Michael Cline. University of British Columbia. Vancouver, British Columbia. bimanual user interface.

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1 Higher Degree-of-Freedom Bimanual User Interfaces for 3-D Computer Graphics Michael Cline Dept. of Computer Science University of British Columbia Vancouver, British Columbia Canada V6T 1Z4 ABSTRACT We compare unimanual and bimanual versions of an interface that use 6 degree-of-freedom elastic rate-control devices for object and camera control in a 3D object docking task. An experiment is presented which compares the performance of these two interfaces as well as the status-quo mouse interface. We nd that the performance benets of the bimanual technique over the unimanual technique are larger for a more complex task which requires more epistemic actions. We also nd that for a trained user, the bimanual interface can usually outperform both the unimanual 6 DOF interface and the unimanual mouse interface. Keywords Bimanual input, 3D interfaces, 6 DOF input devices, camera control, interaction techniques, virtual environments 1 INTRODUCTION Although virtual reality (VR) has nearly two decades of research behind it, human interface technologies for interacting with 3-Dimensional virtual worlds are still in the experimental stage. The mouse has clearly established itself as the user interface of choice for the majority of applications that require interaction in a 2D graphical environment. However, it is not yet clear what is the best interface for tasks in a 3D VR environment, which typically require more degrees of freedom (DOF) than a mouse can provide. People often use both hands cooperatively to perform tasks that might be cumbersome with just one hand. Research has shown that it is possible to take advantage of our natural two-handedness by designing bimanual (two-handed) user interfaces that oer improved performance and ease of use over one-handed devices. Navigating through virtual environments and manipulating 3-D virtual objects are tasks that might benet from a bimanual user interface. Several studies have shown that in a compound task, abimanual interface can be superior to a unimanual interface. Balakrishnan and Kurtenbach [2] note that little formal evaluation of bimanual 3D interfaces has been carried out. Instead, most of the work in bimanual input up to this point has focused on tasks in 2D, using mice or tablets as input devices. Balakrishnan and Kurtenbach explored bimanual camera control and object manipulation for 3D graphics, comparing a twomouse interface to a one-mouse interface. Although participants in their experiment showed strong preference for the two-handed interaction technique, their study showed little hard evidence of temporal benets of one method over the other. This paper intends to build on the work of Balakrishnan and Kurtenbach by exploring bimanual camera control and object manipulation using higher degree-of-freedom input devices. We believe that the two-mouse interface used in their experiment may have been limited in its performance because of the awkward mapping that they were forced to use when applying a low DOF input device to a high DOF task. We therefore believe that by using higher DOF input devices, we are more likely to realize the benets of a bimanual interface in a 3D task. We present the results of an experiment in which a bimanual interface using two 6 DOF controls is compared with a unimanual interface with one 6 DOF control. 2 BACKGROUND Much of the work in bimanual user interfaces [6, 5,2, 8, 9, 1, 7] isdriven by Guiard's [4] theoretical work on Kinematic Chain (KC) model of skilled bimanual action. The component of Guiard's theory that is most relevant tobimanual user interfaces is his principle of right-to-left spatial reference, which says that (for right handers) the right hand follows a frame of reference dened by the left hand. Balakrishnan and Hinckley [1] have shown experimental evidence that the reference principle applies in situations where the spatial separation between the hands does not directly correspond to

