Stanley Tarng
email: stanley.tarng@ucalgary.ca

Aida Erfanian
email: aerfania@ucalgary.ca

Yaoping Hu

Frédéric Merienne
email: frederic.merienne@ensam.eu

Vibrotactile and Force Collaboration within 3D Virtual Environments

Keywords: Cue collaboration, MLE, mean-shifted MLE, VEs

In a three-dimensional (3D) virtual environment (VE), proper collaboration between vibrotactile and force cuestwo cues of the haptic modality -is important to facilitate task performance of human users.

Many studies report that collaborations between multi-sensory cues follow maximum likelihood estimation (MLE). However, an existing work finds that MLE yields a mean and an amplitude mismatches when interpreting the collaboration between the vibrotactile and force cues. We thus proposed mean-shifted MLE and conducted a human study to investigate the mismatches. For the study, we created a VE to replicate the visual scene, the 3D interactive task, and the cues from the existing work. Our participants were biased to rely on the vibrotactile cue for their tasks, departing from unbiased reliance on both cues in the existing work. Assessments of task completion time and task accuracy validated the replication. We found that based on task accuracy MLE explained the cue collaboration to certain degrees, agreed with the existing work. Mean-shifted MLE remedied the mean mismatch, but maintained the amplitude mismatch. Further examinations revealed that the collaboration between both cues may not be entirely additive. This sheds an insight for proper modeling of the collaboration between the vibrotactile and force cues to aid interactive tasks in VEs.

I. INTRODUCTION

Information and feedback of the visual modality can be overwhelming to the human user of a three-dimensional (3D) virtual environment (VE) [START_REF] Ouarti | Haptic motion: Improving sensation of self-motion in virtual worlds with force feedback[END_REF]. Increasingly, the VE incorporates feedback of other modalities such as haptics and auditory to provide the user both a sense of presence and interactivity [START_REF] Ouarti | Haptic motion: Improving sensation of self-motion in virtual worlds with force feedback[END_REF]- [START_REF] Lederman | Relative performance using haptic and/or touch-produced auditory cues in a remote absolute texture identification task[END_REF]. Feedback of the haptic modality is especially important, because this modality corresponds to direct touching an object for interaction. Such touch allows for an interactive experience in the VE to promote a greater sense of presence for the user and, thereby, to enhance user performance [START_REF] Ouarti | Haptic motion: Improving sensation of self-motion in virtual worlds with force feedback[END_REF]. In the haptic modality, two commonly used types of feedback are through vibrotactile and force cues. This arises from both availability and relative ease of acquiring devices, which can delivers these haptic cues.

Vibrotactile and force cues are often delivered to the user collaboratively in either a co-located or dis-located setting. When interacting with an object in the real world, the user expects feedback directly co-located with the object [START_REF] Brown | The Effect of Force / Motion Coupling on Motor and Cognitive Performance[END_REF]. However, co-located cues may be difficult to deliver in the VE due to restrictions in the design and implementation of haptic devices [START_REF] Brown | The Effect of Force / Motion Coupling on Motor and Cognitive Performance[END_REF]. In cases where co-located cues are infeasible, dis-located cues can be delivered to the user instead. While the colocated cues have shown to offer better user performance than dis-located cues [START_REF] Pacchierotti | Enhancing the Performance of Passive Teleoperation Systems via Cutaneous Feedback[END_REF], the dis-located cues nevertheless are a means of improving user performance in the VE [START_REF] Swapp | Interaction with co-located haptic feedback in virtual reality[END_REF]. A general mechanism of collaboration between vibrotactile and force cues would be necessary to facilitate the user performance.

One potential candidate of the mechanism is maximum likelihood estimation (MLE) [START_REF] Ernst | Humans integrate visual and haptic information in a statistically optimal fashion[END_REF], [START_REF] Rohde | Statistically optimal multisensory cue integration: A practical tutorial[END_REF], albeit the existence of information integration theory and signal detection theory [START_REF] Jones | Application of psychophysical techniques to haptic research[END_REF]. In brief, MLE yields a predicted Gaussian distribution of a cue collaboration based on empirical observations of individual cues [START_REF] Ernst | Humans integrate visual and haptic information in a statistically optimal fashion[END_REF], [START_REF] Rohde | Statistically optimal multisensory cue integration: A practical tutorial[END_REF]. The characteristic parameters of the prediction are its mean ( ), standard deviation ( ), and amplitude (A). Based on these parameters, MLE elucidates herein the cue collaboration when the prediction matches its empirical observation. Most existing reports reveal that MLE indeed explains appropriately cue collaborations between visual and haptic cues [START_REF] Ernst | Humans integrate visual and haptic information in a statistically optimal fashion[END_REF], force and position cues [START_REF] Drewing | Integration of force and position cues for shape perception through active touch[END_REF], as well as auditory and visual cues [START_REF] Perez-Bellido | Visual limitations shape audiovisual integration[END_REF].

In contrast, this explanation is inconclusive when applying MLE to interpret empirical observations of collaboration between vibrotactile and force cues [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF]. In the co-located setting of the cues, MLE elucidates the cue collaboration at certain degrees through a mean match between a prediction and an empirical observation. The same match eludes surprisingly in a dis-located setting of the cues, leading to a mean mismatch. Moreover, an amplitude mismatch exists between the prediction and the observation in both settings. The inconclusiveness derived from these mismatches warrants further investigations.

Little work has been done to investigate these mismatches, hence we conducted an empirical study, in which each human participant undertook a 3D interactive task within a VE. We created the VE to replicate the visual scene, the task and the cues from those in the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF]. Departing from a unbiased reliance on either of the cues in the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF], each participant in the current study was instructed to favor the vibrotactile cue over the force cue. In other words, the participant had a biased reliance on the vibrotactile cue. Although assessments of task completion time and task accuracy for all participants validated the replication, we found the same mismatches when applying MLE to explain the cue collaboration. The mean mismatch was interpretable nevertheless using our proposed mean-shifted MLE that linked the weights of the individual cues to the means of their observations. Based on our reliance-biased observations and the reliance-unbiased observations from the existing work, further examinations yielded a linear relationship between the amplitude difference and the difference of the mean-shifted weights. This gives an insight for proper modelling of the cue collaboration, as future work, to aid interactive tasks in VEs.

II. MLE AND MEAN-SHIFTED MLE

In MLE, the prediction ( ) of a collaboration among N individual cues is estimated by summing the weighted empirical observations (

) of the cues as follows [START_REF] Ernst | Humans integrate visual and haptic information in a statistically optimal fashion[END_REF]:

(

where the weight ( ) of the i-th cue's observation is related to its standard deviation ( ), given that each observation is a Gaussian distribution. Thus, the prediction ( ) yielded by Eq. ( 1) is also a Gaussian distribution.

Notwithstanding, what modifications of MLE are needed to remedy the two mismatches found in the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF]? For handling the mean mismatch, we proposed mean-shifted MLE. Departing from a MLE weight , the weights of individual cues in mean-shifted MLE are derived by using the means of the observations of the cues and their collaboration. Considering the vibrotactile and force cues (as N = 2), their mean-shifted weights (W V and W F , respectively, for W 1 and W 2 ) have then the following formulations as: and ,

where V , F , and FV represent the means of the observations of the vibrotactile cue, the force cue and their cue collaboration, respectively. In essence, the mean-shifted weights of the cues are what their MLE weights would be to ensure a mean match between the prediction and the observation of the cue collaboration. The prediction is the sum of the weighted observations of the cues as defined in Eq. ( 1), but with their mean-shifted weights as formulated in Eq. (2).

