A survey on haptic interaction techniques in the exploration of large and scientific data sets

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1 A survey on haptic interaction techniques in the exploration of large and scientific data sets Bob Ménélas (1) Mehdi Ammi (1) Patrick Bourdot (1) Simon Richir (2) (1) CNRS/LIMSI University of Paris-Sud XI BP 133, Orsay (2) P & I Laboratory, ENSAM Angers, France Abstract Capabilities of VR technologies grant a natural environment to researchers and engineers for the exploration of large data sets. During the last two decades several haptic rendering approaches have been developed in order to acquit the visual feedback and allow users to fully exploit the coupling of these two sensory channels. Some skilful results have been archived. In this paper we present a survey on the use of the haptic modality in large data sets exploration. For each haptic technique which is presented in that document, we describe its principals and its effectiveness. Keywords: Haptic rendering, Data exploration, Data set, VR I. Introduction As a result of recent advances in computer simulation, in data storage and in measuring processes, scientific and industrial applications are now increasingly generating large multi-dimensional matrices representing a lot of scientific properties of the studied phenomenon. In these scientific and abstract data sets since most of the time the target is not well defined or necessitates knowledge that can not be formalized, automatic exploration techniques are not relevant for extracting meaningful information. Thus in medical data segmentation, a field where a lot of automatic segmentation methods could be counted, Vidholm and Agmund [37] note that the problem remains unsolved since the methods are not general enough. However, human-centred data miming techniques reveal to be useful for the extracting of meaningful patterns from these resources [29]. Capabilities of Virtual Reality (VR) immersive technologies offer an adequate environment for these approaches. In such system, multisensory feedbacks (visual, haptic, auditory ) and multimodal input (speech, gesture, tracking ) may be exploited in order to provide the most intuitive interaction [9] [28], and thus allow the user to fully take advantage of all his perceptual abilities in the exploration of the data set. The haptic channel can be very useful when the user attempts to precisely locate a feature within a volume, or to understand the spatial arrangement of complex threedimensional structures [2]. Moreover, the haptic sensory-motor channel makes it possible to users to palpate these abstract and scientific data and like tangible objects, hence to enable a better explanation of the studied phenomenon. In the analysis of a large data set resulting from a CFD application through a Visual/Haptic system, the physicist uses a large scale stereoscopic display. He/She can visualize all the data volume or a part of it through different computer graphics techniques (texture based, direct flow visualisation and so on) [32].Thanks to his/her knowledge and his experiments infers a global idea about the evolution of the fluid. On the other hand, the visual feedback is reinforced and sometimes supplemented by the haptic one. In addition this haptic feedback helps the physicist to easily focus his/her analyse on a specific point of interest in the observed phenomenon. While being constrained on a 2D plane, he can more rapidly and easily access a target point within the virtual scene [8] [25] [38]. Fig 1 exhibits a user analysing an unsteady flow through some 2D slices and streamlines in a VR immersive environment by the mean of a visual/haptic system. Figure 1 A accurate positioning of a colored cutting plane, in the CFD immersive application of "CoRSAIRe" Streamlines 2D slices

2 The goal of this paper is to present a survey on haptic rendering techniques used in the exploration of large data sets. Haptic holds a prominent place in the interaction with virtual environments, offering the ability to touch, feel and manipulate virtual objects. This is the force felt when we handle an object, the feedback produced when an object slides on the surface of another one. In VR applications this channel promotes the rendering of geometric or volumetric models. In geometric model, constrained based methods (god-object, proxy 1 ) are usually utilized to convey the touching of a virtual surface. The constrained based method is introduced by Zilles and Salisbury [39] while constraining a virtual point (god-object) on the explored surface. In [33] Ruspini et al. propose a more general constraint based method and simulate additional surface properties such as friction and haptic texture. In the haptic rendering for volumetric data, the force field is computed directly from the volume data without conversion to a geometric model. This paper is organized into four sections. An. We discuss about surface rendering in section 2 while volume rendering is described in section 3. Section 4 concludes