Investigating Natural Interaction in Augmented Reality Environments
using Motion Qualities
Manuela Chessa and Nicoletta Noceti
Universit
`
a degli Studi di Genova,
Dept. of Informatics, Bioengineering, Robotics, and Systems Engineering,
Via Dodecaneso 35, Genova, IT-16146, Italy
Keywords:
Human-computer Interaction, Virtual Reality, Biological Motion Qualities, Reaching Tasks.
Abstract:
The evaluation of the users experience when interacting with virtual environments is a challenging task in
Human-Machine Interaction. Its relevance is expected to further grow in the near future, when the availability
of low-cost portable virtual reality tools will favour a shift – already started – from conventional interaction
controllers to a larger use of Natural User Interfaces, where people are asked to use their own body to interact
with the device. In this paper, we propose the use of motion qualities to analyze reaching movements, as indi-
cators of the naturalness of the users’ actions in augmented reality scenarios. By using such an approach, we
compare three different interaction modalities with virtual scenarios, with the goal of identifying the solution
that mostly resembles the interaction in a real-world environment.
1 INTRODUCTION
Human-Machine Interaction (HMI) in virtual and
augmented reality scenarios will have in the near fu-
ture an increasing presence in our daily activity. In-
deed, in the last year, an growing number of devices
for natural interaction have been developed. Those
tools have led to a shift from conventional interaction
controllers, such as data gloves and motion tracking
setup, toward low cost devices. As an example, the
Leap Motion
1
is a small sensor device that supports
hand and fingers’ motions as input and does not re-
quires neither contact nor touching. Supposedly, such
kind of sensors become a characterizing trait of appli-
cations such as games (Moser and Tscheligi, 2015),
immersive 3D modeling (MacAllister et al., 2016),
mobile augmented reality application (Kim and Lee,
2016), and rehabilitation (Khademi et al., 2014).
Moreover, virtual reality technology has provided
great opportunities for the development of effective
assessment techniques for various diseases, because
they provide multimodal and highly controllable en-
vironments (Iosa et al., 2015). In a virtual world, the
patient does not only react to the stimuli, but can ac-
tually interact with the computer-generated 3D life-
like environment. This provides an entire new realm
1
www.leapmotion.com
of possibilities for evaluation and treatments. In or-
der to obtain effective applications of such technolo-
gies, work has to be done in order to guarantee an
appropriate level of comfort experienced by the end-
user. Towards this goal, Natural Human-Machine In-
teraction (NHMI) aims at creating new interactive fra-
meworks that integrate human language and behavior
into technological applications, inspired by the way
people work and interact with each other (Baraldi
et al., 2009). Such frameworks have to be easy to
use, intuitive, entertaining and non-intrusive. As for
interaction tasks in the real world, the user should not
be asked to use external devices, to wear any kind of
tools, or to learn any specific commands or procedu-
res. Therefore, an interesting challenge for NHMI is
to make systems self-explanatory by working on their
“affordance” (Kaptelinin and Nardi, 2012) and intro-
ducing simple and intuitive interaction actions.
From this perspective, one of the main goal of Na-
tural User Interfaces (NUI) is to improve the quality
of interaction task within virtual and augmented re-
ality scenarios. As a consequence, there is the need
to design qualitative and quantitative evaluations pro-
tocols to evaluate the experience as perceived by the
user. Most commonly, this task has been addressed in
the literature by using qualitative questionnaire (Jai-
mes and Sebe, 2007), by looking at the absolute posi-
tioning error in simple tasks, such as reaching (Solari
110
Chessa M. and Noceti N.
Investigating Natural Interaction in Augmented Reality Environments using Motion Qualities.
DOI: 10.5220/0006268401100117
In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), pages 110-117
ISBN: 978-989-758-227-1
Copyright
c
 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

