domain: info spi info.info-gr

text:
Nicolas Vignais
email: nicolas.vignais@univ-rennes2.fr

Richard Kulpa

Cathy Craig

Sébastien Brault

Franck Multon

Benoit Bideau

Influence of the graphical levels of detail of a virtual thrower on the perception of the movement

Keywords: virtual reality, point-light display, presence, animation

Virtual reality has a number of advantages for analyzing sports interactions such as the standardization of experimental conditions, stereoscopic vision and complete control of animated humanoid movement. Nevertheless, in order to be useful for sports applications, accurate perception of simulated movement in the virtual sports environment is essential. This perception depends on parameters of the synthetic character such as the number of degrees of freedom of its skeleton or the levels of detail (LOD) of its graphical representation. This study focuses on the influence of this latter parameter on the perception of the movement. In order to evaluate it, this study analyzes the judgments of immersed handball goalkeepers that play against a graphically modified virtual thrower. Five graphical representations of the throwing action were defined: a textured reference level (L0), a non-textured level (L1), a wire-frame level (L2), a moving point light display (MLD) level with a normal-sized ball (L3) and a MLD level where the ball is represented by a point of light (L4). The results show that judgments made by goalkeepers in the L4 condition are significantly less accurate than in all the other conditions (p<0.001). This finding means that the goalkeepers' perception of the movement is influenced more by the size of the ball during the judgment task than the graphical LOD of the throwing action. The MLD representation of the movement thus appears to be sufficient for a sports duel analysis in virtual environments.

Introduction

Virtual reality is starting to be used more frequently as a means of understanding sports performance in athletes [START_REF] Capin | Virtual human representation and communication in VLNET networked virtual environments[END_REF][START_REF] Kelly | Design and construction of a bobsled driver simulator[END_REF][START_REF] Bideau | Real handball goalkeeper vs. virtual handball thrower[END_REF][START_REF] Craig | Judging where a ball will go: The case of curved free kicks in football[END_REF][START_REF] Craig | Optic variables used to judge future ball arrival position in expert and novice soccer players[END_REF]. Indeed, this tool offers several advantages over traditional methods for situational analysis in sports [START_REF] Bideau | Virtual reality, a serious game for understanding performance and training players in sport[END_REF]: namely reproducibility between trials [START_REF] Tarr | Virtual reality in behavioural neuroscience and beyond[END_REF], complete control of virtual environment and characters [START_REF] Bideau | Using virtual reality to analyse links between handball thrower kinematics and goalkeeper's reactions[END_REF] and presentation of the visual scene from the subject's view point (egocentric) and not that of an allocentrically positioned camera (e.g. spectator position) [START_REF] Sheridan | Musings on telepresence and virtual presence[END_REF][START_REF] Petit | Scene perception and decision making in sport simulation: a masked priming investigation[END_REF]. In spite of these advantages, using virtual reality as a sports analysis tool does raise new questions. One of the most important is the perception of the movement of synthetic humanoids by the immersed athlete. This perception depends on parameters of the synthetic character such as the number of degrees of freedom of its skeleton or the levels of detail (LOD) of its graphical representation. This study focuses on the influence of this latter parameter on the perception of the movement. The final goal is to determine if there is a threshold on the graphical LOD that ensures that virtual reality can be used for sports applications.

Related Works

Several studies have been carried out on the visual LOD of a movement. In 1973 Johansson demonstrated that humans have the ability to accurately identify actions with limited visual information [START_REF] Johansson | Visual perception of biological motion and a model for its analysis[END_REF]. In these landmark experiments observers could only see points of light attached to strategic body parts as an actor performed an action and were subsequently named moving point light displays (MLD). The results from these experiments showed that in spite of the impoverished visual information, human observers were still able to accurately identify the action being performed. These findings suggest that the human visual system is highly sensitive to biological motion and is therefore capable of extracting relevant information from the dynamic spatiotemporal patterns of the moving point light displays (MLD). The perception of biological motion has since been thoroughly investigated and findings show that humans can reliably perceive gender [START_REF] Kozlowski | Recognizing the sex of a walker from a dynamic point-light display[END_REF], recognize individuals [START_REF] Cutting | Recognizing friends by their walk: Gait perception without familiarity cues[END_REF], categorize actions and motion types [START_REF] Dittrich | Perception of emotion from dynamic point-light displays represented in dance[END_REF] from moving point-light displays alone.

In sport it has also been demonstrated that biological motion information picked up from moving point light displays (MLD) plays an important role in anticipation [START_REF] Williams | Anxiety, expertise, and visual search strategy in karate[END_REF]. For example, the MLD method has been used in different activities such as squash [START_REF] Abernethy | Expertise and the perception of kinematic and situational probability information[END_REF] and tennis [START_REF] Shim | The use of anticipatory visual cues by highly skilled tennis players[END_REF] in order to evaluate differences in perceptual anticipation between novices and experts. This kind of studies revealed that players are able to extract movement information in both video and point-light displays [START_REF] Shim | Perception of kinematic characteristics of tennis strokes for anticipating stroke type and direction[END_REF][START_REF] Ward | Visual search and biological motion perception in tennis[END_REF]. In computer animation, Hodgins and colleagues (1998) have also demonstrated that perception of motion is influenced by the geometric model (polygonal or stick) used for rendering [START_REF] Hodgins | Perception of human motion with different geometric models[END_REF]. It should be noted however that all of these studies have been carried out only using two-dimensional displays. Carrying out complementary studies that use stereovision to assess the role of depth related visual information [START_REF] Slater | Immersion, presence and performance in virtual environments: An experiment with tri-dimensional chess[END_REF] are however necessary.