To interpret the amplitude mismatch, we defined the difference of the amplitudes ( ) between the prediction and observation of the cue collaboration as: ,

where and are the amplitudes of the prediction and the observation of the cue collaboration, respectively. We noted the amplitude difference ( ) as a function (f) of the difference between the mean-shifted weights as below:

. ( 4
)
Examinations based on the observations warrant to refine Eq. ( 4) as a relationship between and the mean-sifted weights.

Figure 1 shows the concepts of MLE and mean-shifted MLE, and illustrates the vibrotactile and force cues. A mean shift ( ) represents the mean difference between the MLE and meanshifted MLE predictions, while the amplitude difference ( ) is defined in Eq. ( 3). Thus, mean-shifted MLE provides a mechanism to examine the collaboration between the vibrotactile and force cues.

III. EMPIRICAL STUDY

We conducted an empirical study to verify the applicability of mean-shifted MLE. For this study, we developed a 3D stereoscopic VE for human participants to undertake an interactive task. The visual scene, interactive task and cues of the VE replicated their originals in the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF]. Comparable hardware were used to deliver similar interactive experience to the participants who were biased to favor the vibrotactile cue over the force cue for the interactive task.

A. Virtual Environment

In brief, the VE was created using Unity 3D game engine (version 5.3.4f). All visual and haptic components of the VE were managed using the C# language. Figure 2 depicts the VE and devices utilized for user interaction. As shown in Fig. 2(a), the stereoscopic visual scene of the VE was projected on a screen (2.4×2.4 m 2 ) using an Acer U5200 projector (Acer Inc., Taiwan) operated at 120 Hz. A pair of 3D shutter glasses and an IR emitter (nVidia Inc., Santa Clara, USA), as depicted in Fig. 2(b), allowed each participant to view the scene. A VibroTac bracelet (SENSODRIVE Gmbh, Germany) presented in Fig. 2(c) delivered a vibrotactile cue to the right hand or the forearm of the participant. As depicted in 2(d), a pair of ear plugs were used to block out the noise generated by the VibroTac bracelet when delivering the vibrotactile cue. The participant wore an E4 wristband (Empatica Inc., Italy) on his/her left wrist to monitor physiological signals in real time. A PHANToM Omni device (Geomagic Inc., USA), as shown in Fig. 2(e), inputted interactive commands and provided the force cue to the participant's right hand. Two Unity 5 plugins were applied to activate the VibroTac bracelet and the Omni device. A vibrotactile plugin was made by the Art et Métiers, France; and As illustrated in Fig. 2(a), each participant sit on a chair and employed his/her right hand to hold the Omni device on a small table for 3D interaction. The chair was positioned at a distance of 2.0 m from the center of the screen. The right elbow of the participant was rested on the small table. The visual scene of the VE consisted of a high-powered transmission line in a mountainous region. The transmission line was supported by two towers and curved towards the ground due to its own weight. The supporting towers were 60.00 m apart. The participant was required to fly and guide a drone along the transmission line with the Omni device. A stereo camera was attached to the front of the drone and served as the viewport of the participant. Attached to the bottom of the drone was a robotic arm, at which end there was a loop-shaped clamp. The clamp covered the transmission line for detecting defects on the line. The defects were indiscernible visually from the rest of the transmission line. Through the VibroTac bracelet and/or the Omni device, the defects were delivered as vibrotactile and/or force cues to the participant, respectively.

The VE was run on a Dell Precision WorkStation T3500 (Dell Inc., USA) under the Microsoft Windows® 7 operating system with an Intel Xeon E5507 CPU, an nVidia Quadro FX4800 graphics card, and 8GB of RAM.

B. Participants

Ten participants (mean age of 27.13 ± 5.13 years old) took part in the study. Being naïve to the purpose of the study, all participants differed from those of the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF]. A preassessment consisted of an Edinburgh handedness test, an Ishihara color-blindness test, and a Randot stereo test. The assessment indicated that each participant was right-handed with regular color vision, and had normal or corrected-to-normal vision with stereo acuity of at least 40" of arc. An ethics approval was attained at our institute for the study.

C. Procedure

The procedure in this study was largely identical to that in the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF], but with one exception. Departing from an unbiased reliance on both vibrotactile and force cues in the existing work, the expectation was a biased reliance on the vibrotactile cue instructed to each participant.

In brief, the interactive task for each participant was to inspect and detect the defects on the curved transmission line using the robotic arm on the flying drone. To move the drone, the participant had to point the stylus tip of the Omni device along the transmission line while pressing the dark-gray button on the stylus. While flying, the clamp of the robotic arm covered the transmission line for sensing its surface. To guide the participant along the line, the Omni delivered a 0.50 N continuous force tangentially to the line.

Additional vibrotactile and/or force cues, as shown in Figs. 1(b) and 1(c), were delivered to the right hand (and/or forearm) of each participant when any defect existed on the transmission line. The vibrotactile cues were provided by the first motor of the VibroTac bracelet in one of two settings: co-located with the Omni device on the right hand or dis-located from the device on the right forearm. The vibration was 1.00 s long at a frequency of about 200 Hz and superimposed on the continuous force. The motor was placed on the skin covering the first dorsal interosseous muscle between the thumb and the index finger in the co-located setting; and at the carpi radialis longus muscle of the forearm in the dis-located setting. Delivered through the stylus of the Omni device to the right hand, the force cues was 0.60 N (i.e., 0.10 N increment on the top of the continuous force) and lasted 1.00 s excluding the pre-and after-ramping of 0.25 s.

While moving the clamp along the transmission line, each participant declared his/her detection of a defect by pressing down both buttons on the stylus of the Omni device. There were 5 cue profiles, with a profile being one testing block, as follows:

V_co: The cue signaled a defect was only a vibrotactile cue co-located with the Omni stylus. V_dis: The cue signaled a defect was only a vibrotactile cue dis-located from the Omni stylus at the forearm. F_only: The cue signaled a defect was only a force cue delivered by the Omni stylus. FV_co: The cue signaled a defect consisted of the vibrotactile cue of the V_co profile in the co-located setting with the force cue of the F_only profile. FV_dis: The cue signaled a defect consisted of the vibrotactile cue of the V_dis profile in the dis-located setting with the force cue of the F_only profile.

The cues and cue profiles were detailed in the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF]. Prior to each testing block, there was a practice block for the participant to learn how to fly the drone and detect defects. After each block, the participant answered one cybersickness [START_REF] Kennedy | Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness[END_REF] and one perceptual questionnaires [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF]. The Empatica wristband was worm by the participant on the left wrist from the pre-assessment to the end of all blocks.

The length of the procedure, including the pre-assessment and a 2-minute break between any two blocks, lasted about 1.75 hours for each participant. The order of the testing blocks were counter-balanced among all participants.

D. Data Collection and Analyses

The steps of data collection and analyses were similar in this study and the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF], except the addition of meanshifted MLE to the analyses in the last step.

