User Localization
Using Wearable Electromagnetic Tracker and Orientation Sensor
Akihiro Hamaguchi, Masayuki Kanbara and Naokazu Yokoya
Graduate School of Information Science, Nara Institute of Science and Technology
8916-5 Takayama-cho, Ikoma-shi, Nara 630-0192, JAPANkanbara, yokoya  @is.naist.jp

Abstract
This paper describes a localization method with wearable electromagnetic sensor and orientation sensor for
wearable computer users. Many user localization methods
have been investigated to realize location-based services
in a wide environment. The localization methods usuallyemploy a hybrid approach in which user’s position is estimated by using positioning infrastructures and dead reckoning such as a pedometer. However, the installation costof infrastructures increases when the area expands, and theerror of the pedometer is frequently caused by failures inwalking locomotion detection and the difference between
ideal and estimated step lengths. If the relative distanceis accurately estimated by dead reckoning approach, the installation cost of infrastructures can be reduced.
This paper proposes a new localization method that improves the estimation accuracy of step length in dead reckoning approach. The proposed method measures user’s orientation and geometrical relationship between user’s heel
and waist with an orientation sensor and an electromagnetic tracker attached to user’s body. When both feet comeinto contact with the ground, user’s position is updated byadding the estimated step length which means the relationship between user’s heels and the position estimated in theprevious step. The proposed localization method has been
experimented using a prototype system to evaluate the accuracy of the proposed method.

1. IntroductionNavigation systems which can provide users withlocation-based information by acquiring the user’s position
with a wearable computer have been developed[1, 2]. In order to realize these systems in a wide area not only indoors
but also outdoors, a number of localization methods havebeen proposed. Especially in indoor environments, localization methods can be divided into two steps: 1) absolute
position measurement from positioning infrastructures andInertial sensor

Electromagnetic
Tracker
Push buttonswitch

Step vector

Figure 1. User’s equipments in the proposed
method.
2) estimation of relative distance by using wearable sensors
such as dead reckoning sensors.
In general, in order to estimate the user’s position, image markers [3, 4] or infrared markers [5, 6] are used as
positioning infrastructures. However, the larger the area ofpositioning infrastructures is, the more the cost is increased.
On the other hand, a typical method in the dead reckoning
approach is to use a pedometer which measures the user’swalking action by using accelerators and a digital compass[7, 8, 9].
In the relative distance estimation by counting steps, it iseasy to accumulate the error of distance due to difference oflength of user’s step or miscount of steps by noise of sensors. To measure the relative distance of user’s movement
accurately, some methods directly measure the distance ofthe foot with accelerator attached to user’s toe[10]. By combining the approach of relative distance estimation with theapproach based on positioning infrastructures, the cost forinfrastructure can be decreased[2, 11]. If the accuracy of
the relative distance estimation increases, the number of devices of infrastructure decreases and the cost of positioning
infrastructure can be low.
In order to decrease the cost for positioning infrastructure, this paper proposed a new method for accurately mea-

Inertial sensor
Electromagnetic
Transmitter

switches attached to user’s both heels, as shown in Fig. 1.
By attaching these sensors to user’s hip, leg, and heel, therelative position of the left and right legs and absolute orientation of user’s hip are estimated continuously. Therefore,
the proposed method can measure the distance of user’smovement if at least one leg is always grounded. In otherwords, when the user walks (not run), the relative position
can be estimated.

Transmitter
coordinate system

Tracker(right)

Tracker(left)

Trackercoordinate system

Tracker coordinate system

Push-button
switch (right)  

Push-button
switch (left)

Figure 2. Hardware configuration of the proposed localization method.

  

To estimate the user’s position, the distance between left
and right legs is added when the pivoted leg is changed. In
this paper, let us call this distance a step vector. The stepvector can be calculated by using parameters acquired fromelectromagnetic tracker and inertial sensor shown in Fig 2.
The step vector   is defined by Eq. (1).

