: if(t > Δtmax)

discard footstep

The step detection algorithm works by constantly

checking if the current acceleration is greater than

. When that happens, the application starts to

check if the footstep pattern is occurring. If a sudden

shift in acceleration occurs in less that Δt

min

, we

assume that it was caused by another movement and

the step is discarded. If the pattern occurs in more

time than Δt

min

but less than Δt

max

, and it reaches λ

creating a pattern similar to the one shown

previously, a step is recorded along with the current

orientation, which is obtained from the digital

compass. If after Δt

max

we still have not detected the

footstep pattern, the step is discarded.

To be able to identify the location of the user

inside a building, we use the last known latitude and

longitude obtained from the GPS as the initial

location of the algorithm. Afterwards, as each step is

detected, we use the orientation obtained from the

device’s digital compass and the defined average

step length to calculate the displacement made. By

adding all the displacements we can calculate the

trajectory taken by the user inside the building and

also his current location.

3.2 Floor Plan Graph and Position

Correction

Although the algorithm described in the previous

section can give an accurate positioning when used

for short periods of time, it can accumulate a

significant amount of error when used for longer

periods of time. To minimize errors caused by steps

that are not detected (false negatives), steps

incorrectly detected (false positives), or errors in the

step size, we make corrections to the position of the

user using information about the building’s floor

plan.

We have chosen to represent the floor plan by

the use of graphs, inspired by the works done by

Stoffel, Schoder and Ohlbach (2008) and Yuan and

Schneider (2010), in which the geometry of the

buildings floor plan is used to define a graph of the

possible paths a user can take.

We have opted to divide the floor plan in

rectangular areas of different sizes, where transition

areas (for example, doors) have the smallest size,

and areas with no transitions (for example, long

corridors with no doors) have the bigger areas. Each

of these areas corresponds to a node in the graph.

Figure 2 shows, on the left, an original floor plan

with the considered areas in red, and in the right the

graph that was defined, with a node for each area.

Figure 2: Definition of the floor plan graph.

The use of the graph allows calculating, with

each step taken, the position of the user inside a

certain node area. If at any time, the calculated

position is outside the current node area, the system

will verify if there is a valid transition from the

current node to another node in the specified

direction. If there is a valid transition, the system

places the user on the next node and calculates his

location in the new node area. If there is no valid

transition in the recorded orientation (i.e. there is no

direct path from the current node to the new one),

the system searches for the nearest position where

the detected movement would be valid and corrects

the user position.

4 APPLICATION PROTOTYPE

Our goal is to develop a point of interest aware

application that can enable a user to roam inside an

unknown building (for example, a shopping mall or

a university campus) and quickly obtain useful

information. The application should aid the user in

finding specific places and also to easily explore the

building while obtaining information about the

points of interest he is near to.

To test and evaluate our approach, we have

developed a simple application prototype for a HTC

Smartphone. We use the Smartphone’s integrated

accelerometer to obtain, in real time, the acceleration

POINT OF INTEREST AWARENESS USING INDOOR POSITIONING WITH A MOBILE PHONE

the device is being subjected to, and the digital

compass, to obtain the orientation of the device.

To obtain information about the points of

interest, we use a SQL Server database that contains

data about the name, location and attributes of each

point of interest. However, to illustrate the proposed

concept, we have defined a simple floor plan

composed of only nine different points of interest.

Figure 3: Prototype showing the desired destination.

The developed application has two main display

modes. In the first mode, used to support a search

task, if the user wants to search for a specific point

of interest, he can select the “Select PoI” button

from the “Options” menu and then search for the

specific point of interest he wants to find (for

example a certain store). When the user selects a

point of interest, it will be highlighted in the

building plan. Additionally, the available

information about that point of interest will also be

shown in the upper part of the interface (Figure 3).

The second mode is used to support an

exploratory task. If the user does not select any point

of interest, the application detects what areas the

user is near to, and shows information about them in

the upper part of the interface (Figure 4).

In our prototype, the main visualization area, in

the bottom part of the interface, shows the path the

users have taken drawn over the building plan and

their current location marked by a blue circle

(Figure 3). An example of path detection is shown in

Figure 4. The position of the user and the path he

has already taken is drawn in blue over the floor

plan. If the user position is automatically corrected

by the application, due to an invalid detected

movement, the interface displays a thick red line to

highlight the correction made.

Figure 4: Prototype showing the path the user has taken.

5 USER EXPERIMENTS

To accurately calculate the position of the user

inside a building, it is essential that we use adequate

step detection parameters and also consider an

accurate step size. To be able to obtain these values,

we performed a set of experiments to allow us to

understand the differences between the walking

patterns and step size of a diverse set of users.

5.1 Procedure

At the start of the experiment, we asked each user

their name, weight and height. Seven users

performed our experiments, four were male and

three female. They had diverse heights and weights,

ranging from 160 cm to 180 cm and 65 kg to 81kg,

respectively.

Afterwards, each user was asked to hold the

mobile device in their hand and follow the

instructions that were presented on the device’s

screen. For each experiment, the user’s were given

indications on the speed at which they were

supposed to walk, and instructed to press a start

button, walk ten steps and press the stop button.

The users were also instructed to stay on the spot

where they stopped, to allow us to measure the

distance they had travelled.

Each user was asked to carry out six tasks, three

of them outdoors and the remaining three inside a

shopping mall.

The users were first asked to walk in their

PECCS 2011 - International Conference on Pervasive and Embedded Computing and Communication Systems

normal walking speed when outdoors, then they

were asked to walk slowly while observing the

surrounding environment and, finally, they were

asked to walk as if they were in a hurry to get

somewhere. These tasks were then repeated while

indoors, where the users were asked to walk in a

normal speed, slowly while watching the shop

windows, and quickly as if they were late.