et al., 2013), or by looking at physiological measu-
res, such as the variation in the heart rate, when per-
forming the interaction tasks in virtual or augmented
scenarios (Chessa et al., 2016). However, if question-
naires or physical measures result in highly subjective
feedbacks, the quantitative analysis adopted so far fo-
cuses on a very limited part of the interaction, leading
to an analysis which only partially evaluate the qua-
lity of the interaction itself.
To overcome this limits, in this paper we propose
to analyse how actions are performed in simple aug-
mented reality scenarios, by adopting motion quali-
ties to provide an investigation of the overall level of
comfort during the interaction based on indicators of
the naturalness of the movements. More specifically,
we address the problem by considering motion featu-
res able to capture on the one hand the geometrical
properties of the movements – i.e. how the action
evolves over time from the spatial point of view – on
the other the dynamic of the motion – in terms of the
hand velocity. We draw inspiration from well known
regularities of biological motion, which are the out-
come of the fact, as human beings, our movements
are constrained by our physicalness. Different, yet re-
lated, theories have been formulated, see for instance
the minimun-jerk and isochrony principles (Viviani
and Flash, 1995). Among them, we specifically re-
fer to the Two-Thirds Power Law – a well-known in-
variant of upper limb human movements (Viviani and
Stucchi, 1992) – which provides a mathematical mo-
del of the mutual influence between shape and kine-
matics of human movements (Greene, 1972).
More specifically, the law describes an exponential
relation between velocity and curvature of the mo-
tion caused by a physical event, and in the case of
human motion (end-point movement in particular) it
has been shown that the exponent is very close to the
reference value of 1/3. In our work, we adopt the
empirical formulation of the law proposed in (Noceti
et al., 2015) and and successfully applied to recog-
nise human motion from visual data. Here, we use
the obtained statistics to compare the quality of in-
teraction in augmented reality scenarios with a com-
parable real world scenario. We consider interaction
sessions in such scenarios in terms of repetitions of
reaching actions towards a common reference target,
presented following different visualization strategies.
More specifically, we compare a classical 2D and ste-
reoscopic visualization; as for the interaction, the use
of a virtual avatar of a hand is compared with more
natural the use of the real hand. It is worth noting that
we take into account a very simple augmented reality
scenario and interaction tasks, in order to focus on the
way the action is performed, by limiting the degree of
freedoms and the perceptual cues that, of course, in-
fluence the movements.
The remainder of the paper is organized as fol-
low. In Sec. 2 we present the augmented reality setup
and provide details of the different visualization mo-
dalities, which are thoroughly compared in Sec. 3 –
where we describe the data collection and the expe-
rimental analysis. Sec. 4 is left to conclusions and
future lines of research.
2 MATERIAL AND METHODS
2.1 The Augmented Reality Setup
The experimental evaluation described in this paper
has been performed by using a setup composed of the
following modules:
- Visualization of the virtual scene on a large field
of view display, which can be used in both ste-
reoscopic and non-stereoscopic mode. In such a
situation, virtual objects appear overlaid onto the
real scene (e.g. the desktop, the surroundings of
the room). Thus, this setup does not represent an
implementation of immersive virtual reality, but
an augmented or mixed reality setup. Indeed, vir-
tual and real stimuli coexist at the same time. The
virtual scene is designed and rendered by using
Unity3D engine.
- Acquisition of the position of the user’s hands, by
using Leap Motion controller, a small USB perip-
heral device designed to be placed near a physical
desktop. The device is able to track the fine mo-
vements of the hands and the fingers in a roughly
hemispherical volume of about 1 meter above the
sensor at an acquisition frequency of about 120
Hz. The accuracy claimed for the detection of
the fingertips is approximately 0.01 mm. Howe-
ver, in (Weichert et al., 2013) the average accu-
racy of the controller was showed to be about 0.7
mm that allows us to effectively use the Leap Mo-
tion in our setup, for the purposes of the experi-
ments. The 3D positions for the 5 fingers, and
for the palm center, of each hand, are available.
Such information is used both inside the augmen-
ted reality environments to render the avatars of
the hands in the modalities in which they are re-
quired, and saved onto files, for the quantitative
evaluation which will be explained in this paper.
Investigating Natural Interaction in Augmented Reality Environments using Motion Qualities
111