Stereoscopic vision and head tracking are immersion factors commonly used in virtual reality [START_REF] Hendrix | Presence within virtual environments as a function of visual display parameters[END_REF]. These two elements are also associated to presence [START_REF] Sanchez-Vives | From Presence to Consciousness through Virtual Reality[END_REF]. In fact, the terms immersion and presence are often confused in the literature. There are two main approaches to define these notions. In the traditional approach, the concept of immersion refers to the objective level of sensory fidelity a virtual reality system provides. In that sense, immersion is defined as "the technical capability of the system to deliver a surrounding and convincing environment with which the participant can interact" [START_REF] Sanchez-Vives | From Presence to Consciousness through Virtual Reality[END_REF]. Immersion is objective and measurable -one system can have a higher level of immersion of another. Consequently, the degree of immersion can be measured independently of the human experience that it engenders [START_REF] Sanchez-Vives | From Presence to Consciousness through Virtual Reality[END_REF].

The traditional approach has also considered the concept of presence as the human response to the virtual reality system. More precisely, this phenomenon is defined as the subjective feeling of "being there" [START_REF] Sanchez-Vives | From Presence to Consciousness through Virtual Reality[END_REF]. [START_REF] Slater | Visual realism enhances realistic response in an immersive virtual environment[END_REF] said that "presence refers to how realistically participants respond to the environment as well as their subjective sense of being in the place depicted by the VE" [START_REF] Slater | Visual realism enhances realistic response in an immersive virtual environment[END_REF]. The level of presence can be evaluated in different ways. The first methodology concerns the measure of presence using questionnaires [START_REF] Witmer | Measuring presence in virtual environments: A presence questionnaire[END_REF]. As this kind of evaluation is subjective, an alternative methodology is to look for objective measures that quantify physiological, optical or physical changes in behaviour. Performance during a motor task can then be considered as one of these physical measures [START_REF] Slater | Immersion, presence and performance in virtual environments: An experiment with tri-dimensional chess[END_REF][START_REF] Slater | Visual realism enhances realistic response in an immersive virtual environment[END_REF]. This concept has been applied in sport. Indeed, [START_REF] Bideau | Real handball goalkeeper vs. virtual handball thrower[END_REF] compared the movement of handball goalkeepers during the parry of the same throws made by real and virtual opponents. The results showed that the goalkeepers' gestures in both environments were similar. However, equivalent motor response does not imply the presence of the subject. Indeed, he can act the same way even if he feels far from being there. The objective evaluation is then correct while the subjective one obtained by questionnaire is not good. In that case, [START_REF] Bowman | Virtual reality: how much immersion is enough?[END_REF] told about the evaluation of the "immersion's effects" instead of the evaluation of presence. Our study can then be considered as a contribution concerning the importance of an effect of immersion: the LOD of a virtual character.

The realism of the visual elements displayed in a virtual application is a key point for immersive systems. In the field of sport, [START_REF] Ma | Presence, workload and performance effects on synthetic environment design factors[END_REF] have analyzed a range of synthetic environment design features suspected to influence athlete's perception in a virtualreality-based basketball free-throw task [START_REF] Ma | Presence, workload and performance effects on synthetic environment design factors[END_REF]. Virtual viewpoint, auditory cue type and visual background were manipulated to see their relative influence. The results demonstrated that viewpoint and auditory cue type significantly impact actors' perception.

The authors noticed that their results do not support those of Barfield et al. in which an increase in the degree of visual realism of a synthetic environment implies an increased level of presence [START_REF] Barfield | Spatial performance with perspective displays as a function of computer graphics eyepoint elevation and geometric field of view[END_REF]. On the whole, studies about the influence of the visual realism of the images on the perception did not found any consensus [START_REF] Slater | The influence of dynamics shadows on presence in immersive virtual environments[END_REF]Welch, Blackmon, Liu, Mellers & Starcks, 1996;[START_REF] Usoh | Using presence questionnaire in reality[END_REF][START_REF] Zimmons | The influence of rendering quality on presence and task performance in a virtual environment[END_REF][START_REF] Sanchez-Vives | From Presence to Consciousness through Virtual Reality[END_REF].

Nevertheless, recent studies seem to demonstrate a positive relationship between the realism of the image and the phenomenon of presence [START_REF] Khanna | Presence in response to dynamic visual realism: a preliminary report of an experiment study[END_REF][START_REF] Slater | Visual realism enhances realistic response in an immersive virtual environment[END_REF]. Note that all these studies were focused on the visual realism of the background in a virtual environment whereas our study is interested in the influence of the LOD of a virtual handball thrower and the ball on perception.

Nevertheless it is important to consider the "uncanny valley" concept when studying the influence of graphical LOD of humanoids in a virtual environment. This concept shows that in any type of human-like object, the graphical representation of the character generates an unpleasant impression for the viewer if this representation is excessively close to the human appearance but not realistic enough [START_REF] Seyama | The uncanny valley: effect of realism on the impression of artificial human faces[END_REF]. It is then important to determine the minimal LOD of humanoids that provides all the needed information about its movement. And even with the increase of computer capabilities, going beyond the "uncanny valley" is a challenging task and before this goal can be reached, all the future level of details of characters must be compared to simpler ones that are before the gap of the "uncanny valley". To this end, in this paper we investigate the effects that different graphical LODs of a throwing movement can have on the performance of immersed handball goalkeepers.

Overview

In order to evaluate the influence of the graphical LOD of character and ball on the handball goalkeepers' perception, we developed a three-step process (see Figure 1): First of all, to realistically animate the synthetic thrower and the ball in the virtual environment, we captured real throwing actions. This led to the creation of a large motion database with throws to various places in the goal.

Secondly, these captured throws were used by the MKM animation engine [START_REF] Kulpa | Morphology-independent representation of motions for interactive human-like animation[END_REF][START_REF] Multon | MKM: a global framework for animating humans in virtual reality applications[END_REF] in order to realistically animate the virtual characters. The rendering of these animations was done with five different LODs.