Finally, we also conducted some experiments

with the device secured on the user’s waist to be

able to obtain a control set with the device placed in

a more stable position.

5.2 Results

After having concluded the experiments, the

accelerometer logs, recorded by the mobile device,

allowed us to create charts displaying the variation

of the acceleration vector norm over time for each of

the tasks conducted by each user.

Using these charts we were able to analyse the

walking pattern of each user and determine, for each

step, what the optimal detection patterns would be.

Regarding the λ

and λ

parameters, we wanted

to understand if the minimum λ

value and the

maximum λ

value were sufficiently different that

they could be used as default values to detect all the

steps. However, as can be seen in Figure 5, the

values obtained are in fact very similar, and as

consequence are not adequate for the algorithm.

Figure 5: λ

and λ

acceleration parameters.

As an alternative, to obtain a set of values that

can detect the majority of the steps, we can use the

average and standard deviation for each parameter.

As a result, the value of λ

can be calculated by

using the average (12.95) and subtracting the

standard deviation (1.71). Similarly, the value of λ

can be obtained by adding the standard deviation

(1.11) to the average (7.53).

Regarding the time interval of the steps

(Figure 6), the average time spent on a step (245 ms)

and the relatively high standard deviation (99 ms)

suggest that a similar approach can also be used to

define the Δ

tmin

as the subtraction of the standard

deviation from the average and Δ

tmax

as the sum.

Figure 6: Time spent taking a step.

The values obtained allow us to detect the

majority of the steps taken by the users, however, as

can be seen by the standard deviation in the previous

charts, there are significant differences in the way

each user walks. For this reason, instead of using

fixed parameter values, it would be better for an

application to acquire information when the user is

outside (where his movement can be detected by the

GPS) to automatically calibrate the parameters.

Figure 7: λ

, λ

and Δ

indoor / outdoor ratios.

However, since the users do not move exactly

the same when indoors as they do while on the

outside, we also need to identify the ratio between

the two types of movements. To achieve this, we

calculated the ratio, for each user, between the

indoor and outdoor experiments at the same speed.

The average ratios are shown in Figure 7.

As important as detecting each step correctly, we

also have to choose the right step size since, in the

long run, it can originate a high amount of error. The

average step size obtained from our experiments is

65 cm. However, there are significant variations

depending on the speed of the movement and also if

it is indoors or outdoors (Figure 8).

POINT OF INTEREST AWARENESS USING INDOOR POSITIONING WITH A MOBILE PHONE

Figure 8: Average step size.

Despite the very diverse step lengths, when we

consider the ratio between each pair of indoor /

outdoor experiments, we obtain a ratio value that is

fairly constant (0.9) and that is directly proportional

to the speed of the user (Figure 9).

Figure 9: Step size indoor / outdoor ratio.

We also wanted to find the relation between the

weight, height and step size of the user. To obtain

the ratio, we calculated the (Height / Weight) / (Step

Size). The average ratio calculated is 0.039 and is

inversely proportional to the speed of the user

(Figure 10).

Figure 10: Height, weight and step size ratio.

Through the use of both the previous ratios and

information obtained from the GPS, when the user is

outdoors, it is possible to calculate with a low error

the average step length for each particular user.

5.3 Evaluation

To understand if the proposed approach was valid,

we conducted a small evaluation phase. This phase

had the goal of determining the accuracy of the step

detection algorithm.

Using information from the previous

experiments we calculated, for each user, their

optimal step detection parameters. After calibrating

the application, we asked the users to walk 50 steps

holding the mobile device in their hand and when

they stopped recorded how many steps the

application had detected. Each user conducted this

evaluation three times.

On average, the algorithm detected 47 steps of

the 50 (standard deviation = 2.5). Although the

number of steps detected can, in the long run, be

responsible for a high error, we believe that it can be

minimized through the use of the floor plan graph

position correction.

6 CONCLUSIONS AND FUTURE

WORK

In this paper, we have presented a point of interest

aware application that uses the information about the

user’s indoor location to display information about

the points of interest that exist in the vicinity of the

user. To be able to correctly identify the position of

the user, we have proposed an indoor positioning

method that does not need previously installed

physical infrastructures in a building. Furthermore,

this approach does not need external sensors,

avoiding the restriction of the user’s natural

movements when using a mobile device and walking

indoors.

We have performed some user experiments that

allowed us obtain valuable data about the differences

in the step patterns originated from a diverse set of

users. This knowledge can give us the insight on

what the best default step detection parameter values

are, and also help us in implementing an automatic

calibration of these values. A particularly important

parameter, which can cause a high accumulated

error, and should thus be automatically adjusted, is

the step size, since it varies not only between

different users, but also depends on the speed and

way the user is moving. Since our aim is to use this

algorithm in mixed environment visualization

PECCS 2011 - International Conference on Pervasive and Embedded Computing and Communication Systems

applications, one solution is to use information from

the GPS when the user is outdoors.

Although there is still work to be done to be able

to use this techniques, in a way they can be accurate

with a diverse group of users, and in complex

environments, the tests that we have done so far

indicate that it is a valid approach.

We intend, in the future, to more precisely

evaluate the precision of the algorithm, taking into

account the position correction using the floor plan

graph. We also intend to test more complex

buildings floor plans, and consequently the detection

of footsteps when going up and down a set of stairs,

and the use of escalators and elevators.

ACKNOWLEDGEMENTS

The work presented here is based on research funded

by the FCT - Fundação para a Ciência e Tecnologia

(Portuguese Science and Technology Foundation)

through the SFRH/BD/46546/2008 scholarship.

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