N. Gu, S. Watanabe, H. Erhan, M. Hank Haeusler, W. Huang, R. Sosa (eds.), Rethinking Comprehensive Design: Speculative Counterculture, Proceedings of the 19th International Conference on Computer- Aided Architectural Design Research in Asia CAADRIA 2014, 729 738. 2014, The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong IMMERSIVE VIRTUAL ENVIRONMENTS: EXPERIMENTS ON IMPACTING DESIGN AND HUMAN BUILDING INTERACTION ARSALAN HEYDARIAN 1, JOAO P. CARNEIRO 2, DAVID GERBER 3, BURCIN BECERIK-GERBER 4, TIMOTHY HAYES 5 and WENDY WOOD 6 1,2,3,4 University of Southern California, Los Angeles, United States {heydaria, jcarneir, dgerber, becerik, hayest, wendy.wood}@usc.edu Abstract. This research prefaces the need for engaging with end-users in early stages of design as means to achieve higher performing designs with an increased certainty for end-user satisfaction. While the architecture, engineering, and construction (AEC) community has previously used virtual reality, the primary use has been for coordination and visualization of Building Information Models (BIM). This work builds upon the value of use of virtual environments in AEC processes but asks the research question "how can we better test and measure design alternatives through the integration of immersive virtual reality into our digital and physical mock up workflows? " The work is predicated on the need for design exploration through associative parametric design models, as well as, testing and measuring design alternatives with human subjects. The paper focuses on immersive virtual environments (IVEs) and presents a literature review of the use of virtual environments for integrating end-user feedback during the design stage. In a controlled pilot experiment, the authors find that human participants perform similarly in IVE and the physical environment in everyday tasks. The participants indicated they felt a strong sense of "presence" in IVE. In the future, the authors plan on using IVE to explore the integration of multi agent systems to impact building design performance and occupant satisfaction. Keywords. Virtual Reality; Prototyping; Design Technology; Immersive Virtual Environments; Feedback.
730 A. HEYDARIAN ET AL 1. Introduction Making design adjustments earlier in a project s life cycle reduces the overall cost of the project (Eastman, 2008). Design adjustments can be better informed by involving end-users early during the design phase of a project in order to meet their expectations and deliver high quality products. However, due to lack of time and a growing number of parties involved in design and construction phases, end-user involvement is usually minimized (Oijevaar et al, 2009). Recently, the use of augmented reality (integrating virtual and simulated information with physical environments), virtual reality (computer-simulated environment that can simulate a physical presence through creating a visualization of a real or imaginary environment), and immersive virtual environments (IVE allowing user interactivity and immersion within virtual environments to provide a feeling of presence) has increased in different domains. In this paper, the authors examine if IVEs can be used as a cost-effective tool to involve end-users during the design phase of a project. In order to examine this, human performance on routine tasks in a physical environment were compared to the performance in an identical IVE (e.g., same room size, objects, lighting). By proving that humans perform similarly in both environments, designers can use end users feedback from virtual environments effectively in making decisions about design alternatives for physical environments. In this paper, the authors explore the use of IVEs for an office space by (1) evaluating the end-users sense of presence within an IVE through questionnaires; and (2) comparing human performance on a set of identical tasks in a virtual design alternative and a physical environment with same architectural settings. These alternatives were created through a custom workflow that translates the design intent from an associative parametric BIM tool to an IVE, in which the geometry, lighting, and coupled configurations of these environments were modified. After completing the assigned tasks, participants filled out questionnaires, measuring the realism of the virtual environment, and user experience in performing the assigned tasks, and users sense of presence. 2. Background In the past two decades, the use of virtual reality has increased in various domains, such as in education (Bailenson et al, 2008; Wagner et al, 2013), the military (Psotka, 1995), and various medical fields (Johnsen et al, 2005). The AEC industry, an industry that relies significantly on visual communication, has also made its transition in to adopting the use of virtual reality in the past decade (Kim et al, 2013).
