Immersive virtual environments versus physical built ...

Automation in Construction 54 (2015) 116–126

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Automation in Construction journal homepage: www.elsevier.com/locate/autcon

Immersive virtual environments versus physical built environments: A benchmarking study for building design and user-built environment explorations Arsalan Heydarian a, Joao P. Carneiro a, David Gerber a,b,⁎, Burcin Becerik-Gerber a, Timothy Hayes c, Wendy Wood c a b c

Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, USA School of Architecture, University of Southern California, Los Angeles, CA, USA Department of Psychology, University of Southern California, Los Angeles, CA, USA

a r t i c l e

i n f o

Article history: Received 26 October 2014 Received in revised form 27 February 2015 Accepted 22 March 2015 Available online xxxx Keywords: Immersive virtual environment Virtual reality Feedback Performance Prototyping Design technology

a b s t r a c t In order for a project to be satisfactory to end-users and completed with high quality, the architecture, engineering, and construction (AEC) industry heavily relies on digital modeling, simulation and visual communication. In the past two decades, the AEC community has examined different approaches, including virtual and augmented reality, to improve communication, visualization, and coordination among different project participants; yet these approaches are slowly being adopted by the industry. Such technological advancements have the potential to improve and revolutionize the current approaches in design (e.g., by involving end-user feedback to ensure higher performing building operations and end-user satisfaction), in construction (e.g., by improving safety through virtual training), and in operations (e.g., by visualizing real-time sensor data to improve diagnostics). The authors' research vision builds upon the value of using immersive virtual environments (IVEs) during the design, construction, and operation phases of AEC projects. IVEs could provide a sense of presence found in physical mock-ups and make evaluation of an increased set of potential design alternatives possible in a timely and costefficient manner. Yet, in order to use IVEs during the design, construction, and operation phases of buildings, it is important to ensure that the data collected and analyzed in such environments represent physical environments. To test whether IVEs are adequate representations of physical environments and to measure user performance in such environments, this paper presents results from an experiment that investigates user performance on a set of everyday office-related activities (e.g., reading text and identifying objects in an office environment) and benchmarks the participants' performance in a similar physical environment. Sense of presence is also measured within an IVE through a set of questionnaires. By analyzing the experimental data from 112 participants, the authors concluded that the participants perform similarly in an IVE setting as they do in the benchmarked physical environment for all of the measured tasks. The questionnaire data show that the participants felt a strong sense of presence within an IVE. Based on the experimental data, the authors thus demonstrate that an IVE can be an effective tool in the design phase of AEC projects in order to acquire end-user performance feedback, which might lead to higher performing infrastructure design and end-user satisfaction. © 2015 Elsevier B.V. All rights reserved.

1. Introduction The cost and complexity of design changes exponentially grow as a project progresses from the planning and design phases to the construction phase; therefore, early design decisions in a project's life impact overall project costs and end-users' satisfaction significantly. In recent ⁎ Corresponding author at: USC School of Architecture, 850 West 37th Street Los Angeles, CA 90089 United States. E-mail addresses: [email protected] (A. Heydarian), [email protected] (J.P. Carneiro), [email protected] (D. Gerber), [email protected] (B. Becerik-Gerber), [email protected] (T. Hayes), [email protected] (W. Wood).

http://dx.doi.org/10.1016/j.autcon.2015.03.020 0926-5805/© 2015 Elsevier B.V. All rights reserved.

years, Building Information Modeling (BIM) has been widely adopted by the AEC industry. As a result, there has been a significant amount of improvement in communication, exchange and interoperability of information among different parties involved in a project [15]. However there exists a lack of end-user involvement during the design process, which has been identified as one of the major issues in current design approaches [11]. User Centered Design (UCD) has shown to be an effective approach to improve the final product based on end-users' needs in many domains, including software design and the automotive industry. Taking the UCD approach used in other industries, Bullinger et al. and Zahedi et al. [11,70] proposed the concept of involving users and acquiring end-user input early during the design phase in order to increase