Finally, the movements of the goalkeepers were captured in real time. These data were then analyzed to determine which responses were successful.

Motion capture and animation process

Throwing movements of top-level handball players were recorded using a VICON motion capture system (Oxford Metrics). The participant and ball movements were captured at 200Hz using 12 infrared cameras. Thirty reflective markers were placed on key anatomical landmarks on each participant. The players were asked to throw the ball 12 meters from the goal aiming at different pre-specified target zones within the goal (no goalkeeper was present).

These captured motions were then used to animate the virtual thrower using the animation engine MKM (Manageable Kinematic Motions) [START_REF] Kulpa | Morphology-independent representation of motions for interactive human-like animation[END_REF][START_REF] Multon | MKM: a global framework for animating humans in virtual reality applications[END_REF]. This engine has already been used and validated for the animation of movements during a sporting duel [START_REF] Bideau | Using virtual reality to analyse links between handball thrower kinematics and goalkeeper's reactions[END_REF] and it offers the following capabilities: motion synchronization, blending, retargeting, and adaptation thanks to an enhanced inverse kinematics and kinetics solver [START_REF] Multon | MKM: a global framework for animating humans in virtual reality applications[END_REF].

Evaluation of the perception of movement

After animating the handball throwing actions, 12 handball goalkeepers (playing in first division and at national level) were placed in the virtual environment (see Figure 2). They all gave their informed consent before participating in the experiment. Mean participant age, height and hand length were 23.2 years (± 4.2 years), 1.85 m (± 0.06 m) and 20 cm (± 0.8 cm) respectively. All participants had normal vision. Three synchronized video projectors (Barco 1208S) driven by an SGI Onyx2 Infinite Reality system were used to project the 3D sports hall environment onto a large cylindrical screen (3.80m radius, 2.38m height and 135° field of vision). Stereoscopic glasses were activated at 60 Hz (30 Hz for the right eye and 30 Hz for the left eye) and synchronized with the system in order to give stereovision. The VICON motion capture system was used to record the goalkeeper's movement during the experiment and also to automatically change his point of view (head-tracking). In order to enhance the feeling of presence, a real goal was placed behind the goalkeeper at the corresponding position of the virtual goal in the virtual handball stadium.

Five graphical LODs of a virtual thrower were tested (see Figure 3):

• L0: a first LOD that defines the reference textured level as presented in [START_REF] Bideau | Using virtual reality to analyse links between handball thrower kinematics and goalkeeper's reactions[END_REF]) (see Supplementary material video0.mpg),

• L1: a second LOD similar to L0 but without texture on the synthetic thrower and ball (see Supplementary material video1.mpg),

• L2: a third LOD based on a wire-frame representation (see Supplementary material video2.mpg),

• L3: a fourth LOD using a MLD representation with 25 points of light (see Supplementary material video3.mpg),

• and L4: a fifth LOD similar to L3 but where the size of the ball was significantly reduced. In this latter condition the size of the ball corresponded to the size of a point of light (cf. video4.mpg).

It can be noticed that the reference textured level provides a sufficient degree of presence as suggested in Bideau et al.'s study [START_REF] Bideau | Real handball goalkeeper vs. virtual handball thrower[END_REF]. In that way, if the performance in a level is similar to the performance in the L0 level, the former level can be rated as a sufficient level of graphical representation in terms of the degree of presence and behavioral realism. The goalkeeper viewed all throws with different graphical LODs in a randomized order. The ball was released when the virtual thrower was 12 meters from the goalkeeper and a cut-off was applied in order to make the ball disappear at 6 meters from the goal. Ball velocities were similar for the three trajectories (20 ± 0.2 m.s -1 ). The goalkeeper was asked to predict where he thought the ball would end up by placing his hand at the correct position in the goal. We encouraged the goalkeeper to make his decision as quickly as possible. The goalkeeper's final hand position was recorded using the VICON motion capture system for each trial. The goalkeepers were equipped with 11 optoelectronic markers: four on their head for head tracking, three on their right hand and four on their left hand (including a dissymmetrical marker needed for the right-left hand distinction). The markers were placed on the hands in such a way that the central position of the hand could be easily calculated.

A total of three different trajectories were presented for the five LODs: upper left, mid right and lower left of the goalkeeper. The goalie hand considered for the error evaluation was respectively the left hand for throws at upper left and lower left and the right hand for throws at mid right. The three different ball trajectories were randomly repeated five times for each LOD. A total of 75 throws (3 trajectories × 5 repetitions × 5 LODs) were displayed. We also randomly included 10 other throws to different target locations in the goal to avoid goalkeepers becoming habituated to the arrival position of the ball in the goal. Each participant had a training period to allow them to become familiar with the environment and the task. During this time the participants were randomly presented with four new throws for each of the displays depicting the various graphical LODs. Trials in the training period were not included in the subsequent analysis.

Results

Data were analyzed using analyses of variance on dependent variables after examination of normality distribution (Kolmogorov-Smirnov test) and equal variances (Levene's test). Posthoc comparisons were conducted with Tukey's HSD test. The limit of significance was set to p<0.05.

