[IEEE 2013 IEEE Virtual Reality (VR) - Lake Buena Vista, FL (2013.3.18-2013.3.20)] 2013 IEEE Virtual Reality (VR) - What is the effect of interface complexity on risk perception tasks?

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  • What is the Effect of Interface Complexity on Risk Perception Tasks?1Vitor Jorge 1Anderson Maciel 1Luciana Nedel 2Jackson Oliveira 3Frederico Faria

    1Universidade Federal do Rio Grande do Sul (UFRGS), 2AES Sul, 3Nexo Art

    ABSTRACTMost human computer interfaces impose a certain level of addi-tional cognitive load to the user task. In this project we study theuse of VR to evaluate the user competency for safe behavior in workenvironments. We propose an assessment of two interfaces withdifferent levels of cognitive load for locomotion in a risk percep-tion task. We describe user experiments with two similar groups ofsubjects, each using one of the interfaces. We identified importantopen questions on the design of this kind of interface.

    Index Terms: H.1.2. [User/Machine Systems]: Human factors

    1 INTRODUCTIONSafety is a permanent concern in work environments. Besides hav-ing the skills for the job, workers have to acquire a number of com-petencies that allow them to behave safely. More than training, toacquire any competency depends on experience, human factors andenvironmental influence. It is then very hard to measure the compe-tency matrix of a worker. Written exams, practice tests in simulatedphysical environments, etc. are often not effective. Accident rate,unfortunately, remains the most reliable assessment parameter.

    We believe that a VR simulator is an effective tool to measurethe competency matrix for safe behavior. It provides the ideal envi-ronment to expose people to danger with no chances to cause themany harm. However, while in a physical work environment workershave to perform tasks at the same time they follow a safe behavior,in a VR simulator they have the additional load of dealing with theuser interface. How does cognitive overload of the interface impactthe risk perception? How does the interface influence behavior?How can immersion help the interface to be transparent? How doescontrol overload (excessive number of DoFs) affect attention andlead to a less safe behavior?

    In this poster we address these questions in the context of a VRsimulator of an office environment. Our main contribution is in auser study to evaluate danger awareness in a virtual environment(VE) and the study of the impact of the user interface in this aware-ness.

    2 RELATED WORKSVR simulators researchers often claim that the cognitive overloadcaused by the interface increases the learning experience. For in-stance, Akioshi et al. [1] evaluated the learning experience in VRmaintenance system applied in construction. They conclude thatthe free exploration of the VE enhances the learning experience.

    However, in the literature of 3D interaction an increase in thenumber of DoFs is often associated with a decrease in user per-formance. Bowman et al. [3] recommend the use of constraints to

    e-mail: vamjorge@inf.ufrgs.bre-mail: amaciel@inf.ufrgs.bre-mail: nedel@inf.ufrgs.bre-mail: jackson.oliveira@aessul.com.bre-mail: fred@nexo.art.br

    (a) (b)

    Figure 1: The user view of the VE and a selectable object (a) beforeand (b) after selection. Notice the highlight on the selected object.

    reduce the DoFs of the user interface. They argue that when weconstrain the VR locomotion to the ground plane, we are in fact re-ducing the problem from 3D to 2D. It is said that if the user has tothink about how to turn left or move forward, then they have beendistracted from the primary task. In a previous study [2], they per-form a comparison of traveling techniques using different numbersof DOFs. They could not show any difference in the task com-pletion times using a reduced DoF technique, but the authors stillpoint out that reducing the DoFs is important for many applicationswhere users do not need the extra DoFs. They recommend furtherstudy on the performance of constrained vs. unconstrained motionin a denser VE with the addition of distractor tasks.

    3 EXPERIMENTSWe designed an immersive VR simulator for the assessment or riskperception (see Figure 1). The user navigates through an officescenario, identifies risky situations, moves their head to place thecross-hair over the target and then selects the object using the gamepad button. In this context, we proposed an experiment to deter-mine how the number of DoFs in the locomotion strategy affectsthe user perception and performance.

    Experts in security inspection defined a set of 46 risks spread inthe VE. We designed a virtual path from which the user can see allrisks. In order to guide them, the path was marked with arrows onthe floor.

