1The Seventh Annual Symposium on Haptic Interface for Virtual Environments and Teleoperated Systems(With the Winter Annual Meeting of the ASME, November 1998)
DSC-Vol.64, proc of the ASME Dynamic Systems and Control Division, ASME, pp.111-118, 1998

Design for Locomotion Interface in a Large Scale Virtual Environment
ATLAS: ATR Locomotion Interface for Active Self Motion

Haruo Noma

Tsutomu Miyasato

ATR Media Integration & Communications Research Laboratories
Kyoto, 619-0288, Japanemail: noma@mic.atr.co.jp

Abstract:
In this paper, we focus on the sense of locomotion, in particular,
the sense one feels while walking in a large space. Walking is the mostbasic way of moving around, and a lot of information can be obtained bydoing so, e.g., sensing, hardness, humidity, and so on of the space. The
locomotion interface we have developed is named ATLAS (ATR
locomotion interface for active self motion). Our design goal was toeliminate the need for a walker to "learn" how to walk and for equipping
the walker with obstructive sensors. We employ a treadmill approachfor ATLAS. In the initial trial state, ATLAS can estimate the working
speed from the walking motion visually, and control the speed of a belt
to synthesize the real feeling of walking in a virtual space. First, we
describe related works of locomotion interfaces and our design. Next,
we show the method utilized for motion analysis and how to control the
belt speed. Finally, we conclude with a description of our first trial ATLAS
and the experimental results.

Introduction
Virtual reality is expected to allow us to experience new things thatcannot be experienced in real life. VR can simulate any point in time,
any position in space and any object. For example, it can allow us to facean extreme situation where the real situation would probably be lifethreatening due to some physical limitation: an extremely high or low
pressure or temperature or a micro or megaworld. It can also reproduce apast scene, enabling dim vistas of our childhood to be opened up.
At ATR-MIC, we are investigating communications between
humans themselves, and between humans and machines. Our group has
been applying VR to generate suitable communications environments
and developing a VR interface. As one of our targets, we are focusing on
the sense of locomotion, in particular, the sense one feels while walking
in a large space.
Walking is the most basic way of moving around. By doing so in
the areas we live, we can find out much information, like the scale,hardness, humidity, and so on of the space. Some VRML viewers, called“walkthrough simulator”s, allow a user to move around a virtual worldon the screen. However, they don't give the user the sensation of walking
because the user controls merely the direction and speed of the viewing
point by using a mouse, like driving a car without realistic feedback for
locomotion.
In this paper, we describe the development of a locomotion interface
named ATLAS (ATR locomotion Interface for active self motion). Our
design goal was to eliminate the need for the user to "learn" how to walkand to equip obstructive sensors. We employ a treadmill approach for
ATLAS. With our first trial ATLAS, we can estimate the working of theuser speed from the motion of his/her feet visually and control the speedof a belt to synthesize a real feeling of walking in a virtual space. First,
we describe related works on locomotion interfaces and our design. Then,
we show details of the motion detection unit and those of the control
system. Finally, we present our first trial ATLAS and experimental results
to show the effectiveness of the method.

Related Works and Design Concept of ATLAS
When we want to go somewhere, we can choose the most suitable
method for getting there: by foot, bicycle, car or airplane. Many
applications have been developed and a lot of research has been done todate to simulate the feeling of locomotion. Most of them use a vehicle
simulator as a training tool or as entertainment, and they are nearlycomplete. In this paper, we limit our discussion to a locomotion interface
for self motion, i.e., walking or running. As a start, we describe previous
research related to locomotion interfaces for self motion. Next, we present
the design concept of ATLAS.
Locomotion Interfaces in VR
Generally, a locomotion interface should cancel the user’s self
motion in a place to allow the user to go to anywhere in a large virtual
space on foot. Several devices have been studied up to now and we classify

