COMPARATIVE EXPERIMENT OF BODY-POSITION BASED

NAVIGATION IN IMMERSIVE VIRTUAL ENVIRONMENTS

Kikuo Asai

National Institute of Multimedia Education, Mihama-ku, Chiba, Japan

Hideaki Kobayashi

The Graduate University for Advanced Studies, Mihama-ku, Chiba, Japan

Keywords: Human-Computer Interaction, Body-Position Based Navigation, Virtual Environment, Immersive Projection

Display.

Abstract: Navigation is one of the basic human-computer interactions in virtual environments, and the technique

should be easy to use, cognitively simple, and uncumbersome. However, the interaction in immersive

virtual environments requires a factor of the sense of immersion as well as efficiency. We have proposed a

body-position based navigation technique that drives a viewpoint with extension and bending of the arms,

rotating both arms, and standing on tiptoes and bending the knees. Using various parts of the body may help

to enhance the sense of immersion in the virtual environment. Depth images obtained from a polynocular

stereo machine are used for tracking the 3D positions of the arms and head of the user in an immersive

projection display. We evaluated the body-position system in experiments in which participants performed

fly-through tasks in a 3D space, and compared the effectiveness of the body-position system with that of a

joystick and a hand-arm gesture interface. The results of the experiment showed that the body-position

system was advantageous on moving around at large areas instead of efficiency or accuracy of viewpoint

control in virtual environments.

1 INTRODUCTION

Navigation is a basic human-computer interaction

(HCI), which is generally a way to move the user

into the location where she/he performs the primary

tasks. Therefore, the interaction technique should be

easy to use, cognitively simple, and uncumbersome

(Bowman and Hodges, 1999). However, these

factors are not enough for immersive virtual

environments (VEs). The navigation in immersive

VEs needs essence to enhance the feeling of being

there so that the HCI makes fly-through content

enjoyable, which takes the user around in the three-

dimensional (3D) virtual world.

Most current fly-through systems employ a 3D

mouse, joystick, or glove as the input device in VEs

(Vince, 1995; Bowman, et al., 2005). Such devices

allow for intuitive control of speed and direction of

movement because they are easy to use and

understand, and are not physically tiring to

manipulate. However, these devices are designed

focusing on efficiency or accuracy, but not

immersion enhancement. We have proposed using

body position as a means of navigation. The user's

viewpoint moves with movements of both the arms

and legs (Figure 1), and hence the user is required to

maintain their balance. Using various parts of the

body and sensory organs may provide the user with

an enhanced sense of immersion, and increase

interactivity with the immersive VE.

Figure 1: Body-position based interface.

106

Asai K. and Kobayashi H. (2007).

COMPARATIVE EXPERIMENT OF BODY-POSITION BASED NAVIGATION IN IMMERSIVE VIRTUAL ENVIRONMENTS.

In Proceedings of the Second International Conference on Computer Graphics Theory and Applications - AS/IE, pages 106-113

DOI: 10.5220/0002078701060113

 SciTePress

The immersive projection display (IPD) (Cruzz-

Neira, 1992; Ihren and Frisch, 1999), surrounds

users with stable wide-angle images giving them

immersive VEs. It uses lightweight stereo glasses,

thereby eliminating the need to wear large headgear

such as a head-mounted display. IPDs are often used

for presenting 3D entertainment contents where the

users enjoy navigation itself in the immersive VEs.

The navigation of the contents is usually done by a

joystick with a magnetic sensor used for pointing at

3D objects. Alternative interfaces should be

investigated to gain the user's sense of presence in

the virtual world.

It is often difficult for users to decide an

appropriate interface device in 3D VEs and

development of efficient interaction techniques,

because the most effective ways for humans to

interact with synthetic 3D environments are still not

clear and may depend on the applications (Herndon,

et al., 1994). This has given rise to studies on design

and evaluation guidelines of the interfaces used in

3D VEs (Gabbard, et al., 1999; Kaur, et al., 1999),

but there is little work on the evaluation of 3D

interaction by body position in immersive VEs and

on the adaptability to their applications in emphasis

on the sense of immersion.

