A Gesture System for Graph Visualization in Virtual Reality EnvironmentsYi-Jheng Huang1* Takanori Fujiwara2† Yun-Xuan Lin1‡ Wen-Chieh Lin1§ Kwan-Liu Ma2¶

1) National Chiao Tung University, Taiwan, 2) University of California, Davis

ABSTRACT

As virtual reality (VR) hardware technology becomes more matureand affordable, it is timely to develop visualization applicationsmaking use of such technology. How to interact with data in animmersive 3D space is both an interesting and challenging problem,demanding more research investigations. In this paper, we presenta gesture input system for graph visualization in a stereoscopic 3Dspace. We compare desktop mouse input with gesture input withbare hands for performing a set of tasks on graphs. Our study resultsindicate that users are able to effortlessly manipulate and analyzegraphs using gesture input. Furthermore, the results also show thatusing gestures is more efficient when exploring the complicatedgraph.

Index Terms: H.5.2 [Information Systems]: Information Interfacesand Presentation—User Interfaces;

1 INTRODUCTION

With the advance of VR technologies, the researchers of visualiza-tion have started to render data in a VR environment, includinggraph visualization. There are several advantages of rendering agraph in the VR environment. For example, stereoscopic displaysprovide additional depth information, which can assist users in pro-cessing graph visualization tasks [24, 42]. The VR environment alsostimulates new demands and possibilities for the interaction designin visualization applications. However, few studies have addressedthe interaction design for a graph in the VR environment. Conven-tionally, a mouse or a touch screen is commonly used as an inputdevice on graph visualization. However, such devices do not haveenough degrees of freedom (DoFs) for operations on a 3D graph,including selection, translation, and rotation in 3D vision providedthrough the VR environment. Additionally, unlike visualization ona 2D screen, users cannot see input devices in the VR environmentwith a head-mounted display (HMD). Hence, input devices thatneed users’ vision, such as a keyboard and a touch screen, are notappropriate [43].

On the other hand, one of common inputs in the VR environmentis gestures, which are intuitive and convenient for interacting withdata as people begin using their hands to interact with things sincetheir birth. Also, virtual hands [32] could provide virtually visibleinput devices in the VR environment. Gestures are an appropriateinput for the VR environment. In this study, we develop a gestureinterface that allows users to interact with a visualization in the VRenvironment with bare hands. We focus on graph visualization. TheVR-based graph visualization system was built based on OculusRift [29] and Leap Motion [22].

To design the gestures, we interviewed several users who havedomain knowledge in graph visualization and considered a set of

*e-mail: jennyhuang914@gmail.com†e-mail: tfujiwara@ucdavis.edu‡e-mail: librechat2514@gmail.com§e-mail: wclin@cs.nctu.edu.tw¶e-mail: ma@cs.ucdavis.edu

operations that are commonly used in graph visualization. One chal-lenge of designing gestures for operations is that the gestures shouldbe easy to learn and perform, and can be precisely recognized in theproposed system. We referred to gestures from previous work [30]and explored several possible designs. In the end, eight gestures aremade in this study. Another challenge of implementing the gestureinterface is that detection of hand motion through Leap Motion isunstable from time to time [31]. This may cause discomfort or con-fusion for users. To provide a better user experience, our systemprocesses captured motions to reduce such shakings. This enablesus to achieve more stable and smoother motion.

Additionally, we evaluate our gesture input design through a userstudy, comparing with a mouse in a VR-based graph visualizationenvironment. The results show that participants are able to completetasks more efficiently for the complicated graph. Our work makesthe following contributions: (1) Proposing a prototype of VR-basedgraph visualization system with gesture control; (2) Assessing theusability of gesture control and mouse control for a set of commonoperations on graphs.