A. Heydarian et al. / Automation in Construction 54 (2015) 116–126

efficiency, quality, and performance of Architecture Engineering and Construction (AEC) projects. However, due to the lack of time and resources, as well as the growing number of parties involved in design and construction phases of AEC projects, end-user involvement is usually minimized and in many cases eliminated [50]. In the past few decades, augmented reality (a physical environment, whose elements are augmented with and supported by virtual input), virtual reality (a simulated virtual environment, representing a physical environment), and immersive virtual environments (IVEs — environments where user interaction is supported within a virtual environment) have given many opportunities to the researchers in various domains, such as the education, health, automation industry, and the military, to increase user involvement and improve the efficiency of their work processes. Similarly, the AEC community has also adopted the use of virtual reality [35,61] and augmented reality [7,10,25] for supporting various construction and building simulations and information visualization [2,6,19], as well as coordination and collaboration among teams [32,40,55,63]. Although the AEC industry has used IVE technologies before (e.g., CAVE and Head Mounted Displays), their primary use has been limited to marketing purposes, visualization of BIMs [11], and education and training of AEC professionals [54]. An IVE can be utilized for engaging end-users in the design process of projects by combining the strengths of pre-construction mockups that provide a sense of presence to users and BIM models that provide the opportunity to evaluate alternative design options in the models in a timely and cost efficient manner [26,41]. Furthermore, building engineers and designers can incorporate IVEs in their work processes as a tool to measure end-user behavior, understand the impact of design features on behavior, as well as receive constructive user feedback during the design phase. The use of IVEs is not only limited to the design phase; such environments can also be used as cost effective and efficient tools during the construction phase to improve site preparation and logistics, safety and training of construction workers [46,66], and collaboration and coordination among team members. IVEs can also be used in the operation phase of buildings to visualize and interact with sensor data available in buildings, and for personnel training and process improvement purposes during building operations [14]. The work presented in this paper builds upon the value of integrating IVEs to explore and address different challenges within the AEC industry, such as user input in early design decision making stages, design alternative evaluation, evaluation of safety measures on a construction site, or emergency response operations in post-construction phase. The authors' long term goal builds on the following research question: “how can we better test project design alternatives and measure user behavior and performance in different alternatives through the integration of immersive virtual reality into our digital and physical mock up workflows?” In order to achieve this long-term goal, the authors aim to test whether IVEs are adequate representations of physical built environments and to measure user behavior within various settings (e.g., different lighting settings). As the first step, it is imperative to evaluate end-users' performance and sense of presence within IVEs and benchmark them to similar physical built environments. This paper specifically presents results from a comparative study, where the authors evaluated the end-user's performance on a set of identical tasks in immersive virtual and physical office environments with the same features. The paper presents the research through a literature review and gap analysis on the use of virtual reality and IVEs for integrating enduser feedback during the design phase, as well as their uses in general within the AEC industry. The paper presents the research methodology, the IVE system for data acquisition, and detailed results and discussion of user performance in everyday office related tasks, through metrics of response–time and performance accuracy, within an IVE compared to a physical built environment setting. Finally, the

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paper is concluded by a discussion and presentation of the planned future work. 2. Use of virtual environments in architecture, engineering and construction In the past two decades, several researchers in different fields that heavily rely on visualization, communication, and interaction (e.g., education [3,65], military [53], and medical fields [34]) have successfully integrated and adapted the use of virtual environments. Other fields, such as mechanical and industrial engineering, have also used virtual and immersive technologies for testing and improving prototypes [42]. For instance, Noor and Wasfy [48] have used an IVE to create an aerospace laboratory for testing structure, aerodynamics, and acoustics of various facilities (e.g., wind tunnels). The AEC industry, an industry that relies significantly on visual communication, has also made its transition into adapting the use of virtual environments in the past decade [36]. During the design phase of a project, IVEs have shown to be effective tools for building mockups as revision tools to analyze and address issues before the construction phase [17,18,26,43,44]. Virtual environments have also been used as collaborative design tools [32], providing a better avenue for information exchange in multi-disciplinary team environments [33,57]. By creating a better sense of realism through its one-to-one scale, immersive virtual mock-ups have been used to provide a better understanding of a project to end-users and stakeholders, resulting in improved communication [4,11,15,26,41,49,69]. Previous research has suggested that these environments have the potential to provide a sense of presence – “presence is defined as the subjective experience of being in one place or environment, even when one is physically situated in another [67]” – found in physical mockups and make evaluation of numerous potential design alternatives in a timely and cost-efficient manner [12,20,59]. IVEs have also proven to be more realistic learning environments, especially for tasks related to spatial performance, such as navigation, path finding, and object perception in comparison to other mediums such as immersive workbenches, computer screens, and hemispherical displays [31,37,38,59]. Virtual environments (including IVEs) are considered to be important tools for the education and training of AEC professionals in BIM models [47,51,54,58,63,68]. Virtual reality has also been used for construction safety training and for developing a better understanding of construction processes for students [39]. Cheng and Teizer [13] introduced a framework for visualizing and simulating construction operation data in a virtual environment for worker training purposes. Although there exists a gap in integrating IVEs into the construction phase, researchers have introduced the use of virtual reality for construction simulation purposes. For example, Kamat and Martinez [35] have used virtual reality models to simulate various construction equipment operations to identify optimal solutions for a construction site. Virtual reality can also be used during the operations phase of buildings. Virtual and augmented reality environments allow users to visualize and interact with real-time data [27,45]. As a result, these environments can be directly used in daily maintenance activities in facilities, by providing efficient and timely access to the site information (e.g., building plans), sensor data, and by identifying malfunctioning equipment and building systems [22,30]. Due to the lack of user input during the design phase of AEC projects, researchers have also used virtual reality models to visualize and simulate possible user interactions, track routes within a designed environment [60,61], simulate user energy-consumption behavior [24], and simulate social and crowd behavior during emergency evacuations [52]. Although in these studies, virtual reality has shown to be a promising tool to understand and improve the design of buildings, the data for these simulations were either collected from similar buildings through observations and were used to define simulation rule sets for