them according to the way in which they cancel motion.
Treadmill
A treadmill is used to cancel the user's motion by moving an infinite
belt in the opposite direction. The main advantage of using a treadmill isthat the user does not have to wear obstacle devices.
One major problem, however, concerns how to control the belt speed
so as to keep the user from falling off. In the case of a motor driven
treadmill, the system has to adjust the belt speed based on the user's
motion. Using a passive treadmill, in contrast the belt is driven bybalancing the user’s weight, so the user never falls off the belt. Thisapproach, however, can generate a flat surface only.
Another major problem is how to change the walking direction.
Brooks [3] and Hirose [10] employed a handle to change the direction.
Mechanical 2D treadmills have been proposed. As a motor driven
treadmill, Iwata [13] developed a 2D infinite plate that can be driven inany direction and Darken [5] proposed an Omini directional treadmillusing mechanical belt. As a passive treadmill, Eyre [7] proposed a
Spherical Projection System employing a huge semitransparent sphere.
A user can walk inside it and it acts a visual screen for projectors, also.
Active Footpad
This method also does not to require the user to wear obstacle devices
and can simulate various terrains. Latham [16] is developing OmniTrek,
which uses two footpads. These footpads are slightly larger than the sizeof a human foot. They track the feet of the user quickly and cancel theuser’s motion so that s/he does not go out of the device. Roston [18]proposed a Whole Body Display that allows the user to walk on stairs,on sand, in mud and so on. Generally footpad has to support the user's
whole weight and track the foot's motion quickly, therefore it requires
sufficient rigidity and a wide band width.
Sliding Interface
Iwata [12] developed a series of sliding interfaces. The user wears
special shoes and a low friction film is put in the middle of the soles.
Since the user’s body is supported by a harness or rounded handrail, thefoot motion is canceled passively when the user walks. The systemmeasures the foot motion and changes the user’s view together withwalking motion.

Fig. 1 First trial ATLAS

2
Pedal Interface
Using a bicycle, the user's motion is different from walking, however
the complicated walking motion is simplified and can be measured easily
by computer. Brogan [2] developed a pedal interface for people training
for bicycle road races. Ensor [6] developed a VRML based bicycle
simulator.
Other Methods
Some research has related the gesture of walking to locomotion.
Choi [4] developed CyberBoots. Four pressure sensors are put on thesole of each foot and a gesture detecting system using fuzzy logic outputs
motion patterns. Kadobayashi [14] developed a gesture interface, called
VISTA Walk, that detects the user’s motion visually. Kobayashi [15]reported a similar device that measures the position of the center ofbalance by a sensor tile.
They translate gestures into commands for moving in a virtual space,so the user has to learn how to walk in a virtual space in advance.
Design Concept of ATLAS
Our goal was to develop an intuitive locomotion interface forwalking with the least amount of equipment on the user's body. To achievethis, we employ a motor-powered treadmill for ATLAS. (Figure 1)
ATLAS consists of a remodeled commercial treadmill and a motion
platform with three axis.
As mentioned above, we have two problems: how to control the
speed of its belt and the walking direction. In this paper, we solve theformer problem. In the following section, we discussed a method forhuman gait detection without an obstacle sensor and a method forcontrolling the speed of the belt.

Gait Estimation for Locomotion Interface
Ideally, if the system could drive the belt at a speed equal to thewalking speed in the opposite direction without a time delay, the walker
would be kept in place. However, it is difficult to sense walking speed
directly, and a mechanical delay can not ignored, so the system has toadjust the belt speed by referencing the user's position and walking speed
in some way.
Research on human gait analysis has been done in the area oforthopedics and biomechanics. Sensor tiles [18,20], body acceleration
meters [1,8] and visual methods [9,21] have often been used. Whereas
most of them record data at once, and analyze it off-line, we havedeveloped a new method that can visually estimate the walking positionand speed from the toes' motion in real time. Toe motion can be easily
measured with a computer vision system without the need for contact.
Here, we discuss the relationship between walking speed and thehuman gait using the results of measured walking motion on a flat floor.
Experimental Method
We used a magnetic position sensor called Fastrack™ to record the
motions: both of the walker's toes and the waist, which represents thewalker's body position. Figure 2 shows the coordinates in the target area.
Subjects were asked to walk from a start line to an end line. The distanceof the path was six meters. Six subjects participated, five men and one
woman, and they were all in their twenties or thirties. The walking pace
was controlled by sound at 120, 96 and 72 steps/min. We asked for three
trials at each pace, so we got 54 samples in total.
Since the effective area of Fastrack™ was limited within thehemisphere of a 150cm radius around the sensor, we arranged themeasurable space at the middle point of the path. The sampling rate was

