Insert Your Own Body in the Oculus Rift to Improve Proprioception

Manuela Chessa, Lorenzo Caroggio, Huayi Huang and Fabio Solari

Dept. of Informatics, Bioengineering, Robotics, and Systems Engineering,

University of Genoa, Viale Causa 13, Genoa, Italy

Keywords:

Head-mounted-displays, Microsoft Kinect, Leap Motion, Virtual Reality, Registration and Calibration,

Self-perception.

Abstract:

A natural interaction in virtual reality environments, in particular when wearing head-mounted-displays, is

often prevented by the lack of a visual feedback about the user’s own body. This paper aims to create a virtual

environment, in which the user can visually perceive his/her own body, and can interact with the virtual objects,

by using a virtual body that replicates his/her movements. To this aim, we have set up an affordable virtual

reality system, which combines the Oculus Rift head-mounted-display, a Microsoft Kinect, and a Leap Motion,

in order to recreate inside the virtual environments a ﬁrst-person avatar, who replicates the movements of the

user’s full-body and the ﬁne movements of his/her ﬁngers and hands. By acting in such an environment, the

user is able to perceive him/herself thus improving his/her experience in the virtual reality. Here, we address

and propose a solution to the issues related to the integration of the different devices, and to the alignment and

registration of their reference systems. Finally, the effectiveness of the proposed system is assessed through

an experimental session, in which several users report their feeling by answering to a 5-points Likert scale

questionnaire.

1 INTRODUCTION AND

RELATED WORKS

The main motivation of the present work is to over-

come the lack of presence of the user own body in

virtual reality (VR) systems, in particular when wear-

ing a head-mounted-display (HMD). The aim of the

paper is to create a virtual environment in which the

user can visually perceive his/her own body, and can

interact with the virtual objects by using a body that

reproduces his/her movements. In particular, the fo-

cus is on having a realistic and natural interaction

through all the body and, in addition, a ﬁne interac-

tion with the hands. VR systems are commonly able

to elicit a strong sense of presence, allowing the user

to perform several uncommon operations in a safe en-

vironment: for example, in medical applications, in

(Ahlberg et al., 2002) it was observed that VR simu-

lation was able to predict the surgical outcome; also in

(Seymour et al., 2002) it was demonstrated the effec-

tiveness of the VR training in improving the operation

room performances.

The use of the hands in a virtual environment, in

order to have a better feeling of presence, was dis-

cussed in (Tecchia et al., 2014) with the aim of train-

ing the user, and in (Beattie et al., 2015) to use CAD

packages. The outcomes of such works consist in

a more natural way of interacting with the environ-

ments, and in the creation of more immersive virtual

systems.

However, the impossibility of having the full con-

trol and a natural perception of own virtual body often

gives a sense of unnaturalness to the user actions and

decreases the effectiveness of transferring the skills

acquired in the virtual environment to the real operat-

ing environment. In fact, one of the most important

differences of performing an experience in a virtual

context or in a real one, is related to the self-body

visual perception. For example, in a CAVE system

(Creagh, 2003), users can still see their body: this has,

of course, a great impact on the experience, especially

in tasks, where the interaction between the user and

the virtual environment is required. Avatar represen-

tation can be used, but it rarely correspond exactly to

the dimensions or current posture of the user.

In the literature, some researchers have used the

Kinect in order to animate an avatar inside a VR envi-

ronment for the Oculus Rift (Lee et al., 2015), but this

work has not the aim of recreating the self-perception

of the HMD user. Recently, in (Sra and Schmandt,

2015) the authors achieved the tracking of the users

body with a Kinect device such that their physical

Chessa, M., Caroggio, L., Huang, H. and Solari, F.

Insert Your Own Body in the Oculus Rift to Improve Proprioception.

DOI: 10.5220/0005851807550762

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP, pages 755-762

ISBN: 978-989-758-175-5

755

movements are mirrored in the virtual world, in a

collaborative environment. Users can see their own

avatar and the other person’s avatar allowing them to

perceive and act intuitively in the virtual environment.

In order to address the proprioception of own body

in VR, we propose to fuse the data acquired by an

RGB-D camera and by a low-price hand-tracking de-

vice, to reconstruct an accurate avatar, which moves

in a coherent way with the user. Some authors have

recently addressed the problem of fusing information

acquired by the RGB-D devices and the Leap Motion,

e.g. in (Penelle and Debeir, 2014) the authors create

an augmented reality system to be used with amputees

patients, and in (Ahmed et al., 2014), where an ap-

proach to fuse Kinect range images and Leap Motion

data for immersive augmented reality applications is

described.

