Regis Dupuy

O Jourdan

G Mioche

A Ehrlich

F Waitz

C Gourbeyre

E Järvinen

M Schnaiter

A Schwarzenboeck

CLOUD MICROPHYSICAL PROPERTIES OF SUMMERTIME ARCTIC STRATOCUMULUS DURING THE ACLOUD CAMPAIGN : COMPARISON WITH PREVIOUS RESULTS IN THE EUROPEAN ARCTIC

INTRODUCTION

The Arctic region is more sensitive to climate change than any other region of the Earth (Solomon, 2007). Clouds and particularly low-level cloud related processes have a major impact on the Arctic surface energy budget (Curry, 1995;Curry, 1996;Morrison, 2011). Observations suggest that boundary layer mixed phase clouds (MPC, mixture of liquid droplets and ice) are ubiquitous in the Arctic and persist for several days under a variety of meteorological conditions (Mioche, 2015;Morrison, 2012;Shupe, 2011;Shupe and Intrieri, 2004). They occur as single or multiple stratiform layers of supercooled droplets near the cloud top from which ice crystals form and precipitate [START_REF] Mioche | Characterization of Arctic mixed-phase cloud properties at small scale and coupling with satellite remote sensing[END_REF]. The microphysical properties of these clouds have a large impact on the surface radiative fluxes and Arctic climate feedbacks (Kay, 2012;Kay and Gettelman, 2009) but are often poorly predicted by models (Forbes and Ahlgrimm 2014). In particular, a correct representation of MPC ice nucleation process and liquid-ice partitioning in numerical models is needed for both weather forecasts and climate prediction (Barret, 2017a,b).

In May-June 2017, a suite of "in situ" microphysical probes was deployed on-board the AWI Polar 6 aircraft during the ACLOUD experiment in the Svalbard region (Arctic Cloud Observations Using airborne measurements during polar Day). ACLOUD is a joint project of different German universities and research institutes embedded in the Transregional collaborative research center (AC)3. The general goal of ACLOUD is to gather a comprehensive data set of what will contribute to a process level understanding of clouds in the Arctic and their role in the amplified climate change observed in this region.

This campaign gave us the opportunity to explore the microphysical properties of summer arctic stratocumulus clouds over different surface conditions (open sea, sea ice and marginal sea ice zone). Cloud vertical profiles of liquid and ice water content, number concentration and effective size are presented under different surface conditions air mass origin.

These results are compared to previous studies from [START_REF] Lloyd | Observations and comparisons of cloud microphysical properties in spring and summertime Arctic stratocumulus clouds during the ACCACIA campaign[END_REF] performed during the ACCACIA campaign (summer/spring cloud over open sea), Young (2016, spring cloud transition from sea ice to open sea) as well as with cloud vertical profiles established by [START_REF] Mioche | Characterization of Arctic mixed-phase cloud properties at small scale and coupling with satellite remote sensing[END_REF] over the Greenland and Barents seas. Hereafter, these studies will be referred as L15, Y16 and M17 respectively.

INSTRUMENTATION

The instrumentation used in the present study was installed on the Polar 6 aircraft of the Alfred Wegener Institute (AWI). It is a modified DC-3 aircraft of the type Basler BT-67 with an endurance of up to 2300 km and a ceiling of 7.6 km altitude. Its cruise speed is between 10 and 150 knots and can operate for 6 to 7 hours with a take-off weight of 14 tons.

The Small Ice Detector (SID-3) instrument (Kaye, 2008;Vochezer, 2016) measures the size distribution of cloud particles from 5 to 50 μm. From a sub-sample of particles the spatial intensity distribution of the light scattered in the angular range of 7º to 23º is recorded as a two-dimensional scattering pattern. These patterns can be analyzed for the particle shape and sub-micrometer scale complexity (Vochezer, 2016;Schnaiter, 2018).

The Cloud Droplet Probe (CDP) is a forward scattering optical spectrometer using a single mode diode laser at a wavelength of 0.658 μm (Lance, 2010;Wendisch, 1996). The instrument counts and sizes individual droplets by detecting pulses of light scattered from a laser beam in the near-forward direction (4º -12º), (using a calibrated sample area of 0.32 mm 2 ). Sizes are accumulated in 30 bins with variable sizes (1 μm wide for size lower than 14 μm or 2 μm wide for larger droplets). The CDP was equipped with anti-shattering tips to reduce possible shattering artifacts (Korolev, 2011). Microphysical quantities were derived from the measurements , such as liquid water content (LWC) and effective droplet diameter (Deff) by integrating the cloud particle number size distribution (PNSD). The acquired PNSDs have been corrected to minimize the effect of Mie sizing ambiguities using a Monte Carlo inversion method.