Briefly, both objective and subjective data were collected in the first step during each testing block. Objective data were logged by the Emptica wristband to monitor each participant's physiological signals for cybersickness; and by the VE to yield the participant's performance, including task completion time and task accuracy of detecting defects. Subjective data was gathered using the cybersickness and perceptual questionnaires. The later covered the components of perceived usefulness, effectiveness, pleasure and workload of the VE. The first three components were constructed using a variation of the Likert scale, which had a continuous bar bounded between 0% and 100%. For consistency, each question related directly to the participant's actions [START_REF] Cairns | Experimental methods in human-computer interaction[END_REF]. The last component utilized the NASA task load index (TLX) [START_REF] Hart | Nasa-Task Load Index (NASA-TLX); 20 Years Later[END_REF]. All subjective data, except cybersickness ones, were converted to numeric for comparison.

In the second step, cybersickness was assessed using both the physiological signals recorded by the wristband and the responses gathered from the cybersickness questionnaire. Should a participant suffer from cybersickness, his/her data was excluded from analyses.

To validate our replication and determine the baseline for using MLE, other objective and subjective data were compared in the third step using one-way repeated-measure analyses of variance (ANOVA) [START_REF] Montgomery | Design and Analysis of Experiments[END_REF]. Any significant difference was evaluated further through a Bonferroni post hoc test [START_REF] Montgomery | Design and Analysis of Experiments[END_REF]. Data eligibility for ANOVA analyses was determined by using Anderson-Darling normality tests [START_REF] Forbes | Statistical Distributions[END_REF].

In the last step, both MLE described in Eq. ( 1) and meanshifted MLE were applied to the same data. The mean-shifted weights, WV and W F , were calculated from Eq. ( 2) for the individual vibrotactile and force cues, respectively. The weights were then used to compute the prediction of the collaboration between the vibrotactile and force cues. All analyses of this step were based on task accuracy in this study and the existing work.

IV. RESULTS

None of the participants suffered from cybersickness. This was confirmed by the physiological data and the responses to the cybersickness questionnaire. Thus, the data of all participants were used in the analyses.

Normality tests on other objective and subjective data showed that all the data were normally distributed and therefore eligible for ANOVA analyses. Using the objective data, the task completion time for each testing block was on average similar as depicted in Fig. 3. An ANOVA analysis of the completion time revealed no significant difference among all testing blocks [F(4, 49) = 1.65, p > 0.05]. As each block had the same defects, a similar time was thus needed to complete these blocks.

The task accuacy for each testing block gave however a contrast picture, as shown in Fig. 4. An ANOVA analysis of the accuracy revealed a significant difference among all blocks [F(4, 49) = 24.08, p < 0.01]. Bonferroni post hoc tests inidcated that the difference came from the F_only block, compared to other blocks pairwisely. That is, the F_only block was significantly less accurate than all other testing blocks. Hence, the vibrotactile cue enhanced the accuracy of detecting defects. A comparison between all above results and the corresponding ones in the existing work was to validate our replication of the visual scene, interactive task and cues in the VE.

As summarized in Table 1, ANOVA analyses of the subjective data revealed that no significant difference existed among the testing blocks for perceived usefulness, effectiveness and pleasure. The workload gave however a significant difference. Bonferroni post hoc tests yieded that the difference arose from the V_co vs. F_only blocks [F(1, 9) = 1.84, p < 0.05] and the V_co vs. FV_co blocks [F(1, 9) = 2.46, p < 0.05].

Since the participants were instructed to favor the vibrotactile cue over the force cue, their data were biased towards the reliance on the vibrotactile cue. For the reliancebiased data, MLE and mean-shifted MLE were applied to the task accuracy of each testing block. This estimated Gaussiandistributed observations of the inidvisual cues, and yielded Gaussian-distributed observations and predictions of their cue collaboration. The observations and predictions were derived for the co-located and dis-located settings, as presented respectively in Figs. 5(a The same MLE and mean-shifted MLE estimations were carried out for the reliance-unbiased data from the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF]. The estimations gave Gaussian-distributed observations and predictions as depicted in Figs. 6(a) and 6(b), correspoding respectively to the co-located and dis-located settings. Comparisons between Figs. 5 and 6 allowed for verifying the applicability of mean-shifted MLE.

V. DISCUSSION In this study, the outcomes of both task completion time and task accuracy agreed with those in the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF]. This validates that our replication of the visual scene, interactive task and cues was as good as their originals. The reliance biased towards the vibrotactile cue seems to have no effect on both the completion time and task accuracy. The effect of the vibrotactile cue on the task accuracy conformed well with that in the existing work and other studies on sensing surfaces with tactile cues [START_REF] Lederman | Haptic perception: A tutorial[END_REF].

Moreover, our ANOVA analyses of the subjective data revealed similar trends of the F_only block as in the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF]. The F_only block was on average less effective and pleasurable compared to other blocks, even though their means were not statistically differentiable. Although the means of workload was higher in this study than in the existing work, the standard deviations of the workload were at the similar level for all corresponding blocks. Notwithstanding, the standard deviations of usefulness, effectiveness and pleasure were larger in this study than in the existing work. This might result from the reliance biased towards the vibrotactile cue.

When interpreting observations of the cue collaborations, MLE failed for three cases presented in Figs. 5(a), 5(b), and 6(b). One exception was the case of a reasonable mean-match as shown in Fig. 6(a). The MLE predictions of the collaborations based on the reliance-biased data unmatched their corresponding observations, as seen in Fig. 5. The observations were close to those of the V_co and V_dis blocks, as well of the mean-shifted MLE predictions. The closeness was especially pronounced in the dis-located setting, as depicted in Fig. 5(b). The participants might entirely ignore the force cue that was delivered at a dislocation from the vibrotactile cue, making its mean-shifted MLE prediction plausible.

The participants utilized however the force cue to a certain degree, when the cue was delivered at a co-location with the vibrotactile cue. This manifested as an amplitude mismatch between the observation of the cue collaboration and the meanshifted MLE prediction, as depicted in the case of Fig. 5 (a). The similar amplitude mismatch existed in the dis-located setting, as seen in another case of Fig. 6 (b). While differing from the deliveries of the cues (co-located vs. dis-located), the participants in both cases favored clearly the vibrotactile cue but yielded the amplitude mismatches. Compared to MLE, meanshifted MLE elucidated well the observations of the cue collaborations in all 4 cases of Figs. 5 and6. Each case interpretable by MLE and/or mean-shifted MLE, nevertheless, had an amplitude mismatch.

To examine the amplitude mismatch, we regressed the mean difference ( ) obtained using Eq. ( 3) against each meanshifted weight (WV or W F ) for all reliance-biased and -unbiased cases. As depicted in Fig. 7, the regression between and W V (or W F ) follows a Sigmoidal curve [START_REF] Hu | A model of the coupling between grip aperture and hand transport during human prehension[END_REF]: ,

where W represents a mean-shifted weight; and the parameters of , , W 0 and C are respectively the upper limit of , a positive coefficient, a constant weight at which equals to /2 and a constant. Using the Gauss-Newton method [START_REF] Hu | A model of the coupling between grip aperture and hand transport during human prehension[END_REF], a non- linear least square fitting of the available against W V yielded a regression coefficient R 2 = 0.99 and the parameters of Eq. ( 5), as presented in Fig. 7. So did the fitting of all against W F with its fitting parameters. The relative symmetry of the two sigmoidal curves implies that there might be a linear relationship between and the difference of W V and W F . This linear relationship is evident by a least square regression, as shown in Fig. 8 with R 2 = 0.92. This indicates that the function in Eq. ( 4) is a linear relationship between and W V -W F . This relationship suggests that the collaboration between vibrotactile and force cues might not be necessarily additive for humans to undertake 3D interactive tasks. More values would be imperative for validating the linear relationship in the future.