The aim of our work is to set up an affordable

VR system, which combines the Oculus Rift HMD, a

RGB-D device, i.e. the Microsoft Kinect, and a Leap

Motion, in order to allow a user to perceive his/her

own body inside the VR environments also by taking

into account ﬁne details, such as the ﬁngers’ move-

ments, thus bridging the gap between the HMD sys-

tems and the CAVE system.

2 MATERIAL AND METHODS

The proposed system has been designed using differ-

ent sensors to track of the entire body of the user. The

depth and color images from the Microsoft Kinect

provide a quite robust tracking of the users move-

ments and at the same time the Leap Motion

pro-

vides a more precise tracking of the users hands and

ﬁngers position. We have also developed a calibra-

tion method that computes the rigid transformation to

align the two different frames of references expressed

from the two sensors. After the calibration, the data

from the Kinect and the Leap Motion (LM) are fused

together and used to control a ﬁrst-person 3D avatar,

which is showed inside a virtual environment, by us-

ing as HMD the Oculus Rift

(OR). The virtual en-

vironment has been created by using Unity3D 5

which also makes the assets for acquiring data from

Kinect and LM available in its store. The data fusion

gives some advantages: more accurate tracking of the

users hands and ﬁngers, and extension of the tracking

range by providing hand locations when they are not

visible to the Kinect.

https://dev.windows.com/en-us/kinect

https://developer.leapmotion.com/

https://developer.oculus.com/

https://unity3d.com/5

2.1 Description of the System Setup

Figure 1 shows an overview of the VR system de-

scribed in the paper. The user stands in front of

the Kinect at a distance of about 1.5m wearing the

OR. The Leap Motion is attached on the OR, and the

Kinect is located in a table. The user is free to move

over an area of about 1.5m

in order to interact with

the 3D objects in the virtual environment. The area

where the objects appear is set to be in the reaching

range for the entire users body.

Figure 1: Overview of the proposed system.

The device used for Virtual Reality is the Oculus

Rift DK2: its lenses distortion allows the user to have

a really immersive experience with an estimated ﬁeld

of view of 100 degrees. The device has a resolution

of 2160x1200 pixels and connected via HDMI 1.3 and

via USB2.0. The OR is very comfortable with a high

frame rate of about 90Hz and contains several sensors

such as the accelerometer, the gyroscope and mag-

netometer, which are used to track user position and

movement while wearing it. It is also present an ex-

ternal camera, the OR camera, which is available to

track the OR with an update rate of 60Hz and estimate

its location in a Cartesian coordinates system centered

on the camera.

To make the system works ﬁne, it requires a

slightly powerful computer and a large bandwidth to

manage the data ﬂow from the two sensors simulta-

neously. The used machine speciﬁcation are: a PC

with Mother Board Asrock Z77 Extreme4, equipped

with graphic card NVIDIA GeForce CTX 960, pro-

cessor Intel Core i5 2500k 3.30 GHz, 8GB of RAM,

operating system Windows 10 Pro 64bit.

2.2 Body Tracking

The data stream for tracking the user is acquired by

the Kinect, through a synchronization of depth and

color images with a resolution of 640x480 pixels at

a frame rate of 30Hz. The images are analyzed by

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756

the Kinect MS-SDK Assets, available in the Unity as-

set Store, which uses the Kinect Runtime provided by

Microsoft to make the tracking information suitable

to move an avatar. The asset gives information on the

tracking of twenty joints of the users body which are

then aligned with those acquired by the Leap Motion

(more details on the alignment procedures are in Sec-

tion 2.4).

2.3 Hand Tracking

The accurate tracking of the users hands and ﬁngers

are performed by the Leap Motion, which is a small

sensor connected via USB 2.0 and placed on the Ocu-

lus Rift support in front the users eyes. It is composed

of two wide-angle infrared CCD cameras with an ac-

quisition frequency of about 120 Hz and the detection

ﬁeld is approximately a hemisphere of 0.5m radius

above the sensor.

The technology behind the Leap Motion has not

be released by the manufacturer yet but the accuracy

announced for the position detection of the ﬁngertips

is approximately 0.01mm. For more details, in (We-

ichert et al., 2013) the authors have conducted a study

on the accuracy and robustness of the Leap Motion.

The acquired data are elaborated by the Leap Motion

SDK, which makes available the locations of the cen-

ter of the palm and of each single ﬁnger. The infor-

mation is expressed in a Cartesian coordinates system

centered on the device.