The Cloud Imaging Probe (CIP) and the Precipitation Imaging Probe (PIP) measure the size and shape of cloud and precipitation particles (Baumgardner, 2011). The measurement principle of these Optical Array Probes (OAPs) is based on the linear array technique to acquire the two-dimensional black and white images of particles. As the particles pass through a laser beam, the shadow they cast on a photodiode array is used to record their dimensions. The PNSDs are computed from the OAP images, but the assessment of the median mass diameter (MMD) and the ice water content (IWC) relies on the definition of the crystal diameter and its mass-

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* Corresponding author address : Regis Dupuy, LaMP, Bat. Physique 8, 4 Avenue Blaise Pascal, TSA 60026, CS 60026, 63178 AUBIERE CEDEX, FRANCE diameter relationship (Brown and Francis 1995). Possible contamination of shattering/splashing of ice/liquid particles on the instruments tips have been identified and removed using inter arrival time statistics (Field, 2006) and image processing.

A similar instrument payload was used for L15, Y16 and M17 dataset and a comparison of every instrumentations can be seen in table 1. ~25 µm to 1.6 mm Ice particle size distribution, shape, IWC Table1 : List of the probes used for M17 study (in red), ACCACIA campaign (L15 and Y16 studies, in blue) and this study (in black) to characterize the liquid and ice properties of the Arctic Stratocumulus Clouds.

Cloud Probes

Size

DATA PROCESSING AND CLUSTERING

Meteorological conditions

The meteorological conditions used in this study are precisely described in M17 and [START_REF] Knudsen | Synoptic development during the ACLOUD/PASCAL field campaign near Svalbard in spring 2017[END_REF]. Table 2 summarizes the synoptic regimes encountered during the airborne campaigns. Mean cloud top temperature (CTT) and air mass origin (based on HYSPLIT back trajectory computation) are also displayed as they are used in our cloud observation classification.

Since the temperature was always above -15°C during ACLOUD, the M17 COLD regime was not included in the comparison.

Liquid / Ice discrimination

The discrimination of liquid and ice particle samples is based on the circularity of particles(see Crosier et al., 2011). Cloud particle images with value larger than 1.25 are classified as ice crystals. This method is only valid for images of particles with surface larger than 16 pixels and induces that the minimum diameter of particles identified as ice crystal is 125 microns for the CIP. In order to be consistent with previous studies, only particles with size lower than 50 µm are considered to be liquid water droplets. Table2 : List of meteorological periods and their associated number of flights defined by M17 (in red) and [START_REF] Knudsen | Synoptic development during the ACLOUD/PASCAL field campaign near Svalbard in spring 2017[END_REF]. The number of vertical profiles has been added in brackets for ACLOUD.

Regime

Surfaces overflown

The classification of the underlying surface is based on the average sea ice concentration measurements produced by the University of Bremen from AMSR2 satellite data) :

• Above 90 % is defined as "Sea Ice" (SI)

• Between 10 and 90 % is the "Marginal Ice Zone" (MIZ) • Lower than 10% is defined as "Open Water" (OW)

• Land correspond to NaN values.

It is important to note that M17 cloud samples were only taken over open water.

Altitude normalization

Since cloud top and cloud base heights exhibit large variability, the altitudes are normalized following the method presented in M17. The cloud top and cloud base refer to the liquid phase layer, i.e., the cloud layers containing liquid droplets (mixed-phase or liquid only). The liquid layer is identified based on the CDP and the CIP measurements. when the liquid water content is greater than 0.001 g/m 3 , the liquid extinction is above 1 km -1 and the ice extinction is lower than 0.5 km -1 .

Within the liquid layers (Eq. 1) and below the cloud base (Eq. 2) the normalized altitudes Zn are defined as follows :

where Zn is the normalized altitude, Z the altitude corresponding to the aircraft measurements, and Zt and Zb the cloud top and base altitudes, respectively. Thus, Zn = 1 corresponds to the top-of-the-cloud liquid-containing layer and 0 to its base. Negative values characterize regions of ice precipitation below the cloud layer and the altitude of -1 defines the ground level according to Eq. (2).

To obtain representative statistical results, the cloud layers have been stratified in 10 levels with intervals of Zn = 0.2 . The vertical profiles of MPC microphysical properties presented hereafter are obtained by averaging the in situ measurements over each normalized altitude layer.

RESULTS

Dataset statistical robustness

To characterize the robustness of our statistical analyses, Table 3 and4 show the number of samples within each data cluster according to the underlying surface and the meteorological conditions. Each sample represent a 10 second average of the measurements performed by ACLOUD probes (lowest time resolution of the SID3).

These tables show that nearly no sampling was performed over Land. Thus the "over land" measurements sub-sample will not be included in the following. The cold period is hardly representative as well as the MIZ during the normal period. However since cold and normal periods exhibit similar air mass origins and mean cloud top temperature, these two periods are merged leading to the following clusters :

LIQUID samples

• ACLOUD North = Cold Period + Normal Period • ACLOUD South = Warm Period The cloud vertical profiles are computed separately for each of these dataset clusters and presented in the following section for a better analysis and discussions of the results. While an earlier study by [START_REF] Lloyd | Observations and comparisons of cloud microphysical properties in spring and summertime Arctic stratocumulus clouds during the ACCACIA campaign[END_REF] has shown that five times more ice crystal concentrations were found in summer than in spring in this region over open water, few ice crystals are measured over open water (<1L -1 ) during ACLOUD (cf. Fig. 2). Ice crystals are mainly found in the lowest cloud layer but no general trend can be depicted as the ice properties show a large variability. It is important to note though that L15 was based on only one flight and may not be statistically comparable.