(a) RT (b) 2D (c) 3D AV (d) 3D HAND
Figure 1: The augmented reality setup in the 4 modalities (a) Reaching of a real target; (b) Reaching of a virtual cube,
visualized monoscopically by using the hand’s avatar; (c) Reaching of a virtual cube, visualized stereoscopically by using the
hand’s avatar; (d) same task of the previous, but by using the users’ real hands.
2.2 Visualization and Interaction
Modalities
We have devised 4 different scenarios of interaction,
with a common virtual background displayed on the
monitor and representing a standard office environ-
ment, with books and a penholder (see Figure 1). The
target to be reached has the form of a small red cube.
The scenario has been intentionally kept very simple,
in order to better focus on the reaching task towards
the cube. Indeed, the aim of the paper is to analyze
how a simple action (i.e. to reach an object) is perfor-
med, by looking at the 3D trajectories, velocities, and
curvatures of some salient points of the users’ arms.
To analyze human motion in such simple scenario is
a good starting point to better understand interaction
modalities in more complex AR enviroments.
In the following we provide some details on each
visualization strategy:
- Real target (henceforth referred to as RT, Fi-
gure 1(a)): this modality is expected to provide
a baseline, and it is the reference scenario against
which the interaction with virtual scenes is com-
pared to. A real cube, whose dimension are
3cm × 3cm × 3cm is placed in a known position
in front of the user.
- 2D target with avatar (2D, Figure 1(b)): A vir-
tual cube, whose dimensions and 3D positions are
the same of the real one is displayed between the
user and the display. When the user acts in the
scene to perform the required task the avatars of
his/her hands are shown. The scene is rendered
monoscopically, and only one camera is used in-
side the game engine. The camera is positioned
at the same distance of the user’s position (com-
puted as the mean of the distance of his/her eyes)
with respect to the monitor, whose position corre-
sponds to the focal plane of the virtual camera.
- 3D target with avatar (3D AV, Figure 1(c)): diffe-
rently from the previous case, the scene is rende-
red stereoscopically. Two cameras with asymme-
tric frustums to correctly implement the off-axis
rendering techniques (Solari et al., 2013) are po-
sitioned inside the virtual scene. it is worth noting
that the off-axis technique has been ad-hoc imple-
mented, since it is not native in the Unity3D en-
gine. When the user acts in the scene to perform
the required task, the avatars of his/her hands are
shown.
- 3D target with real hand (3D HAND,Figure 1(d)):
the virtual scene and the stereoscopic rendering
are as in 3D AV, but no avatar is shown. The in-
teraction is instead performed with the real user’s
hands. Conversely to the second and the third sce-
narios, this modality represents a true “mixed re-
ality” environment, in which a user is asked to in-
teract with virtual objects, by using his/her own
body.
3 EXPERIMENTAL ANALYSIS
In this section we discuss in details the experimen-
tal comparison between the different modalities of in-
teraction. After having introduced the data we col-
lected, we will discuss kinematics and dynamic pro-
perties of the reaching movements.
3.1 Data Collection
Subjects. 15 subjects, males and females, from 24
to 45 years old, participated in the experiments. They
are all confident in the use of technology (everyday
use of PC and peripheral) but they did not use the
Leap Motion before, neither previously tested the se-
tup. All the subjects have normal or corrected-to nor-
mal vision.
Task. All the 4 modalities of visualization and
interaction have been tested by all the subjects in a
pseudo-random order, to avoid bias effects due to
the pre-exposure to a given modality. The subjects
VISAPP 2017 - International Conference on Computer Vision Theory and Applications
112

(a) (b) (c) (d)
Figure 2: A visual summary of the collected trajectories on the plane X-Z. The temporal series are color-coded according to
the user.
were seated in a fixed position, about 60 cm from
the monitor, the workspace was represented by a
volume of about 100cm × 60cm, the leap motion
was positioned on a desktop between the user and
the monitor. For all the 4 modalities each user was
asked to perform 20 reaching movements towards
the nearest-upper corner of the cube (virtual or real
depending on the considered modality). In the case
where the hand’s avatar was present, the reaching
was performed with the virtual index of the preferred
hand. In the other cases, the real index of the
preferred hand was used. All the participants were
right-handed. No specific constraints were given to
the participants about the starting and ending point of
each reaching movements, or about the trajectory or
velocity. The only reccomandation was to move in
the manner they felt more comfortable. To simplify
the analysis only the first starting position and the last
rest position were fixed. Figure 2 provides a glance
of the obtained trajectories (shown in the X-Z plane
for better interpretation) from which the variability of
the data may be appreciated.
3.2 Estimating Motion Features
The collected trajectories are first analysed in order to
detect the dynamic instants corresponding to tempo-
ral points where (i) a reaching instance starts (hence-
forth referred to as starting points), and (ii) the fin-
ger reaches the target (contact points). A pair of tem-
porally adjacent starting-contact points delimit a rea-
ching action, where a peak in the velocity can be de-
tected thanks to the well-known bell shapes charac-
terizing biological motion (Morasso, 1981). Figure 3
reports an example where dynamic instants and velo-
city peaks are marked on a trajectory described with
the Z coordinate (left) and on the corresponding tan-
gential velocity (right).
Further, we describe the reaching movements per-
formed by the users in terms of tangential velocity
and curvature estimated instantaneously (Noceti et al.,
2015). Given a certain 3D location P
t
= (x
t
, y
t
, z
t
)
acquired at time t, the velocity vector can be easily
obtained as
V
t
= (V
x
t
,V
y
t
,V
z
t
) = (x
t
− x
t−1
, y
t
− y
t−1
, z
t
− z
t−1
)
(1)
Similarly, the acceleration can be obtained as diffe-
rence between consecutive velocity estimates
A
t
= (A
x
t
, A
y
t
, A
z
t
) = (V
t
−V
t−1
,V
t
−V
t−1
,V
t
−V
t−1
).
(2)
An empirical formulation of the curvature is the fol-
lowing
C
t
=
||V
t
× A
t
||
||V
t
||
3
(3)
from which the radius of curvature can be computed
as the reciprocal.
It is worth noting that, without loosing in genera-
lity, the acquired data have been smoothed with a run-
ning average filter to remove noise due to the acquisi-
tion setup.
3.3 Motion Analysis
We now present the comparison between different in-
teraction modalities analysing the motion features we
introduced in the previous section. Later, we will le-
verage on the use of the Two-Thirds Power Law as a
mean to evaluate motion naturalness.
If not otherwise stated, we adopt Two-way ANOVA
methods to compare distributions of measures from
the different modalities. The influence of the type of
visualization and of the subject are taken into account.
Geometry of the Reaching Task. First, we consi-
der how participants used the space while performing
the reaching actions. We report in Figure 4 a visual
representation of the distribution in space of dynamic
instants. It can be observed how the spread characte-
rizing the area of the starting points (squares) is redu-
ced in the middle of the movement where the velocity
peak is achieved (asterisks).
Contact points further shrink in the area corre-
sponding to the target. To provide an overall idea
of the spatial extent of the performed movements,
Investigating Natural Interaction in Augmented Reality Environments using Motion Qualities
113