As given in Eq. ( 1), MLE assume an entirely additive effect among cues in collaboration. That is, the cues reinforce positively by each other. This might be true in the collaboration between visual and haptic cues [START_REF] Ernst | Humans integrate visual and haptic information in a statistically optimal fashion[END_REF]. So do other cue collaborations between visual and auditory cues, as well between force and position cues [START_REF] Ernst | Humans integrate visual and haptic information in a statistically optimal fashion[END_REF], [START_REF] Drewing | Integration of force and position cues for shape perception through active touch[END_REF], [START_REF] Perez-Bellido | Visual limitations shape audiovisual integration[END_REF]. Although remedying the mean mismatches resulted from MLE, meanshifted MLE took the same additive assumption as MLE. This explains the amplitude mismatches produced by both MLE and mean-shifted MLE, as depicted in Figs. 5 and6. Thus, the interpretation of the collaboration between the vibrotactile and force cues might need a model without additive assumptions.

VI. CONCLUSION

Replicating the existing work [START_REF] Erfanian | Force and vibrotactile integration for 3D user interaction within virtual environments[END_REF], we proposed meanshifted MLE to handle the mean and amplitude mismatches produced by MLE for the collaboration between vibrotactile and force cues. While mean-shifted MLE remedied the mean mismatch, the examination of the amplitude mismatch yielded promisingly a linear relationship between the mismatch and the difference of mean-shifted weights. Future work needs to acquire sufficient cases to gain insights into multi-sensory collaboration in virtual environments.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Collaboration and cues: (a) concepts of MLE and mean-shifted MLE on a cue collaboration; (b) a vibrotactile cue; and (c) a force cue.

  ) and 5(b).

Fig. 3 .Fig. 4 . 1 .

 341 Fig. 3. Average task completion time. [Error bars are standard errors.]

Fig. 5 .Fig. 6 .

 56 Fig. 5. Estimations of MLE and mean-shifted MLE on the reliancebiased data in the (a) co-located and (b) dis-located settings.

Fig. 7 .Fig. 8 .

 78 Fig. 7. Sigmoidal fit of the force and vibrotactile weights with different in amplitude of accuracy distributions


Antoine Lassagne

Andras Kemeny

Javier Posselt
email: javier.posselt@renault.com.

Frederic Merienne
email: frederic.merienne@ensam.eu

Performance Evaluation of Passive Haptic Feedback for Tactile HMI Design in CAVEs

Keywords: Haptics, human-machine interfaces, virtual reality

This article presents a comparison of different haptic systems, which are designed to simulate flat Human Machine Interfaces (HMIs) like touchscreens in virtual environments (VEs) such as CAVEs, and their respective performance. We compare a tangible passive transparent slate to a classic tablet and a sensory substitution system. These systems were tested during a controlled experiment. The performance and impressions from 20 subjects were collected to understand more about the modalities in the given context. The results show that the preferences of the subjects are strongly related to the use-cases and needs. In terms of performance, passive haptics proved to be significantly useful, acting as a space reference and a real-time continuous calibration system, allowing subjects to have lower execution durations and relative errors. Sensory substitution induced perception drifts during the experiment, causing significant performance disparities, demonstrating the low robustness of perception when spatial cues are insufficiently available. Our findings offer a better understanding on the nature of perception drifts and the need of strong multisensory spatial markers for such use-cases in CAVEs. The importance of a relevant haptic modality specifically designed to match a precise use-case is also emphasized.

INTRODUCTION

D UE to the recent improvements in high performance immersive systems, haptic feedback has become increasingly sought after by users who want to touch what seems real. Furthermore, haptic modalities seem to be an efficient way to improve interactive capabilities of Virtual Reality (VR) systems. The feeling of touching virtual objects is not easily attained as technological barriers are numerous. Currently few companies offer devices for natural interactions with the environment which can be used in an industrial context.

RELATED WORK 2.1 Brief History

Haptic systems have been proposed since the very beginning of the development of VR. For example, in 1967 the GROPE project [START_REF] Brooks | Project GROPE-HapticDisplays for scientific visualization[END_REF] was attempting to develop a force feedback mechanical arm with 2, 3 and finally 6 degrees of freedom (DOFs). Various more advanced systems emerged as VR started to grow in the early 90s, due to significant technological advancements which allowed for much greater computing power. For instance, Cybergloves Systems ß proposed various products, from simple vibrotactile gloves to full haptic workstations with whole hand kinesthetic and cutaneous feedback. The SPIDAR collection [START_REF] Sato | Space inteface device for artificial reality-SPIDAR[END_REF], human-scaled systems made of taut strings and stepper motors, also emerged and became popular during these years. Meanwhile, Sensableß was releasing the Phantom Omni [START_REF] Massie | The phantom haptic interface: A device for probing virtual objects[END_REF] (now called Geomagic Touch), which was one of the first affordable haptic devices. For over a decade, Haptionßhas been largely deploying haptic devices in spite of finding only a few early customers among the largest OEMs.

It has been established that haptic systems are necessary to improve the presence, the immersion and the performance [START_REF] Brooks | What's real about virtual reality?[END_REF], but also to reduce the cognitive load generated when vision is required to compensate for a lack of other perceptual cues [START_REF] Robles-De-La-Torre | The importance of the sense of touch in virtual and real environments[END_REF]. With the recent maturity of high performance display systems and the large consumer deployment of head mounted displays (HMD), one can expect an increased interest in haptics in the years to come.

Haptics & CAVEs

Cave Automatic Virtual Environments (CAVEs) and HMDs have different constraints which often require a specific implementation. CAVEs do not overshadow the real environment, while HMDs do. HMDs thus do not need to deal with real objects mixed with virtual ones, like the human body, or with the intrusiveness of the systems.

CAVEs and similar display systems are broadly used by the automotive industry as VR tools. This is partly due to the high graphic quality and the ergonomics required by driving simulators, which deploy a lot of common technologies with CAVEs [START_REF] Kemeny | From driving simulation to virtual reality[END_REF], but also to the simplified conception process offered by VR. However, haptic implementation is still troublesome in these immersive systems where the real environment cannot be fully hidden by the virtual one.

These devices thus have major criteria to meet to be compatible with CAVEs and industrial applications.

Visual intrusiveness is a critical issue in CAVEs, as physical objects are visible. This may cause a critical reduction in immersion [START_REF] Scheibe | Tactile feedback at the finger tips for improved direct interaction in immersive environments[END_REF], with the usual results. Physical intrusiveness also needs to be minimized to fit ergonomics and accessibility requirements. All perceptual stimuli should be synchronized with the visible self to avoid any visuo-haptic conflict. Haptic modalities (textures, kinesthesia, visual feedbacks, etc.) must respond to the use-case requirements. Industrial requirements must also be considered, which can be gathered through usability. There exists several definitions of usability with many different criteria like effectiveness, efficiency, learnability, attractiveness, security, operability, etc. [START_REF] Assila | An approach and a tool for subjective and objective evaluation of the usability of interactive systems: Application to passengers information assistance system[END_REF]. In this study, we consider efficiency, effectiveness, and satisfaction of subjects (ISO 9241-11). Several systems can be suited for use in CAVEs. Large scale SPIDARs [START_REF] Buoguila | New haptic device for human scale virtual environment Scaleable-SPIDAR[END_REF] (Fig. 1), haptic arms, haptic gloves, or even non-contact haptic systems [START_REF] Sodhi | AIREAL: Interactive tactile experiences in free air[END_REF], [START_REF] Carter | UltraHaptics: Multi-point mid-air haptic feedback for touch surfaces[END_REF]. Unfortunately, none of these are compatible with our use-case, due to deficiencies in fidelity, ergonomics, flexibility, robustness or flexibility.