Vertical

Nevertheless, the ice particles properties (concentration and size) measured over the MIZ with south air mass origin seem similar to the spring cases over open water with the same air mass origin.

b) Impact of the underlying surface

Figure 3 shows that the main differences observed in the liquid phase properties can be attributed to the large scale meteorological conditions and the air mass origin (fewer and larger droplets during Normal-Cold period) linked to the aerosol concentrations.

However, changes in droplet concentration values with the underlying surface are also observed. Over MIZ and under southerly flow, a higher droplet concentration is found at cloud top followed by a steep decrease at lower levels. This could be indicative of changes in the droplet activation conditions (supersaturation, heat flux...) linked to the surface type. Ice crystal concentrations show that MIZ > SI > OW with a maximum of 2L -1 .

These trends have to be compared to previous results of [START_REF] Young | Observed microphysical changes in Arctic mixed-phase clouds when transitioning from sea ice to open ocean[END_REF] who showed that:

• Ice crystal properties were constant with surfaces with concentrations between 0.5 and 1.5L -1 • Droplet concentration : MIZ > SI > OW Yet, these measurements were conducted in March during one flight only and a mean top cloud temperature of -20°C.

Outlooks

The study presented in this poster is a preliminary study. We plan to carry on by first analyzing the aerosol concentration of particles larger than 100 nm to study their impact on cloud droplets. Particles larger than 500nm will be considered to investigate their impact as Ice Nuclei. This study will also be combined with an other study on small ice crystals performed by F. Waitz (see poster 72). Future campaigns are planned in this region (AFLUX 2019 andMOSAIC 2020) in order to increase the number of cloud samples with different air mass origins and surfaces overflown.

  Profiles a) Summer / Spring comparison Figure 1 shows the cloud vertical profiles for the liquid phase over open water in Spring and in Summer. Higher liquid water content is found in Summer (ACLOUD) compared to M17 Warm North. However similar values and behavior are encountered when comparing the summer profiles to the Spring M17 Warm South LWC data. Significant discrepancies in the droplet number concentrations are observed. High droplet concentrations are associated with low effective sizes and air mass originating from the South. This could be indicative of a possible Twomey effect but aerosol concentration are still needed to confirm these first results.

Figure 1 :

 1 Figure 1 : Vertical profiles (normalized altitudes) of liquid droplet extinction coefficient (a), number concentration (b), mass content (c) and effective diameter (d) median values for ACLOUD North (blue line), ACLOUD South (red line), M17 Warm North (black line) and M17 Warm South (green line). Shade of blue and red represent the interquartile values for ACLOUD North and ACLOUD South respectively.

Figure 2 :

 2 Figure 2 : vertical profiles (normalized altitudes) of large (Dp>125 mm) ice crystals extinction coefficient (a), number concentration (b), mass content (c) and effective diameter (d) median values for open water (full line), marginal ice zone (dashed lines) and sea ice (dotted lines) surfaces overflown during ACLOUD North (blue lines), ACLOUD South (red lines), M17 Warm North (black line) and M17 Warm South (green line) meteorological conditions.

Figure 3 :

 3 Figure 3 : vertical profiles (normalized altitudes) of liquid droplet extinction coefficient (a), number concentration (b), mass content (c) and effective diameter (d) median values for open water (full line), marginal ice zone (dashed lines) and sea ice (dotted lines) surfaces overflown during ACLOUD North (blue lines) and ACLOUD South (red lines) meteorological conditions.

Cold Period Normal Period Warm Period

	Sea Ice > 90%	0	1019	1146
	90% > MIZ > 10%	307	48	556
	10% > Open Water	56	419	548
	Land	0	45	0

Table 3 :

 3 Number of 10 second averaged samples of the cloud liquid phase for each cluster defined by the meteorological conditions and surfaces overflown during ACLOUD campaign.

	ICE	Cold	Normal	Warm
	samples	Period	Period	Period
	Sea Ice > 90%	0	297	1357
	90% > MIZ > 10%	93	9	676
	10% > Open Water	21	264	49
	Land	0	26	0

Table 4 :

 4 Number of 10 second averaged samples of the cloud ice phase for each cluster defined by the meteorological conditions and surfaces overflown during ACLOUD campaign.