We examined performance of the body-position

based navigation system by making an experiment

of fly-through tasks in 3D VEs in comparison with a

joystick and a hand-arm gesture interface often used

in IPDs.

2 RELATED WORK

Various techniques have been developed for

applications to HCI using body motion. Mapping

head movements to navigation has been dedicated to

hands-free fly-through applications in VEs, although

the sensors have often been implemented in

interfaces using a wired approach. A head-directed

system (Fuhrmann, et al., 1998) determines speed

and direction of navigation. The advantage of such a

system is to be simple requiring no additional

hardware except a head tracker, but using a head

direction leads to limitation of the view direction.

Both head and foot movements have been

mapped into viewpoint motion (LaViola, 2001).

This system used a floor map as a world in miniature

to move to the desired location in the virtual world,

and detected the body's lean to enable movement

over a small distance. The interaction technique is

suited for moving to specific places, but not for

moving around in VEs such as fly-through

applications.

The physically connected systems constrain

natural movements of the user within VEs. The user

often has to be aware of any cabling in their

immediate vicinity. Holding interface devices leaves

the user the feeling of machine manipulation.

Studies have also been performed that focus on

wireless interaction without attachment of tethered

trackers. Body balance was mapped to navigation

according to weight shifts detected using weight

sensors (Fleischmann, 1999). The user controls

speed and direction through the entire body and

balance. However, the weight sensors needed a large

platform that makes the implementation difficult in

the system requiring a floor display such as IPDs.

A vision-based interface is then one of the most

suitable candidates for applications in which the user

moves the body within VE systems, allowing full

freedom of movement. The ALIVE system (Maes, et

al., 1997) is a gesture-language system in which the

user interacts with virtual creatures, and the

movements are controlled by the position of the

user's head, hands, and feet, through vision-based

tracking. They have been applied to the control of

avatars rather than 3D navigation. Magic Carpet

(Freeman, et al., 1998) was designed for navigation

in a 2D space, but a 3D VE was not considered. The

positional interpretation provided by vision-based

tracking has been mapped into navigation of 3D

game controls or inside a 3D Internet city by

movement of the user's body (Wren, et al., 1997; F.

Sparacino, et al., 2002).

All these works have been addressed on design

issues of HCI in VEs, but not on evaluation issues of

user performance, though the prototypes have been

developed for demonstrations. HCI by body position

has yet to be examined as a possible interface for

navigation in 3D VEs, particularly for a vision-based

system with an IPD.

3 SYSTEM OVERVIEW

3.1 Assignment of Body Position

We utilize both arms and legs for navigation, so that

the movements contribute to maintaining balance.

The user is therefore required to control viewpoint

motion and maintain their balance simultaneously.

Here, the basic premise of these assignments is an

intuitive understanding of the relationship between

body position and viewpoint motion. Figure 2

illustrates the assignment of body movements to

COMPARATIVE EXPERIMENT OF BODY-POSITION BASED NAVIGATION IN IMMERSIVE VIRTUAL

ENVIRONMENTS

107

viewpoint motion. The movements are limited to the

following 3 degrees of freedom (DOF) in order to

simplify the control parameters:

 Upward and downward movement of the head;

standing on tiptoes and bending of knees.

 Horizontal movement of hands; extending and

bending arms.

 Left and right rotation of both arms around the

axis of the body.

These 3 DOF correspond to vertical motion, forward

and backward motion, and counterclockwise and

clockwise rotation motion in VEs, respectively.

Figure 2: Assignment of body position.

Flexibility of body movements may generate the

various mapping methods between body position

and viewpoint motion. We decided to use the above

assignment, after the preliminary test obtained the

result that the above assignment was more suitable

for navigation by body position in VEs, than the

other two; 1) the body movements are identical to

the above but the viewpoint moves at the same

direction to that of extending and bending arms, and

2) upward and downward movements of the arms

are assigned to the vertical motion, instead of

standing on tiptoes and bending of knees.