2 RELATED WORK

VR environments, such as HMDs and CAVE-style displays [14],provide 3D vision. This advantage has been utilized in visualiza-tions required to convey spatial information [11, 19, 27, 39]. As forinformation visualization, the VR environment has potential bene-fits derived from using an extra dimension and a (virtually) largescreen space. Teyseyre and Campo [36] and Brath [9] providednice overviews of 3D information visualizations. While most ofthese existing 3D information visualizations have been applied on2D displays, several studies focused on evaluating the VR environ-ment [3,17,18,40–42]. Furthermore, after consumer HMD products,such as Oculus Rift [29], became available, information visualiza-tion studies using HMD is being more popular. Kwon et al. [21]applied a spherical layout, rendering methods including depth rout-ing and LineAO [15], and node highlighting interaction for graphvisualization in the VR environment. Their user studies showed theirmethod performs better than traditional 2D graph visualization in theVR environment. Cordeil et al. [13] compared an HMD and a CAVE-style display for collaborative analysis of network connectivity inthe VR environment through the user studies. Although the HMD ismuch less expensive than the CAVE-style display, their conclusionsuggested that the HMD can provide a comparable experience forcollaborative analysis and users may complete the tasks faster. Eventhough these existing studies [13, 21] provided effective methodsfor information visualization with the HMD, they only supportedhighlighting interactions.

Regarding interactions for 3D objects in a general research area,traditionally a mouse is used for the interactions. However, themouse has only two DoFs, and thus would not be suitable for inter-acting with objects in 3D environments. 3D interactions that requireusers to make some gestures in 3D space, e.g., virtual hand [32] andvirtual pointing [26], could provide more DoFs. Various 3D interac-tions have been developed and surveyed in the past [4, 7, 20]. Onewidely spread method is the virtual hand with hand gesture recogni-tions [28, 34]. This method could utilize hand and finger positions,movements, and angles to provide interactions. Recently, Pium-somboon et al. [30] proposed user-defined gestures for augmented

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2017 IEEE Pacific Visualization Symposium (PacificVis)18-21 April, Seoul, Korea978-1-5090-5738-2/17/$31.00 ©2017 IEEE

reality. These gestures could be applied to VR applications. Hence,we adopt some of them to form the gesture set of our interface. Wepropose and evaluate these interactions in this paper.

3 DESIGN AND IMPLEMENTATION OF GESTURE INTERFACE

We provide eight operations as illustrated in Figure 1 to allow usersto interact with a graph, which are (a) move a node, (b) move alink, (c) highlight a node, (d) highlight a link, (e) rotate a graph, (f)translate a graph, (g) group: merge a group of nodes into a large node,and (h) ungroup: separate the large node back to individual nodes.The eight operations intend to cover most selection operations usedin graph visualization. In this work, we do not cover operationsrequiring a text entry (e.g., searching and filtering). The designsof the operations are based on the studies by Lee et al. [23] andRoth [35]. Lee et al. [23] defined a list of tasks which are commonlyperformed in graph visualization. Roth [35] proposed a taxonomyof interaction primitives for visualization.

Our system applies Oculus Rift DK2 [29] as an HMD and LeapMotion [22] as a sensor device to provide an interface for gesturesin the VR environment. The VR developer mount [22] is used tomount Leap Motion on Oculus Rift DK2. This mounting positioncan keep Leap Motion in front of the user’s viewpoint and avoidschanges in gesture recognition accuracy that depends on the user’sposition relative to Leap Motion.

We design the gestures in Figure 1 with two considerations:intuitiveness and feasibility. About the intuitiveness, we refer touser-defined gestures proposed by Piumsomboon et al. [30]. Theyintroduced a set of 44 user-defined gestures for selected tasks bysummarizing 20 subjects’ opinions. However, some of these gesturesrequire advanced implementations to work effectively. In particular,some gestures cannot be robustly captured by Leap Motion andcauses low accuracy of recognition, such as a single select gesturesuggested by them. In this paper, six out of eight gestures, (a, b, e-h),are from their work [30]. The design of (c-d) gestures, which aims tomake gestures easy to be recognized with an infrared stereo camera,is inspired from the gesture design of Microsoft HoloLens [25].

To recognize the gestures in Figure 1 with Leap Motion, we de-velop simple gesture recognition methods in our system for real-timeinteractions. Table 1 lists the features and criteria used in our gesturerecognition methods. We extract these features from the informa-tion provided by Leap Motion Orion SDK and set correspondingthresholds to recognize different gestures. We use grabStrengthand pinchStrength that are variables provided by Leap Motion fordetecting the gestures of moving a node/link, translating a graph,and rotating a graph. grabStrength indicates how close a hand isto being a fist and pinchStrength represents the holding strength ofthe pinch pose. The orientations and velocities of palms are used torecognize the gestures of grouping and ungrouping.