ACKNOWLEDGMENT AND REFERENCES

This work was primarily funded by EECLAT project (Expecting EarthCare, Learning from A-Train) supported by the french Centre National des Etudes Spatiales (CNES). We gratefully acknowledge the support from the Transregional Collaborative Research Center (TR 172) "ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3 ", which is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft; DFG) and from the Chantier Arctique -Pollution in the Arctic System (PARCS) project, funded by CNRS. The authors thank the Alfred Wengener Institute (AWI) team for managaging and operating aircrafts in the arctic. Brown, P. R. A. and Francis, P. N., 1995 : Improved Measurements of the Ice Water Content in Cirrus Using a Total-Water Probe, J. Atmos. Ocean. Tech., 12, 410-414. Crosier, J. and al., 2011 : Observations of ice multiplication in a weakly convective cell embedded in


Mohammad Ali Mirzaei

Jean-Rémy Chardonnet
email: jean-remy.chardonnet@ensam.eu

Frédéric Merienne
email: frederic.merienne@ensam.eu

Comparing Real and Virtual Object Manipulation by Physiological Signals Analysis: A First Study

Keywords: Virtual Reality, Object Manipulation, EMG, CAVE

Virtual reality aims at reproducing reality and simulating actions like object manipulation tasks. Despite abundant past research on designing 3D interaction devices and methods to achieve close-to-real manipulation in virtual environments, strong differences exist between real and virtual object manipulation. Past work that compared between real and virtual manipulation mainly focused on user performance only. In this paper, we propose using also physiological signals, namely electromyography (EMG), to better characterize these differences. A first experiment featuring a simple pick-and-place task on a real setup and in a CAVE system showed that participants' muscular activity reveals a clearly different spectrum in the virtual environment compared to that in reality.

INTRODUCTION

Object manipulation has attracted much attention among other interaction tasks especially in virtual reality (VR). Obviously, object manipulation depends on environmental conditions such as the lighting, the presence of other objects and their location in the scene and so on. With the rapid development of VR for the last couple of years, reproducing high fidelity simulations in VR became a crucial issue. For instance, with the emergence of industry 4.0, simulations of manual tasks such as pick-and-place, manufacturing, handling operations are more and more frequent. These manual tasks need a precise evaluation to be validated [START_REF] Fumihito | Fast grasp synthesis for various shaped objects[END_REF].

When it comes to simulation, VR requires technologies and methods allowing to interact with the virtual environment. Strong differences usually exist between object manipulation in the virtual and the real environments because of missing sensory feedbacks such as gravity, roughness, pressure or temperature. In this regard, a huge piece of work has been proposed in the past [START_REF] Argelaguet | A survey of 3d object selection techniques for virtual environments[END_REF]. Though we observe for a few years great progress in natural interfaces and interactions [START_REF] Bowman | Questioning naturalism in 3d user interfaces[END_REF], these techniques are still not fully mature [START_REF] Mirzaei | Improvement of the real-time gesture analysis by a new mother wavelet and the application for the navigation inside a scale-one 3d system[END_REF][START_REF] Alibay | The Usability of Speech and/or Gestures in Multi-Modal Interface Systems[END_REF]. We still see, through newly commercialized VR headsets, that the most popular interaction interface remains based on ray tra-cing. These devices allow easy interaction, avoiding any occlusion issues.

In this paper, we propose to compare between real and virtual object manipulation. Not only the task performance is compared but also the physiological signals that are derived from this activity are considered. We are interested in better characterizing the differences between reality and VR. As a first study, we consider a very simple yet common manual task that is pick-and-place. Indeed, we do not want to consider more complex manipulation tasks as they require much more dexterity, especially for users who are not familiar with VR, which in turn means much more parameters monitoring.

RELATED WORK

One strong issue in VR is to make users feel present in the virtual environment [START_REF] Witmer | Measuring presence in virtual environments: A presence questionnaire[END_REF]. Early work has shown that high quality 3D graphics do not suffice to get high sense of presence. Other sensory feedback like tactile or haptic feedback showed to enhance the level of interaction [START_REF] Sturman | Hands-on interaction with virtual environments[END_REF][START_REF] Talati | Visual and tactile guidance of dexterous manipulation tasks: An fmri study[END_REF]. Haptic interfaces were quickly developed to provide the sense of weight, friction, touch (see for example early devices like [START_REF] Burdea | Force and Touch Feedback for Virtual Reality[END_REF][START_REF] Kim | Tension based 7-dof force feedback device: Spidar-g[END_REF][START_REF] Koyama | Multi-fingered exoskeleton haptic device using passive force feedback for dexterous teleoperation[END_REF] or more recent devices like [START_REF] Ma | Design and optimization of a five-finger haptic glove mechanism[END_REF][START_REF] Choi | Wolverine: A wearable haptic interface for grasping in vr[END_REF]), all challenging with imagination to provide users with realistic experience in manipulating virtual objects. The main drawbacks of such devices are: they require to be worn, meaning they are mostly intrusive, and calibration is often needed prior to be used. Léon et al. proposed a hands-on non-intrusive haptic manipulation device that allows a large variety of hand and finger configurations [START_REF] Léon | Dexterous grasping tasks generated with an add-on end-effector of a haptic feedback system[END_REF]. This device is intented to simulate virtual manipulation tasks requiring high dexterity. However, the main issue is its small workspace due to the haptic arm, which may lead to repeated arm movements to move a virtual object on a large span.