3.2 Viewpoint Control

The image sensor must detect the 3D positioning of

both edges of the hands and the top of the head.

However, we cannot simply apply color or motion

segmentation techniques to the extraction of the

target, because the background images also change

in the screens of the IPD. Depth images are acquired

for extracting the body shape by the polynocular

stereo machine (Kimura, et al., 1999). Here, we use

a primitive structure to simplify the body position,

created by generating a straight line between the

edges of both hands.

To maintain stability at a point and smooth

motion of the viewpoint, the threshold and scale

were adjusted based on the properties of the body-

position movements using a trial and error process.

In forward and backward driving, the width between

the both hands when the arms are outstretched is

defined as the maximum forward speed, and two

thirds of the maximum width is linearly adjusted to

represent a speed of zero. When the hands are

brought together to less than two thirds of the

maximum width, the viewpoint begins to move

backwards.

3.3 Implementation

Figure 3 shows the system configuration of the IPD.

The VE system TEELeX (Asai, et al., 1999)

incorporates a 5.5 multi-screen display designed to

generate stereo images with the circular polarization

method. The acoustic system is also installed into

the display for 3D spatial sounds with 8 speakers.

The user's location is taken around the center of the

floor screen (3 m x 3 m) along the forward-

backward axis diagonal to the screen corners. The

multi-camera equipment is placed behind the user at

the edge of the screens, such that it does not obstruct

the view of the user.

Figure 3: System configuration.

An Onyx2 graphics computer [SGI] is used to

generate images of the virtual world. The

polynocular stereo machine is a Pentium III 1 GHz

PC, connected to the graphics computer. The body-

position data from the polynocular stereo machine is

multicast with UDP/IP, designating the receiving

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address. The polynocular stereo machine generates

depth images with the size of 280 x 200 pixels at the

frame rate of 30 frame/sec, which have the

resolution of 4 mm at 1 m, 15 mm at 2 m, and 34

mm at 3 m from the camera, respectively. Figure 4

shows examples of (a) depth image, and (b)

extracted skeleton with tracking points.

Figure 4: Examples of (a) depth image, and (b) extracted

skeleton with tracking points.

4 EXPERIMENT

We performed an experiment comparing the body-

position system with a joystick and a hand-arm

gesture interface. The experiment was designed to

address evaluation issues on user performance

including interaction style and cognitive capability

in navigation.

4.1 Method

4.1.1 Participants

21 participants took part in the experiment, all of

whom were aged from 18 to 28 and had normal

vision. Most of the participants had experience of

stereoscopic views, but none had experienced

navigation using the body-position system in an IPD.

4.1.2 Tasks

We prepared two different tasks for measuring

characteristics of the performance. The tasks were

designed as follows (Obstacle and Hallway), in

which the users were required not only to move

directly to a place but also to move dynamically with

their surrounding condition and to cope with the

cognitive loads in a VE.

In Obstacle, a participant is required to reach a

goal as fast as possible while avoiding obstacles in

the VE. A sky-blue transparent sphere 5 m in

diameter is put as the goal at the opposite end of the

VE from the initial point. Blue-white mottled

tetrahedrons and brown textured cubes are placed

within an area of 60 m from the center of the VE.

Figure 5 (a) shows a schematic layout of the

obstacles in the VE. The position of the goal is

initially fixed, but is modeled so as to elude the user

as the user approached. The speed of the goal is set

at 0.3 times the viewpoint speed. The 5,000

tetrahedron and cube obstacles are randomly located

and revolved around the center of the VE at 0.5 rpm.

Therefore, collisions occur even when the viewpoint

is motionless. When the viewpoint collides with the

obstacles, the viewpoint rebounds elastically

resulting in time loss.