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how the end-users would behave in different situations or the rule sets for end-user behavior were determined based on software calculations. Therefore, these precedent simulations lacked actual end-user input and feedback for the building being designed, a critical distinction for the work presented in this paper and going forward. Gerber [23] discusses the current design methodology, which is enabled and dependent on parametric design thinking. Through the use of IVEs, such systems might become more intelligent by allowing design alternatives to be more rapidly evaluated by design teams as well as end-users. IVEs have allowed researchers to study user-built environment interactions through a more effective approach by enabling more control over different variables, which are usually difficult or almost impossible to be controlled or manipulated in physical settings (e.g., spatial and geometric and tectonic design variations, as well as environmental variables). For example, in a pilot test, Heydarian et al. [28] have used IVEs to evaluate the effects of different modes of lighting controls (remote controls for lighting vs. regular light switches) on enduser energy-consumption behavior. IVEs have also been used in emergency egress to measure the effects of static and dynamic signs in a case of an emergency in a building [16]. Although previous research in the AEC community shows promising progress in virtual reality and IVE areas, it is important to systematically evaluate end-user interactions within a virtual environment and benchmark them to physical built environments. In order to get feedback from end-users and understand behavioral changes within alternative designs, as the first step, it is imperative to evaluate whether IVEs are adequate tools to represent real-life scenarios. To achieve this goal, in this paper the authors study users' performance and sense of presence within such environments and benchmark these metrics to real-world settings. 3. Research methodology The objective of the study presented in this paper is to evaluate whether IVEs are adequate representations of physical environments, specifically office environments, by examining the difference in endusers' performances in office-related activities within a physical office space and a virtual office space. Different studies have identified lighting as one of the major factors that influence occupants' performance in indoor environments [8,9,56]. For instance [21] has identified tasks, such as reading speed and comprehension [62,64], object location and identification, and writing as highly affected by illuminance, amount of glare, and spectrum of light. Therefore, in order to compare end-users' performance between virtual and physical office space, three parameters were measured: (1) user performance when given simple office

related tasks (e.g., reading speed, comprehension); (2) user perception of color recognition and object identification; and (3) user's sense of immersion and presence. In order to assess the IVE's effects on the endusers' performance, the speed and accuracy of the performed tasks were recorded and compared to the physical office environment with very similar settings. In addition, through a set of questionnaires, the authors measured the participants' sense of presence within an IVE. The following subsections explain the details of the research methodology. 3.1. Design of experiment and hypothesis A single occupancy office space at the University of Southern California was selected as the test case. The office included regular office furniture (e.g., a desk, bookshelves, chairs, and a computer), a white board, and a window (Fig. 1). The office included two sources of lighting: (1) natural light coming through the window and (2) two artificial light fixtures (three fluorescent light bulbs on each fixture). Four different artificial light settings were available through different combinations: (1) no light bulbs on, (2) two light bulbs on, (3) four light bulbs on, and (4) six light bulbs on. A set of identical 3D virtual models of the office with the same dimensions, similar furniture, and similar lighting conditions was created (Fig. 2). Previous research has suggested that different lighting conditions and variations in luminance levels and color may influence interpersonal behavior and user performance on tasks that are primarily cognitive in nature [5,21]. Therefore, the authors have created two conditions for both the virtual and physical environments with similar settings: a dark and a bright office space. Artificial light settings 1 and 4 were selected for representing the dark and bright conditions of the room, respectively. The blinds were kept fully open for the bright condition and half open for the dark condition. The lighting in the dark condition was adequate for the participants to be able to see the objects and perform the assigned tasks. The participants were assigned to read a passage both in the virtual environment and physical environment with similar lighting settings and then answer a set of questions based on their comprehension of the passages. As a second task, they were assigned to identify and count the number of books with a specific cover color in a bookshelf both in the virtual and physical environments. In order to ensure that the lighting settings in the 3D models were similar to those in the physical environment, the illuminance levels in the physical environment for lighting conditions 1 and 4 were measured several times between 3:00 PM to 6:00 PM on different days using an illuminance meter (Minolta T-10). The averaged values were used to setup the lighting levels for the 3D models. In order to keep the differences between the bright and dark conditions in the physical environment

Fig. 1. Physical single occupancy office space with dark and bright conditions.