Despite abundant proposals, most of these devices were not validated through comparison with object manipulation in real environments. The question of comparing real and virtual tasks is in fact not trivial. Indeed, many parameters may encounter such as the system latency, the perceived realism of the virtual scene, the available field of view, stereoscopy, and so on. Early work attempted to compare between reality and virtual reality in training situations [START_REF] Kenyon | Training in virtual and real environments[END_REF]. Though the context was a little bit different than ours, results showed that virtual worldtrained users were significantly more performant than untrained users but that real world-trained users did not perform significantly better than untrained users. Graham and MacKenzie compared real and virtual pointing, showing that physical pointing leads to better results [START_REF] Graham | Physical versus virtual pointing[END_REF]. However their study was conducted on a 2D interaction basis whereas here we address 3D interaction. Later work in a different context showed better performance in virtual environments and even better in mixed reality (also called dual reality) environments, compared to the one in real environments [START_REF] Raber | The comparison of performance, efficiency, and task solution strategies in real, virtual and dual reality environments[END_REF]. However, the interaction task in virtual reality consisted in a 2D interaction on a touch screen, whereas interaction was done in 3D space in the real condition. Nevertheless, mixing reality and virtual reality seems an interesting alternative.

Several past research attempted to reduce differences between reality and VR, as in [START_REF] Kitamura | A manipulation environment of virtual and real objects using a magnetic metaphor[END_REF] where a manipulation method was designed by adapting reality to virtual environment constraints. However the proposed method requires constraining reality, which restricts practical usage. Chapoulie et al. proposed a framework to analyze finger-based 3D object manipulation considering several devices that were designed to be identical in both reality and VR [START_REF] Chapoulie | Finger-based manipulation in immersive spaces and the real world[END_REF]. Results on performance showed greater errors in virtual environments than in reality, with differences becoming more apparent as the complexity of the devices increased. In fact they pointed out that reproducing identically real setups in virtual reality is very complex. Also they compared between wand-like and natural interfaces, showing a tendency to better results with wand-like devices. Here we do not aim at considering finger-based interaction. We will rather stick to wand-like interfaces to cope with the fact that even newly commercialized VR headsets come with such devices.

In all past work reported above, comparison between real and virtual environments have been conducted measuring user performance (such as the completion time, task errors, cognitive load through subjective questionnaires, e.g., NASA-TLX [START_REF] Hart | Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research[END_REF]). To the best of our knowledge, physiological-based parameters have hardly been considered. Electromyography (EMG) or electroencephalography (EEG) has mostly been used in human-computer interaction research work as a mean to interact with virtual contents (for example [START_REF] Tisančín | Emg sensors as virtual input devices[END_REF][START_REF] Lotte | Combining BCI with Virtual Reality: Towards New Applications and Improved BCI[END_REF]), rather than as an analysis tool of interaction tasks. Through this first study, we contribute in exploring EMG as a tool to characterize differences between real and virtual object manipulation. We consider here a simple pickand-place task.

EXPERIMENT DESIGN

An experiment was designed in both a real and a virtual environments to compare user performance, user perception as well as the muscular activity during a simple object manipulation task.

Participants

10 participants were recruited on a voluntary basis within the university students to perform the experiment. No requirements regarding the level of knowledge in VR were specified when recruting the participants. There was a briefing to give enough information about the test procedure and we collected the participants' consent to perform the experiment. No participants got compensation to the experiment. None of them were familiar with VR, however they already experienced it at least once. All were right-handed and none of them reported any health issues.

Setup

The real setup consisted in a 1.5 m-long table on which three wooden cubes (15 × 15 × 15 cm 3 and an approximate weight of 1 kg) were arranged. Three squared marks were drawn on the table to indicate where to place these cubes (Figure 1.b).

The setup was reproduced in a virtual environment (VE) with the same scale (Figure 1.a). We ensured consistency in the scale in the VE by superimposing a virtual cube on a real one in the virtual setup, then adjusting the size of the virtual cube manually. The virtual setup consisted in an immersive room, allowing scale-one immersion, through a home-made software based on OpenSceneGraph and VRPN [START_REF] Taylor | VRPN: A Deviceindependent, Network-transparent VR Peripheral System[END_REF]. The immersive room was a 3×3×3 four-sided CAVE system with a 1400×1050 px resolution per side with stereoscopic vision, integrating an infraredbased tracking system to track the user's location in the CAVE and in the VE. Manipulation of the virtual cubes was performed using an ART Flystick 2 interaction device that has several buttons and a trigger. The Flystick was also tracked in position. Here we did not consider tactile or haptic feedback to avoid biases due to the intrusivity of devices incorporating such feedback.