(a) Obstacle (b) Hallway

Figure 5: The layout of the VE.

In Hallway, a participant is required to reach a

goal through a 3D hallway as fast as possible while

memorizing objects such as 3D models, pictures,

and Kanji idioms with their loci and directions. This

information gathering task was based on the work by

D. A. Bowman et al. [1998]. The hallway is

composed of 50 blocks of cube with 3 m edges, as

shown in Figure 5 (b). 12 objects are placed in the

hallway presented with transparent-green walls. We

used the popular pictures such as the Mona Lisa and

the Scream of Munch, and the familiar idioms with

four Kanji characters. When the viewpoint collides

with the wall of the hallway, the viewpoint rebounds

softly instead of getting out of the hallway.

4.1.3 Apparatus

The images were presented via the stereoscopic

display at a resolution of 1000 x 1000 dots per

screen, and the sounds were output from the 8

speakers. In the tasks, collision sounds notify the

participant of the collision besides the background

color change to red synchronously. The standing

position of the participant was basically fixed around

the center in the IPD.

COMPARATIVE EXPERIMENT OF BODY-POSITION BASED NAVIGATION IN IMMERSIVE VIRTUAL

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109

We used a joystick and hand-arm gestures

(Figure 6) as interfaces compared with the body-

position based navigation system. Wanda

○,

[Ascension] was implemented as a joystick, which

has been popularly used as a navigation device in

IPDs.

Figure 6: The compared interfaces (a) joystick and (b)

hand-arm gestures.

Figure 7 shows the hand-arm gestures. Flathand

and fist movements were mapped into adjustment of

the speed, and spreading and bending the arm

controlled the forward and backward directions,

respectively. Shifting the hand left and right rotates

the viewpoint left and right, and shifting the hand up

and down moves the viewpoint up and down. The

hand shapes are detected by resistive bend-sensing

of CyberGlove

○,

[Immersion] that transforms hand

and finger motions into joint-angle data. The

positions of the arm are detected by two magnetic

sensors of FASTRAK

○,

[Polhemus], which are put

on the back of the hand and the upper part of the arm.

Figure 7: The hand-arm gestures; (a) flathand and fist

movements for the speed control, (b) spreading and

bending, rotation, and upward and downward movements

of the arm.

4.1.4 Measurements

We measured the task completion time for each trial,

in which participants complete a series of

manipulations for the joystick, the hand-arm gesture

interface, and the body-position system. In the

Obstacle task, the number of collisions and the loci

of the viewpoint were recorded as accuracy

measurements for the navigation.

In the Hallway task, the cognitive load is defined

as accuracy of the information memorizing task. We

counted the number of

object/direction/location/surface sets the participant

got exactly right, and several variations of partially

correct objects, directions, loci, and surfaces. A

single response variable V that would encompass all

of these values was formulated as

(1)

where n expresses the kind of object. a is the number

of object/direction/location/surface sets exactly

correct, b represents responses that have three of

four aspects (object/direction/location/surface)

correct, and c is responses where two of four aspects

correct, and d is responses where only one of the

aspects are correct. The location over one block was

regarded as a correct answer.

4.1.5 Procedure

The trials were performed from the Obstacle task for

each participant. The Obstacle and Hallway tasks

had the trial seven times and twice, respectively. The

participants proceeded to the trials using a second

interface after the trials using the first interface.

In the body-position system, each task began

with capture of the initial position. The participants

were told to fix their bodies in a straight standing

position with both arms outstretched at shoulder

height, being advised to turn their palms upwards so

as to reduce fatigue. In the hand-arm gesture

interface, each task began with calibration of the

hand shapes that slightly depend on a user. The

recursive movements of flathand and fist are needed

for capturing the hand shapes. The joystick did not

need any calibration for the manipulation.