The first part of the analysis looks at how the different graphical LODs influenced the participants' prediction about the final position of the ball in the goal. To estimate successful judgments, the hand and the ball were both represented by spheres (see Figure 4) for the purposes of analysis. A judgment was considered successful if the sphere around the hand made contact with the ball at its arrival position in the goal. The mean percentage of correct trials shows that there is no statistical difference between the different graphical LODs (see Figure 5). The only noticeable difference lies between L4 and the other four LODs (L0=22.2%±4.7; L1=22.8%±5.2; L2=20.4%±5.7; L3=20%±3.3; L4=12.5%±5.6).

radial error

Nevertheless, this difference was not found to be significant according to a two-way repeated measures analysis of variance (F(4,88)=2.368; p=0.067). Moreover, there was no significant influence of the LOD on the mean percentage of correct trials into each of the three zones aimed. In the second part of the analysis, a repeated measures analysis of variance revealed however a significant difference for the radial error between the different graphical LODs (F(4,84)=12.187; p<0.001). A Tukey HSD post-hoc analysis shows that the radial error for L4 was significantly greater than all the other conditions (L0=36.1cm±2.5; L1=33.9cm±2.7; L2=35.2cm±3.4; L3=35cm±2.9; L4=54.2cm±2.9) (see Figure 6). Besides, there was a significant influence of the LOD on the radial error into the mid right zone (L0=39.6cm±2.8; L1=37.9cm±3.3; L2=41.1cm±2.9; L3=38.5cm±2.6; L4=61.7cm±3.1) and into the lower left zone (L0=35.7cm±2.5; L1=30.3cm±3.1; L2=32.8cm±2.7; L3=30.3cm±2.9; L4=63.4cm±3.2). This influence was also characterized by a significant difference between L4 and all the other LOD in both zones (p<0.001). However there was no significant influence of the LOD on the radial error into the upper left zone (L0=32.9cm±2.5; L1=33.4cm±3.1; L2=31.5cm±2.6; L3=36cm±2.8; L4=37.5cm±3.2)

Discussion

This paper aims at studying the influence graphical LOD of a virtual thrower and ball have on a goalkeeper's perception of the realism of a simulated movement. Perception in this case was evaluated by quantifying the goalkeeper's judgments about the final position of the ball in the goal.

The results show that the first four graphical LOD (L0, L1, L2 and L3) do not significantly affect the goalkeepers' judgments. As the first graphical level L0 corresponds to a sufficientfor-presence LOD for the handball goalkeeper [START_REF] Bideau | Real handball goalkeeper vs. virtual handball thrower[END_REF], we can conclude that the impoverished graphical levels L1, L2 and L3 are able to induce a sufficient level of presence. This statement agrees with [START_REF] Ma | Presence, workload and performance effects on synthetic environment design factors[END_REF] who showed that a streamlined visual background does not significantly influence the level of presence. As L3 is the simplest of the four LODs, it means that an MLD representation of the movement can induce a sufficient level of presence in this virtual environment.

These results are very relevant when considered in light of other research in this area. Indeed previous studies that have looked at the effects of the graphical LOD in motion recognition tasks [START_REF] Bobick | An appearance-based representation of action[END_REF] have shown that poor motion representations are still sufficient to accurately recognize actions. For example, Ahlström et al. have shown that it was easier to identify a walker in an MLD representation than in a blurred video representation [START_REF] Ahlström | Perception of biological motion[END_REF]. Other studies have also shown how personal characteristics pertaining to individuals can also be picked up through MLD representations, such as [START_REF] Stevenage | Visual analysis of gait as a cue to identity[END_REF] who have demonstrated how gait patterns represented in a MLD display can be used to reliably predict identity of the walker [START_REF] Stevenage | Visual analysis of gait as a cue to identity[END_REF]. In other words, the spatiotemporal pattern of the movement of point of lights attached to the body parts reliably reproduces the key informational elements necessary to determine identity. All these studies used only 2D displays and usually involved action recognition.

Using 2D displays instead of 3D displays may introduce a methodological bias particularly in studies interested in perception [START_REF] Hendrix | Presence within virtual environments as a function of visual display parameters[END_REF]. Moreover, other studies have shown how the additional motion-in-depth information provided by 3D displays improves perception of action [START_REF] Beintema | The influence of biological motion perception on structure-from-motion interpretations at different speeds[END_REF][START_REF] Mazyn | The contribution of stereo vision to one-handed catching[END_REF]. This study shows that an MLD representation can be sufficient to perceive movements even in stereoscopic virtual environments.

In this paper, both thrower and ball representations were modified to correspond to different graphical LODs. The only significant difference was found between level 4 (L4) and all the others. Indeed, we observed a significantly greater radial error for the L4 level when compared to the other four levels and yet the only difference between L3 and L4 levels is the size of the ball (respectively equal to 15cm and 5cm). This means that the reduction of the size of the ball affects the accuracy of the goalkeeper's judgment. This result can be explained by the phenomenon of expansion: an object moving in depth in the visual field causes the size of the image projected on the human retina to increase or decrease [START_REF] Clifford | The perception and discrimination of speed in complex motion[END_REF]. In this study, the expansion rate of the ball is the same between L3 and L4 so it seems that there is a threshold on the size of the ball below which the visual information of the ball are not sufficient to predict its final position in the goal. Our results in virtual environments are in accordance with Berthier et al.'s study carried out in real situations [START_REF] Berthier | Visual information and object size in the control of reaching[END_REF]. Indeed, these authors have shown that hand speed profiles change when the size of the object they are trying to catch decreases. In both studies, the size of the object has an influence on the response of the participant.

Conclusion

To conclude, the results presented in this study help to clarify the influence different LODs of a virtual thrower and ball have on the goalkeepers' perception of movement. The goalkeepers were equipped with stereoscopic glasses in order to have the in-depth information of the 3D virtual environment. Their perception was evaluated by quantifying their judgments about the final position of the ball in the goal. This study shows that a simplification of the graphical representation of the virtual thrower and ball up to an MLD model does not influence goalkeepers' judgments. So, the graphical model of the thrower does not seem to be the most important parameter for perceiving movement. Moreover, this study demonstrates that a reduction of the size of the ball in the MLD level influences goalkeepers' responses.

Consequently it seems that the objects that gets close to the participant (such as the ball) influences the perception of the throwing action. In future studies, it will be interesting to analyze the influence of the reduction of the ball size on the perception of the movement of a throwing action in the other LODs from the fully textured one (L0) to the wire-frame one (L2). It could also be interesting to compare these results with the same experiment carried out with only 2D projections.