To recognize the highlighting gestures, a template pose shown asthe left hand in Figure 1(c) is predefined. At run-time, our systemwill calculate the distance D between the template pose and thecurrent pose, where D = (Q−Qi)

T W(Q−Qi) and Q and Qi arecolumn vectors describing the joint angles of the template pose andthe current pose, respectively; W is a diagonal matrix that weighsthe influence of different joint angles. Weights will be lower if thejoints are closer to finger tips. If D is less than a threshold, thesystem considers that a user is performing a highlighting gesture.

Furthermore, to provide visual feedback and better user experi-ences, the user’s hand motion is smoothed and displayed in the VRenvironment since the raw hand motion obtained from Leap Motionis inaccurate and sometimes shakes unnaturally [31]. We reduce theshaking by applying a virtual spring (P-controller [8]) at each jointand setting the rest pose as a template pose. We define a templatepose for each gesture in the gesture set. Thus, when a user intendsto perform a gesture and his/her current pose is close enough to apredefined template pose of a gesture, the displayed hand motion

Table 1: The features and criteria for recognizing each gesture.

Gestures Features Criteria(a) Move a node/(b) Move an edge

grabStrength &pinchStrength

grabStrength≥ 0.7 orpinchStrength≥ 0.7

(c) Highlight a node/(d) Highlight an edge

The joint anglesof a hand D < 100

(e) Rotate a graph/(f) Translate a graph pinchStrength pinchStrength≥ 0.7

for both hands

(g) Group/(h) Ungroup

Theorientationsand velocitiesof palms

Palms face each other(the angle between t-wo palms’ orientationis greater than 170 de-grees), and the relativevelocity of two palmsis greater than 0.11 (g-roup) or less than -0.06(ungroup)

would be steadily locked to the template pose as the virtual springwill resume the current pose back to the template pose if the originalhand motion from Leap Motion has any jerks.

Additionally, we visualize virtual hands [32] in the system byusing Leap Motion Orion SDK. The virtual hands allow users to seetheir input devices (i.e. their hands) in the VR environment evenwhen they use the HMD.

4 USER STUDY

To evaluate the usability of our system, a user study is conductedto compare the users’ performance and experiences about the inter-actions with a mouse and the proposed gestures to carry out vari-ous graph visualization tasks. We choose the comparison with themouse because it is a selection device commonly used in the graphvisualization. The design of the mouse operations is based on 3D vi-sualization systems or 3D commercial software, such as BioLayoutExpress3D [1], CGV [37], Maya [5], and Unity 3D [38]. Please seethe supplemental material for details of the mouse operations.

Experimental Design. We adopted the between-subjects design.Based on a pre-experiment survey, the participants were dividedinto two groups with roughly equal capability: a “gesture group”uses gestures and a “mouse group” uses the mouse as an interface.Each participant would perform nine tasks on three different graphlayouts in the same order, including: (a) a brain graph, (b) a force-directed graph, and (c) a BioLayout graph as shown in Figure 2(a-c).We chose these graphs with different complexity to observe how itaffects the performance of gesture and mouse control. The braingraph, which has 136 nodes and 254 edges, was generated in theexisting work [16]. Each node represents a region in the brain andeach edge represents a correlation of the functional connectivitybetween each region. We set a threshold to obtain edges that havehigh correlations and applied 3D mean-shift edge bundling [6]. Toconvey anatomical positions of the nodes, each node is set in its 3Dspatial position. The force-directed graph with 77 nodes and 254edges presents character co-occurrence in Les Miserables. Relatedcharacters are closer while unrelated characters are farther apart. Thedata was downloaded from [2]. This graph is sparse. The BioLayoutgraph contains 312 nodes and 2198 edges. We downloaded the datafrom [1] and used BioLayout Express3D [1] to generate the layout.The graph is relatively dense and the nodes of a group are gatheredtogether.

We design the nine tasks based on the previous studies [23,33] andsuggestions from experts. The tasks are listed in Table 2. Task 1.1to 1.3 in the brain graph are the operations used by neuroscientistswhen they are exploring brain data. Task 2.1 to 3.3 are summarizedtasks which are usually performed in graph visualization.