Tasks

As shown in Figure 1.a, the object manipulation task in the virtual environment consists in a simple pickand-place task, that is, replacing the three blue cubes into the three green ones using the Flystick device. Selection is performed by pointing the Flystick at the center of the target cubes then pressing the trigger on the Flystick when enough close to the center of the target cube (less than 20 cm). The selected cube is then attached to the Flystick until the trigger is released. Using the trigger button allows simulating grasping. During displacement, the participant keeps pressing the trigger while positioning the selected cube. The blue cubes have to be carefully placed inside the marker cubes (the green cubes) during positioning. All three blue cubes have to be placed inside the green marker cubes in order (the first cube inside the first marker, and so on).

The same task is performed by hand in the real environment (Figure 1.b). The participant grabs the first cube, holds it up and places it inside the first red square marked up on the left side of the table (Figure 1.b), the same task is repeated for each cube.

Muscular Activity

As the object manipulation task we consider here is a simple pick-and-place task, the active limbs are mostly the arms, with a flexion/extension movement of the elbow. The hands are also solicited to grasp the objects. Here we will not consider hand and finger movements as the aim is not to study grasping configurations, nor grasping stability. The biceps muscle was therefore considered as an appropriate place to measure the muscular activity for this task, as the biceps functions as an important flexor of the forearm, which is needed to pick and place objects.

To measure the muscular activity, we use threelead wireless BIOPAC surface electromyography (sEMG) sensors1 . The sensors are connected to an amplifier and an acquisition stand via wireless connection, allowing wireless data logging by a PC. The EMG signal data are transmitted at a rate of 2 kHz, providing a high resolution wireless EMG waveform at the receiver's output.

The positive and negative leads were placed at the origin and end of the biceps muscle respectively and the ground lead right on the elbow. As the arm can be either opened, semi-opened or closed, we ensured stable and solid positioning of the sensors on the arm regarding body temperature variation and movement tension to get robust and meaningful signals.

Procedure

Each participant followed the procedure below:

1. Three BIOPAC sensors were placed on the participant's right arm as described in the previous section.

2. The participants were asked to lift off the three cubes one by one from the right side of the real table, to move them in a semi-circular path to trigger flexion of the elbow and to precisely place them on the three red squared marks on the left side of the table.

3. An EMG signal was recorded during this task. This measurement was considered as a reference measurement.

4. The participants were then introduced to the CAVE system. They were briefly explained how to use the Flystick and were able to train for two minutes.

5. The participants had to pick the virtual blue cubes, to move them in a semi-circular path and to place them in the green cubes trying to be as precise as possible. 6. An EMG signal was recorded during this task.

Throughout the experiment, the time of task completion was recorded in both environments.

At the end of the experiment, a presence questionnaire [START_REF] Witmer | Measuring presence in virtual environments: A presence questionnaire[END_REF] was filled out by the participants. We also asked the participants' preference between manipulation in the real and the virtual environments. The presence questionnaire and EMG are used here as psychological (subjective) and physiological (objective) measurements respectively.

The participants took less than 10 minutes to complete the whole experiment.

Object Manipulation Evaluation Criteria

We propose three criteria to evaluate object manipulation. The length of the movement, ϕ 1 , the amount of rotation, ϕ 2 , and the completion time, t, are considered as time space features for the comparison between the real and the virtual environments (1).

criteria = {ϕ 1 , ϕ 2 ,t} (1)
3.6.1 Criteria 1: The Length of the Object Movement Trajectory (ϕ 1 )

The first feature is the length of the movement, which is a criterion to assess a given task (selection, movement and placement of a cube) in different environments. Besides, it can be used to compare a manipulation mechanism or to compare the same task in two different environments. As a result, the longer the length of the movement, the harder the task. The movement in the real environment is considered as a semi-circular path with an average radius (r) and an average length πr. Theoretically, the length of a given continuous curve is calculated by (2) in 3D space.

ϕ 1 = b a   dx dt 2 + dy dt 2 + dz dt 2   dt (2)
Equation ( 2) can be easily substituted by (3) for a curve with sampled data with ∆t = 1 s.

ϕ 1 = n-1 ∑ i=1 ∆P i
(3)

∆P i = (x i+1 -x i ) 2 + (y i+1 -y i ) 2 + (z i+1 -z i ) 2
where n indicates the number of points along the path.

3.6.2 Criteria 2: The Amount of Rotation (ϕ 2 )

The amount of rotation along the circular path in a plane is defined by (4) as specified in [START_REF] Tai | Unified definition of divergence, curl, and gradient[END_REF].

∇ × F = 2π 0 F(x, y, z)d r (4)
where F is the movement trajectory. Since the movement is semi-circular, ( 5) and ( 6) are used to calculate the amount of rotation for a continuous and a discrete functions respectively.