(a) (b)
Figure 3: Dynamic instants and velocity peaks are marked on a trajectory described with the Z coordinate (left) and on the
corresponding tangential velocity (right). Starting and contact points are marked with squares and circles, respectively. Peaks
velocities are marked with asterisks.
(a)
Figure 4: A visual representation of the distribution in the
3D space of dynamic instants.
we provide a visual representation of the displace-
ments between pairs of temporally adjacent starting
and contact instants as 3D points (see Figure 5). It
can be noticed a clear separation between observati-
ons from RT and 3D HAND with respect to the remai-
ning two scenarios. As a further evidence, we com-
puted the average magnitude for each scenario, obtai-
ning (in cm) 51.79 ± 11.30 for RT, 31.77 ± 11.24 for
2D, 34.69 ± 11.30 for 3D AV, and 44.98 ± 10.75 for
3D HAND. Though starting and contact points seem
to discriminate between avatar-based modalities with
respect to real hand ones, an ANOVA test on the posi-
tions of the velocity peaks does not reveal a significant
difference between the population of the four scena-
rios. All the contact points are correctly located in the
vicinity of the virtual or real targets.
Figure 6 shows the average reaching errors and
their standard deviations, estimated as the distance be-
tween contact points and exact target positions. As
expected, the lowest errors are for the RT modality,
followed by 3D HAND scenario.
Figure 5: A visualization of vectors connecting starting and
contact points (consecutive in time) reveals a clear separa-
tion between scenarios where avatar is used and scenarios
where the real hand is employed.
Figure 6: Absolute average errors and associated standard
deviation of the target localization (position of the contact
point) with respect the known target position.
Such errors are due to different causes: in the real
scenario the users could make the cube swing, thus
causing an error in the localization of the target. In
the steroscopic 3D scenarios, the users’eyes position
was not tracked, thus causing a localization error as
VISAPP 2017 - International Conference on Computer Vision Theory and Applications
114

explained in (Solari et al., 2013). This does not affect
the generality and validity of the experiment.
We then consider the populations of curvature (Eq.
3) estimations in the different scenarios. Significant
effects of modality F(3, 446.48) = 24.18, P < 0.05
and subject F(13, 114.55) = 6.2, P < 0.05 factors
have been observed. Also, an interaction between the
two factors F(39, 43.25) = 2.34, P < 0.05 has been
detected as statistically significant. More specifically,
modalities RT and 3D HAND resulted similar with
P = 0.16; a similar observation holds for modalities
2D and 3D AV, with P = 0.96. An investigation on
the statistical distributions of curvature among par-
ticipants reveals the presence of two subjects whose
measures statistically diverge from the rest of the po-
pulation.
We report in Figure 7(a) the curvature between a pair
of starting and contact point, sampled so to have a
common number of observations and averaged for
each scenario. It can be noticed how modalities RT
and 3D HAND are characterized by lower curvature
(i.e. more “direct” reaching, related to the minimum-
jerk) which may be indicative of higher velocity.
In summary, all the observations consistently indicate
that the presence of the avatar tends to spatially con-
straint the movements of users, providing more com-
pact temporal observations and an expected lower le-
vel of naturalness of the motion.
Dynamics of the Reaching Task. We computed the
tangential velocity in each point as in Eq. 1. A two-
way ANOVA reveals significant effect of the moda-
lity factor (F(3, 557.52) = 81.10, P < 0.05) and of
the subjects (F(13, 100.42) = 14.61, P < 0.05), as
well as an interaction between them (F(39, 38.76) =
5.64, P < 0.05). The comparison among modalities
shows strong similarities between RT and 3D HAND
(P = 0.40) and between the avatar-based modalities
(P = 0.79). 3 subjects out of the 14 show a statisti-
cally significant divergence of their velocity profiles
with respect to the rest of the population. In Figure
7(b) we report the sampled velocity bells representing
the reaching dynamic and corresponding to the curva-
ture plots in Figure 7(a). The average bells show that
2D, 3D AV, 3D HAND and RT are characterized by
an increasing overall velocity, which may be in turn
interpreted as an indication of the increasing level of
comfort experienced by the users in the scenarios.
Combining Curvature and Velocity. We conclude
our analysis by evaluating the pertinence of the obser-
ved motion to the Two-Thirds Power Law. We consi-
der the following formulation of the law
(a)
(b)
Figure 7: Average curvature and velocity of reaching acti-
ons, sampled to obtain comparable representations of equal
length.
V
t
= k