Passive Haptics

Passive haptics, also called Augmented haptics or Pseudo-haptics were also studied in many experiments. The idea is to oppose a tangible passive object, co-located with the virtual object, to the actions of the user [START_REF] Bayart | R ealit e augment ee haptique: Th eorie et applications[END_REF], [START_REF] Hoffman | Physically touching virtual objects using tactile augmentation enhances the realism of virtual environments[END_REF], [START_REF] Lecuyer | Interfaces haptiques et pseudo-haptiques[END_REF]. The subject merges the real and virtual environments in a global one, with visual and haptic feedback, like in the experiment conducted by Hoffman [START_REF] Hoffman | Physically touching virtual objects using tactile augmentation enhances the realism of virtual environments[END_REF]. Research showed that it significantly increases the sense of presence and global immersion [START_REF] Insko | Passive haptics significantly enhances virtual environments[END_REF], [START_REF] Carlin | Virtual reality and tactile augmentation in the treatment of spider phobia: A case report[END_REF].

This method is affordable, safe, and does not need computation time. It provides a robust kinesthetic feedback as well as the appropriate cutaneous sensations, although it does not address every possible situation. Still, it is suited for a large number of use-cases, especially when it simulates simple shapes, and can render satisfactory kinesthetic feedback. Kinesthesia is the key to performance in eye-coordinated tasks whereas cutaneous feedbacks are more effective when rendering only simple alerts [START_REF] Hale | Haptic rendering -beyond visual computing -Deriving haptic design guidelines from human physiological, psychophysical, and neurological foundations[END_REF]. Some systems seek a compromise between several haptic modalities like textures, forms, compliance, temperature, inertia, friction, whereas focusing on a precise modality makes an efficient haptic system easier to attain. Despite all of these strong advantages, there are a couple of drawbacks.

One or many tangible objects need to be accurately adjusted, calibrated, or even built, for every change in the displayed scene, which decreases the flexibility of the virtual technologies. When using passive haptics, loading a simulation may take more time than launching a visual environment with a few clicks. Real objects cannot be overshadowed by any virtual object in CAVE-like displays. This causes unusual image overlap and corresponding distorted distance perception [START_REF] Fuchs | Le Trait e de la R ealit e Virtuelle[END_REF], thus only the foreground can render haptics. An accurate spatial calibration between real and virtual objects may be difficult to attain. This is mainly due to imprecisions in the virtual position of the eye, and unfortunately it causes great offsets between visual and haptic perception [START_REF] Wann | Natural problems for stereoscopic depth perception in virtual environments[END_REF], [START_REF] Chen | Manually locating physical and virtual reality objects[END_REF], [START_REF] Kenyon | Size-Constancy in the CAVE[END_REF]. The position of the eye is difficult to take into account, as people have important morphologic disparities [START_REF] Dodgson | Variation and extrema of human interpupillary distance[END_REF]. Passive haptics are implemented in Renault CAVE IRIS (Immersive Room and Interactive System) to allow the users to sit in a car and handle a steering wheel. However, the virtual parts are totally occulted by physical ones which cannot be modified easily. The physical parts need to be calibrated as accurately as possible and tracked with precision to match multiple environments, and these constraints rarely allow for a perfect visuo-haptic synchronization.

Sensory Substitution

Sensory substitution is a substitution to haptic feedbacks by other sensory information, such as visual or auditory. These lighter solutions were considered to add interactive functionalities to VEs without heavy haptic implementation. Several studies concluded that visual or auditory cues helped users to know if and when a virtual collision occurred [START_REF] Chan | Touching the void: Direct-touch interaction for intangible displays[END_REF], [START_REF] Hu | Visual cues for imminent object contact in realistic virtual environment[END_REF], [START_REF] Madison | Use of interreflection and shadow for surface contact[END_REF], [START_REF] Petzold | A study on visual, auditory, and haptic feedback for assembly tasks[END_REF], [START_REF] Kitagawa | Effect of sensory substitution on suture manipulation forces for surgical teleoperation[END_REF], [START_REF] Sreng | Using visual cues of contact to improve interactive manipulation of virtual objects in industrial assembly/maintenance simulations[END_REF], especially realistic cues like shadows or inter-reflections. Nevertheless, cues must be optimized for their effect to be positive. According to Kitagawa et al. [START_REF] Kitagawa | Effect of sensory substitution on suture manipulation forces for surgical teleoperation[END_REF], auditory cues may become intrusive while continuous and they are not as precise as visual ones while discrete. On the other hand, continuous visual cues are proven effective, but need to be seen by users while auditory ones do not.

Several studies concluded that the more visual cues there are, the stronger the space perception [START_REF] Ernst | Merging the senses into a robust percept[END_REF] and the easier the interaction becomes, although too many cues may increase the cognitive load or intrusiveness [START_REF] Petzold | A study on visual, auditory, and haptic feedback for assembly tasks[END_REF]. There were also experiments conducted to investigate which visual cues could be used, and when. Sreng et al. [START_REF] Sreng | Using visual cues of contact to improve interactive manipulation of virtual objects in industrial assembly/maintenance simulations[END_REF] concluded that visual glyphs like arrows or spheres were appreciated in the perception of distances and effort, whereas light sources were more effective to perceive contact locations. In general terms, continuous visual cues can provide continuous feedback capabilities from software and thus allow subjects to gain precision and accuracy.

CONTEXT OF THE WORK

The automotive industry investigates virtual technologies to avoid the production of physical prototypes which are expensive and time consuming, but haptic modalities are needed in VEs lacking interaction capabilities [START_REF] Scheibe | Tactile feedback at the finger tips for improved direct interaction in immersive environments[END_REF]. Three haptic systems are tested in this experiment to evaluate if they could be of interest for dashboard HMI engineering design. This study is focused on flat HMIs like touchscreens to satisfy the need of testing new designs of car computer interfaces while being in the cockpit, in context, not necessarily while driving. Such a system would allow the designers to evaluate the integration of their software creations inside the car, and the usage of the HMI with the right posture. Nowadays, HMIs are only tested on classic computers before they are integrated in an expansive physical prototype, not always in the exact configuration of the car.

This context is compatible with haptic systems which render only flat objects. The implementation is thus simplified and three interactive methods are considered.

A sensory substitution based, visual-only, interaction system. An additional passive haptic device made of a transparent glass subjectively calibrated. A classic tablet fixed on an articulated arm. The systems are evaluated in accordance with three criteria.