Ow

X

3

1.8

Y Sensor
sourceSubject A
Subject B
Subject C
Subject D
Subject E
Subject F

1.6

ZMean stride S (m)Stride1.4

Walking direction

Measurable areaof the sensor

1.2
1.0
0.8
0.6

OL

S

OR
Subject

0.2
0OB

Position
sensor

5.39 10 1 V

6.10 10 1

0.4

Start position

0.5

1.0

1.5

2.0

Walking speed V (m/s)

Fig. 2 Experimental setup

Fig. 5 Speed versus mean stride on ground

2.0

1.5

Body
Left toe
Right toe

1.5

Walking direction

Toe-off

Stride S

Position Z (m)50Swing duration t2 (sec)

1.0

Heel-contact

-0.5

t2

1.18 10 1 V

Subject A
Subject B
Subject C
Subject D
Subject E
Subject F

5.32 10 1

1

0.5

-1.0
t1

t2

-1.5
Stance duration

Swing duration

0

-2.0
1.5

2

2.5

3

3.5

4

0

0.5

1.0

1.5

2.0

4.5

Walking speed V (m/s)

Time (sec)Fig. 3 Typical walking motion

Fig. 6 Speed versus swinging duration on ground

2

1.5

1.5

Stance duration t1 (sec)Body position
Left toe position
Right toe position
Middle position of toes
Filtered middle position

1

0.5

0

Subject A
Subject B
Subject C
Subject D
Subject E
Subject F

1

0.5

-0.5

t1

0.774 / V

0

-1
2

2.5

3

3.5

4

4.5

5

5.5

0

0.5

1.0

1.5

2.0

Walking speed V (m/s)

Fig. 4 Estimation of walker's position on groundFig. 7 Speed versus stance duration on ground4
factor (F(5.48)=0.54, p<.743).
The regression lines of V to t1 and t2 are indicated in the graphs
and following equations.

40Hz and the precision of the data was 0.8mm.
Figure 3 is a typical time series graph that indicates the motion of
the body and two toes. We manually worked out the average stride S, theduration of stance phase t1, the duration of swing phase t2, and the walking
speed V from them. Here, the “Stride” is the distance between the heelof a footprint and the toe of the previous footprint of the same foot.
“Stance phase” is the phase in which the foot continues to touch thefloor. “Swing phase” is the phase during which the foot is swinging
forward.

t1 = 7.74 × 10 −1 V −1

(2)

Design of the Speed Controller for the Treadmill
Based on the previous results, we built a speed controller for thetrial ATLAS. The goal of the controller is to keep the walker at a pointon the treadmill by adjusting the belt speed using visually obtained gaitparameters.
Before starting this discussion, we will give some assumptions anddefinitions. First, the belt speed and the walker's toe positions and speeds
can be measured in real time. A detailed method for obtaining themvisually is taken up in the next section. Next, comparing the toes' motion
and belt speed, the walking phase can be detected automatically and the
speed estimation unit updates its output when the stance phase is switchedto swing phase. Furthermore, the first order hold sampler outputs
estimated walking speed to the controller in real time. Next, figure 8
shows the definitions for this section. The upper left character represents
the coordinate with which they are referenced. “w” is the coordinate“world” and “t” is the coordinate “Treadmill” fixed on it. wVbelt is theTbelt speed, wVwalker is the walking speed. X is the walker's )position on)w 'the treadmill. Vwalker is the estimated walking speed, and wVbelt , T X aredesired values.
T
The aim of the controller is to achieve automatic regulation for X
even if the walker changes walking speed, provided that the transit
response of the walker's position is the critical dumping and theacceleration of the belt speed is limited to prevent the walker from fallingdown.
As mentioned previously, ideally the belt speed should be equal
and in the opposite direction to the walking speed. However, the treadmill