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Fig. 2. Virtual single occupancy office space with dark and bring conditions.

and IVE as close as possible, the authors ensured the ratios between the dark and bright conditions in the physical environment (measured by the illuminance meter) matched to the ratio between the dark and bright conditions in the 3D models. Due to the changes in lighting conditions at different hours throughout the day and different times of the year, the authors ran the experiments between 3:00 PM to 6:00 PM for a period of one month (April 2014). In order to ensure the bookshelf and

the books within the 3D models looked realistic, a similar number of books with real covers of existing books were created (Fig. 3). The participants were assigned to perform a set of similar tasks (reading a passage and counting the books in the bookshelf) both in the physical environment and in the IVE. The passages were different in each of the four settings however, the reading levels were kept consistent to ensure there would not be any biases. To ensure the reading

Fig. 3. Bookshelf in the IVE vs. physical environment.

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levels were consistent, the authors used ACE Reader [1], reading speed and comprehension software. The software ensures the reading levels of the text are consistent by running the text through common readability formulas, in which they took into account factors, such as the number of syllables per word, and number of words per sentence. The results of the participants' performances were first compared within the dark and bright conditions (within group comparison) in each environment (IVE and physical) and then the difference was compared between the physical environment and IVE (between groups comparison). This was done to understand whether the IVE is an adequate representation of the physical environment for office related activities. Fig. 4 shows the two environments in bright and dark conditions. For comparing the performance of the participants in the reading task, two parameters were measured: (1) reading speed and (2) comprehension (answering questions based on the passage). The changes (Δ) in performance for reading 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 measured parameter for identifying and counting similar colored books task was to identify the correct number of the specified colored books in the bookshelf within 30 s. To determine whether there was a difference in user performance between the two environments; the difference ‘between’ the Δ for the physical environment and the Δ for the virtual environment was compared. As shown in Fig. 5, rooms a and a′ were two conditions for the dark rooms and b and b′ were two conditions for the bright rooms for the physical environment and virtual environment, respectively. The changes in performance between a − b and a′ − b′ are shown by Δ1 and Δ2, respectively. The authors hypothesized that in order for the IVE to be an adequate representation of the physical environment, there would be a significant difference in performance between the bright and dark conditions in both the virtual environment and the physical environment with the comparison of these differences supporting the prediction that Δ1 ≈ Δ2. The authors explored if there is a significant difference in performance between the deltas in the physical environment and IVE rather than exploring if there is a significant difference in performances

Physical Environment a’

a

Performance in Physical Room

Dark

b

Bright

IVE

Δ1 Performance in Physical Room

Performance in IVE Room

b’

=

Δ2

Performance in IVE Room

Fig. 5. Experiment hypothesis.

between the physical environment and IVE in dark conditions and bright conditions independently. The long-term goal is to use the IVEs to examine how changes in design alternatives affect user behavior, user satisfaction, and user performance rather than precisely depict a given condition in IVE. Therefore, by examining the change in deltas, the authors can control for other miscellaneous factors that may be affecting user performance and perception in IVEs other than the manipulated lighting, such as the level of detail and quality of the 3D models, the individual differences in navigating within the IVE, and etc. 3.2. Model and apparatus The basic geometry and structure of the 3D environment was designed in Revit© 2013. The model was then exported to 3ds Max© to create a more photo-realistic representation of the physical environment. In 3ds Max© objects, such as furniture (e.g., desk, bookshelf, chairs), lighting and reflection, texture, and materials were added. To make the 3D models more photo-realistic, the models were rendered

Fig. 4. Bright and dark conditions in IVE (right) vs. physical environment (left).