1
C
t

β
(4)
and adopted PCA (Jolliffe, 2002) to estimate the
function parameters k and β in the linear relation
log(V
t
) = log(k)+ βlog

1
C
t

(5)
While we can not have a prior on the value of
k, we expect β to be close to the reference value
of 1/3, which characterise human motion. A di-
vergence with respect to such a value may indicate
a decrease in the naturalness of the motion. The
Two-Way ANOVA test suggests that both modalities
and subjects influence the outcome (with, respecti-
vely, P(3, 0.18) = 92.90, P < 0.05, and P(13,0.75) =
386.70, P < 0.05). Also, an interaction between fac-
tors is observed (P(39, 0.09) = 44.24, P < 0.05). Fi-
gure 8 reports average and standard deviation of the
estimated βs. Only the RT scenario is characterized
by a population of βs not significantly different from
the distribution with
1
3
mean. The other scenarios –
3D HAND, 3D AV and 2D – progressively approach
Investigating Natural Interaction in Augmented Reality Environments using Motion Qualities
115

the reference exponent, indicating an increasing level
of smoothness of the movements.
Figure 8: Average and standard deviation of the estimated
βs.
3.4 Discussion
In order to provide a further qualitative evaluation
of their experience, we asked the participants to pro-
vide us with a ranking of their preferences when in-
teracting with the virtual scenarios. Table 1 shows
the obtained percentages, from which it is possible to
state that the most appreciated modality (in terms of
easiness and positive feeling in the interaction) is 3D
HAND. Overall, stereoscopic scenarios ranked first in
∼ 86% of the cases. The quality of interaction, as
self-reported by the users, are thus in line with the
outcomes of the analysis we proposed. However, it is
worth noting that this last investigation shows a hig-
her variability of the users’ acceptance with respect
to the considerations resulting from the motion-based
quantitative evaluation – where the highest pertinence
of 3D HAND to RT is consistently inferred, thus con-
firming the importance of a qualitative evaluation of
interaction to assess AR scenarios.
Table 1: Ranking of preference (in percentages) as self-
reported by the users.
1st 2nd 3rd
3D HAND 57.14 21.43 21.43
3D AV 28.57 57.14 14.29
2D 14.29 21.43 64.29
4 FUTURE DEVELOPMENTS
In this paper, we have used motion qualities to analyze
reaching movements, as indicators of the naturalness
of the users’ actions in augmented reality scenarios.
We have considered a simple AR setup, in order to
focus on a reaching movement, trough different visu-
alization modalities.
It may be possible that the results are affected by
the precision of the Leap Motion. Nevertheless, the
use of more precise, and more expensive or invasive,
tracking device is out of the scope of the paper and it
is far from our idea of obtaining Natural HCI by using
low-cost and off-the-shelf devices.
The obtained results show, in a consistent way,
that the interaction in a stereoscopic environment by
using own real hand is the most similar to the inte-
raction in a real-world scenario. This confirm the va-
lidity of the proposed approach.
As a future work we plan the extend the evalu-
ation of interaction naturalness in virtual and aug-
mented scenarios, by considering more complex tasks
and richer environments. Moreover, we plan to
jointly analyze quantitative motion qualities, sub-
jective users’ evaluation, and physiological measures,
with the aim of guiding the development of effective
Natural Human-Machine Interaction systems.
ACKNOWLEDGEMENTS
The authors would like to thanks all the participants
to the data collection.
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