Estimation of Speed
Figure 5 shows the walking speed V versus mean stride S from alltrials. Markers represent the subjects. Using an analysis on the varianceof S per subject, the subject was a significant factor (F(5.48)=8.13,
p<0.001.) Focusing on one subject, the stride becomes longer inproportion to the walking speed, but the coefficient of the regression line
greatly depends on the subject. Therefore, it is not enough to use stride
for speed estimation.
Figure 6 presents the walking speed versus the mean duration ofswing phase t2. Makers are the same as for the previous results. An
analysis on the variance of t2 per subject showed that the subject was nota significant factor (F(5.48)=1.02, p<.419).
On the other hand, Figure 7 shows the walking speed versus themean duration of stance phase t1 from every trial. An analysis on thevariance of t1 per subject showed that the subject was not a significant
tX

walker =(1)

These results indicates that the duration of stance phase t1 is ininverse proportion to the walking speed, and the duration of swing phaset2 is also proportional to walking speed. Comparing the response, therelation between t1 and V is better than that between t2 and V, therefore,
we employed speed estimation using the duration of the stance phase on
the treadmill.

Estimation of Position
First, we estimate the body position from the toe position. Figure 4
shows the time series graph of three position data: body, left toe andright toe, and two calculated results: middle point between toes and filteredresults of the middle point. Comparing the measured body positions, themiddle point shows a 0.5Hz pulsatory motion that was caused by therepetition of the swing and stance phases. Therefore we processed a LPF
with a cut off frequency of 0.2Hz to apply this calculated value. Body
motion is not as quick as feet motion, so the delay by the LPF can beignored. As shown in the result, the filtered result followed the body
position well. In the trial ATLAS, we employed this method to estimate
the position on the treadmill.

tV

t2 = −1.18 × 10 −1 V + 5.32 × 10 −1w
wV
walker + Vbelt
w

Vwalker
Speed estimation unit
stance phase
Analyzing

First order
hold

t1

World

Controllerw'
Vwalker

Estimationof speed

T

wV
belt

X̂

Treadmill

+
+

+
-

G(s)
w

V̂beltLimiter

+
+
w

Vbelt

s
TreadmillxFig. 8 Dynamics of ATLAS

Fig. 9 Block diagram of speed controller

T

1
S

X

has a mechanical delay and our speed estimation unit update output once
every step. Therefore our speed controller employs a primary feedforward
using the estimated walking speed and PI feedback unit using theestimated walker's position on the belt to cancel the offset caused byerror of the speed estimation unit. Figure. 9 shows a block diagram of
the controller. Here, G(s) is treadmill and α , β , γ are the gain for theeach element of the controller.
)
When T X is equal to the origin, the transfer function is shown inequation (3).
T

1γ TX 
'X =  wVwalker + G( s)(α wVwalker+ β TX +
)
s
s 

(3)

where the nonlinear elements are ignored. The feedforward gain α
should be the inverse function of G(s) ideally, however this is impossible.
As shown in the block diagram, the system contains a few nonlinear
elements, so it is difficult to find the out optimal parameter bymathematical analysis. In the trial ATLAS, we adjust α to 0.6 within
the limit of the conditions.
The offset that is caused by the insufficient feedforward gain andestimation error can be compensated by the feedback elements. We
employ a typical PI feedback in the trial system. Adjusting gain β , γfor PI feedback, we considered the stability of the feedback system.
First, we regard wVwalker as noise to the feedback loop, characteristic
equation is taken as equation (4).

1 + (β +γ1)G ( s ) = 0
s
s

(4)

By considering that G(s) can be modeled as the first order systemwith dead time, G(s) can be written as equation (5) by Pade approximation.

G( s ) =e −0.09 s
1
1 − 0.045s=⋅
1 + 0.1S 1 + 0.1s 1 + 0.045s

5value, the overshoot converges faster. However, stability is gradually
lost as β and γ become large. By considering that transit response shouldbe the critical dumping, the shaded area in Figure 10 will be reduced.
We decide the gains by making an adjustment with the trial ATLAS
within the results, and we put β =6, γ =5 under this limitation.