Performance should be sufficient to allow effective interactions and feelings of confidence. System Usability Scale-Industrial usage requires a tool to be accessible to a large panel of users, and compatible with a large panel of use-cases. Relevance. Each haptic modality can respond to additional use-cases which must fit the context. Finally, this experiment is carried out to evaluate how a subjective visual calibration, quickly made by each subject before beginning the experiment, can improve the quality of the visual coherence between the real and virtual parts. To have an efficient haptic feedback able to give self-confidence to the engineers who use the tool, real and virtual objects need to be perceived precisely in the same place.

RESEARCH PLAN

Scientific Context

This research aims at studying the added value of haptic feedback in interacting in immersive VEs. It is focused on virtual touchscreen-like HMI design.

Three haptic modalities are evaluated. We dealt with different scientific barriers : CAVE-related questions like intrusiveness, visuo-haptic synchronization, and space perception. CAVEs cause specific issues that HMDs do not. Objects cannot be visually intrusive, physical and virtual parts are mixed within the environment (especially the body of the subject), space perception can be biased, anthropological parameters have an influence, etc. Haptic system related questions, specific to the modality (kinesthesia, cutaneous, sensory substitution, etc.) and the actual device implemented. We also seek to study industry-compatible interaction systems, with every related additional constraint (ergonomics, robustness, accessibility).

Research Questions and Hypothesis

Our research questions are related to the added value of tangible interfaces in the context of a full visual immersive system (CAVE type):

To which extent a tangible interface brings a better sense of interaction in virtual immersion? What is the precision of interaction in using a tangible interface? It is expected that a tangible interface is an added value for precision as well as user's confidence in the interaction, thanks to the space calibration process done with kinesthesis and haptic feedback. Thus, our hypothesis are the following:

The precision of the interaction is better when using tangible feedback. The tangible interface brings a better comfort to the user in his interaction. The study aims to grow the knowledge on transparent passive haptic systems and find new ways to improve them.

EXPERIMENTAL STUDIES

Virtual Environment (VE)

This experiment took place at Renault, in the P3I (Plateforme d'Int egration Immersive Industrialis ee) CAVE (Fig. 2). This CAVE is a 4-faced immersive room, with front, floor, and two side walls. It is 2.7 m high, 4.32 m deep, 3.6 m large and has high definition video projectors displaying every pixel in a 2.25 mm square.

Head tracking is provided by 8 ARTtrack infrared cameras, as well as hand and finger tracking using an active fingertracking device (Fig. 6) with an alleged 1-millimeter precision.

The VE is computed and displayed by Oktal SCANeR Studio ß1.4, at a 60 Hz frame rate, working with Catia V5 via TechViz middleware. Renault's Megane 4 interior is displayed and the outside environment is a reproduction of Guyancourt, France (Fig. 3).

A tracked car seat, co-located with the virtual seat allows subjects to sit down just like they would in a real car.

Experimental Conditions

Interaction Systems

Three different haptic modes are tested during the 3 phases (described Fig. 4) called phase X, Y and Z.

Phase X -Sensory substitution. Only the CAVE is used and no tangible haptic system is added. A green button, when pressed, would color itself into a darker green, and then turn back to white when released. Although it does not provide any force feedback, it does offer a way of interacting. Phase Y -Passive haptics. A transparent glass subjectively calibrated is placed in the CAVE to provide a tangible contact and a robust depth marker to rely on. Plexiglas is chosen for its great transparency, we can see in Figs. 3 and6 that the system is nearly invisible. Phase Z -Classic tablet. The tablet is fixed on an articulated arm. The exact same software is displayed on it, and the interaction system is the multi-touch screen instead of the finger tracking glove. The tablet used is a Samsung Galaxy A6 with more than sufficient specifications

Methodology

The 3 phases are randomly ordered to avoid a learning bias, and they are attempted in a row in order for the subjects to have fresh memories of what they felt in the previous test. The phases are separated by questionnaires : one questionnaire about the interaction implementation and one presence questionnaire (adapted from Witmer and Singer's [START_REF] Witmer | Measuring presence in virtual environments: A presence questionnaire[END_REF]), for each interaction modality.

Within each phase, the subjects interact with the designated haptic mode as they would interact with the integrated touchscreen of a modern vehicle. The HMI is a simple 4-button interface, as illustrated in Fig. 5.

Phases Sequencing. Each phase consists of 2 series, which are differentiated by the size of the buttons. The buttons of the first series are much bigger than the ones of the second series, as shown in Fig. 5.

A series consists of 20 successive interactions on the interface. To trigger every interaction, one of the 4 buttons on the interface turns green. The subject is tasked to touch the center of the button when this occurs. Once it is touched, the button turns grey again and the interface waits for a random duration before triggering the next interaction.

Subjects are instructed to touch precisely the center of the button, with a constant speed. They are also asked to put their hand on their knee between each interaction, to ensure regularity. The subjects are aware that their interaction durations are timed and their accuracy is being measured.

At the end of a series, we save the collected data : response duration, relative error of each interaction, and the subjects' comments verbatim.

Latency

In addition to the usual latencies produced by the CAVE, we face two specific delays during our experiment.

Streaming latencies: the HMI is streamed to the CAVE from another computer through the network. The HMI is refreshed 100 ms late due to this configuration. Interaction latencies: the tracking system and its network flow causes a 50 ms latency. These latencies occur only in virtual modes and not when subjects interact with the tablet. They are identical during both virtual modes as the tangible Plexiglas is only a passive object.

Subjective Calibration

For every haptic mode, subjective calibration is conducted instead of analytic calibration.

The physical parts are adjusted by the subjects until they coincide with virtual parts to their own vision (when there are physical parts). The interaction engine is localized where the subjects can see it. They are asked to successively touch 3 corners of the virtual touchscreen (except when using the tablet), and the interaction system is mapped on this calibration, as represented in Fig. 7. Every action is theoretically taken into account 0.5 mm before the subject actually touches the glass to avoid erratic behavior. In early experiments, analytic calibration (provided by measuring the interpupillary distance (IPD) of subjects and tracking the passive object) did not provide satisfactory accuracy. The subjects were complaining about a spatial offset between what they could see and what they could touch. Measuring the IPD obviously improves depth and scale perception, but is not always enough. It is measured to get a more accurate position of the eyes of the user, interpolated from his tracked 3D glasses, but there are other parameters influencing distance perception accuracy. Nose height and eye depth should be measured, as well as anything that has an influence on the location of the 3D glasses. The 3D glasses should also have sufficiently robust positioning on the subject, and be placed the same after a subject removes them and wears them again. When the location of the actual eye of the subject is not precisely known by the system, the vision frustrums cannot be computed accurately and this lead to spatial bias.

These constraints, in addition to the privacy issues caused by the company in the case that we collected and stored morphologic data, led us to consider a subjective calibration. It was found more accurate by early testers, but still not perfect. Further experiments should improve subjective calibration, perhaps combining it with analytic calibration, as it does not resolve all issues. Notably, the subjects should keep the same posture as during the calibration while interacting, and the same gaze direction (a straight forward gaze in our experiment) as well. If the subjective calibration is performed while looking straight forward, switching to lateral vision would corrupt it.

Subject Panel

20 subjects took part in this experiment. To ensure confidentiality, each one of them is a Renault employee working in Guyancourt. Coming from various fields of work, half of them were totally VR-naive (they had never used a virtual reality immersive system before). The other half were experts. Males and females, most of them were between 25 and 50 years old. Their specificities were known by questionnaires, oral questions, and verbatim records. Their feelings were collected between each phase via our questionnaires.