the paper. II. Surface rendering In the surface rendering approaches, the haptic feedback simulates the response of touching a virtual surface, allowing the analysis of local properties, and thus improves the accuracy of the user s gesture. Such haptic rendering process is very pertinent for isosurface exploration in volumetric data set, indeed in Medical Imaging domain, haptic segmentation system speeds up and facilitates the user interaction [35]. In [37] Vidholm et al. related that, in surface segmentation, users work more efficiently when they are guided by a haptic feedback than without. Several techniques were developed in this class of methods. Traditional approaches aim at extracting a surface based representation from the volumetric one. The Marching Cubes (MC) algorithm [20] is the most common algorithm used for the computation of the boundaries of the volumetric data. Once the surface is generated, the haptic feedback is computed through a classic collision detection module coupled with penalty based method [25] [26]. If it is true that using the surface based representation let having a stable feedback, we can however note that since the surface is estimated in a pre-computed time this method does not allow real time modification of the data. To overcome this 1 The proxy is the perceptive representation of the haptic interface in the virtual scene limitation, Galyean et al. in [12] and later Körner et al. in [18] exploited a local utilisation of the MC algorithm. In this solution, the voxel data in the neighbourhood of the probe 2 position is used to generate a surface through points with similar values. With this method, real time updating is possible because the surface is generated on the fly. However there is a direct relation between the computation time and the amount of data. This tends to restrict the application of such method to non complex data. To overcome limitations related to the local approximation, Adachi et al in [1] propose the use of an intermediate representation like a virtual plane for controlling the haptic interface. In order to assume a very fast haptic loop without dependence on the amount of data, this method updates the virtual plane at a low frequency while maintaining a high update rate at force control loop of the interface. Mark et al. in [25] and later, Chen et al. [6] have illustrated this model with a haptic rendering method for isosurface without any explicit isosurface extraction. In this algorithm, a virtual plane is used, as an intermediate representation of the isosurface, to compute the pointwise interaction force applied to the haptic interface. In [5] Chen et al. implement this model in a virtual sculpting system enabling real time data modification like melting, burning, constructing, stamping, and peeling. The classical approach for haptic rendering of isosurface was exhibited in 1996 by Avila and Sobierajski [2]. Their work addresses the haptic exploration of all the data volume or a part of it like an isosurface. For isosurface rendering this method does not require any intermediate representation of the surface. Indeed, the generated feedback F is expresses as a retarding and stiffness forces directly approximated by the penetration distance to the virtual surface via a difference in the field value. r r r F = S + R In this equation S is related to the stiffness and is consequently directed according to the volume gradient at the probe position, while R expresses the environment viscosity opposed to the haptic interface motion (Fig. 2). Note that this contribution works well with general data volumes, presenting a very fast haptic loop without using any surface representation. However, some unwanted vibrations can occur in regions having high frequency data. In such regions, due to the high gradient the difference in the field value is very strong hence does not approximate the penetration distance of the probe in the isosurface. 2 A probe is defined as the effective representation of the haptic interface in the virtual scene

3 Figure 2. Representation of various components of the generated force F To render the haptic interaction with different types of volumetric data, mimic the touching of hard and soft contents e.g., Computed Tomography (CT) data, Lundin et al. in [24] adapted the proxy method [33] defined for the surface representation to the volumetric one. In the proposed approach the isosurface is locally approximate by a virtual surface defined by the local gradient at the proxy position. Hence the proxy is not constrained by the geometrical representation of the isosurface but by the local gradient at the probe position (Fig 3). The displacement of the proxy is assumed with several rules related to the nature (hard, soft) of the data in the environment of the proxy position. The low is the density of these data, more rapidly the proxy will be able to move on this virtual surface. Thus the haptic feedback generated with this method does not only render the presence of the isosurface but also provide some