Trial ATLAS
Overview of The Trial ATLAS
We built the trial ATLAS using the previously mentioned method.
Figure 11 gives an overview. We arranged a CCD camera with an infrared
light filter and an infrared lamp in front of the treadmill. Putting small IR
reflection markers on the walker's each toe, the markers’ points, in short
toe position, can be measured while the subject walks on the belt without
having to be concerned with noise from other light source such as theceiling lamp, and so on. We employed a video tracking system called
QuickMug™ manufactured by OKK. QuickMug™ can track eight brightmarkers simultaneously at 60Hz. Since the results of one trace produce a
2D data set, so we assume that the walker’s feet are always on the belt.
Under this assumption, the system translates the 2D tracking data into
the positions of the feet on the belt. The accuracy of tracking is 0.3cm inthe worst condition. Furthermore, we use Fastrack™ to measure thewalker’s head direction, to support the head tracked visual images as an
optional extension.
Our altered treadmill is a commercially available product. The
permissible walking area of the belt is 145 cm (D) x 55 cm (W). The belt
speed can be controlled by a PC, from 0 to 4.0m/sec continuously, andits time delay is 0.09 sec and time constant is 0.10 sec within walking
speed. The treadmill is arranged on a custom made motion platform withthree axes. It can be tilted and can maintain the walking surface in any
direction.
Two computers, a PC and a SGI ONYX™ control these devices.

(5)

Where parameters are determined from the trial ATLAS. Putting
equation (5) into equation (4), we apply it into Routh's method. As aresult, Figure 10 indicates that the feedback works well when gain β
and γ are within the shaded area. As β becomes larger value, the peakof the response of the walker's position decreases. As γ becomes a larger

Magnetic
sensorReel

J3
IR CCDcamera&IR lamp
Treadmill&
3 axis motion platform

25

J2
J1

20
Unstable

ONYX

Foot motion

Head motion

15

Visual image feedback
Belt & motion
platform
driver10
StableMagnetic
position
sensorVideo
tracker
Body motion

Locomotion
display drivercontroller

50Projector
screenSensorcontroller
PC

0

5

10

15

20

System controller&
Virtual world simulator
Motion analyzer&
Locomotion feedback

25
Ethernet

Fig. 10 Feedback gain for stable convergenceFig. 11 Overview of trial ATLAS

6
The PC acts as an interface I/O for the video tracker, the magnetic tracker,
the treadmill, and the motion platform. All data is exchanged via ethernet.
The motion analyzing subsystem in the ONYX™ distinguishedwalking phases by comparing the belt speed and the toe speed. The
controller described in the previous section is built into the locomotion
feedback. These subsystems operated at 60(Hz).
A virtual world simulator subsystem maintains a database for a largescale virtual space and manages the user’s position in the world. A visual
image feedback subsystem generates images of the world on a projector
screen in front of the walker.

1.8
Subject A
Subject B
Subject C
Subject D
Subject E
Subject F

1.6

Mean stride S (m)

1.4

1.2

1.0
On the
floor

0.8

0.6
1

S

5.91 10 V

4.32 101

On thetreadmill

0.4

0.2
0

0.5

1.0

1.5

2.0

Walking speed V (m/s)

Fig. 12 Speed versus mean stride on ATLAS
1.5

Swing duration t2 (sec)t2

2

3.73 10 V

6.21 10

Subject A
Subject B
Subject C
Subject D
Subject E
Subject F

1

1

On thetreadmill

0.5
On the
floor

0
0

0.5

1.0

1.5

2.0

Walking speed V (m/s)

Fig. 13 Speed versus swinging duration on ATLAS
1.5

On thefloor

Subject A
Subject B
Subject C
Subject D
Subject E
Subject F

Stance duration t1 (sec)On thetreadmill

1

0.5

t1

0.46 V 1.2

0

0

0.5

1.0

1.5

2.0

Walking speed V (m/s)