Before the actual experiment began, each

participant was allowed to practice controlling the

viewpoint until they felt controllable with the

interface. During the practice, participants were

advised to master staying at one point in the VE, and

were allowed to ask any questions about the task at

that time. After the word "Start" appeared on the

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screen, the participants were instructed to reach the

goal as quickly as possible and the word "Goal" was

displayed to notify the participant of the end of the

trial.

4.2 Results

One out of 21 Participants was retired because she

did not feel well during the experiment, and the data

were excluded from the analysis. The rest of

participants experienced all the tasks, but had some

trials that were not completed and some extras were

done in the tasks.

4.2.1 Obstacle Task

Table 1 lists the experimental results of the task

performance for each interface in the Obstacle task.

Each value indicates the average among the trials

experienced by the participants, and the value inside

the parenthesis is the standard deviation. The

Obstacle task had 139 trials for the joystick, 140 for

the hand-arm gestures, and 139 for the body-position

system, respectively. The joystick and body-position

system included one participant who did not

complete the trial once.

Table 1: Results of the Obstacle task.

Trials Time

[sec]

Collision

number

Speed

[km/h]

(a) 139 27.9 (12.1) 0.88 (1.7) 19.6

(15.9)

(b) 140 24.0 (14.5) 0.84 (1.9) 19.5

(6.9)

(15.1)

(a) Joystick, (b) Hand-arm, and (c) Body-position

A one-way ANOVA was performed for the

effect of interface on the completion time, collision

number, and speed in the VE. The effect of

completion time on the interface was statistically

significant at F(2,415) = 15.29, p < 0.01. Post hoc

analyses were conducted in order to compare all

possible pairs of the interfaces. The analyses show

that the trials with the body-position system were

completed significantly faster than those with either

joystick or the hand-arm gestures. There was no

significant difference between the joystick and the

hand-arm gestures in the completion time.

The effects of collision number and speed on the

interface had no significant difference at F(2,415) =

1.54 (p > 0.01) and F(2,415) = 6.13 (p > 0.01),

respectively. However, the speed effect had a trend

toward significance on the interface. Post hoc

analyses comparison of the pairs of the interfaces

showed that the navigation with the body-position

system had significantly higher speed than that with

the hand-arm gestures, and a trend to be faster than

that with joystick.

4.2.2 Hallway Task

The table 2 lists the results of the task performance

for each interface in the Hallway task. Each value

indicates the average among the trials experienced

by the participants, and the value inside the

parenthesis is the standard deviation. The Hallway

task had 34 trials for the joystick, 35 for the hand-

arm gestures, and 31 for the body-position system,

respectively. Although we designed that each

participant had the trial twice for each interface, 6, 5,

and 9 participants did complete the trial only once

for the joystick, the hand-arm gestures, and the

body-position system, respectively, because they felt

uncomfortable during the trials.

Table 2: Results of the Hallway task.

Trials Time

[sec]

Memorizing

value

Speed

[km/h]

(a) 34 120.3 (35.0) 29.8 (8.8) 7.4 (3.2)

(b) 35 147.0 (32.6) 26.7 (8.6) 5.4 (1.2)

(a) Joystick, (b) Hand-arm, and (c) Body-position

The results of ANOVA showed significant

effects of completion time and motion speed on the

interface at F(2,97) = 17.48 and F(2,97) = 16.93, p <

0.01, respectively, but no significant effect of the

interface on the information memorizing at F(2,97)

= 1.44, p > 0.01. Post hoc analysis comparison

showed that trials with the joystick had a

significantly shorter time of completion comparing

with trials with the body-position system and the

hand-arm gestures. The completion time had a trend

toward a significant difference between the body-

position system and the hand-arm gestures. Post hoc

analyses of the motion speed showed the similar

result to the completion time. Besides, the trials with

joystick had the larger standard deviations in the

completion time and the motion speed, compared to

those with the hand-arm gestures and body-position

system.