∇ × F = π 0 F(x, y, z)d r (5) ∇ × F = - n-1 ∑ i=1 (x i+1 -x i ) cos(θ) cos(φ) ∆P i - n-1 ∑ i=1 (y i+1 -y i ) cos(θ) sin(φ) ∆P i + n-1 ∑ i=1 (z i+1 -z i ) sin(θ) ∆P i (6)
where θ and φ represent the parameters of the sphere coordinates. x i , y i , z i are the coordinates of the i th point P on curve F and n represents the number of points on F. We assume that the reference semicircular path lies on the XZ plane (the vertical plane on which the cubes lie), meaning y = 0 and φ = 0. Moreover, the resolution of θ is chosen as π n for simplicity. As a result, (6) can be rewritten as (7). Using (7) the total rotation along the path can be calculated.

∇ × F = ϕ 2 = - n-1 ∑ i=1 (x i+1 -x i ) cos i π n ∆P i + n-1 ∑ i=1 (z i+1 -z i ) sin i π n ∆P i (7)

RESULTS

4.1 Data Analysis in the Time Domain A paired Student t-test provided a significant difference between the length of the average movement in the virtual (M = 1.19 m, SD = 0.28) and the real (M = 0.68 m, SD = 0.05) environments (p = .039, t(9) = 5.41). Besides, we performed a one-way ANOVA test to be sure that the differences between each cube displacement were not significant in both environments, which was indeed the case (p = .141).

Amount of Rotation

The amount of total rotation can be calculated by ( 7). The results of this calculation are shown in Figure 2 right. A comparison between both environments showed that in the virtual environment this value is significantly higher than in reality (p = .0035).

Completion Time

Figure 3 shows the time taken to complete the manipulation task both in the virtual (black) and the real (gray) environments. A paired Student t-test provided a significant difference between the time of completion in the virtual (M = 79.78 s, SD = 43.20) and the real (M = 24.16 s, SD = 5.095) environments (p = .0026, t(9) = 3.62). Therefore, the participants spent more time in the virtual environment than in the real one (M v = 79.78 > M r = 24.16) to complete the manipulation task. Moreover, the variation in the virtual environment is much larger (SD v = 43.20 > SD r = 5.095) than in the real environment.

EMG Signal

An example of a logged EMG signal in the time domain is shown in Figure 4. We can observe a clear difference between the real and the virtual environments. As shown on the top signal, three activities corresponding to the displacement of the three cubes can be distinguished (separated by the red bars) in the real environment while in the virtual environment the activities (bottom signal) are not easily distinguishable, although the tasks in both environments are close to each other. This is the only information we can get in the time domain. There are high and low components in the signal that can hardly be differentiated.

Data Analysis in the Frequency Domain

Because the time representation of the EMG signals does not provide clear clues on the muscular activity, especially in the virtual environment, the recorded EMG signals are converted to the time-frequency space [START_REF] Boashash | Time Frequency Analysis, A Comprehensive Reference[END_REF].

Figure 5 shows the time-frequency representation of the EMG signal for three participants. Red colors indicate frequency components with high power while blue colors indicate frequency components with very low power. From this representation, activities can be clearly distinguished in the real environment, with three sets of components corresponding to the displacement of the three cubes. Most of high power components are below 100 Hz, which corresponds to normal muscle activities [START_REF] Sadoyama | Frequency analysis of surface emg to evaluation of muscle fatigue[END_REF]. Whereas in the virtual environment, we can observe much more components with lower power, which supposes more movements with smaller amplitude of the biceps.

Questionnaire Data Analysis

First, we asked the participants to rate the level of their satisfaction in both environments on a 7-point Likert scale (Figure 6). As seen, the highest rate has been given to the real environment, in average 5.9. A paired Student t-test showed a significant difference between the real (M = 5.9, SD = 1.17) and the virtual (M = 4.5, SD = 1.19) environments (p = .0013, t(9) = 3.45). Then, from the presence questionnaire, the control (CF), sensory (SF) and distraction (DF) factors were calculated as three main factors with the correction factor proposed by [START_REF] Witmer | Measuring presence in virtual environments: A presence questionnaire[END_REF]. The results of the calculation are shown in Figure 7. In average, the score associated with CF and SF is higher than DF. From a paired Student t-test, CF (M = 7.049, SD = 1.32) and SF (M = 5.93, SD = 1.37) are significantly higher (p = 2.71 × 10 -6 , t(9) = 4.32 and p = .0009, t(9) = 3.75, respectively) than DF (M = 3.9, SD = 0.79).