Fig. 14 Speed versus stance duration on ATLAS

Results of The Trial ATLASIn this section, we evaluate the proposed method by using the trial
ATLAS.
Adjustment of Speed Estimation UnitFirst, we confirm the motion analyzing subsystem for the treadmill.
Since previous results were obtained from walking on the ground, it is
not to be denied that walking on the treadmill is as same as walk on the
ground. Here we measured "Stride", “Duration of stance phase” and“Duration of swing phase” on the treadmill again.
Six subjects were asked to walk on ATLAS. They were same subjects
who participated in the previous experiment. One important difference
was that the belt speed was fixed at 0.4, 0.8, 1.2, 1.6 or 1.8 (m/sec),
meaning that walking was not purely a self motion. Using the motion
analyzing subsystem of ATLAS, ten steps under each condition were
measured.
Figures 12, 13 and 14 show the results for the motion analyzer of
ATLAS. The axes and markers were the same as in Figures 5, 6 and 7.
The black lines indicate the regression functions for the result. The dottedlines are from the results on the ground for reference. Compared with theresults on the flat floor, the output from the motion analyzer showed thesame tendency as the results on the ground. The stride on ATLAS isobserved to be a little shorter than the stride on the ground. The durationof the swing phase is longer than the same value for walking on the
ground and the duration of stance phase is shorter. This is due to thedifference in the algorithm of phase detection. Therefore, we proofread
the equation of the speed estimation using the results.
−1

t1 = 4.6 × 10 V

−1.2

(6)

When the motion analyzing unit fails to distinguish phase detection
and output a shorter stance duration, the estimated speed is too fast.
Therefore the output from the speed estimation unit is limited in maximum
2.0 (m/sec). This estimation parameter is used in the following
experiment.
Results from Walk After the Block Task
We conducted a simple experiment to the examine controllabilityof walking speed with "walking after the block task." On the screen in
front of ATLAS, two blocks were displayed, and they moved from thebottom to the top of the screen. One was controlled by the system, andthe other block was synchronized with the subject's motion. When thesubject walked faster, the second block moved faster. In this condition,subjects were asked to walk after the first block at the same speed. The
system controlled block began to move at 5sec after the task started.
The initial speed of the block was 0.8 m/sec. When 20sec passed, it
accelerated to 1.6m/sec, and slowed down to 0.8m/sec at 30sec. Whenthe task clock reached 40sec, the block stopped.
Figure 15 shows a time series graphs of one typical result on the7Walking areaon the belt

0.6

0.6

0.4
0.2

0.5

0
-0.2
-0.4
Right footLeft footEstimated body

-0.6

(I)

-0.8
0

10

(II)

20

30

40

Time (Sec)(a) Time vs. walker's position on the belt
2.5

0.4

0.3

0.2

0.1

(II)

I-Feedback
Velt-Speed

1.5

Speed (m/s)(I)

Feedforward
P-Feedback

2

Average error of speed (m/sec)

Walker's estimatedposition on the belt (m)

0.8

0

1
0.5

A

B

C

D

E
F
Subject

G

H

I

Fig. 16 Experimental results

0
-0.5
0

10

20

30

40

(b) Time vs. belt speed, feedbacks and feedforward

Left footRight foot

(I)

(II)

Swing
phase
Stance
phase
Swing
phase
Stancephase

Time (Sec)Time (Sec)(c) Results of motion analysis

Fig. 15 Typical result of walking motion on ATLAS
trial. Figure 15(a) shows the estimated subject's position and the repeatedmotion of the subject's toes on the belt. The walking direction was minus
axis in the graph and the walkable belt area is illustrated on the rightside. It shows that the subject was always kept within 0±0.1m even when
the walking speed changed while walking. The belt speed and output
from feedforward and two feedback elements are shown in Figure 15(b).
Since the subject's position was kept in place, the belt speed can be
regarded as the walking speed. Therefore, it can be seen that this subject
could walk after the system controlled block well in the trial. Observing
the transit response, the subject's position shows a peak when he speeded
up at 21sec. At that time, the PI feedback responded faster thanfeedforward, and soon the PI-element took turn primary control to thefeedforward element as we has designed it. Figure 15(c) shows the resultsof the motion analyzer during the two shaded periods in Figure 15(a)and (b). As the walk became faster, the detected duration of the stancephase became shorter.
We used twelve subjects: Two of them were experienced subjects
and the others were novice to the ATLAS. They were all in their twenties