to texture in 3ds Max© using multiple computers as a render farm. The rendered models were then imported in Architecture Interactive (AI) powered by Worldviz's Vizard virtual reality toolkit as an .osgb file. AI was used as a tool for visualization and interaction within the virtual office space. The Vizard toolkit was used to (1) connect a Microsoft© Xbox Kinect, an Oculus Rift DK1 Head-Mounted Display (HMD), and a Wand tracker together and link them to the imported 3D model; (2) visualize the 3D rendered model in order to identify the necessary adjustments needed to be made prior to bringing the model to AI; and (3) script different interactive options that a participant could have with the model within its python platform (e.g., turning the light switch on/off). The computers used for rendering the models were a Microsoft© Windows workstation with NVIDIA© 3000M graphics card, Microsoft© Windows laptop with Intel® graphics card, and an iMac with AMD Radeon HD 6750M graphics card. The workstation was used to connect all the IVE equipment together and run the Vizard toolkit. To increase the sense of presence and allow participants to realistically interact with the IVE, the Microsoft© Xbox Kinect was used to track the participants' body displacement (3 degrees-of-freedom — DoF), the HMD was used to track the head rotation (3 DoF), and the Wand tracker was used to navigate through the room, providing 4 DoF. Fig. 6 illustrates the modeling steps and the apparatus used for this experiment. 4. Experiment details Prior to running the experiment, a pilot study was ran by the authors to ensure the models, apparatus, experimental procedure, passages, books, and questionnaires were designed adequately for this experiment [29]. Based on the findings of the pilot study, the authors modified the quality of the models (lighting, furniture, and books), changed the passages to ensure the reading difficulties were the same for all four environments, and improved the questionnaire that was administered at the end of the experiment. 4.1. Recruitment Prior to recruiting participants for the experiment, the study was approved by the Institutional Review Board (IRB Approval # UB-1300503). In order to test the hypothesis, a power analysis was conducted and it was determined that 120 participants total would be more than sufficient (N.99 power) to detect any-sized effect in differences between Δ1 and Δ2. 120 participants (61 males and 59 females) were recruited by advertising through the psychology human subject pool available at the University of Southern California (USC), as well as in numerous engineering and architecture classes. The participants either voluntarily participated in the experiment or received credits for their participation. The participants were undergraduate and graduate students at USC, between the ages 18 and 33 years old (M = 21, SD = 2.5). About 55% of the participants were non-engineering and non-architecture majors and the remaining 45% were split evenly between various engineering and architecture majors (e.g., civil, environmental, and industrial engineering, computer science and architecture). None of the participants

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reported any prior experience with IVEs. Since participants were later asked to identify objects based on color, they were asked if they had normal or corrected visual acuity prior to the experiment. If the participants felt any motion sickness at any point during the training or the actual experiment, they were provided with full credits (when appropriate), thanked, and dismissed. 4.2. Procedure Prior to starting the experiment, participants read and signed an IRBapproved consent form. They were then given training using a different virtual environment (a virtual industrial kitchen), on how to navigate within an IVE. Another virtual environment than the actual experiment setting was used for the training purposes to avoid any learning effects and biases that could possibly affect the participants' decision making during the experiment. The training served to help participants adjust to the Oculus Rift and get used to the Wand for navigating in the environment. The participants were provided with instructions on how to use the Wand tracker to navigate within the space, on how to turn their head around to get a feeling of the Oculus Rift head tracking system, and on other simple tasks such as crouching, grabbing and moving objects in the virtual environment. Once the participants felt comfortable with the navigation tools and the IVE, they were asked to remove the HMD and report how they felt, their experience within the IVE, and whether they felt any sickness. After the participants completed the training, to eliminate any order effects, the participants were randomly assigned to one of the four groups shown in Fig. 5. The participants were assigned two sets of tasks in each environment, which included (1) reading a passage and answering questions based on the passage, and (2) identifying and counting books with a specified color in the bookshelf within 30 s. They were also randomly assigned to start either with the reading task or counting the books in the bookshelf. In this way, the authors were able to eliminate many potential confounds (order effects, learning effects, and fatigue effects) and isolate the variables of interest (independent variables' – physical vs. IVE – effect on the dependent variables of interest — comprehension, object identification, and reading speed). Prior to performing the reading task, the participants were instructed to read the passages however they felt most comfortable (out loud or quietly however it was kept consistent for all four environments). Participants were also motivated to pay attention to the passages, as they were required to answer a few questions about the passages. For the dark and bright conditions in the physical environment, the participants were additionally instructed to not move the papers from the desk (this was to ensure that the available lighting and reflection are similar among all of the participants). A stopwatch was used to record the amount of time it took each participant to read the passage, starting from the moment the participant began reading. Once completed with the reading, the participants were provided with a set of comprehension questions based on the passages. For the IVE section of the experiment (Fig. 7), once the participants finished

Fig. 6. Modeling steps and apparatus.

