ᚑくᄢቇℂᎿቇ⎇ⓥႎ๔
J. Fac. Sci. Tech., Seikei Univ.
Vol.47 No.2 (2010) pp.49-51
㧔․೎⎇ⓥ⾌ߦଥࠆ⺰ᢥ㧕㪬㫊㪼㫉㩷㫃㫆㪺㪸㫃㫀㫑㪸㫋㫀㫆㫅㩷㪹㫐㩷㪸㩷㫎㪼㪸㫉㪸㪹㫃㪼㩷㫊㪼㫅㫊㫆㫉㩷
Shuhei Terada*1, Yusuke Enomoto*1, Hisayoshi Endo*2, Dai Hanawa*3, Kimio Oguchi*4
ABSTRACT㧦GPS acquires location information by interrogating several satellites so acquisition is

problematic indoors. Dead Reckoning is one solution; the path from a known departure point iscalculated.

This technique needs the length of stride, the velocity, and the estimated regression

equation. However, this technique is limited to known individuals and normal walking action. This

paper introduces a self-location estimation method for indoor environments that uses multiple regression
equations to handle various walking styles.

Keywords㧦acceleration sensor, navigation system, Dead Reckoning, multiple regression equations(Received September 24, 2010)

1. Introduction

There are two approaches to self-localization in

WSNs (Wireless sensor networks) are becoming

pervasive [1].

This raises the feasibility of various

sensor-based services [2], [3].

In addition, more

services are plotting useful information on maps.

Thus

many navigation systems have been developed forpedestrians and car drivers in recent years.

Providing anavigation system to walkers via their mobile telephones
is attracting attention.

This technology displays both a

map and the user’s present location on the mobiletelephone.

A recent concept is the three-dimensionalbuildings.

GPS (Global Positioning System)-based(3D) map for navigation to rooms or sites inside

navigation systems are becoming popular due tosignificant advances in ICT (Information Communication
Technology).

GPS acquires location information byinterrogating several satellites so acquisition is patchy

indoor environments. First is the co-sited sensor.
Self-location

isacquired

fromtheenvironment.

Examples include Camera, IC tags and so on.
approach is Dead Reckoning.

The other

The user carries awearable sensor such as an acceleration sensor, angularvelocity sensor, magnetic sensor and so on. The path iscalculated from a known departure point [4]. Co-sited
sensors have several weaknesses.
install.

They are expensive to

Their installation locations must be carefullyconsidered.

Therefore, our approach is based on therelatively low cost solution of Dead Reckoning.

Inaddition, current mobile phones contain various sensors.

This technique needs the length of stride, the velocity,
and a regression equation to estimate the distancetraveled and turns taken.

However, existing systems arelimited to known individuals and normal walking action.

This paper introduces a self-location estimationindoors.

method for indoor environments that uses multiple*1styles.

㧦Master Student, Graduate School of Science and Technology

㧦Master Student, Graduate School of Engineering, and*2

Dai Nippon Printing Co., Ltd.

(present affiliation)

㧦Assistant Professor, Faculty of Science and Technology*3(hanawa@st.seikei.ac.jp)

regression equations enabling to handle various walking

2.

㧦Professor, Faculty of Science and Technology*4(oguchi@st.seikei.ac.jp)

MethodThe proposed method is intended to estimate thevelocity of the user in real time.

Multiple regression

equations that can estimate velocity were made from㧙㧙acceleration data in terms of x and y peak-to-peak values.

the acceleration data captured from walking subjects, six

We identify the type of movement to get a better estimate

healthy males ranging from 21 to 24 years.

of distance moved.

Each wore a three-axis acceleration sensor (Hitachiclassify the movement type.

wireless-T with sampling frequency of 100Hz) on theright waist.

For this, thresholds are used toThose values were then categorized into 4 groups in

As for coordinates, x-axis is travelingterms of x and y values.

direction, y-axis is vertical direction, and z-axis is

The 4 groups were a) small x

horizontal direction normal to the direction of travel.

and y value, b) small x and large y, c) large x and small y,walk: “Slow walk”, “Normal walk”, and “Brisk walk”,

were further categorized into 2 sub-groups.

and d) large x and y value. Here, group b), c), and d)

Subjects moved 20 m three times using three kinds ofregression equations were created from the characteristicand “Jog”.

3.

Multipledata of each subject.

Data analysis

We obtained 7 groups in total, so that 7 multiple

regression equations were evaluated.

values of 576 points generating the 7 multiple regression

Time period for the walk cycle was evaluated usingsmoothened acceleration data.

The peak to peakequations were here used.

A cycle was judged

The 2 explanatory variables are the peak-to-peakwhen the acceleration value started from the median,passed through the pre-set upper threshold, fell to thevalue of an x-axis cycle (traveling direction) and thereached the median again.

The objective variable is velocity. This is obtained bypeak-to-peak value of a y-axis cycle (vertical direction).

median, passed through the pre-set lower threshold, andThe peak-to-peak value ofdividing moving distance by the time.

each cycle in x and y coordinates was evaluated for 4
cycles of each of the 4 movements.

Figure 1 plotsWĞĂŬͲƚŽͲƉĞĂŬǀĂůƵĞŽĨƚŚĞĂĐĐĞůĞƌĂƚŝŽŶ ŽĨǆĂǆŝƐ;ŵ'Ϳ䌹㩷ϯϱϬϬ^ƵďũĞĐƚ^ƵďũĞĐƚ^ƵďũĞĐƚ^ƵďũĞĐƚ^ƵďũĞĐƚ^ƵďũĞĐƚ&:ŽŐϯϬϬϬϮϱϬϬϮϬϬϬƌŝƐŬ ǁĂůŬEŽƌŵĂůǁĂůŬϭϱϬϬϭϬϬϬ^ůŽǁǁĂůŬϱϬϬϬ㪇㪉㪇㪇㪋㪇㪇㪍㪇㪇㪏㪇㪇㪈㪇㪇㪇㪈㪉㪇㪇㪈㪋㪇㪇y ĂǆŝƐ;ŵ'ͿWĞĂŬͲƚŽͲƉĞĂŬǀĂůƵĞŽĨƚŚĞĂĐĐĞůĞƌĂƚŝŽŶ ŽĨǇ(KI2GCMVQRGCMCEEGNGTCVKQPXCNWGQHGCEJE[ENG

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4. Result

convergence to a single line because the standard

deviation (SD) is rather high. This may be the cause ofthe mean error.

The distance moved was estimated by using themultiple regression equations.

The moved distance was

6. Conclusion and future works

calculated from the length of stride and the number ofsteps.

The length of stride was calculated from thevelocity of one step and the cycle of one step.

Accuracy is expected to be improved by repeating theThevelocity of one step was estimated from the acceleration

classification.

yields the moved distance where D is Distance, T : time

applied to anyone.

data by the multiple regression equations.

Larger data sets are necessary to createreally effective universal regression equations that can beEquation (1)These experiments considered 4 styles of movement.

of ith cycle, vi : velocity of ith cycle, and n : the numberof steps.

However, humans use a much wider range of styles, so

regression equations and evaluated their performance.

analyzed.

additional data from various cases must be collected andWe used different data sets in making the multiple

The measured mean error in estimated distance traveled

References

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Table 1 sammarizes the[3]6CDNG6JGOGCUWTGF/'CPF5&
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5. Discussion

characteristic

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ofSociety”

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In addition, we formed multiple regression equations
compareNeelam

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