DISCUSSION AND LIMITATIONS

From the results, the participants spent more time with larger movements in the virtual environment than in the real one ( ∼ = 3 times), which shows object manipulation in the virtual environment is harder than in the real environment. One reason lies in the fact that we did not provide any tactile or haptic feedback to the participants in the virtual environment, which confirms past work on the importance of adding other sensory cues than just visual feedback. Another reason may lie in the choice of participants who were not familiar with VR. Further experiments should be conducted with VR users. Also we observed a much larger variation of the length of the movement trajectory in the virtual environment than in reality. This huge difference, 9 times, originates from different training experiences. None of the participants experienced virtual object manipulation in the near past. However, surely they experienced similar object manipulation tasks in real environments. Since the participants did not have the same experience in the virtual environment, they recalled their experience from the real environment. Because there is a strong difference in terms of sensory input to the cortex between the real and the expected inputs, the calculated motor command in the brain either is overestimated or underestimated which in turn creates a large deviation from the mean value in the vir-tual environment. Again, other experiments should be conducted with VR users to see whether these differences of variation tend to decrease.

Regarding EMG, we see a clear difference of muscular activity between the real and the virtual environments. The time-frequency representation of the EMG signals showed much more components with lower power in the virtual environment. Indeed, in the real environment, complete feedbacks (visual, tactile, sound) are provided by the sensory organs to the brain, consequently the brain can recall the appropriate pattern and generate accurate motor commands to the muscles. In the virtual environment, some sensory information is missing; as a result the brain cannot extract the appropriate pattern and therefore, it cannot generate very accurate motor commands to the muscles. Typically, the participants performed several times small flexion/extension movements of their forearm to precisely position the cubes in the virtual environment.

Here, we considered real cubes of approximately 1 kg whereas the Flystick weighs around 300 g, which may have affected the measurements. Unrealistic texture rendering in the virtual condition may also have influenced movements as past work showed texture to have an influence on weight perception [START_REF] Flanagan | Effects of surface texture on weight perception when lifting objects with a precision grip[END_REF]. Further experiments should be done with exactly the same conditions in both environments.

Another limitation of our study is that in the virtual condition, the participants had to hold the Flystick all the time, even when they did not grasp any virtual cube, which could bias the comparison with the real condition. Though EMG signals were provided from the biceps thus without any link with hand movements, further investigation should be carried out to verify this aspect, using for example optical-based hand trackers.

Looking at subjective data, the presence questionnaire revealed that the control and the sensory factors were much higher than the distraction factor. It means the user interface was well designed for an object manipulation task in virtual environments, was not distractive and was capable to involve several sensory inputs. To verify this claim, we have compared DF with the participants involvement (INV). A paired Student t-test showed INV (M = 3.9, SD = 0.785) to be significantly higher than DF (M = 5.75, SD = 1.64) (p = .0038, t(9) = 3.95) as shown in Figure 8.

Finally, as other sensory cues were missing in the virtual environment, not surprisingly, the participants preferred object manipulation in the real environment than in the virtual one, with a level of satisfaction significantly higher in the real condition than in the virtual one. Investigation should be made to determine the role of the interaction technique, apart from the effect of multi-sensory cues, as in the virtual condition we imposed an interaction strategy, whereas in the real condition, the participants were free to choose their own grasping strategy.

CONCLUSION

We evaluated through a first study a simple manipulation task using EMG and objective criteria, and compared a real and a virtual environment situations. EMG signals revealed clear differences between the real and the virtual environments, and time space features showed the virtual condition to require more arm movements than in the real condition. These differences originated from a gap between the actual and the expected sensory inputs to the brain. As virtual reality aims at reproducing real situations, characterizing differences between the real and the virtual environments could help better design interaction devices and methods, so that user experience can be enhanced in virtual environments and be close to real manipulation tasks.

Future work will include in-depth investigation with more participants accustomed to VR and different object manipulation techniques closer to real manipulation, e.g., using finger trackers that do not require holding any device all the time. We will also consider tactile/haptic feedback and see how physiological signals behave accordingly, knowing that past research showed tactile feedback to enhance interaction in virtual environments.

Figure 1 :

 1 Figure 1: Object manipulation in (left) a virtual and (right) a real environments.

4. 1 . 1

 11 Length of the Movement TrajectoryBy applying (3) the length of the performed movement can be calculated for each cube. The results of this calculation are shown in Figure2left.

Figure 2 :

 2 Figure 2: Length of the movement trajectory (top) and amount of rotation (bottom) in the virtual (blue) and the real (orange) environments for the three cubes with the order shown in Figure 1.

Figure 3 :

 3 Figure 3: Time of task completion in the real and the virtual environments.

Figure 4 :

 4 Figure 4: EMG signals recorded in the real (top) and the virtual (bottom) environments for one participant.

Figure 5 :

 5 Figure 5: Time-frequency representation of the EMG signal in the (a) real and the (b) virtual environments for three participants.

Figure 6 :

 6 Figure 6: Level of satisfaction in both environments.

Figure 7 :

 7 Figure 7: Sub-scores provided by the presence questionnaire in the virtual environment.

Figure 8 :

 8 Figure 8: Level of distraction and involvement in the object manipulation task in the virtual environment.

Comparing Real and Virtual Object Manipulation by Physiological Signals Analysis: A First Study

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CHIRA 2018 -2nd International Conference on Computer-Human Interaction Research and Applications