2 the position of the hands as sensed by the input device. In other words, even if the hands work in two separate kinesthetic frames, each with its own independent origin, the principle of right-to-left spatial reference still applies. Hinckley, Pausch, and Prott [7] experimented with a bimanual user interface on an object-alignment task in a 3D environment. They concluded that the use of both hands forms a hand-relative-to-hand frame of reference that can help the user gain a better sense of the space that they are working in. Balakrishnan and Kurtenbach [2] conducted two experiments comparing a one-mouse and a two-mouse user interface for target selection and docking tasks in a 3D environment. In the object selection experiment, the bimanual interface was about 20 percent faster than the unimanual interface. The results from their object docking experimentshowed no signicant dierence in average task completion time between the bimanual and unimanual interface. In their conclusions, they suggest that the performance of the bimanual interface may have suered because the interaction style deviated from Guiard's KC model: the interface encouraged a parallel, symmetric style of interaction. Studies [2, 9]have pointed out that two-handed interfaces are useful not only for pragmatic actions (actions which are directed towards a task's goal), but also for epistemic actions (actions which facilitate cognition). For example, in the 3D docking task experiment mentioned in [2] it is noted that when using the two handed interaction technique, the users tended to move the camera around more, in order to enhance their perception of the 3-D scene. 3 CONTEXT AND GOALS OF THIS PROJECT Little research has been done that uses 6 DOF input devices in a bimanual user interface. Therefore, much of the research done with bimanual interfaces has concentrated on tasks with low degrees-of-freedom such as a 2D connect-the-dots task [1, 8], or a task where the user positions and scales a 2D object [3]. Some notable exceptions are [5, 10]. In Balakrishnan and Kurtenbach's [2] comparison of the one-mouse and two-mouse interfaces, we believe that the performance of their bimanual interface was limited by the awkward mapping they were forced to use because they applied a low DOF input device to a high DOF task. Combined camera and single object manipulation can use up to 12 DOF (not including camera zoom). Since the docking task in their experiment used a sphere, rotation was irrelevant, reducing the object manipulation to 3 DOF. The camera movement was restricted to inward pointing views on a sphere, requiring only 2 DOF. Thus the combined task was still a 5 DOF task, one more degree than their interface. To deal with this, they restricted object translation to a plane parallel to the X-Y plane of the camera. This restriction is what hurt the performance of the interface, because it required the hands to work together in a complex way, breaking the separation between the tasks of the two hands. For example, in order for the user to move the object along the Z dimension, they would have torst rotate by 90 degrees so that the old z-axis became the new x or y axis. The interface did not comply with Guaird's KC model very well. We hypothesize that if the above experiment was duplicated using 6 DOF controls instead of mice, then the dierences between the performance of the unimanual and bimanual methods would be more apparent. The questions this study attempts to answer, along with the experimental hypotheses, are: 1. Can a bimanual interface that uses two 6 DOF controllers provide advantages over a unimanual 6 DOF interface? HYPOTHESIS: The bimanual interface will always perform better than the unimanual interface 2. How do the bimanual and unimanual 6 DOF interfaces compare with a unimanual mouse interface? HYPOTHESIS: Even novice users of our system were \expert mouse users", so we suspect that for novice users, the mouse may be faster than the 6 DOF interface. However, for an expert user, we suspect that the 6 DOF interfaces will perform better than the mouse interface. 3. How does the complexity of the task aect the relative performance of the unimanual and bimanual approaches? HYPOTHESIS: A more complex task will require more epistemic actions. Since the bimanual interface allows the user to more easily make epistemic actions, we suspect that the relative advantages of bimanual over unimanual will be greater in more complex tasks. 4 EXPERIMENTAL PROCEDURE The experimental task was a docking task (see Fig. 1) similar to that of [2]. The user's goal is to move a docking object inside a docking station. After a trial is completed, the docking station and object are randomly repositioned.