 ½    ¾    ½    ¾  :step vector in the world coordinate system,(1)

whereFigure 3. Illustration of summing up step vectors.
suring the relative distance of user’s movement. Fig. 1 illustrates an overview of the proposed localization method.
The user equips an electromagnetic tracker, inertial sensor
and push button switches. The electromagnetic tracker measures the relationship between right and left feet, and theinertial sensor measures the absolute orientation against thereal world. The method estimates the user’s movement distance by summing up the relationship between left and rightfeet when both left and right legs are grounded. That is, bydetecting the switch timing of the pivot foot, the step vector, which means the relationship between left and right legs
when both legs are grounded, is added incrementally.
This paper is structured as follows. Section 2 describes
the proposed localization method with wearable electromagnetic tracker and orientation sensor. In Section 3, experimental results with a prototype system are described.
Finally, Section 4 summarizes the present work.

2. Localization with 3D position and orientation sensors

   This section describes the proposed localization methodwith an electromagnetic tracker, inertial sensor and pushbutton switches. A user equips an electromagnetic sensor(Polhemus Inc.: 3Space Fastrak), an inertial sensor (Intersence, Inc.: inertiaCube2) at user’s hip, and push button : absolute orientation of hipin the world coordinate system, ½  ½ : position of cnemis  :
 ¾  ¾ :(position of electromagnetic tracker)
in electromagnetic sensor coordinate system,
pose of cnemis
in electromagnetic sensor coordinate system,
distance between cnemis and heel (constant).

Here, f and b mean front and back side legs, respectively.
Fig. 3 illustrates the process of estimating user’s position.
The estimated distance is defined by Eq. (2).

     (2) 

In the proposed method, it is ideal that both left and rightheels are grounded at the same time when the step vector
is added. However, in a human walking action, the heel ofback leg usually leaves from the ground before groundingof the heel of front leg as shown in Fig. 4. Therefore, theerror of height accumulates as shown in Fig. 5. In order toovercome the problem, we correct the error by estimating
the  which denotes the height of heel in each step, asshown in Fig. 6. In fact, the parameter  is estimated bysome trials of walking along a path whose distance is known
in advance. Equation for position estimation considering
the heel error is defined as follows:

No.4
No.5

West direction [m]0.0Movement of heel

hheel

-1.0

-2.0

両足の踵が接地している 後側の踵が浮いている
Figure 4. Movement of heel when step vector

-3.0
1.0
Height [m]is added.

calibrated (No.4)
uncalibrated (No.4)

0.5
0.0
-0.5

Height [m]1.0calibrated (No.5)
uncalibrated (No.5)

0.5
0.0
-0.5
0.0

1.0

2.0

3.0
4.0
5.0
South direction [m]

6.0

7.0

8.0

Figure 7. Estimated trajectories of walking
along a straight line with and -out calibration.

Figure 5. Estimation without heel height calibration.

1.5
No.4
No.5
1.0

West direction [m]

0.5

0.0

-0.5

-1.0

Figure 6. Estimation with heel height calibration.

-1.5

¼



  

0.5

 ½    ¾    ½    ¾    (3)  .

3. Expriments
In order to confirm the feasibility of the proposed
method, we have carried out the experiments. First, to examine the accuracy of estimated results, three kinds of paths
are evaluated: a straight line, a circle and stairs. Figs. 7, 8
and 9 show the estimated trajectories of three paths. Figs.
7 and 8 illustrate the results of walking along a straight line
whose distance is 9m and a circle whose radius is 1.5 m,respectively. Fig. 9 shows the results when a user goes up
stairs to the upper floor. In this experiment, we have estimated the height error  in advance from the three results of user’s walking along the paths whose distances areknown. We can confirm the proposed method can estimate
user position accurately. Here, the error is defined as a ratioof the difference between true and estimated positions andcalibrated (No.5)
uncalibrated (No.5)
0.0

-0.5
-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

South direction [m]Figure 8. Estimated trajectories of walking
along a circle with and -out calibration.

4.5
4.0
3.5
3.0
Height [m]where  

calibrated (No.4)
uncalibrated (No.4)
0.0

-0.5

Height [m]Height [m]

0.5

2.5
2.0
1.5
1.0
0.5
calibrated(No.4)
uncalibrated(No.4)
Ideal

0.0-0.5-1.5 -1.0 -0.5

0.0South direction [m]0.5-5.5 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5
West direction [m]0.0Figure 9. Estimated trajectories of walking up
stairs with and -out calibration.