Participants. We recruited 14 participants (7 males and 7 fe-males) through the Internet. The age of participants ranged from

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Figure 1: The gesture set used in our system. Please see also the accompanying video for an illustration of these gestures.

Table 2: The tasks used in user study.

1. Brain graph(Task 1.1) Find the names of two marked nodes, and the value

of the link connecting them.(Task 1.2) Find all nodes connected with two marked nodes.(Task 1.3) Following the previous task, what are the values of

the edges between the found nodes and the marked nodes?2. Force-directed graph(Task 2.1) Find the nearest node to a marked node.(Task 2.2) Find all nodes connected with two marked nodes.(Task 2.3) Find the shortest path between two marked nodes.3. BioLayout graph(Task 3.1) Find all nodes connected with two marked nodes.(Task 3.2) How many nodes in a group colored with yellow?(Task 3.3) Following the previous task, what are the colors of

each of the groups connecting to the yellow group?

19 to 32. Both the gesture and the mouse groups have seven partic-ipants. Eight of them have computer science background, equallydistributed in the two groups. The others have backgrounds on en-vironmental engineering, civil engineering, biomedical, chemistry,economics, and early childhood education.

Procedure. The participants were invited to our laboratory, andgiven a pre-experiment survey concerning the questions about pre-vious knowledge of the VR platforms and computer games. Weseparated them into the mouse group and the gesture group accord-ing to this survey to make the capability of the two groups similar.Next, we introduced the experiment and gave a tutorial of the system.The tutorial began with a video illustrating each operation, and thenthe participant put on the VR headset and practiced each operationwith a simple graph (Figure 2(d)) until s/he felt skilled enough. Afterpracticing, each participant needed to take a test where we askedparticipants to perform the eight operations in a random order. Theparticipant was required to pass this test to start the experimentaltasks. The participant was required to finish the current task be-fore starting the next one. After finishing all tasks, we asked theparticipants to fill in a questionnaire about their using experiences.

5 RESULTS

We provide the experimental results in four aspects: accuracy, diffi-culty of each operation, completion time and user experience.

Difficulty of Operations. Figure 3 shows the difficulty level ofeach operation rated by the participants in the questionnaire witha five-point Likert scale (five means the most difficult). Since thevariables are ordinal and the distribution of the difficulty level ofeach operation had heteroscedasticity of variance, we applied theBrunner-Munzel test [10]. Looking at each operation, the Brunner-

(a) Brain graph (b) Force-directed graph

(c) BioLayout graph (d) The graph for practice

Figure 2: The graphs provided in the user study.

Munzel test revealed the significant differences in following oper-ations: move a node (p < .009), move an edge (p < .02), rotate agraph (p < .003), and group (p < .02). The gesture was easier tomove a node, move an edge, and rotate a graph. Conversely, themouse was easier for the grouping operation.

Accuracy. We define accuracy rate as the percentage that partic-ipants were able to give the correct answers on their first answer.Figure 4 shows the result of accuracy rate through all tasks for eachgraph and of each task. Accuracy rate also did not follow a normaldistribution and had heteroscedasticity of variance. Therefore, weused the Brunner-Munzel test. However, there was no significantdifferences in accuracy rate for each graph and each task.

Completion Time. Figure 5 compares the average completiontime for the practice, tasks in each graph, and each individual task.The practice included a tutorial of the system, practice of the system,and a simple test. The completion time also did not follow a normaldistribution and had heteroscedasticity of variance. The Brunner-Munzel test revealed significant differences in the completion timefor the practice (p< .03), the tasks of the BioLayout graph (p< .02),and the task 3.2 (p < .05). The gesture group spent more time thanthe mouse group in the practice task. Conversely, the gesture groupspent less time than the mouse group in the task 3.2 and the tasks ofthe BioLayout graph.

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Figure 3: Participants rated the difficulty of performing each operationwith gesture and mouse input. The average scores, standard errors,and the operations with significant difference are plotted.

Figure 4: Accuracy rates and their standard errors for each tasks. Thelabel (a-c) indicate the accuracy rates on different graphs: (a) braingraph, (b) force-directed graph, and (c) BioLayout graph.