3 100 cube trials for each of the three interfaces. The author had roughly 5 hours of cumulative experience with each oftheinterfaces. Figure 1: Object docking task The 6 DOF interface used to conduct the experiments was an elastic rate control device (see Fig. 2) similar to Zhai's EGG interface [11]. The user controls this device by moving (translating or rotating) a tennis ball which is suspended on elastic bands. The position and orientation of the ball are sensed using a Polhemus Fastrak. This kind of interface has a number a qualities that are desirable for the docking task. One reason why rate control is benecial is that it can eliminate the need for clutching. Also, the integral transformation in rate control acts as a low pass lter that removes high-frequency noise (jittery hands), producing a smoother trajectory [12]. In this experiment, three interfaces were compared against each other: a two-handed interface with two 6 DOF controls, where the left hand controls the camera view and the right hand controls the position of the docking object a one-handed 6 DOF interface, where object control mode and camera control mode were alternated by pressing a mode switch in the left hand a one-handed mouse interface, where the user drags on the docking object to move it, or drags on any other point to change the azimuth and elevation of the camera view (this is the same an the unimanual interface used in [2]) In order to determine how the complexity of the task aects the relative performance of the interfaces, the docking task consisted of two phases. In the rst phase, the task was to dock a sphere inside a cube. Thus, rotation of the object was irrelevant. The second phase required the user to place a smaller cube within a larger cube, requiring the user to properly orient the docking object. The task was nished when all 8 corners of the docking object were within the docking station. In both phases, the docking station was slightly larger (about 20 percent) than the docking object, so the user had a certain margin of positional and rotational error. Six novice users participated in the experiment. Each of the six participants performed 10 sphere trials and 10 cube trials on all three of the interfaces. To cancel out any learning eect, each participant used the interfaces in a dierent order. To gather data comparing the interfaces for an experienced user, the author performed 100 sphere trials and Figure 2: The elastic rate control device used in the experiment The EGG interface's self centering nature facilitates rate control, because the user can simply let go of the control to cause the controlled object to stop moving. The EGG especially lends itself to camera control, because beingabletoquickly stabilize the camera is desirable. The elastic rate control is also a good choice for object control in the docking task because the user needs to be able to reliably keep the docking object in one place while they move the camera around to examine the scene. With an isomorphic (one-to-one) controldisplay mapping, the user would be required to hold their hand steadily in one spot. We attempted to select the mappings for the 6 DOF rate control devices so that they would allow the user to complete the task in a minimum amount of time. For the translational motions of the docking object, we used a non-linear mapping:

4 v x =(k c x ) 2 v y =(k c y ) 2 v z =(k c z ) 2 where v x, v y,and v y are the velocities of the docking object in the x, y and z directions, c x, c y,andc z are the coordinates of the control relative to the elastic centre of the device, and k is a constant. This mapping allowed the docking object to have a wide range of velocities, yet still allowed ne control of the object. Rotational motion of the control was mapped linearly onto rotational velocity of the docking object. The camera control was simplied by the choice of rotational mapping. Rather than having the camera rotate in place, camera rotation was performed by rotating the camera about the centre of the virtual world. Thus, if no translational motions were applied to the camera, it would always point towards the centre of the world, so that the docking object and docking station were within view. This is similar to the waycameracontrol was done with the mouse in [2], where the camera was restricted to inward-looking views from a sphere of possible camera positions. 