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Fig. 7. Illustrates the view from within the IVE of a participant sitting behind the desk to read a passage. Top left is a superimposed photograph of the participant.

with the reading task, in order to minimize the chance of any motion sickness, they were asked to remove the HMD and answer the questions on a piece of paper. Since the questions were designed for specifically measuring the participants' comprehension, the change of mediums did not have any effect on the answers they chose. The passages and questions chosen for each environment were different from other settings while the reading difficulty level was kept consistent by selecting the passages all at the sixth grade reading level (newspapers are usually written at this level). For the object identification and counting task, the participants were instructed to count the number of books with a specified cover color. Prior to performing the task, an example was shown to the participants to help them understand how to identify whether a book should be counted or not (this was due to the fact that some of the books in the bookshelf had more than one color in their covers). Once the participants understood the task, they were given 30 s to count the books with a specified color. The assigned colors were different between the four environments but kept consistent among the participants (e.g., dark IVE = blue books, bright physical environment = black books). The participants were asked how many books they found and the number was recorded. Once the tasks in each environment were completed, the participants were given a five-minute break and were taken to the next environment. The explained procedure was repeated until the participants

went through all four environments. Fig. 8 shows some of the participants performing the assigned tasks in the IVE. 4.3. IVE experience questionnaire Upon the completion of the tasks in all four environments, the participants were provided with a set of questionnaires, in which they were asked about their interaction and immersion in the IVE as well as the realism of the IVE compared to the physical environment. In order to understand an individual's (1) sense of presence and immersion within the IVE, as well as other mediums (e.g., watching TV, playing video games, reading books), and (2) interaction and experience within the IVE, the authors designed a questionnaire that included self-made questions, as well as questions adapted from [67]'s questionnaire, which focused on immersive tendency and presence within virtual environments. The questions were on a five-point or a seven-point Likert Scale (Fig. 9). There were a total of 32 questions in the questionnaire. Table 1 shows a sample of the questions with the averages and standard deviations for each question. The categories of focus, gaming, and immersion were adapted from the sub-scale of “immersive tendency” and the control factors, distraction factors, and involvement and control categories were adapted from the “presence” sub-scale in [67]. The last category of the questions, IVE interaction, was self-made and used to understand the individuals' IVE experience. 5. Analysis and discussion of the results

Fig. 8. Participants performing the assigned tasks within the IVE.

Out of the 120 participants, eight participants were not able to fully complete the experiments as four participants felt motion sickness during the experiment, two participants had larger sized glasses and they were not able to wear the HMD comfortably, and two participants were unable to complete the experiment due to time limitations. Therefore, the analysis is based on 112 participants' data. The results of the analysis follows the hypothesis that there exists significant differences in participants' performances between the dark and bright conditions in each environment (virtual and physical) for the following three categories: (1) comprehension, which is the ratio of correctly answered questions to all questions for each passage, (2) reading speed, which is the ratio of the reading speed and the number of words per second, and (3) object identification, which is the deviance from the correct number of colored books. In order to compare the Δs and differences between the bright and dark conditions, a pair sample t-test was conducted for comprehension, reading speed, and object identification. If the p-values for a pair sample t-test are below 0.05, there is a statistically significant difference between the two samples, and if it is above 0.05, there is no statistically


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Fig. 9. A sample question from the IVE experience questionnaire.

Table 1 Sample presence and immersion questions. Sample questions Focus How physically fit do you feel today? (5 point scale) How good are you at blocking out external distractions when you are involved in something? (5 point scale) Are you easily disturbed when working on tasks? (5 point scale) Gaming How many hours a day do you play video games? Immersion and involvement Do you ever get extremely involved in projects that are assigned to you by your boss or your instructor, to the exclusion of other tasks? (5 point scale) Do you ever become so involved in a television program or book that people have problems getting your attention? (5 point scale) Do you ever become so involved in a movie that you are not aware of things happening around you? (5 point scale) How frequently do you find yourself closely identifying with the characters in a story line? (5 point scale) Do you ever become so involved in a daydream that you are not aware of things happening around you? (5 point scale) How well could you examine objects from multiple viewpoints? (from different angles in the room) (5 point scale) Control factors How quickly did you adjust to the virtual environment experience? (5 point scale) How much were you able to control events? (5 point scale) How responsive was the environment to actions that you initiated or performed? (5 point scale) How natural did your interactions with the environment seem? (5 point scale) How much did your experiences in the virtual environment seem consistent with your real-world experiences? (5 point scale) Distraction factors How distracting was the control mechanism? (control mechanism is the remote control device — the WAND) (5 point scale) How aware were you of events occurring in the real world around you while performing the assigned tasks in the virtual environment? (5 point scale) IVE interaction How realistic and natural was your sense of moving around in the virtual environment? (5 point scale) How difficult was it to read the given passage in the virtual environment compared to the physical environment? (5 point scale) Did you feel like it took longer to read the passage in the virtual environment compared to the physical environment? (3 point scale) Was it more difficult to identify different colors of the books in the virtual environment compared to the physical environment? (7 point scale) Comparing the dark room in the virtual environment and the dark room in the physical environment, how similar did you feel these two environments were? (5 point scale) Comparing the bright room in the virtual environment and the bright room in the physical environment, how similar did you feel these two environments were? (7 point scale) Did the virtual environment become more real than the “physical environment”? (physical environment being our normal everyday environment) (7 point scale)