0.5

EndStart

26.0
25.0

7

24.0
23.0
22.0
21.0

6

20.0
19.0
18.0
17.0

5

16.0

Height [m]

15.0
14.0
13.0

4

12.0
11.0
10.0
9.0

3

8.0

Figure 10. Estimated trajectories of walking
along a square(one side is 50m).

7.0
6.0
5.0

the true total distance. Errors in the three paths are 7.2%,4.2% , and 6.0%, respectively. Figs. 10 and 11 show experimental results of estimating two long paths. One is asequence of walking along a square whose one side is 50m
and the other is that of going up stairs to 7th floor. Errors inestimation are 5.2% and 2.6%, respectively.

4. Conclusions
This paper has proposed a new localization method that
improves the estimation accuracy of step length dead reckoning approach. The proposed method measures user’s orientation and geometrical relationship between user’s heel
and waist with an orientation sensor and an electromagnetic
tracker that are attached to user’s body. In experiments, wehave evaluated the accuracy of the proposed method withthe prototype system.

References[1] T. Höllerer, S. Feiner, T. Terauchi, G. Rashid and D. Hallaway: “Exploring MARS: Developing Indoor and Outdoor User Interfaces to aMobile Augmented Reality System,” Computers & Graphics, 23, 6,
pp. 779–785, Dec. 1999.
[2] R. Tenmoku, M. Kanbara and N. Yokoya: “A Wearable AugmentedReality System Using Positioning Infrastructures and a Pedometer,”Proc. 7th IEEE Int’l Symp. on Wearable Computers (ISWC’03),
pp. 110–117, Oct. 2003.
[3] M. Kalkusch, T. Lidy, M. Knapp, G. Reitmayr, H. Kaufmann andD. Schmalstieg: “Structured Visual Markers for Indoor Pathfinding,”
Proc. IEEE Int’l Augmented Reality Toolkit Workshop (ART’02),
Sep. 2002.
[4] L. Naimark and E. Foxlin: “Circular Data Matrix Fiducial System
and Robust Image Processing for a Wearable Vision-Inertial Self-4.023.0
2.0
1.0
0.0-2.0 -1.0 0.0 1.0 2.0
South direction [m]-6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0
West direction [m]Figure 11. Estimated result of walking upstairs(from first floor to 7th floor).
Tracker,” Proc. Int’l Symp. on Mixed and Augmented Reality (ISMAR’02), pp. 27–36, Oct. 2002.
[5] D. Hallaway, T. Höllerer and S. Feiner: “Coarse, Inexpensive, Infrared Tracking for Wearable Computing,” Proc. 7th IEEE Int’l
Symp. on Wearable Computers (ISWC’03), pp. 69–78, Oct. 2003.
[6] M. Maeda, T. Ogawa, K. Kiyokawa and H. Takemura: “Trackingof User Position and Orientation by Stereo Measurement of Infrared
Markers and Orientation Sensing,” Proc. 8th IEEE Int’l Symp. on
Wearable Computers (ISWC’04), pp. 77–84, Oct. 2004.
[7] M. Kourogi and T. Kurata: “Personal Positioning based on Walking
Locomotion Analysis with Self-Contained Sensors and a Wearable
Camera,” Proc. 2nd IEEE and ACM Int’l Symp. on Mixed and Augmented Reality (ISMAR’03), pp. 103–112, Oct. 2003.
[8] S.-W. Lee and K. Mase: “Activity and Location Recognition Using Wearable Sensors,” IEEE Pervasive Computing, 1, 3, pp. 24–32,Jul.–Sep. 2002.
[9] J. W. Kim, H. J. Jang, D.-H. Hwang and C. Park: “A Step, Strideand Heading Determination for the Pedestrian Navigation System,”
Journal of Global Positioning System, 3, 1–2, pp. 273–279, 2004.
[10] E. Foxlin: “Pedestrian Tracking with Shoe-Mounted Inertial Sensors,” IEEE Computer Graphics and Applications, 25, 6, pp. 38–46,
Nov.–Dec. 2005.
[11] D. Hallaway, T. Höllerer and S. Feiner: “Bridging the Gaps: Hybrid
Tracking for Adaptive Mobile Augmented Reality,” Applied Artificial Intelligence Journal, Special Issue on AI in Mobile Systems, 18,6, pp. 477–500, Jul. 2004.