or thirties. Before the test, we gave short instructions about ATLAS andleft them to use it freely until they could start, walk and stop on ATLAS.
At this training phase, three novice subjects were finally not able to walkand gave up on the experiment. They could not walk at a constant speed
as they desired and all showed a reciprocating movement on the belt on
the belt while walking.
After the training phase, we asked them to walk after the block ten
times. The first five trials were ignored as a training phase for the task,
then, in the latter five trials, we measured the difference in speed between
two blocks, because we focused on walking speed controllability.
Figure 16 indicates the average of square rooted difference of the
speed in each subject. Here, A and B are experienced subjects, so theirresults were regarded as the optimal value. It is likely that the novice
subjects C, D, E, F, G, H were able to control their walking speed as wellas the experts. Subject I only had trouble controlling the walking speed.
The subjects who gave up on the trial were able to maintain their
walking speed on the ATLAS when gains of the feedback and thefeedforward were reduced. Therefore, it is possible that the reason forthis is that the stability of the positional feedback loop that includes boththe subjects and the ATLAS was lost in the trial. Since we cannot say forcertain about the feedback including a subject, we left this problem as a
subject for future works.
Moreover, some subjects reported that it was difficult to stop
walking. ATLAS monitors toes motion constantly, and manages thecontrol mode: staying, starting, walking and stopping automatically.
When the walker intends to stop walking, s/he will stop her/his foot motion
usually. It takes 0.1sec for ATLAS to detect this motion, and it takes
almost 0.1sec to stop the belt because of mechanical delay. Totally, beltruns for 0.2sec after the walker stops. Therefore, the walker has to reduce
speed enough before the stop motion and this requires some skill on thepart of the walker. A solution to this is an important subject for future
work also.

Conclusion & Future WorkIf we focus only on the effectiveness of the moving speed in a virtual
space, we should choose a beam-like method to change the standing point.

The reasons why we walk using our self motion are as follows: concerns
about our heath, to become refreshed, to train ourselves, to observe orcontemplate things, for rehabilitation purposes, and so on. Here, walking
is not the object but a method. From this point of view, it should be notedthat a locomotion interface should offer the feeling of a real walking
motion to the user as possible.
The purpose of our research is to obtain such an interface and, in
this paper, we have proposed a locomotion interface that uses a treadmill
activated by the user’s self motion. The treadmill method has two
problems: how to control the speed and how to control the direction ofwalking. By only putting two small IR markers on the toes, we solved
the first problem. Our method can estimate the walking motion and adjust
the belt speed of the treadmill to keep the walker in place on the belt.
These sensors never disturb the user’s motion. We have built a trial model
named ATLAS, and have confirmed our method from the experimental
results.
The experimental results showed that even novice users were able
to control their walking motion after a few training session. On the other
hand, a few of them could not control this motion very well. One reason
for this is that our proposed method does not have flexibility towarddifferent personalities. Grieve [9] reported that the relation between the
stance phase and the walking speed differs according to generations. We
have to develop way to adjust such system parameters in future.
As for the second problem concerning the walking direction, we
realized the need to direct the walking by changing the face direction(measured by Fastrack™ with the trial model). Needless to say, however,this is not the natural way that we are aiming for. Therefore, we are now
investigating a solution with a more intuitive approach.