    We evaluated two interaction techniques. The first allows theuser to turn, strafing and walk in the virtual world using a joystick.The other technique permits the user to go only forward or back-ward on a constrained path using the same joystick. The secondtechnique is expected to reduce cognitive overload as it cuts downDoFs in locomotion interaction. In either cases the user can seethe VE in any direction through the HMD. Figure 2 shows the dif-ferences between the techniques. The users are asked to select theobjects that represent danger (potential risks) in the virtual world.In addition, all VE objects, with the exception of the walls, are se-lectable.

    Apparatus. A zSight Sensics HMD provided immersive visu-alization. To improve the user immersion, presence and proprio-ception, a MS Kinect sensor was used to track the user movementsand generate a skeleton that allows them to see their own limbs.The control of locomotion and selection is made through a standardgamepad to prioritize accuracy.

    Population. The test subjects were divided in two groups: theConstrained Locomotion Group (CR), in which the number of DoFs


    IEEE Virtual Reality 201316 - 20 March, Orlando, FL, USA978-1-4673-4796-9/13/$31.00 2013 IEEE

  • Figure 2: Illustration of the differences between two interaction ap-proaches we evaluated.

    FR CR

    Ms(s) 29.08 (10.56) 30.45 (11.02)Mr(r) 20.23 (4.90) 22.18 (6.50)Mt(t) 503.40s (132.19s) 527.39s (234.11s)

    MFP(FP) 25.7% (15.9%) 25.5% (12.2%)

    Table 1: Statistics collected

    is reduced to one, and the Free Locomotion Group (FR) in whichthe users could freely navigate. The CR and FR groups were com-posed initially by 11 and 13 subjects respectively, mostly male, with2 females per group, with a mean age of 26.7 years old. There isno significant difference between groups in gaming expertise andprevious training on safety at work.

    Procedure. To prepare the test subjects, an instructor passedon written instructions on the basic definitions of risk and safetyin the work environment. The user was instructed on how to usethe VR equipment, and was invited to fill a subject characterizationform with basic identification and previous experiences in safetyperception, video games and VR devices. After that, the users wereplaced in a basic training VE where they practiced as much as theywanted. To avoid user fatigue or cybersickness from prolonged useof VR equipment, they were allowed to rest for 1 to 5 minutes.The user was placed in the experiment VE and asked to select themaximum number of potential risks they could identify, withoutany time constraint. After the end of the test, users filled anotherform related to the risks perceived, and interface usability.

    Collected data and metrics. We computed for each group theaverage object selection count, Ms, the average number of risks se-lected, Mr, and the average time to complete the task, Mt , alongwith the corresponding standard deviations s, r and t . To assessaccuracy, i.e. a selected object is actually a risk, we computed thenumber of false positives, FP = Se/Sc for each user, where Se is thetotal number of risks missed by a user in the observed group and Scis the total number of selections. Then, we computed the averagefalse positives for each group, MFP.


    Table 1 contains the statistics collected from the experiment. Re-garding the effects of DoF constraint on the number of correct se-lections, the mean results are slightly better using the constrainedlocomotion strategy.

    From the questionnaires, the CR group stated that the systemwas easy to use, while users of the FR group state that they coulddo the task but they think that they took too long. Even thoughmost users using CR felt they have a good or regular performanceusing the system, most of the users stated that the most difficultaction was not the interface but the selection of risks. The mostinteresting is that both groups have a similar point of view regardingthe difficulties of using the system.

    5 OPEN QUESTIONSFrom our user studies we identified the following questions thatshould be addressed in the design of VR simulators for risk percep-tion analysis.

    Does limiting the number of DOFs help or harm the trans-parency of the interaction? We could not detect any significantadvantage or disadvantage of using a constrained technique over afree locomotion strategy technique. A possible reason is that, in theVE, the HMD yaw DoF is somewhat overlapped with the need toturn right or left, minimizing the demand for turning on locomotion.

    Does the answer above apply for the risk perception task?There is a chance that the risk perception task is a special type oftask. That is, risk perception might be something more instinctivethan other tasks not associated with individual sensation of safety.In this case, finding risks triggers a stronger commitment than othertasks. This would minimize the effects of changing the number ofDoFs.

    Does previous training in safety and risk detection help inanyway? Our test subjects sample was mainly composed of peoplewith no special training in risk perception. This might have resultedin a group searching for only those risks that were learned throughinstinct and personal experience and not training. We hypothesizetha