IMMERSIVE VIRTUAL ENVIRONMENTS 731 With the advent of virtual and augmented reality along with the advances in the field of human-computer interaction, AEC professionals have the opportunity to bring their design alternatives into IVEs for evaluating them in a coupled fashion with end-user feedback (Maldovan et al, 2006). To involve end-users in the design phase, (Dunston et al, 2007) brought healthcare organization end-users (e.g., doctors, nurses, etc.) into an IVE in order to evaluate the proposed design of a new hospital. Previous research has suggested that IVEs can reduce the amount of time needed to modify designs, provide detailed information about a potential design to the reviewers, and improve the communication between owners, architects, and engineers (Majumdar et al, 2006; Maldovan et al, 2006). These environments can be utilized as a new approach for involving end-users by combining the strengths of preconstruction mock-ups and BIM models (Bardram et al, 2002; Dunston et al, 2007); they can provide the sense of presence found in physical mock-ups and make evaluation of numerous potential design alternatives possible in a timely and cost-efficient manner (Shiratuddin et al, 2004; Chan and Weng, 2005; Eastman, 2008). Additionally, such immersive environments can potentially be used as a tool for building designers and engineers to study enduser behaviour and satisfaction within a design alternative. To confirm if IVEs are adequate for analysing and comparing design alternatives, it is important to study users performance within such environments and compare it to the real-world settings. Prior research has shown that IVEs can be used effectively to measure performance, such as in examining behavioural compliance to different emergency exit cues (Duarte et al, 2013). Additionally, other research has examined how perception of statues varies within IVE, physical environments, and augmented reality (Huang and Wang, 2008). However, there is a need to further examine how performance in everyday tasks in IVE compares to physical environments when features of the design alternative are changed (e.g., lighting, geometry). 3. Methodology The authors of this paper examine whether end-users performances on daily office related tasks (e.g., reading, writing, communication, etc.) differ between an immersive virtual office environment and a physical office environment. To evaluate if an IVE is an adequate representation of a physical environment, specifically two parameters were measured: (1) user performance when given simple tasks, such as reading a passage; and (2) user perception of colour and brightness by identifying coloured objects in the room. These parameters were measured based on the speed and accuracy of the
732 A. HEYDARIAN ET AL performed tasks to determine whether IVE has any negative effects on users vision and performance on the given tasks. 3.1. EXPERIMENT AND HYPOTHESIS An identical 3D virtual model of an office room at the University of Southern California with the same dimensions and objects (e.g., desk, bookshelf, chairs, etc.) was created (Figure 1). The physical office environment had a window for natural light and four available lighting settings: (1) no light bulbs on, (2) two lights bulbs on, (3) four light bulbs on, and (4) 6 light bulbs on. Previous research has suggested that different lighting conditions and variations in illuminance level and colour may influence interpersonal behaviour and human performance on tasks that are primarily cognitive in nature (Baron et al, 1992). Therefore, lighting settings two and four were selected for representing dark and bright conditions of the room, respectively. Two 3D virtual models were created based on these two conditions (i.e., dark and bright). The experiments in the physical environment also used two conditions of the physical room: dark and bright. Participants were asked to perform similar tasks that measured their performance and perception in all four environments. Figure 1. Photos of the physical environment (left) and virtual models of the physical environment (right), representing the two conditions The changes (Δ) in performance for speed and accuracy were then determined within each environment (e.g., between the bright and dark conditions in the physical environment and between the bright and dark conditions in the virtual environment). The user performance between the two environments was compared by determining if there was any difference between the Δ for the physical environment and the Δ for the virtual environment.