Mean, (SD) 3.57 (1.03) 3.27 (1.00) 3.11 (0.91)

0.50 (1.03)

3.17 (0.91)

2.91 (1.00)

2.97 (0.95) 3.52 (0.94) 2.70 (1.02) 3.72 (0.96)

3.68 (0.85) 3.55 (0.76) 3.65 (0.85) 3.01 (0.98) 3.40 (0.98)

2.88 (1.03) 2.38 (1.19)

3.12 (0.95)

significant difference. For comprehension, the p-value shows that there is a significant difference between the mean values in the dark IVE and bright conditions IVE (p = 0.006 b 0.05) and there is also a significant difference between the mean values in the dark physical and bright physical conditions (p = 0.001 b 0.05) as hypothesized. However, there is no significant difference between the Δ1 (the difference between dark and bright mean values in the physical environment) and Δ2 (the difference between dark and bright mean values in the IVE) (p = 0.947 N 0.05). For reading speed, there is a significant difference between the mean values in the dark IVE and bright IVE conditions (p = 0.000 b 0.05) and the dark physical and bright physical conditions (p = 0.004 b 0.05). As hypothesized, there is no significant difference between the Δ1 and Δ2 (p = 0.641 N 0.05). For object identification, the pair sample t-test analysis points to similar conclusions as the other two categories, in which there is a significant difference between the means in the dark IVE and bright IVE conditions (p = 0.024 b 0.05) and the dark physical and bright physical conditions (p = 0.049 b 0.05). However, there is no significant difference between the Δ1 and Δ2 (p = 0.864 N 0.05). Table 2 summarizes the analysis. By computing the Δs for each parameter, no significant differences were found between the two environments for all parameters; it was concluded that Δ1 ≈ Δ2. Most importantly to our design of the experiment and our future work is the fact that the statistical analysis shows that participants performed similarly in the IVE and physical environment. A power analysis confirmed that the authors had more than a 99% chance of detecting even a small effect (difference in deltas between the physical and virtual environments) with their sample size. Fig. 10 shows the means and standard deviations for each condition between IVE vs. physical environment. The comprehension values indicate the average and standard deviation of the correct number of questions that were answered by the participants; the reading speed values indicate the number of words per second that the participants were able to read the given passage; and the counting books values indicate the deviation from the correct number of books (absolute value of counted books − correct number of books). As it is shown in Fig. 10, based on the means for each assigned task, the participants were faster by a few seconds in their reading speed in the physical environment; this was due to the fact that participants had to use the Wand tracker to move up and down to be able to get a good view of the passage without getting dizzy and motion sickness, slowing down their reading speed.

2.62 (1.07) 1.80 (0.56) 4.60 (1.48)

Table 2 Comprehension, reading speed and object identification tasks' results. Δ1 = Δ2

Dark and bright VR

Dark and bright physical

2.90 (1.07)

Comprehension t-Test −0.066 p-Value 0.947

−2.83 0.006

−3.279 0.001

3.41 (0.95)

Reading speed t-Test −0.468 p-Value 0.641

−3.653 0.000

−2.93 0.004

Object identification t-Test −0.172 p-Value 0.864

2.291 0.024

1.987 0.049

3.43 (1.61)

124


Physical Environment

Comprehension µ = 3.09, SD = 0.76 Reading Speed µ = 3.91, SD = 1.04 Counting Books µ = 6.49, SD = 4.12

Dark

b Bright

IVE a’

a

Δ1



b’

Δ2


Fig. 10. The means and standard deviations for each condition between IVE vs. physical environment.