Figures Captions

Figure 1 :

 1 Figure 1: Three-step process: data acquisition; real time experiment that includes motion animation, motion degradation and motion capture; performance comparisons.

Figure 2 :

 2 Figure 2: A handball goalkeeper immersed in the virtual environment.

Figure 3 :

 3 Figure 3: Graphical LOD of the thrower/ball representation. From left to right,

Figure 4 :

 4 Figure 4: A schematic representation of the radial error (i.e. the distance between the

Figure 5 :

 5 Figure 5: Mean percentage of correct responses for all goalkeepers and for each of the

Figure 6 :

 6 Figure 6: Mean radial errors for all goalkeepers and for each graphical LOD. L4 was

Figure 1 :

 1 Figure 1: Three-step process: data acquisition; real time experiment that includes motion

Figure 2 :

 2 Figure 2: A handball goalkeeper immersed in the virtual environment.

Figure 3 :

 3 Figure 3: Graphical LOD of the thrower/ball representation. From left to right, conditions L0,

Figure 4 :

 4 Figure 4: Representation of the radial error. It is the distance between the virtual ball and the

Figure 5 :

 5 Figure 5: Mean percentage of correct responses for all goalkeepers and for each of the

Figure 6 :

 6 Figure 6: Mean radial errors for all goalkeepers and for each graphical LOD. L4 was found to

Supplementary Material Captions

Video0: View of the reference textured level (L0) Video1: View of the first-level degradation without texture on the synthetic thrower (L1) Video2: View of the second-level degradation using a wire-frame representation (L2) Video3: View of the third-level degradation using a point light display representation (L3) Video4: View of the fourth-level degradation condition (L4)


domain: info spi

text:
Nicolas Vignais
email: nicolas.vignais@univ-rennes2.fr

Richard Kulpa

Cathy Craig

Benoit Bideau

Virtual thrower vs. real goalkeeper: influence of different visual conditions on performance

Keywords: virtual reality, motor responses, visual information

In order to use virtual reality as a sport analysis tool, we need to be sure that an immersed athlete reacts realistically in a virtual environment. This has been validated for a real handball goalkeeper facing a virtual thrower. However we currently ignore which visual variables induce a realistic motor behaviour of the immersed handball goalkeeper. In this study we used virtual reality to dissociate the visual information related to the movements of the player from the visual information related to the trajectory of the ball. Thus, the aim is to evaluate the relative influence of these different visual information sources on the goalkeeper's motor behaviour. We tested 10 handball goalkeepers who had to predict the final position of the virtual ball in the goal when facing: only the throwing action of the attacking player (TA condition), only the resulting ball trajectory (BA condition), and both the throwing action of the attacking player and the resulting ball trajectory (TB condition). Here we show that performance was better in the BA and TB conditions, but contrary to expectations, performance was substantially worse in the TA condition. A significant effect of ball landing zone does, however, suggest that the relative importance between visual information from the player and the ball depends on the targeted zone in the goal. In some cases, body-based cues embedded in the throwing actions may have a minor influence than ball trajectory and vice versa. Kinematics analysis was then combined to these results to determine why such differences occur depending on the ball landing zone and consequently how it can clarify the role different sources of visual information play on the motor behaviour of an athlete immersed in a virtual environment.

Introduction

Virtual reality is now being used as a tool to analyze and understand motor behavior in sport [START_REF] Katz | Virtual reality in sport and wellness: promise and reality[END_REF][START_REF] Bideau | Virtual reality, a serious game for understanding performance and training players in sport[END_REF]. This promising technology has a number of advantages over video presentation or real-game situations. Firstly, all factors can be controlled and manipulated in a systematic manner, ensuring reproducibility between trials [START_REF] Tarr | Virtual reality in behavioral neuroscience and beyond[END_REF]. Secondly, the effects of these modifications on resulting behaviour can be monitored in real-time. Thirdly, the immersion of the subject in the virtual scene in an egocentric position allows the optical information gleaned from the virtual world to correspond directly to what the participant would see in a real sporting situation [START_REF] Cutting | How the eye measures reality and virtual reality[END_REF]. Finally, the participant's view point is stereoscopic which has previously been highlighted as an important factor when performing interceptive tasks (Mazyn, Lenoir, Montagne & Savelsbergh, 2004). Given these advantages, this technology has been exploited to analyze and understand players' performance in different sports such as soccer [START_REF] Craig | Judging where a ball will go: The case of curved free kicks in football[END_REF], tennis (Molet et al., 1999), handball [START_REF] Bideau | Using virtual reality to analyse links between handball thrower kinematics and goalkeeper's reactions[END_REF][START_REF] Vignais | Virtual environments for sport analysis: perception-action coupling in handball goalkeeping[END_REF] and rugby (Brault, Bideau, Kulpa & Craig, 2009). Moreover this technology can be employed to test team sport strategies [START_REF] Metoyer | Animating athletic motion by example[END_REF] or to train athletes in a simulator [START_REF] Kelly | Design and construction of a bobsled driver training simulator[END_REF]. With respect to this latter option, [START_REF] Kelly | Design and construction of a bobsled driver training simulator[END_REF] have successfully designed a complete bobsled simulator which displays a graphical representation from the driver's viewpoint on a cockpit-mounted monitor.

In spite of these advantages, using virtual reality as a sports analysis tool does raise new questions. One of the most important is the motor response of the athlete immersed in a virtual environment. In fact, the motor behaviour of a subject can be considered as one of the physical measures of presence [START_REF] Slater | Immersion, presence and performance in virtual environments: An experiment with tri-dimensional chess[END_REF][START_REF] Slater | Visual realism enhances realistic response in an immersive virtual environment[END_REF]. In sport, this concept has been used to validate a virtual sport situation. Indeed, [START_REF] Bideau | Real handball goalkeeper vs. virtual handball thrower[END_REF] compared the movement of handball goalkeepers during the interception of the same throws made by real and virtual opponents. The results showed that the goalkeepers' gestures in both environments were similar. Nevertheless, of the virtual sport situation used in this case study induced handball goalkeepers to respond realistically, the reason of this motor behaviour remains unanswered. Are some visual elements displayed in the virtual scene more important than others to enable handball goalkeepers to react in a realistic way?