IMMERSIVE VIRTUAL ENVIRONMENTS 733 As shown in figure 2, rooms a and a are considered for the dark rooms and b and b are considered for the bright rooms for the physical environment and virtual environment, respectively. The changes in performance between a b and a b is shown by Δ1 and Δ2. The authors hypothesized that in order for the IVE to be an adequate representation of a physical environment, Δ1 Δ2. In order to not reject this hypothesis, a p-value greater than 0.05 is needed as a result of the statistical analysis. Physical World a a IVE Dark Performance in Physical Room Performance in IVE Room Bright Δ 1 Δ 2 = b b Performance in Physical Room Performance in IVE Room Figure 2. Experiment Setup 3.2. MODEL AND APPARATUS The base structure of the IVE was designed in Revit 2013. 3ds Max was used to modify the model, add materials, and lighting. The model was then exported to the IVE software: Architecture Interactive. The system configuration is composed of a Microsoft Xbox Kinect, an Oculus Head Mounted Display (HMD), a tracker, a Microsoft Windows graphics workstation with NVIDIA 3000M graphics card. To increase the sense of presence and to allow participants realistically interact with the IVE, the Kinect was used to track the body displacement (3 Degrees-of-Freedom - DoF), the HMD was used to track the head rotation (3 DoF), and the tracker was used to navigate through the room, providing 4 DoF. Figure 3 shows the procedure for creating the models and the apparatus used for this experiment. Alternative Models Immersive Virtual Environment 3D Model Rendering Interactivity tool Oculus Goggles 3 DoF Head Rotation Revit 3D Max Architecture Interactive Xbox Kinect 3 DoF Body Displacement Modeling Adjustments Tracker / Controller 4 DoF Rotation + Displacement Figure 3. Modelling and apparatus
734 A. HEYDARIAN ET AL 3.2. EXPERIMENT PROCEDURE In order to test our hypothesis, an experiment was conducted with 9 participants. The participants were graduate students of the University of Southern California, between the ages of 21 to 35 years old. An IRB (Institutional Review Board) approval was obtained and all participants completed a consent form. None of them reported any prior experience with IVEs. Since participants were later asked to identify objects based on colour, they were simply asked if they had normal or corrected visual acuity through a questionnaire for the purpose of this pilot experiment. First, the participants were trained how to navigate within an IVE different from the environment used in the actual experiment using the tracker. They were provided with instructions on how to move from one side of the room to another side using the tracker as the main navigation tool. Other tasks such as crouching, turning head, and grabbing and moving objects in the room were also part of this training. Once the participants felt comfortable with the IVE, they were asked to remove the head mounted display and were asked about their general feeling about the environment. This precaution was used to ensure the participants were not getting any motion sickness and they have felt comfortable with the virtual environment. The participants were then randomly assigned to one of the four settings (figure 2) in order to eliminate any order effects. In each environment the participants were given two tasks of (1) reading a passage on a computer screen; and (2) identifying books of a specific colour in 30 seconds. For the first task, they were specifically told to ensure they read the passages thoroughly as they will be asked comprehension questions about it. The duration to read the passage was recorded. For the second task, at the end of 30 seconds, they were asked how many books they found in the bookshelf and/or around the room and this number was recorded. Once the two tasks were completed, they were given four multiple choice questions about the passage and the number of correct responses was recorded. Once each participant completed the first environment he/she was asked to take a five minute break and was taken to the second environment. The same procedure as the first environment took place but they were assigned a different passage and were asked to count a different colour of books; this was done to reduce the learning effect that the participants could have during the experiments. Participants were randomly assigned to the IVE or the physical environment. Within IVE and the physical environment, they were then randomly assigned to complete the tasks first in the dark environment or bright environment. Once the participants completed their assigned tasks for each of the four settings, they were given a questionnaire form to fill out about their experience
IMMERSIVE VIRTUAL ENVIRONMENTS 735 in the IVE. They were asked to describe their sense of presence in the environment, whether they thought that the virtual office environment was a good representation of the physical office room, and if they thought their performance was affected by the light settings in the room. Figure 4 shows some of the participants using the IVE. Apparatus Set-up Reading Task Object Detection Figure 4. Participants Interacting with IVE 4. Results The results indicate that the there are no significant differences between the Δs. Three Δs from each environment (physical and virtual) were computed for each of the following parameters between the dark and bright conditions: (1) comprehension, which was the ratio of correctly answered questions to all questions for each passage, (2) speed, which was a ratio based on the participants speed of reading and the number of words (word/seconds), and (3) object detection, which was the ratio of number of found books of a specified colour to the total number of books of a specified colour in the room. The Δs computed for each parameter were then compared between the physical and virtual environment. An independent sample t-test was performed for each of these three parameters and no significant differences were found between the environments for all parameters. Table 1 shows the participants Δs for each of the six settings along with the averages. Table 2 shows the p- values associated with the t-test analysis. In terms of performance on the assigned tasks, it appears that participants performed similarly in the IVE and physical environment. However, participants did appear to have some navigation related trouble in the IVE. This could be improved with more training and practice in the future studies.