Our numerical analysis is only based on 17 subjects, as there were subjects unable to succeed every phase or who provided aberrant results.

Measurements

Three indicators are used to evaluate the performance of the subjects. Every action is timed from the moment when a button turns green to when it is touched by the subject. Vertical and horizontal relative errors are measured by the software as the corresponding projection of the distance between the interaction and the actual center of the button. The third indicator is the questionnaire ratings. We also analyze verbatim records (Subjects are asked to comment each difficulty they encounter and to report whatever is crossing their mind) to complement these indicators.

The subjects were allowed to compare their different questionnaires when filling them out, and were all told that our interest is only in the differences between modes, not in the absolute values.

RESULTS

Execution Duration

Data on execution duration can be observed in Fig. 8 . The Student's t-Test for paired samples was used for the comparison of two phases (the differences are normally distributed according to Shapiro-Wilk Normality Test). We found a significant difference (p < 0:05) of execution duration between haptic modes. The fastest mode was with the tablet (Z), with which the subjects' interaction took an average time of 1.5 seconds. Compared to this duration, both phase X (sensory substitution) and Y (Plexiglas) are slower. Phase X is also significantly slower than Y, twice the value of Z. However, this does not prove on its own that phase Z is superior. Lots of subjects did not appreciate it for the immersion experience, although every one of them emphasized that the tablet offered a more reliable interaction than any other system.

We identified two reasons for the poor performance in the virtual phases :

The reliability of perception was a handicap for a few subjects whose spatial perception drifted over the course of the experiment. Some of them could not get to the end of the visual-only phase, and most of them sometimes had to try twice or more to achieve an action. This phenomenon also happened with the transparent glass, but was stronger and more frequent without it. The subjects would go deeper and deeper through the button, recalibrating their perception until they would not see the button on the same place than before. A perception recalibration and a one minute pause could often solve the problem, as discussed later. There were six recalibrations needed during phase X, and just one during phase Y. Irresolution, meaning the subjects slow their moves due to a lack of self-confidence, is also a major cause. Even when removing every interaction slowed by a reliability issue, the average duration of phase Y is longer than phase Z. The subjects are not as confident as they would be in real conditions and they slow their moves accordingly. This situation is worse during phase X, and there are greater disparities: standard deviation between means of subjects is almost twice as high as during phase Y and many subjects achieved a poor performance.

Relative Error

Relative error is analyzed and represented in Fig. 9. It was measured for each interaction and represents its position compared to the center of the button which was supposed to be touched. Although the A.R.T finger tracking device was found to be conclusive, we prefer to analyze repeatability to avoid any bias due to a constant shift in the tracking system. Once again, Student's t-Tests for paired samples were used to analyze differences among the modalities (the differences are normally distributed according to Shapiro-Wilk Normality Test).

While phase Y and Z have approximately the same mean standard deviation, phase X has a significantly higher value (p < 0:05). There are also more disparities between subjects, as the variance of variances is much higher. These differences are mainly due to perception uncertainty and shifts occuring during this phase. Different subjects reported that they felt the button was getting further and further away from them, until their interaction would not work anymore. On the computer, the operator could see their interactions progressively drifting to the left, because their move would intersect the virtual screen earlier than they thought. The software was designed to prevent them from realizing that they were shifting ; they only receive the information that they touched the button or not, not where they had touched it.

Finally, we can see on the graph that the standard deviation of standard deviations of relative errors is higher during phase Z. We assume that some subjects did not take phase Z as seriously as X and Y, as they all were familiar with tablets. Even though we specifically asked them many times to touch precisely the center of the buttons, the subjects were less focused during this phase.

Subjects Judgment

Each mode received specific recurrent reports from subjects.

Sensory Substitution Mode

This mode could be frustrating when the subjects thought they had touched the button but the system would not take it into account. When the calibration was correct, the subjects reported this mode as surprisingly pleasant to use, and the color cue was much appreciated. Several subjects reported that they enjoyed the fact that there were two color changes, one when they would press the button and one when they would release it.

Passive Plexiglas Mode

Being able to forget about depth issues and to lower the cognitive load and the uncertainty induced was much appreciated, but subjects sometimes had to put more pressure on the glass when the system would not react. Some subjects were especially happy with the glass placement, reporting that this precise calibration rendered a nice feeling. The system lacked a bit of robustness and rigidity and it can still be improved. The A.R.T glove structure could sometimes interfere between the glass and the skin, leading to a quality contact deterioration.

Real Tablet Mode

The tablet provided excellent interaction during phase Z, but impaired the VE due to contrast and brightness disparities. It caused a drastic reduction in immersion, and some subjects even wondered in which purpose the CAVE served. These subjects thought that the tablet experience Fig. 9. Standard deviation of mean relative error for each subject. Again, a few users could be efficient with system X or Y, bust most of them lacked accuracy and regularity.

would have been better without the CAVE, as the 3D glasses deteriorated the displayed image with their polarization. On the other hand, the subjects who were expecting an immersive experience reported that this phase was weird, easy to succeed but unpleasant and not necessarily an effective tool to work with.

Questionnaires

Concerning the questionnaires, phase Y obtained on average higher scores (nearly 50 percent) than phase X in the system specific category, while phase Z received even higher scores than phase Y. This questionnaire is mainly focused on the performance, with questions about self confidence in interaction, delay perception and global experience quality. Considering these results added to the corresponding performance, we assume that the glass helped people in their virtual interactions. The verbatim records confirm this assessment. Finally, several subjects reported that even so their performance felt better during phase Z, the very low immersion made it feel irrelevant to the use-case of virtual HMI design.

Delay Perception

Although latency is well known, we still asked subjects to rate the delay they perceived from 1 to 5, 1 being the best rate. We discovered that perceived latency is related to the performance of the subjects. The subjects who had difficulty interacting often blamed latency. For that reason, the mean rate of perceived latency obtained is a lot higher in sensory substitution mode (2.6) than in passive haptics mode (1.5). On the other hand, people who had no issue interacting reported that the latencies were detectable but low.

Confidence

In the presence questionnaire, we asked the subjects to rate their feeling of being qualified while using the current system from 1 to 7. We had significant results (p < 0:01 according to Wilcoxon signed rank test) showing that subjects felt much more qualified during phase Y than during phase X, as we can see in Fig. 10. Phase Z obtained heterogeneous grades, as subjects suffered from the lack of immersion : They may have felt confident in their interaction, but they did not in their evaluation of the vehicle HMI in context, which would be the real goal.

The remaining results of the presence questionnaire are less relevant, as phase Z obtains the same mean score than Y, only a little higher than X, whereas many subjects complained about the low quality of immersion while they were using the tablet. Witmer and Singer's questionnaire may not be appropriate for our research, as it contains only a few questions dedicated to haptics and interactions.

DISCUSSION

Preferred System

In this experiment, none of the interaction modes were unanimously favored by all the subjects, regardless of the significant usability differences found in the results. They were asked to defend their point of view. Their arguments led us to conclude that when first seeing the system, every subject had his own idea of how it could serve specific usecases, and these ideas ruled their expectations. Depending on what was their initial opinion about virtual technologies and if they were expert in HMIs development for car interiors, they nearly had chosen their system before beginning the experiment.