In most ball sports, successful anticipation of what an opponent is going to do next can give a substantial competitive edge. The temporal constraints within a player must act are often very tight, and so early recognition of an opponent's actions or anticipation of where the ball is going can increase the player's chances of making a successful counteracting move. Much of the research to date has focused on identifying the sources of visual information that players use to anticipate an opponent's next move [START_REF] Abernethy | Can the anticipatory skills of experts be learned by novices?[END_REF]. While some studies have suggested that successful anticipation is determined by the perception of spatialtemporal information in the trajectory of the ball [START_REF] Bastin | Prospective strategies underlie the control of interceptive actions[END_REF]Craig et al., 2006;[START_REF] Lenoir | Rate of change of angular bearing as the relevant property in a horizontal interception task during locomotion[END_REF][START_REF] Mcleod | The generalized optic acceleration cancellation theory of catching[END_REF], others have shown that successful anticipatory behaviour depends on the accurate perception of body-based cues [START_REF] Jackson | Anticipation skill and susceptibility to deceptive movement[END_REF][START_REF] Panchuk | Gaze behaviors of goaltenders under spatialtemporal constraints[END_REF][START_REF] Savelsbergh | Visual search, anticipation and expertise in soccer goalkeepers[END_REF][START_REF] Williams | Visual-search strategies in experienced and inexperienced soccer players[END_REF]. The general consensus suggests that when the temporal constraints are too tight (e.g. a penalty kick in soccer), players do not have enough time to act on ball trajectory information and consequently use body-based cues [START_REF] Savelsbergh | Anticipation and visual search behaviour in expert soccer goalkeepers[END_REF]. In spite of this conclusion, the relative contribution of body-based versus ball flight information in performance has not been properly tested.

Here, we suggest to present body-based cues and ball trajectory information in isolation in order to better assess the relative importance of both types of visual information for performance. Using immersive interactive virtual reality, this study allows dissociation between body-based cues and ball flight information. The relative importance of these two visual elements in performance will be assessed in order to understand why an athlete tends to respond realistically to a virtual sport situation. To this end, we investigate the effects of different visual conditions of a virtual throwing action on the performance of immersed handball goalkeepers.

Overview

In order to evaluate the influence of the different visual conditions of a throwing action on the handball goalkeepers' performance, we developed a three-step process (see Erreur ! Source du renvoi introuvable.): First of all, to realistically animate the synthetic thrower and the ball in the virtual environment, we captured real throwing actions. This led to the creation of a large motion database with throws to various places in the goal.

Secondly, these captured throws were used by the MKM animation engine [START_REF] Kulpa | Morphology-independent representation of motions for interactive human-like animation[END_REF][START_REF] Multon | MKM: a global framework for animating humans in virtual reality applications[END_REF] in order to realistically animate the virtual character. The rendering of these animations was done with three different visual conditions.

Finally, the movements of the goalkeepers were captured in real time. These data were then analyzed to determine which responses were successful.

Motion capture and animation process

In order to animate the virtual character and ball, real handball throwing actions were recorded. A VICON motion capture system (Oxford Metrics, Oxford, UK) was used to record movement kinematics from three top-level handball players. All the throwers were righthanded and had at least ten years experience playing handball in the top French league at the time of the study. Each player was equipped with 36 reflective markers placed on anatomical landmarks to precisely reconstruct the 3D position and orientation of each limb segment, with six markers being reserved for the ball. Each player was asked to throw the ball 12 m from the goal, aiming for different pre-specified target zones within the goal (no goalkeeper was present). For each throw, the participant and ball movement were captured at 200 Hz using 12 infrared cameras. Nine trajectories that arrived close to the centre of each zone were selected and used in the subsequent virtual animation part of the study (see Figure 2). Kinematic throwing data, captured using the VICON system, were then incorporated into the MKM (Manageable Kinematic Motion) animation engine [START_REF] Kulpa | Morphology-independent representation of motions for interactive human-like animation[END_REF][START_REF] Multon | MKM: a global framework for animating humans in virtual reality applications[END_REF] to animate the virtual handball player in real time. Based on these captured motions, MKM can automatically synchronize, blend, and adapt actions to different morphologies and to external constraints such as foot contacts. Virtual ball trajectories for the nine zones were calculated using another software module that incorporates data obtained from the motion capture part of the study. Ball velocities were similar for all nine trajectories (20 ± 0.2 m•s -1 ), making ball flight time very nearly the same for all nine zones.

A realistic handball stadium was created using 3dsmax (Autodesk Inc., San Rafael, CA, USA). The size of the virtual stadium was scaled so that one unit in the virtual environment corresponded to 1 m in reality. The virtual court was 20 m wide and 40 m long with a clearly marked semi-circular area (6 m radius) corresponding to the goal zone.

Evaluation of goalkeepers' performance

After animating the handball throwing actions, 10 elite male handball goalkeepers (playing in the top league and at national level) were placed in the virtual environment (see Figure 3). Mean age of the participants was 22.6 years (stdev=4.9 years), mean height was 1.81m (stdev=0.09m) and mean weight was 78.2kg (stdev=13.1kg). All participants had normal or corrected-to-normal vision. All participants gave their informed consent before the study began.

In order to enhance the behaviour of subjects, a real goal (3 m × 2 m) was placed where it was virtually represented in the computer generated environment. Goalkeepers were encouraged to touch the real goal posts to enhance the correspondence between virtual and real distances.