2.4 Calibration

Before the calibration step the data acquired by the

two sensors are used to produce a virtual avatar as in

Figure 2.

To align the data of all sensors, the calibration

phase is done in two steps:

Figure 2: Own avatar inside the VR environment by using

uncalibrated data.

• Rigid transformation between common points ac-

quired by the Kinect and the Leap Motion, com-

puted just once.

• Live corrections to overcome the residual offset

present between the Kinect and the Leap Motion

tracking, and management of the head position

over time, computed every frame.

Thanks to the Leap Motion Core Assets plug-in for

Unity3D, the Leap Motion and the Oculus Rift are al-

ready very well aligned: the two cameras of the Leap

Motion allow us to use the Oculus-Leap system as a

pass-through head mounted device, and to verify the

correctness of the alignment embedded in the plug-in.

The data obtained by the Kinect is properly low

pass ﬁltered, in order to remove most of the noise,

maintaining realistic movements of the body.

2.4.1 Rigid Transformation

To compute the rigid transformation, we use the

“least-square rigid motion using SVD” technique

(Sorkine, 2009), implemented in C# language, in or-

der to have a fast and reliable implementation of this

technique in the Unity3D environment. The method

can be formulated in the following way:

(

t) = argmin

R,t

∑

i=1

k(Rp

+ t) − q

where:

• R is the rotation matrix between the two sets of

points (and

R the computed estimate) ;

• t is the translation vector between the centers of

mass of the two sets of points (and

t the computed

estimate);

• P = {p

, p

, ..., p

} are the Leap Motion samples;

• Q = {q

, q

, ..., q

} are the Kinect samples;

• n is the number of samples.

Such a method requires several correspondences be-

tween the two systems to align them; in our case, we

have some common joints tracked by both the Kinect

and the Leap Motion: the centers of the hands, the

wrists, the elbows.

The result of the rigid transformation, done on a

single set of samples, often leads to have an alignment

visually incorrect; this is due to multiple factors:

• possible coplanar structure of the points: hands,

wrists, elbows almost on the same plane;

• noise that affects the Kinect joints: in particular

hands and wrists;

• frequent mismatch between the centers of the

hands, due to the noise and to the worse accuracy

of the Kinect, with respect to the Leap Motion.

Insert Your Own Body in the Oculus Rift to Improve Proprioception

757

To overcome this problem, we have taken in consider-

ation the use of less joints and more samples in time.

We decided to remove the centers of the hands from

the list of matching points (so we take in considera-

tion 4 points for each frame, the two wrists and the

two elbows) and 500 samples over time: during the

calibration period, the user have to move his/her arms

a little bit, paying attention that both are tracked by

the two sensors.

In order to maintain the correct distance between

the Oculus Rift point of view (POV) and the hands, as

well as its correct rotation, looking at the hands dur-

ing the phase of rigid transformation, we set the Ocu-

lus child-node to the middle point of the Leap Motion

matching points in the Unity3D environment: consid-

ering that, as explained in (Sorkine, 2009), the rota-

tion is done around the middle point, this ensures to

maintain a correct behavior of the POV.

After that, the visual result of the rigid transfor-

mation is far better than before, in particular for what

concerns the legs (see Figure 3). The Kinect and the

Leap Motion hands, however, still do not perfectly

overlap over time, because of the different precision

and the position of the two sensors.

Figure 3: Own avatar inside the VR environment, after the

calibration and the rigid trasformation.

2.4.2 Live Corrections

In order to have a unique body structure, which shows

a continuity between the arms tracked by the Leap

Motion and the rest of the body tracked by the Kinect,

we have to better fuse the data from the two sensors.

In particular, we choose to use only the positions pro-

vided by the Leap Motion, because of its better preci-

sion, when the arms are tracked by both sensors, and

use the data obtained by the Kinect, when the hands

are out of the range of the Leap Motion.

Thanks to the previous rigid transformation, the

legs are visually correct, even if they are not so

aligned as the hands. In second place, we manage

the movements of the point of view of the user in

a way that is consistent with the movements of the

head, to let the user move around having his/her vir-

tual body, which moves in a coherent way with his/her

real movements. There are two procedures to do this:

• Link the Oculus POV to the head joint tracked by

the Kinect: this solution allows the user to move

in all the area in which the Kinect is able to track

the user, but, because of the noise that affects the

measurement, the VR camera tends to suffer of

small oscillations, that have a bad impact on the

general sense of immersion of the user;

• Use the Oculus Positional Tracker (i.e. the OR

camera): this is the best solution in term of real-

ism of the movements of the head, but the area is

limited to the range and the direction of the Ocu-

lus Positional Tracker.