information related to the nature of this surface. Using this method a user can easily make the difference between bones, skin and muscles. However, since the proxy is constrained by the local gradient, one has to note that within high gradient data, this virtual surface does not approximate the isosurface. Figure 3. Coupling between the proxy and the probe in surface haptic in Lundin et al. [24] Motivated by problems related to shock structures and vortices visualization in data sets resulting from CFD applications, Lawrence et al [19] exploit the accuracy of the haptic channel for local properties to complement the visual view of theses structures. Thanks to the haptic feedback the user can be alerted on the presence of any secondary shock (even invisible) contained in the main one. This method allows free motion in regions having low gradients. Within the shock region, the forces applied to the user result in behaviour similar to a ball on a hill. The shock surface can only be penetrated from the low density side ( ρ < ε) by pushing against the rendered force hence allowing users to easily understand regions representing high and low density without cluttering the visual display with additional data (Fig 4). For vortex visualization, they implemented two added capabilities: an exploratory and an identification mode. In the exploratory process the haptic device acts as a 3D mouse allowing users to explore local properties of streamlines graphically displayed. In the vortex core identification, a torque characterizing a vector that can be the vorticity, the acceleration or the jerk is rendered to the user. When the identification mode is activated in areas with vorticity the user is informed on the shape of the vortex core. Figure 4. Haptic rendering of a secondary shock Lawrence et al. [19] More recently, in order to haptically explore large data sets resulting from CFD simulations, Ménélas et al. in [27] have presented a haptic rendering method of isosurfaces. This work addresses the problem of the haptic rendering of data sets containing high gradient data. In such 3D regions, due to the tightening thickness of the isosurface, traditional haptic rendering methods of surfaces rendering usually induce some perturbations in form of vibrations during the exploration process [2]. In order to insure a better haptic feeling this work combines the Avila method [2] to the proxy one [33] through a 3D adaptation of the Bresenham algorithm [10]. During displacements in the volume, a force is generated when the user hand crosses the isosurface from a greater value to a lower one. This force is cancelled when he crosses the isosurface in the opposite direction. The position of the proxy is then computed thanks to the gradient at the position of the cross (Fig 5). Moreover their exploration method is assuming without having any intermediate geometrical representation, and so offers a fast haptic rendering loop. The results have been confirmed through psychophysical experiments aimed at following up an isosurface.

4 α*n Proxy position Probe position Figure 5. Computation of the proxy position in Ménélas et al. [24] III. Volume rendering Gradient direction In surface rendering models, the haptic feedback brings great information on the explored surface. The user has access to this shape and several local properties. However this approach does not provide any information related to internal structures of the data volume. On the contrary volumetric models can deliver a great deal of knowledge about all the data volume. In the volume rendering process, the data presented at the probe position is haptically conveyed to the users through the mean of a metaphor. In such methods conversely to surface rendering one the probe freely explores all the data volume. The mapping of the field value on a viscosity provides a simple way to correlate the field value on the probe velocity (viscosity mapping). While moving in the data volume the user has a viscosity feedback proportional to the field value at the probe position. Hence regions where data values are large feel more viscous to the user. Pao et al. explored this method in [31], and later by Aviles and Ranta in [3]. Later evaluations of van Reimersdahl et al. [36] reveal that this metaphor is very useful for rapidly scanning a volume in order to identify interesting regions. The viscosity mapping is suitable to inform about the value distribution in the volume but is not relevant for the analysis of a specific point. However, since the force feedback is directly proportional to the probe velocity, the low speed required by point analyse may produce a force feedback close to zero. Aviles and Ranta [3] have also implemented a 3D haptic grid in the analysis of geoscientific data in order to allow users to be able to easily and precisely locate a specific point. The gradient scalar is also used in some work in order to inform about the relative distribution of scalars in the volume. In [15], Iwata et al. suggested a technique for the understanding