Three synchronised video projectors Barco 1208S (Barco, Courtrai, Belgium) driven by a SGI 83 Onyx2 Infinite Reality (Silicon Graphics, Sunnyvale, CA, USA) were used to project the 3D sports hall environment onto a large cylindrical screen (3.80 m radius, 2.38 m height, and 135º field of vision). A set of glasses synchronised with the system enabled stereovision ( 60Hz). The Vicon motion capture system (12 cameras) was used to record goalkeeper's movements and was coupled to the virtual reality display. As the two systems were linked and the goalkeeper's head was tracked, it was possible to change the goalkeeper's perspective in the virtual world in real time (delay < 20 ms).

Three experimental conditions representing the different types of visual information were randomly presented to the participants. The 'ball only' condition (BA) involved the goalkeeper judging the arrival position of the ball while seeing only the trajectory of the ball from the point of release (12m) to the cut-off point (6m) (see Video1.avi), the 'thrower only' (TA) condition involved the goalkeeper making an action when presented with only the complete throwing action of the thrower (pre-release to release point) (see Video2.avi), and the 'thrower and ball' condition (TB) involved the goalkeeper making a judgment when presented with both the throwing action and the resulting ball trajectory (see Video3.avi). These three experimental conditions allowed us to systematically isolate the two visual information sources a goalkeeper might use to guide his actions. Two response modes were then performed: a motor task condition and a judgment task condition.

• Judgment task condition: in this experiment, the goalkeeper was equipped with only 11 reflective markers: 4 on their head for head tracking, 3 on their right hand, and 4 on their left hand to record final hand positions. The markers were placed on the hands in such a way that the central position of the hand could be easily calculated, with the additional marker allowing the left hand and right hand to be distinguished during analysis (see Figure 4a). After the end of the throwing action, goalkeepers were asked to then move their hand as quickly as possible to the position in the goal where they thought the ball would have ended up.

• Motor task condition: this experiment required the goalkeeper to get their limb to the right place at the right time to stop the virtual ball. The goalkeeper reacted to the same randomised throws displayed in the judgment condition. The goalkeeper was asked to react as if he was in a real match situation. Each goalkeeper was equipped with 36 optoelectronic markers placed on the same anatomical landmarks as in the motion capture part of the real handball throwing actions (see Figure 4b). Before the experiment began, each participant was given a training period to familiarise them with the environment and the task. During this time the participants randomly viewed a sample of three throws from each of the three visual information conditions (three BA throws, three TA throws, and three TB throws). Trials from the training period were not included in the subsequent analysis.

A total of four different trajectories were randomly presented for the different visual conditions. They corresponded to zones 1, 3, 4 and 6 (see Figure 2). The four different ball trajectories were randomly repeated five times for each condition giving a total of 20 throws per condition. 10 other throws to different target locations in the goal were randomly included so as to create more variability in end arrival position for the goalkeeper. In fact a total of 140 trials ([(5 repetitions per zone × 4 zones aimed × 3 visual conditions) + 10 other throws] × 2 response modes) were presented to each goalkeeper. A short break was given after each block of 30 trials.

In order to estimate and compare goalkeeper's perceptual and motor responses, two kinds of data analysis were performed:

• Judgment task condition: the hand and the ball were both represented by spheres (see Figure 4a) during the process analysis. We calculated the radial error which was the difference between the final position of the hand and the virtual ball's arrival position in the goal mouth. As zero radial error equated a correct response we used this measure to calculate the percentage of successful judgments.

• Motor task condition: a detection of the collision process was used in the virtual environment by representing the goalkeeper's limbs as cylinders (trunk, arms, forearms, thighs, shanks and feet) and spheres (head and hands) from joint centre positions (see Figure 4b). This full body representation enabled us to determine if there was a collision between the virtual ball and the goalkeeper in real time (a visual feedback was displayed after each throw). The collision detection process provided us the percentage of successful interceptions.

Results

The first part of the analysis looked at how the different visual conditions influenced the participants' effectiveness. The percentage of successful responses was also analysed.

During the judgment task, a prediction was considered successful if the end-position of the hand sphere was in contact with the end-position of the ball sphere (see Figure 4a).

During the motor task, a parry was considered successful if the representation of the interceptive limb was in contact with the ball sphere (see Figure 4b). As expected, the participants were most successful when they were presented with TB information during the judgment task (21.3±9.7%) and during the motor task (31.2±5.9%) (see Figure 5a and5c). The participants were only marginally less successful for the BA condition (judgment: 21.1±7.3%; motor: 29.1±6.7%), but performance was statistically worse with the TA information (judgment: 8.6±7.9%; motor: 14.5±8.6%) after a two-way repeated measures analysis of variance (judgment: F(2,54)=46.082; p<0.001; motor: F(2,54)=100.226; p<0.001). This marked reduction in performance for the TA condition was seen in all zones except zone 1 (see Figure 5b and 5d).

The second part of the analysis focuses on the influence of the different visual conditions on goalkeepers' accuracy. With this aim, the radial error was calculated and examined (see Figure 4). A repeated measures analysis of variance revealed a significant difference for the radial error between the different visual conditions during the judgment task (F(2,54)=803.501; p<0.001) and during the motor task (F(2,54)=257.762; p<0.001). A Tukey HSD post-hoc analysis shows that the radial error obtained for the TA condition (judgment: 61.3±5.3cm; motor: 47.8±7.3cm) was significantly greater than the BA condition (judgment: 31.8±1.4cm; motor: 18.2±2.9cm) and the TB condition (judgment: 26.2±1.8cm; motor: 13.6±4.4cm).