In order to obtain the most realistic solution, we

use the second solution; the position computed by

the Oculus Positional Tracker is affected by a little

drift-offset respect to the position obtained from the

Kinect, but it seems not to be relevant in the truthful-

ness of the body perception (see Figure 4).

Figure 4: Own avatar inside the VR environment: ﬁnal re-

sult after calibration process.

Finally, Figure 5 shows the system work ﬂow: at

each start we have to perform the calibration phase,

during which the system collects the data for the rigid

transformation, only if they are available from both

the Kinect and the Leap Motion. Once the system is

calibrated, we apply live corrections to use the best

data and animate correctly the hands, if the Leap Mo-

tion is tracking them. However, as explained before,

there is the possibility to go out of range of the Ocu-

lus camera: in this case, a warning message invites

the user to step back into the tracking range.

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Figure 5: The system work ﬂow: (i) at each start a calibration is performed, during which the system collects the data for the

rigid transformation (only if they are available from both the Kinect and the Leap Motion); (ii) once the system is calibrated,

live corrections are operated to use the best data and correctly animate the hands (if the Leap Motion is tracking them).

2.4.3 Adding a Mesh to the Avatar

With the aim of giving an even more realistic sense

of presence in the VR, we added a human mesh to

the scene as the body of the user (see Figure 6), but

see (Lugrin et al., 2015).

Figure 6: Adding a human mesh to the tracked skeleton.

We animated the mesh taking inspirations from

the skeleton we reconstructed and scaling in a proper

way to ﬁt the user, but several problems came out.

The main issue consists in the different way of ani-

mating the skeleton and the mesh. The former is an-

imated by giving to the joints the positions taken by

the Kinect and the Leap Motion and by connecting

them with simple structures, like parallelepipeds.The

latter is animated by giving to each bones of the skele-

ton linked with the mesh a correct rotation. So, the

challenge was to calculate, from the ﬁrst skeleton we

made, the correct angles to be given to the second

skeleton in order to obtain the same movements. This

work required a proper mapping of the movement of

the ﬁrst skeleton to a set of rotations to apply to the

second one, in relation to the starting position of the

mesh, in particular the initial rotation of every bone

of the second skeleton. Another problem involves the

structure of the second skeleton and the noise which

affects the Kinect data: each bones is nested with the

previous one. In this way, the noise that affects a sin-

gle joint, propagates to the following, altering the ﬁ-

nal result. The ﬁnal result is a more realistic body,

which may lead to a good feeling of presence in the

virtual environment, but the positions of the hands re-

sult to be less realistic than in the previous case, thus

hampering a proper interaction with the virtual ob-

jects of the scene. This is due to the propagating noise

coming from the Kinect data. A solution to this prob-

lem represents one of the future steps of this work.

3 EXPERIMENTAL EVALUATION

To validate the quality of the proposed system, we

have created an experimental session, which was at-

tended by 14 subjects: the participants were both male

and female, with ages ranging from 20 to 50, and

with normal or corrected-to-normal vision. In this ex-

periment, we recreate a simple skeleton consisting of

small spheres, in place of the joints tracked by the

Kinect, and parallelepipeds, in place of the bones of

the subject, except for the hands, for which we used

the ones of the model included in the Leap Motion

Insert Your Own Body in the Oculus Rift to Improve Proprioception

759

Core Assets for Unity3D. A simple skeleton for the

body, composed of basic objects, allows us to bet-

ter manage the data acquired by the Kinect and to

provide a coarse interaction with the virtual environ-

ment. Meanwhile, a more complex but tested model

for the hands guarantees us a better feeling of pres-

ence and a more accurate interaction with the virtual

objects. Figure 7 shows a snapshot of the experi-

mental session: on the left there is an image from

the Oculus Rift, where it is possible to see the re-

construction of the user’s hands and the virtual ob-

jects with which he/she can interact, on the right

there is the acquisition from the point of view of

the RGBD sensor. Moreover, a video showing the

system and the experimental session is available at

https://youtu.be/2UaxkyZbeLQ.

Figure 7: A snapshot of the experiment: (left) scene from

the Oculus Rift, and (right) from the RGB-D camera.

After the calibration phase, carried out for each

subject, the users were free to move in an area of

about 1.5m

and to explore the movements of the

skeleton, to ensure that they were coherent with their

movements; then some ﬂoating cubes were shown

and the subjects were free to interact with them, by

moving them with the hands and with the entire skele-

ton.