4.3 Discussion

We examined the user performance of the navigation

using the body-position, compared with the joystick

and the hand-arm gestures. We expected that

COMPARATIVE EXPERIMENT OF BODY-POSITION BASED NAVIGATION IN IMMERSIVE VIRTUAL

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111

different techniques of navigation would produce

different levels of user performance. We found that

the task worked as a significant factor that

dominated the results as well as the interface was the

significant variable.

In the Obstacle task, the body-position had a

significant effect on the task performance, and the

body-position system was superior to the joystick

and the hand-arm interface in the completion time

and the motion speed. One of our concerns is that

the body-position technique works to drive the

motion speed into being kept, and did not simply

result in improving performance for the navigation

in VEs, because more collisions were observed at

higher speeds when using the body-position

interface, while there was not clear correlation

between the collision frequency and speed for

individuals.

One possible scenario is that the body-position

interface promoted the participants willingness to

travel at higher speeds. This caused situations in

which it was easy to collide with the obstacles. The

participants who could evade collisions well,

reached the goal in a shorter period of time.

Otherwise, unavoidable collisions due to high-speed

movement resulted in longer times. Another scenario

is that the participants tended to dodge obstacles by

controlling direction, but not speed when using the

body-position interface. Stopping the motion was

often observed until the obstacles passed when using

the joystick. Keeping the velocity zero was a simple

process at the joystick because the participants could

just let go off the lever. Conversely, when using the

body-position interface, the participants had to

search for zero motion position for forward and

backward directions.

In the Hallway task, the joystick had an

overwhelming effect in the completion time and the

motion speed, though the excursion level was quite

high between the participants. The significant effect

of the joystick could be interpreted as the properties

where the joystick makes a user possible to navigate

VEs subtly. The travel with the joystick was quite

stable, accurately controlling the 3D position,

direction, and speed for the viewpoint. The large

excursions of the completion time and the motion

speed between the participants was possibly caused

by differences of the experiences using the similar

devices. The excursions between the participants

were not so large in the body-position system and

the hand-arm gestures, where the participants had no

previous experiences, as in the joystick.

Surprisingly, the navigation techniques did not

affect information memorizing scores, though we

expected that different control techniques would

produce different levels of cognitive loads. To

interpret the results, we checked the participants'

behavior from the recorded video. We found that

most of the participants took strategies for

performing the Hallway task with careful

information memorizing. The participants focused

on memorizing the objects, and passed slowly

through the hallway, sometimes stopping at the

objects for ordering their memory. Thus, the lack of

significant difference implies that the information

memorizing took priority over the completion time,

rather than simply that the level of cognitive loads

did not differ between the interfaces.

Overall, the results of the experiment indicated

that the body-position system was advantageous on

moving around at large areas instead of efficiency or

accuracy of navigation in VEs, while the joystick

appears to be advantageous on accurate viewpoint

motion control. The hand-arm gestures had the

middle characteristics between the body-position

system and the joystick. It is suggested that the

body-position based navigation is suitable for an

application getting users enjoying fly-through itself

with some entertaining elements.

5 CONCLUSIONS

We have developed a body-position based

navigation system as a vision-based interface in an

immersive VE. In our implementation, the body-

position enables us to navigate VEs via arm and

head movements without the need for devices

attached to the body. Stable position tracking is

achieved using depth images in the IPD. We

conducted an empirical evaluation by comparing the

body-position system with the joystick and the hand-

arm gesture interface. The results of the experiment

showed that the performance for the interfaces

depended on the task, and the body-position system

was advantageous on moving around at large areas

instead of efficiency or accuracy of navigation in

VEs. This suggests that the body-position interface

tends to suit applications in which amusement and

enjoyment are important, and conversely may not be

suitable for applications that require a high

efficiency.

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ACKNOWLEDGEMENTS

This study was supported in part by Grants-in-Aid

for Scientific Research (18500754).

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COMPARATIVE EXPERIMENT OF BODY-POSITION BASED NAVIGATION IN IMMERSIVE VIRTUAL

ENVIRONMENTS

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