6 DISCUSSION

Gesture Design. As shown in section 5, the participants providedbetter evaluation for the gesture about move a node, move an edge,and rotate a graph. A possible reason is that the gestures for theseoperations are easy to learn and recall. The participants can usetheir hands to approach, grab, and move, just as they do in reallife. As for the operation of rotating a graph, because the mousehas only two DoFs, it is not easy to precisely control the rotation in3D space [12]. On the contrary, the gesture of rotating a graph hasthree rotational DoFs. Participants can rotate a graph more easily.On the other hand, the participants evaluated that using a mouseis better for the grouping operation. A possible reason is that thegrouping operation with a mouse is much simpler than the gesture.The gesture need two stages: making the hands parallel, and thenclapping the hands, while the participants in the mouse group neededonly double clicking. Participants also gave the worst evaluationon highlighting gestures within the proposed gestures. It may bebecause we asked participants to straighten their fingers when theywere practicing. We gave this instruction because it provides better

Figure 5: The completion time of completing tasks in different systems.The average time and standard error is provided. The tasks withsignificant difference are marked. The label (a-c) indicate the totaltime spending on different graphs: (a) brain graph, (b) force-directedgraph, and (c) BioLayout graph.

recognition with Leap Motion. However, the participants may feeluncomfortable and fatigue easily. We might be able to find bettergestures for these. A good gesture design should consider all theissues of intuitiveness, feasibility, and ergonomics.

Influence of Graph Complexity on the Efficiency of GesturesIn this paragraph, we discuss the potential effects of graph com-plexity on the efficiency of gestures. The force-directed graph wasthe simplest among the three graphs. As the nodes were sparselydistributed, the participants were able to complete the task withfewer operations. The completion time was short on both the mouseand gesture groups. On the other hand, the BioLayout graph wasthe most complicated graph in the user study, and the gesture groupperformed statistically significantly better in this graph. One pos-sible reason is that the BioLayout had many connections betweeneach node and they were densely distributed. Thus, the participantsneeded to move many nodes and edges to figure out the relation-ship between nodes for completing the tasks. The gestures of movenodes/edges are easier to perform in the gesture system. This influ-ences the participants performance in task 3.2 significantly because,in both the mouse and gesture systems, most participants countednodes by moving nodes. One noteworthy thing is in task 3.3, al-though the grouping operation using a mouse was rated as easier thanusing gestures, the mouse group was not able to complete the task3.3 faster. As we observed that the participants often forgot to utilizethe grouping operation, it can be inferred that the gesture group mayperform better when completing task 3.3 without grouping.

Evaluation of Gesture System. The required time for learningthe gestures were longer than using a mouse as shown in Figure5. This is not surprising since the mouse is more familiar for theparticipants as an input device. Despite the longer learning time, theparticipants can still learn the gestures within 11 minutes on average.As for the effectiveness of the gestures, the participants in the gesturegroup were able to complete the tasks with the similar/less time whencompared with the mouse group. About the difficulty of using eachgesture, there was no particularly difficult gesture. All gestureshad a difficulty rating from 1 to 3 on average. The most difficultgestures were the highlighting operations with gestures. About theuser experience of the system, according to the questionnaire, mostparticipants agreed that our gesture system was intuitive, easy tolearn, and interesting. They also thought that they were able toexplore the data in the VR environment. We are also interested inthe advantages and disadvantages of our system. Five participantsmentioned that intuition was the best part of the system, and fiveparticipants thought the accuracy of recognizing gestures could beimproved. We could refine the gesture system based on the resultsobtained in the user study to made it more effective and intuitive.

7 CONCLUSIONS AND FUTURE WORK

In this paper, we designed a set of gestures and implemented agesture interface that allows users to interact with graph visualizationin the VR environment. The user study revealed that compared withmouse input, using gesture input is more efficient and effective forthe complicated graph. There are some possible directions for futureresearch. First, to obtain more confident results from a user study, wewill apply a randomized order for the tasks. Second, the users maysometimes feel tired when using our system for a while. Besidessolving the fatigue problem from the VR system, a better gesturedesign considering ergonomics is desirable.

ACKNOWLEDGMENTS

This research was sponsored in part by UC Davis RISE program,U.S. National Science Foundation under Grant No. DRL-1323214,and the Ministry of Science and Technology of Taiwan under GrantNo. 104-2917-I-009-016, 104-2628-E-009-001-MY3 and 105-2221-E-009-095-MY3.

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