The aim of the experimental validation was to test

the level of immersivity and the experience of the

users that tested the proposed system. The sensa-

tion of presence (or level of immersion) is often mea-

sured by means of self-rated questionnaires (Gorini

et al., 2011). Several examples can be found in the

literature: in the UCL Presence Questionnaire partic-

ipants are required to provide ratings on a seven level

Likert scale (Slater et al., 1994), and in the Indepen-

dent Television Company Sense of Presence Inven-

tory (ITCSOPI) users are evaluated postexposure by

providing scores on a ﬁve level Likert scale (Lessiter

et al., 2001). In this paper, we have taken inspiration

from the questionnaires present in the literature, and

at the end of the session, users were asked to answer

a questionnaire with 13 close-ended questions. Sub-

jects rated their feelings on a 5-points Likert scale,

where 1 indicated negative feelings at all and 5 in-

dicated the most positive experience. The questions,

listed below, were related to: the quality of the VR

scenario (Q1-Q4), the truthfulness of the virtual body

(Q5-Q9), the sense of immersion in the VR (Q10-

Q13).

Q1 How often do you play videogames?

Q2 How much was the scenario immersive?

Q3 How much lag did you notice in the scenario?

Q4 How much did the visual aspects of environment

involve you in the scenario?

Q5 How strongly did you feel your virtual body was

truly?

Q6 How much did you feel your virtual body follow

your real movement?

Q7 How much did the virtual body help you to in-

crease the sense of presence in the environment?

Q8 How much natural was your interaction with the

object?

Q9 How closely were you able to examine your

hands?

Q10 How much did your experiences in the virtual

environment seem consistent with your real world ex-

periences?

Q11 How compelling was your sense of moving

around inside the virtual environment?

Q12 How much did the screen resolution negatively

affect your sense of presence?

Q13 Did you feel more immersed in the VR over

time?

The distribution and the statistics of the responses

to the 13 questions are reported in Figure 8.

In general, the feeling about the developed system

Figure 8: Questionnaire results: box plots of the scores (on

the 5-point Likert scale) given by the subjects to the 13

questions. The red lines represent the median scores, the

blue boxes are scores between the ﬁrst and the third quar-

tiles.

VISION4HCI 2016 - Special Session on Computer VISION for Natural Human Computer Interaction

760

was good. Question Q2 about immersivity reported a

median value of 4, together with Q5 about the posi-

tive feeling about the user’s own virtual body inside

the VR environment. Nevertheless, users were critic

about the natural interaction with the objects (ques-

tion Q8, median 3) and about the sense of moving

around inside the environment (question Q11, median

3). Of course, in such a system the sense of touch

is not addressed, though a tactile feedback, also by

means of a sensorial substitution, might help the user.

To sum up, the goals of this experiment were: (i)

to assess the performances of the proposed system, for

what concerns the responsiveness of the reconstructed

skeleton to the real movements of the user; (ii) to ver-

ify the effects of a full-body controllable avatar on the

feeling of presence in the VR. This experiment con-

ﬁrms the validity of the developed system, even in its

ﬁrst prototypal implementation.

4 CONCLUSION AND

DISCUSSION

In this paper, we have presented a method to insert the

avatar of the user own body, which gives the visual

feedback and replicates the movements of the user, in

a VR environment, enjoyed through an HMD. In par-

ticular, our scope was to improve the sense of pres-

ence by using immersive devices, such as the Oculus

Rift. In order to replicate the movements of both the

body of the user, and of his/her hands, by also taking

into account the ﬁne details of the ﬁngers, we have

proposed to use both a Microsoft Kinect, which ac-

quires the entire body of the users, and a Leap Mo-

tion, a low cost device used to measure and track the

ﬁngers. Such devices must be accurately registered in

order to obtain stable and robust measures, with re-

spect to a coherent reference system. To this aim, we

present a 2 steps procedure to achieve such a registra-

tion: (i) a rigid transformation through a least-square

SVD, from which we estimate the roto-translation to

align common points acquired by the two devices;

(ii) a live correction, by also taking into account the

movements of the users, captured through the Oculus

Positional Tracker.

We assessed the proposed VR system through an

experimental session, attended by 14 subjects who an-

swered to a self-reported questionnaire. The results

show that the users have a positive feeling about the

proposed system, in particular with respect to the im-

mersivity and to the sense of presence.

ACKNOWLEDGEMENTS

The authors would like to thank Prof. George Dret-

takis and Dr. Adrien Bousseau of the GRAPHDECO

group, INRIA Sophia-Antipolis, France.

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