of a scalar field variation. During the field exploration, a constant direction force proportional to the field value at the probe position is transmitted to the user. With this metaphor the haptic interface is attracted or repulsed (according to the sign of the transfer function) by regions having high scalar values. The generated feedback is very suitable for volumes having low frequency data, producing a soft push towards regions of interest. However, if the magnitude of the attractive force is too high, or in the case of high frequency data, unstable behaviour can occur in the form of vibrations. Fritz and Barner in [11] exhibit a set of haptic rendering methods for the exploration of different types of data. Their framework implement a number of algorithms depend on the dimensional type of the data and the expected representation. One dimensionnal data is presented in a plane as abscissa. Point in the space is rendered by computing their collision to a virtual sphere surrounding the probe. By connecting a set of points, a haptic line is also simulated. For the two dimensional data type, they propose a 2D line graph while a 3D mesh surface and a 3D vector field hap-tic rendering metaphors are presented for 3D data type. In [13] Gibson et al. introduce volumetric methods for modelling complex anatomy and tissue interactions. To simulate rigid, elastic and plastic materials two stages are detailed in the deformation algorithm. In the first step, some constraint rules describe the way that elements react to the haptic interface interaction. In the second one, the 3D ChainMail algorithm is exploited to compute how this interaction affects the immediate environment on the solicited element. Other interactions like, cutting, tearing, and suturing are also presented through a set of very simple algorithms. To cut an element, the link on this element to others is simply deleted, while they are linked in a suturing process. Thanks to this approach data sets resulting from medical imaging can be analyse by using haptic feedback, indeed during the exploration process bone and muscle are distinctly identify by the haptic feedback. In order to enhance scientific visualization capabilities Durbeck et al. [7] integrate a haptic interface into a scientific visualization tool, thus allowing users to simultaneously see and feel a vector field. In the implemented system, the haptic display presents each vector as a force correspondding to the vector s magnitude and direction while the graphic one presents a subset of the vector field as streamlines or as arrow glyphs. This visual/haptic system has proved to be useful for displaying vector fields. In the exploration task the haptic feedback remains the feeling produced when one put his finger into a flow: vectors act upon his fingertip, drawing it in the same direction as the local flow field. If a user does not oppose the movement, his hand draws a flow line within the vector field. In order to reduce difficulties involve in the visualization of some large multi-dimensional data sets, Pao et al. in [31] have introduced the haptic modality in the exploration loop. On the basis of

5 human capabilities, they suggested that the haptic feedback can provides a complementary information channel to convey certain data properties. Their investigation proposes a set of method which aims at carrying the information present within the data set to the user by the meaning of the haptic channel. We summarize these methods as follow (Fig 6): Gradient Viscosity Allow free motion in local regions having the same value. This allows fast intuitive exploration of local isosurface structures Pseudo-gravity Assign mass to each field voxel proportional to the field value, creating a force field where the user s hand is attracted to regions of large density. Orientation Constraint Informs on the orientation of the field by limiting the hand of the user according to the direction of the vector field. Torque Nulling Produce a torque proportional the field magnitude at the end effector position whenever this one is not aligned with the vector field. Transverse damping Facilitates the following up of a streamline by applying on one hand a large viscosity in directions transverse to the field direction and on the other hand forces in the field direction proportional to field magnitude. Relative drag Render the force that would be applied to a particle moving along a streamline. Vortex Torque Provide torques proportional to the local curl or vorticity of the field. It hence offers a natural rendering of some properties of a vector field that are hard to visualize. distance field conveys the distance between the probe position and the final destination. The second distance field encodes the distance to a surface to which the user must avoid any contact. To help the user in his task the second distance field is interpreted as a potential value, the closer the user approaches the surface, the stronger a repulsive force will push him back along the gradient of this distance