736 A. HEYDARIAN ET AL Table 1- Physical and virtual environment parameters Δ for each participant Comprehension (Δ Ratio of Correct ans.) Physical Environment Reading speed (Δ word/sec) Object Detection (Δ Ratio of correct ans.) Immersive Virtual Environment Comprehension (Δ Ratio of Correct ans.) Reading speed (Δ word/sec) Object Detection (Δ Ratio of correct ans.) P1 0.250 0.011-0.129-0.250-0.014 0.080 P2-0.250 0.001 0.018-0.250-0.113 0.030 P3 0.000 0.314 0.043 0.250-0.307-0.037 P4 0.000 0.203 0.008-0.250 0.046-0.005 P5-0.250-0.203 0.058 0.000-0.008 0.008 P6 0.000 0.167-0.059-0.250-0.082 0.091 P7 0.250-0.110 0.418 0.250 0.131-0.080 P8 0.250-0.220 0.075-0.250 0.175 0.000 P9 0.250-0.342-0.007 0.000-0.253 0.000 Avg 0.056-0.020 0.047-0.083-0.047 0.010 Table 2- t-test Results for each Δ Δ Comprehension Δ Reading Speed Δ Object-Detection (Real vs. VR) (Real vs. VR) (Real vs. VR) t-test(p-value) 0.185 0.765 0.500 The object identification test was a measure of participant s colour perception in both environments. The lack of difference in the Δs between the IVE and physical environment in the object detection task suggests that participants had similar colour perceptions in both of these environments. The participants indicated that the IVE appeared very realistic, in terms of the setup of the room, as well as, their depth perception when compared to the physical environment. They stated that the tasks were comparable in difficulty in the IVE compared to the physical environment. Additionally, the participants indicated that they felt that they were physically inside of the office room in the IVE. These responses to the exit questionnaire suggest that participants felt a sense of "presence" in the IVE similar to the presence in the physical environment. 5. Limitation and Future Work Although serving as a first step toward the research goals, this study had limitations. There was a small sample size of 9 participants which will be increased for the full experiment through a proper power analysis. There were a few features of the model (e.g., outside window view, additional objects in the room, etc.) that need to be improved to better represent the physical room in the IVE. The authors measured participants presence through preliminary
IMMERSIVE VIRTUAL ENVIRONMENTS 737 set of questionnaires, but for the full experiment the authors will use more established instruments and questionnaires such as those suggested by (Witmer and Singer, 1998). As part of the future work, the authors will look in to effects of lighting and geometrical changes in an IVE on human performance and energy-use behaviour. The authors will also use IVEs to explore the integration of multi agent systems to impact building design performance and occupant satisfaction. 6. Conclusion End-user feedback during the design phase is vital in enhancing satisfaction and increasing quality of the end product. The authors aimed to explore if IVEs can be an efficient way to involve end-users. This paper demonstrated that there were no differences in human performance on everyday officerelated tasks in a physical environment and in an IVE. Additionally, followup questionnaires revealed that participants thought that the virtual environment was a satisfactory representation of the physical environment and that they felt a sense of "presence" within the IVE. With performance being similar in these environments, virtual environments can be used as a way to explore design alternatives and various design changes, such as lighting and geometry changes in a room, and their impact on human perception and behaviour. More comprehensive tests with larger sample sizes are among the authors future directions. Acknowledgement This project is part of the National Science Foundation funding under the contract 1231001. Any discussion, procedure, results, and conclusion discussed in this paper are the authors views and do not reflect the views of National Science Foundation. Special thanks to all of the participants and to the researchers that contributed to this project; specifically to Saba Khashe for her contributions in preparing and running the experiment. References BAILENSON, J. N., YEE, N., BLASCOVICH, J., BEALL, A. C., LUNDBLAD, N. & JIN, M. 2008. The Use of Immersive Virtual Reality in the Learning Sciences: Digital Transformations of Teachers, Students, and Social Context. Journal of the Learning Sciences, 17, 102-141. BARDRAM, J., BOSSEN, C., LYKKE-OLESEN, A., NIELSEN, R. & MADSEN, K. H. 2002. Virtual video prototyping of pervasive healthcare systems. Proceedings of the 4th conference on Designing interactive systems: processes, practices, methods, and techniques. London, England: ACM. BARON, R., REA, M. & DANIELS, S. 1992. Effects of indoor lighting (illuminance and spectral distribution) on the performance of cognitive tasks and interpersonal behaviors: The potential mediating role of positive affect. Motivation and Emotion, 16, 1-33. CHAN, C.-S. & WENG, C.-H. 2005. How Real is the Sense of Presence in a Virtual Environment? Applying Protocol Analysis for Data Collection. Digital Opportunities: Proceedings of the 10th International Conference on Computer-Aided Architectural Design Research in Asia. New Delhi: Architexturez Imprints.
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