Reference
[1]

[2]

[3]

[4]

[5]

[6]

K. Aminian, P. Robert, E. Jequier, and Y. Schutz, "Estimation of
Speed and Incline of Walking Using Neural Network," IEEE. Trans.
on Instrumentation and Measurement, vol. 44, pp. 743-746, 1995.
D. C. Brogan, R. A. Metoyer, and J. K. Hodgins, "Dynamically
Simulated Characters in Virtual Environments," SIGGRAPH'97
Visual Proc., pp. 216, 1997.
F. P. Brooks Jr., "Walk Through-a Dynamic Graphics System for
Simulating Virtual Buildings.," proc. of 1986 Workshop on
Interactive 3D Graphics, pp. 9-21, 1986.
I. Choi and C. Ricci, "Foot-Mounted Gesture Detection and its
Application in a Virtual Reality Environment," proc. of IEEE Int'lconf. on System, Man and Cybernetics, pp. 4248-4253, vol.5, 1997.
R.P. Darken, W.R. Cockayne, D. Carmein, “The Omni-Directional
Treadmill: A Locomotion Device for Virtual Worlds”, Proc. of
UIST’97, pp. 213-221, 1997.
J. R. Ensor and G. U. Carraro, "Pelton: A VRML-Based Bicycling

8
Simulator," SIGGRAPH'97 Visual Proc., pp. 198, 1997.
[7] J. Eyre, http://www.ndirect.co.uk/~vr-systems/sphere1.html
http://www.bbc.co.uk/tw/9798/9804sphere.shtml[8] D. A. Farris, G. C. Urquizo, D. K. Beattie, T. O. Woods, and D. G.
Berghaus, "A Simplified Accelerometer System for Analysis of
Human Gait," Experimental Techniques, vol. 17, pp. 33-36, 1993.
[9] D.W. Grieve, R.J. Gear, “The Relationship Between Length ofStride, Step Frequency, Time of Swing and Speed of Walking forChildren and Adults”, ERGONOMICS, vol. 5, No. 9, pp. 379-399,
1966.
[10] M. Hirose and K. Yokoyama, "Synthesis and transmission ofrealistic sensation using virtual reality technology.," Transactionsof the Society of Instrument and Control Engineers, vol.33, no.7,
pp. 716-722, 1997.
[11] J. M. Hollerbach, "Locomotion Interfaces",
http://
www.cs.utah.edu/~jmh/Locomotion.html.
[12] H. Iwata and T. Fujii, "Virtual Preambulator: A Novel Interface
Device for Locomotion in Virtual Environment," proc. of IEEEVRAIS'96, pp. 60-65,1996.
[13] H. Iwata and Y. Yoshida, "Virtual walk through simulator with
infinite plane (in Japanese)," proc. of 2nd VRSJ Annual conference,
pp. 254-257, 1997.
[14] R. Kadobayashi, K. Nishimoto, and K. Mase, "Design and
Evaluation of Gesture Interface for an Immersive Virtual Walkthrough Application for Exploring Cyberspace," proc. of Third IEEE
International Conference on Automatic Face and Gesture
Recognition (FG98), pp.534 -539 ,1998.
[15] M. Kobayashi, S. Shiwa, A. Kitagawa and T. Ichikawa, "Tilting
Disc: A Real Scale Interface for Cyberspace", proc. of SID 98,
pp.333-336,1998[16] R. Latham, "Device for Three Dimensional Walking in VirtualSpace", http://www.cgsd.com/OmniTrek.html.
[17] G. P. Roston, T. Peurach, “A Whole Body Kinesthetic Display
Device for Virtual Reality Applications”, Proc. of IEEE
International Coonference on Robotics and Automation, pp. 30063011, 1997.
[18] R. W. Soutas-Little, J. C. Otten, and A. J. Hull, "Use of Computer
Aided Design and Analysis in Gait and Athletic Footwear
Evaluation," ASME AMD, vol. 84, pp. 349-352, 1987.
[19] N. Talbert, "Toward Human-Centered System," IEEE Computer
Graphics and Applications July/August, pp. 21-28, 1997.
[20] J. Yamato, K. Sudo, A. Tomon, and M. Arai, "Walking PatternAnalysis using Large Area Pressure Sensor Array and Video Images(in Japanese)," Tech Report of IEICE, RU95-92, pp. 79-82,1995.
[21] T. Yokoi, A. Takahashi, and K. O. Byun, "Self-Organization ofLower Limb Motion in Human Locomotion," Robotics andMechatronics, vol. 8, pp. 364-371, 1996.

9