Subjects who preferred phase X were mostly VR experts who expected an interactive system, not necessarily in this particular context. In their opinion, they were testing an interaction module to use with CAVEs in various applications. Subjects who preferred phase Y were VR naive and experts. They appreciated having a tangible and precise force feedback to oppose their moves and enhance their performance, in contrast with the sensory substitution mode which could lead to difficulties. In their opinion, they were testing a vehicle dashboard HMI in a realistic environment, just like if they were in a physical car, to simulate the car interior specificities. Subjects who preferred phase Z wondered why VR would be of any interest for dashboard HMI development. In this state of mind, they were not paying attention to the whole VE and they reported that they would have preferred being simply in their office with the tablet. With this study, we emphasize that haptic modalities are use-case dependent and need to be implemented accordingly. There is also a performance factor, which granted phase Y better rates than other phases.

Application Area and Limitations

We observe that these haptic implementations would not fit the same application area.

Sensory substitution can be implemented in most scenarios, as it just needs collision detection, but this flexibility has a price paid in usability. Transparent passive haptics, as we implemented it, only work in specific situations, like car flat tactile HMI design. Targets must be flat, static, and have simple shapes. Improving the usability of sensory substitution may allow its use in a wider application area than passive haptics, but it is not conclusive for our use-case yet.

Recalibrations

Lots of subjects had their spatial perception shifted after a while during phase X, losing their references in the VE progressively. In the end, some of them would see the buttons in another place and their following interactions were all in error. Six subjects needed a perception recalibration during phase X and 4 subjects were able to take benefit from it. The recalibration consisted of asking them to look somewhere else on the scene, to look at themselves, to close their eyes for a few seconds. After those simple acts, they were once again able to interact.

These recalibrations were required only once during phase Y. We suppose that the tangible Plexiglas is a strong enough depth mark on which subjects' perception system can rely.

Discrete recalibration of subjects perceptions were studied in the past [START_REF] Atkins | Experience-dependent visual cue recalibration based on discrepancies between visual and haptic percepts[END_REF], and continuous recalibration may enhance the usability of fully visual modalities when perception robustness lacks.

Inter-Subject Disparities

Subjects adapted differently to phases X and Y, and more disparities were revealed during phase X. Both standard deviation of mean execution durations and standard deviation of standard deviations of relative errors are significantly lower during phase Y than during phase X. Additionally, there were more subjects failing phase X, or frustrated by it, than any other phase. This makes us assume that sensory substitution is more difficult to handle, and subjects with a lower level of adaptability were handicapped. As expected, people who were familiar with CAVEs and stereoscopy achieved a decent performance during phase X, whereas VR-naive subjects often could not. The added value provided by the passive transparent glass was more pronounced with these subjects who approached the performance attained by experts.

Finally, and once again, phase Z provided the lowest performance disparities, in spite of the subjects' overall opinion.

Learning in the Loop

A simple in-the-loop learning process was tested with a few subjects in sensory substitution mode. As the subject interacted, the operator informed them orally of their spatial error (long shift on the right, short shift on the top...). Results were impressive and subjects were able to be as accurate as during phase Z after only a few guided interactions and without additional information. A dedicated experiment with several different software-based in-theloop learning cues would certainly be of interest, and would probably allow companies to find a reliable interactive modality which could be used in various work fields.

To synchronize the perception of the subjects with the visual and tracking system, we are not limited to the method of teaching users how to adapt their perception via visual or oral guidance. A strong self-correcting software could also be a solution which would need further study to be efficient and reliable.

Driving Simulation Situation

A simulated driving situation would introduce major differences, and thus require further studies.

A moving environment continuously renews all spatial cues (depth, scales) and it may significantly reduce the perception shifts we encountered. Interacting with the HMI while driving is less important than safety. Users cannot keep their sight on the screen for too long, thus they will use either central vision for short durations or peripheral vision, which is not as efficient as central vision in many aspects [START_REF] Strasburger | Peripheral vision and pattern recognition: A review[END_REF], [START_REF] Soichiando | Central and peripheral visual reaction time of soccer players and athletes[END_REF]. The context itself also has influence over the haptic performance [START_REF] Atkins | Experience-dependent visual cue integration based on consistencies between visual and haptic percepts[END_REF] and may influence the results as users would have a different focus.

CONCLUSION

In this article, we compared different haptic modes for tactile dashboard HMI design in a CAVE in order to measure the added value of tangible interfaces. We tested a simple sensory substitution system based on discrete visual cues, a passive haptic system made of a tangible transparent glass, and a classic tablet. The transparent passive haptic system allowed significant improvements in usability compared to sensory substitution. It enhanced usability through significant gain in accuracy, efficiency and selfconfidence bringing a better user experience. We could thus show that the proposed tangible interface provides significantly more robust spatial haptic cues. Finally the tablet in spite of good performance was not well accepted as natural interface by subjects who reported a reduction in immersion.

PERSPECTIVES

In-the-Loop Learning. More multisensory cues could be added to continuously help users interact. In addition, software would take advantage of detecting theses shifts an adapt in real time.

Driving Simulation Context. Driving situations modify mental workload and performance while interacting with HMI. Future work will be carried out to study haptic modalities in driving situations.

Fig. 1 .

 1 Fig. 1. A SPIDAR, providing kinesthetic feedback through taut strings.

Fig. 2 .

 2 Fig. 2. P3I: Plate-forme d'Int egration immersive industrialis ee.

Fig. 3 .Fig. 4 .

 34 Fig. 3. The passive slate, nearly invisible in the environment during phase Y.

Fig. 5 .

 5 Fig. 5. There are only four buttons in the HMI, with two different scales.

Fig. 6 .

 6 Fig. 6. A.R.T. Finger tracking device and the passive glass.

Fig. 7 .

 7 Fig.7. The software maps the interaction system with the subjective calibration proceeded by the subject.

Fig. 8 .

 8 Fig. 8. Mean duration of interaction for each subject. Apart from a few points, we can see a global tendency where phase Z is faster than phase Y, which is faster than phase X.

Fig. 10 .

 10 Fig. 10. The feeling of being skilled between phases X Y.

ACKNOWLEDGMENTS

This work was supported by a cooperation between the Renault Group and Arts et M etiers Engineering School. The authors would like to thank particularly the Renault Group for providing the materials and software we used in this experiment.

Javier Posselt received the PhD degree in robotics from the Universit e Pierre et Marie Curie, in 1996. He is a member of the Renault's Advanced Driving Simulation & Virtual Reality Department and since 2010 has been a project manager in virtual and mixed reality systems. For 15 years, he has been the project manager on projects involving industrial process simulation (virtual factory), and digital mockup assessment using virtual and mixed reality systems. He managed French national and international collaborative projects in the area of virtual reality. He was the project manager of the first high quality projection CAVE of Renault inaugurated in 2013.

Frederic Merienne received the PhD degree from the National Polytechnical Institute of Grenoble (INPG), in 1996. He is currently a professor with Arts et M etiers and has been director of the Image Institute, a research team of the Le2i Laboratory, since 2004. His research interests are focused on virtual immersion linked with engineering, cultural heritage, and health applications. He is the author of many scientific papers in virtual reality and related disciplines. He is involved in different projects with industrial partners, and initiated international collaborative projects in the area of virtual reality with universities in the USA, Australia, Colombia, and Malaysia.