Discussion

The aim of this study was to see how different sources of visual information influence the goalkeepers' motor behaviour in a virtual environment. By separating the presentation of body-based movement cues and ball trajectory information, we were able to look at the relative contribution of each of them individually and together. The results show that body-based cues alone do not provide sufficient information for expert goalkeepers to accurately predict where the ball is going in the goal. Indeed, regarding the final limb position results, we obtained extremely larger errors for the TA condition compared to the BA and TB conditions. Although the BA condition was significantly more successful than the TA condition, it does not seem that seeing the throwing action with the ball trajectory (TB condition) improve performance compared to the BA condition, except for the goalkeepers' accuracy during the judgment task. However, we observed a decrease in successful responses in all zones, except zone 1, for the TA condition. This finding could be explained by the biomechanical analysis of the throwing actions (see Figure 7). As it can be seen from Figure 7, the patterns of final limb positions are very similar for throwing actions in zones 1 and 3. Segmental positions and joint angle values of both throwing actions seem very close. It can be suggested that participants have confused the throwing actions for zone 3 for that of zone 1 (see Figure 7a). Moreover it is possible that goalkeepers were able to identify kinematic cues of the throwing action for zone 1. Given these similarities in final limb position, the last part of the analysis aims to see how the throwing actions differed. In other words, what biomechanical parameters changed most significantly from one throw to another? The analysis of body segment angles showed greatest differences in the elbow angle (see Figure 7b). Differences, though small, were also found for dipping of the shoulder and elevation of the elbow respectively to the shoulder. Although biomechanically these differences in limb segment orientation partly determine where the ball will land in the goal, they are probably too subtle for the goalkeeper to use them as reliable visual information, except for throws in zone 1.

These findings are very interesting when considered in light of other research in this area. Previous studies have attempted to show that expertise in goalkeeping resides in the ability to pick up pertinent information from the movement kinematics of the attacking player [START_REF] Savelsbergh | Visual search, anticipation and expertise in soccer goalkeepers[END_REF][START_REF] Savelsbergh | Anticipation and visual search behaviour in expert soccer goalkeepers[END_REF][START_REF] Franks | Cues for goalkeepers. high-tech methods used to measure penalty shot response[END_REF][START_REF] Williams | Visual-search strategies in experienced and inexperienced soccer players[END_REF]. In a virtual environment it seems that handball goalkeepers are able to use body-based cues only for specific throwing actions. Overall it would appear that body-based information on its own is not sufficient to make accurate judgements about (and consequently movements toward) where the ball will end up in the handball goal. Information picked up from the ball trajectory is necessary to refine judgement and motor tasks. This kind of visual information helps the handball goalkeeper to react in a realistic way in the virtual environment. A recent study by [START_REF] Jackson | Advance visual information, awareness and anticipation skill[END_REF] also revealed the importance of ball flight information in making anticipatory judgements in tennis. Using a spatial occlusion technique to selectively omit key components of the tennis serve, they found the most detrimental effects in performance occurred when no ball information was present. Expert tennis players' performance in judging where the ball would land on the court was severely compromised, with the percentage of correct judgements falling to chance level.

Interestingly when these same players were asked to judge the utility of different sources of information (body-based vs. ball), they claimed positional information pertaining to the arm and racquet was most informative.

Conclusion

To conclude the results presented in this study help to clarify the role different sources of visual information play on the motor behaviour of an athlete immersed in a virtual environment. They reinforce the importance of being able to track at least part of the ball trajectory to correctly predict where the ball is going to end up. However, bodybased cues can be significant depending on the throwing action. Future studies will attempt to see which kind of visual cues of the throwing movement can be detected.

Moreover, these findings have to be compared with other virtual sport situations.

Acknowledgments

Authors want to thank the Bunraku team for all the support and resources made available during this study and in particular Julien Bilavarn for the work done.

Figures Captions

Figure 1 :

 1 Figure 1: The three-step process includes data acquisition, real time experiment and performance comparison

Figure 2 :

 2 Figure 2: handball goal mouth divided into nine zones. Although all of the nine zones were aimed at by the thrower, only zones 1, 3, 4 and 6 were used in the forthcoming analysis.

Figure 3 :

 3 Figure 3: A goalkeeper immersed in the virtual environment.

Figure 4 :

 4 Figure 4: Position of the optoelectronic markers, simplification of segment reconstruction and computation of the radial error in the judgment task condition (a) and in the motor task condition (b).

Figure 5 :

 5 Figure 5: Mean percentage of correct responses for all goalkeepers during the judgment task (a) for each zone aimed (b), and during the motor task (c) for each zone aimed (d) (***p<0.001).

Figure 6 :

 6 Figure 6: Mean radial error (and its lateral and vertical components) for all goalkeepers during the judgment task (a) for each zone aimed (b), and during the motor task (c) for each zone aimed (d) (*p<0.05; **p<0.01; ***p<0.001). Lateral and vertical components have not been represented in the goal (b and d).

Figure 7 :

 7 Figure 7: Segment configuration of the throwing action at ball release in frontal plane for each zone aimed (a) and histogram of related joint angles (b). The different joints represented are: left shoulder (LS), right shoulder (RS), right elbow (RE) and right wrist (RW).

Figure 1 :

 1 Figure 1: The three-step process includes data acquisition, real time experiment and

Figure 2 :

 2 Figure 2: handball goal mouth divided into nine zones. Although all of the nine zones

Figure 3 :

 3 Figure 3: A goalkeeper immersed in the virtual environment.

Figure 4 :

 4 Figure 4: Position of the optoelectronic markers, simplification of segment

Figure 5 :

 5 Figure 5: Mean percentage of correct responses for all goalkeepers during the judgment

Figure 6 :

 6 Figure 6: Mean radial error (and its lateral and vertical components) for all goalkeepers

Supplementary Material Captions

Video1.avi: view of the virtual throwing action in the 'ball only' condition (BA).

Video2.avi: view of the virtual throwing action in the 'thrower only' condition (TA).

Video3.avi: view of the virtual throwing action in the 'thrower and ball' condition (TA).