field. Experiments reveal that with appropriate parameters, the user was guided towards the target point, while maintaining a position out of the surface to avoid. They also propose the use of other vector fields as force field functions to explore flow data phenomena, such as vortices or swirls. In [36] van Reimersdahl et al. present the VISTA FlowLib framework for interactive visualization and exploration of unsteady flow by extending the methods of Lawrence et al. Their gravity scalar method assigned a mass to a pre-computed distance field. With this method users are attracted or repulsed by specified value. For fast and qualitative exploration of a complex data set, the Viscosity scalar slows down the movement of the user in regions of interest, whereas the Gravitational Line renders a streamline as an attractive line. These experiments conclude that within a scalar field framework, the Gravity Scalars method is best suitable to identify a chosen value, while the Viscosity Scalar method is best to identify zones containing a range of values. To take advantage of a directional constraint, Itkis et al. [14] provide intuitive exploration modes for volumetric data sets. Their work provides a unified framework for different data modalities and effects such as texture and friction by the meaning of several motion rules and transfer functions. Thus to guide the user in vector field data, the proxy can be constrained along a streamline (Fig. 7). Haptic interface Gradient viscosity Pseudo gravity Streamlines Orientation constraint Torque nulling Figure 6. Haptic method for 3D field exploration in Pao et al. [31] Bartz et al. in [4] experiment a haptic navigation scheme based on two distance fields. The first Figure 7. A user exploring a vector field while being constraint on the streamlines in Itkis et al. [14] In such model, any effort to move the haptic interface in a direction perpendicular to the current orientation of the field results in a strong opposing force. They have also experiment this approach in an exploration task of a tensor field. To indicate the tensor orientation, the motion of the proxy is free in the direction of the major eigenvector, but constrained in the two other directions. They

6 conclude that the effort required for tracing out curves indicating fiber distribution and connectivity is lower than efforts necessitated by numerical methods. After the surface rendering, Lundin et al. in [21] extend their works to a general, proxy-based approach for volume exploration. Local properties of the volumetric data is simulated through a set of haptic primitives. A viscosity mode is implemented by using a point primitive, while a line primitive is using to convey the orientation of vector field, and a surface-and-friction mode for representing implicit and penetrable surfaces. In this system the computation of the proxy position results in a compromise; a balance between the feedback of the haptic device and the haptic primitive. In [22] they modify the proxy model to explore CFD data. The proxy is neither constrained by a surface [33] nor by data features [21]. However, whenever the probe moves in a direction perpendicular to the flow field one, this modify model produces a viscosity feedback. Then guide the user to follow the direction of the flow. They also adapt this model for extending the work of Lawrence et al. to guide the user to vortex cores, by generating viscosity feedback whenever the probe is moving out of the vortex. Since the probe is in the vortex, the interior of tube-like : the shape of the vortex can be felt. More recently in [23] Lundin et al. present a method enabling haptic interaction with MRI data. IV. Conclusion This paper presented an overview on the intervention of haptic in the exploration of large data sets in a VR immersive environment. Through this work we understand that the haptic rendering let on the one hand to reinforce and supplement the visual feedback, on the other hand to improve the accuracy and speed up the exploration task. Moreover this modality grants access to different properties of scalar or vector fields. Several haptic rendering methods have been developed in the last two decades. However, only a few of them are validated trough psychophysical criterions. Some benchmarks and evaluation processes are needed in order to highlight which methods are best suitable for each expected task. Furthermore, these approaches are limited to the considered applicative context. Improvements are thus required to extend approaches to other application fields. V. Acknowledgements This study was partially supported by grants from the CoRSAIRE ANR project, and DIGITEO of Région Ile-de-France in the context of the SIMCoD project. References [1] Adachi, Y., Kumano, T. and Ogino, K. Intermediate Representation for Stiff Virtual Objects. Proceedings of IEEE Virtual Reality Annual International Symposium, Research Triangle Park. 95, , (1995) [2] Avila, R. S. and Sobierajski, L. M. 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