5 EXPERIMENTAL RESULTS Figure 3 shows the average trial completion times for novice users for both the sphere and the cube task. For the sphere task, the bimanual interface performed much worse than either the unimanual or mouse interface. However, for the cube task, the performance of the bimanual interface was 32 percent faster than the unimanual interface. This evidence supports hypotheses 2 and 3, but refutes hypothesis 1. seconds Figure 3: users Unimanual Bimanual Mouse Sphere Task Cube Task Average trial completion times for novice Figure 4 shows average trial completion times for the expert user. This data supports all three hypotheses: in both tasks, the bimanual interface performed better that the unimanual interface, and the 6 DOF interfaces performed better than the mouse in most cases. The task complexity seemed to have a large eect on the relative performance of the unimanual and bimanual interfaces: for the sphere task, bimanual was 28 percent faster than the unimanual, whereas in the more complex cube task, the bimanual was 39 percent faster than the unimanual. seconds Unimanual Bimanual Mouse Sphere Task Cube Task Figure 4: Average trial completion times for expert user Data was also collected that measured the translational and rotational ineciency of the user's motions. Ineciency is a ratio of the user's actual path length to the shortest possible path length [12]. For translational motions, the shortest path is a straight line from the starting position to the goal. For rotations, the shortest path is a rotation about a single axis from the start position to the goal position. The ineciency data we collected was inconclusive. It was erratic, and did not consistently support or refute any ofourhypotheses. 6 CONCLUSIONS Our experiments haveshown that using a 6 DOF bimanual interface can be benecial over using a unimanual 6 DOF interface. Our data suggests that the bimanual interface was most benecial when the task was complex and required many epistemic actions. For novice users, the unimanual mouse interface performed better than either of the 6 DOF interfaces. For the expert user, the mouse performed comparably to the 6 DOF interfaces on the sphere task (slightly better than unimanual, slightly worse than bimanual). However, on the cube task the 6 DOF interfaces performed much better than the mouse interface. Subjectively, the expert user found the 6 DOF interfaces easier and more pleasant to use than the mouse, especially on the more complex tasks. However, Novice users found the 6 DOF interfaces dicult to adjust to. In light of this, it seems that the bimanual 6 DOF in-

5 terfaces can be benecial, but their benets are only fully realized for expert users on complex, compound 3D tasks. Examples of where this technology may be applicable are computer aided design, or for tele-operation of a robot with many degrees of freedom. 7 ACKNOWLEDGEMENTS We would like to thank all the volunteers who participated in the experiment. We would also like to thank Dr. Sidney Fels for his helpful comments, and for supplying the Polhemus Fastrak and writing the drivers that allowed its use to be possible. REFERENCES [1] R. Balakrishnan and K. Hinckley. The role of kinesthetic reference frames in two-handed input performance. In Proceedings of UIST'99, pages 171{178, [2] R. Balakrishnan and G. Kurtenbach. Exploring bimanual camera control and object manipulation in 3D graphics interfaces. In Proceedings of ACM CHI 99 Conference on Human Factors in Computing Systems, volume 1 of Object Manipulation Studies in Virtual Environments, pages 56{63, [10] R. C. Zeleznik, A. S. Forsberg, and P. S. Strauss. Two pointer input for 3D interaction. In Proceedings of the Symposium on Interactive 3D Graphics, pages 115{120, New York, Apr. 27{ ACM Press. [11] S. Zhai. Investigation of feel for 6DOF inputs: Isometric and elastic rate control for manipulationin 3D environments. In Proceedings of the Human Factors and Ergonomics Society 37th Annual Meeting, volume 1 of COMPUTER SYSTEMS: 3D Input and Display, pages 323{327, [12] S. Zhai and P. Milgram. Quantifying coordination in multiple DOF movement and its application to evaluating 6 DOF input devices. In Proceedings of ACM CHI 98 Conference on Human Factors in Computing Systems, volume 1 of 3D, pages 320{ 327, [3] W. Buxton. A study in two-handed input. In Proceedings of CHI'86, pages 321{326, [4] Y. Guiard. Asymmetric division of labour in human skilled bimanual action. Journal of Motor Behaviour, 19:486{517, [5] K. Hinckley, R. Pausch, D. Prott, and N. F. Kassell. Two-handed virtual manipulation. ACM Transactions on Computer-Human Interaction, 5(3):260{302, [6] K. Hinckley, R. Pausch, D. Prott, J. Patten, and N. Kassell. Cooperative bimanual action. In Proceedings of ACM CHI 97 Conference on Human Factors in Computing Systems, volume 1 of PA- PERS: Handy User Interfaces, pages 27{34, [7] K. Hinckley, R. Pausch, and D. Prott. Attention and visual feedback: The bimanual frame of reference. In Proceedings of the 1997 Symposium on Interactive 3D Graphics, pages 121{126, [8] P. Kabbash, W. Buxton, and A. Sellen. Twohanded input in a compound task. In B. Adelson, S. Dumais, and J. Olson, editors, Proceedings of the Conference on Human Factors in Computing Systems, pages 417{423, New York, NY, USA, Apr ACM Press. [9] A. Leganchuk, S. Zhai, and W. Buxton. Manual and cognitive benets of two-handed input: An experimental study. ACM Transactions on Computer-Human Interaction, 5(4):326{359, 1998.