Meanwhile, the participants were able to count the number of books faster in the IVE in comparison to the physical environment since they could easily change their elevation in the IVE (e.g., in some cases participants had to stand on their toes to count the books on the upper book shelves in the physical environment). On the other hand, they were able to easily change their elevation by pushing up or down using the Wand tracker in the IVE. The authors also analyzed the IVE experience questionnaire results in order to evaluate the participants' sense of immersion and presence. The participants indicated: • Participants were relatively focused (μ = 3.3 — on a 5-point Likert Scale) during the experiment. • 89% of the participants did not play video games for more than an hour a day — 68% of the participants indicated that they did not play any video games on a daily basis. • Participants indicated that they were relatively (μ = 3.2 — on a 5-point Likert Scale) immersed in different mediums. Their abilities to control the events happening in the IVE was moderately similar to those in the physical environment and their interaction within the IVE felt moderately natural. • Participants were relatively (μ = 3.9 on a 7-point Likert Scale and μ = 3.0 on a 5-point Likert Scale) unaware of the events happening in the physical environment, while they were immersed within the IVE. • Their navigation within the IVE was moderately realistic and similar to their navigation in the physical environment. • It was moderately more difficult to read the passages in the IVE than it was in the physical environment; therefore, they felt it took slightly longer to read the passages in the IVE. • Their perceptions of the color of the books were moderately similar when the IVE and the physical environment were compared. • The bright room in the IVE was a slightly better representation of the bright room in the physical environment than the dark room was. To understand whether participants' sense of presence within other mediums (e.g., movies/TV shows, gaming, and reading books) had any effect on their IVE experience, the authors conducted a linear OLS (ordinary least squares) regression analysis predicting the difference in performance between the IVE and physical environment (|Δ1 − Δ2|) using the participant's presence questionnaire responses and concluded that none of the questionnaire measures had significant effects on |Δ1 − Δ2|. For example, the authors found there was no significant effect of the number of the gaming hours on the |Δ1 − Δ2| for comprehension (β = −.015, p = .84), for reading speed (β = −.039, p = .36), and for object identification (β = − .131, p = .63).

Although the results are not statistically significant, they directionally suggest that for one hour increase in gaming there is a 0.039 unit reduction in |Δ1 − Δ2| for comprehension, a 0.015 unit reduction in |Δ1 − Δ2| for reading speed, and a 0.131 unit reduction in |Δ1 − Δ2| for object identification. This finding implies that gamers perform directionally more similarly in IVE as they do in a physical environment. The authors also investigated if gaming influenced participants' perception of the IVE (only the questions on a 5-point Likert scale were used). The authors found a marginally significant effect of number of gaming hours on participants' IVE interaction (β = .12, p = .075); this finding indicates that one hour increase in gaming results in a 0.123 unit increase in participants' IVE experience scale on a 5-point Likert scale. This finding suggests that gamers think that the IVE is more similar to the physical environment than the non-gamers do. Lastly, the authors examined if gender (male vs. female) had a significant effect on the IVE experience. Conducting an independent sample t-test, the authors found no significant difference in comprehension, reading speed, and object identification performance between the IVE and physical environment (|Δ1 − Δ2|) and no significant difference in perception of the IVE between males and females. 6. Limitations and future work Although the participants felt the IVE experience was somewhat realistic, one of the major limitations of the experiment was that participants' navigation through the virtual environment was not realistic (e.g., walking from the entrance to the desk, walking from the desk to the bookshelf, and etc.), however this limitation should not affect the performance measures, only their perception of similarities between the IVE and physical environment. Although at this point it is costly, having precision position tracking sensors in the room would allow the participants to walk naturally within the IVE and increase their sense of presence. Although the models were rendered in high quality settings, the Oculus Rift DK 1 has a pixel limitation using the highest quality setting of (640 × 800 per eye) that could be experienced by the participants. This resolution limitation has been addressed and improved in Oculus Rift DK 2 (960 x 1080 per eye), which the authors will be using in their future experiments. Another limitation with this experiment is that the sample was comprised of mainly undergraduate students. It is possible that older adults might not perform similarly in an IVE compared to a physical environment. In this experiment, there were three measures of performance (i.e., comprehension, reading speed, and object identification). The authors used these measures as examples of user performance in an office environment and are assuming these effects generalize to other measures of performance. However, there are other tasks that occupants of an office building perform on a daily basis. More complex tasks and design interactions can be studied in future research to ensure that the effects experienced in this experiment are scalable to other tasks and building types. The long-term objective of the authors is to build on the presented work and use IVEs to collect and integrate real-world end-user information into the simulated realm of architectural design alternatives, design features and design exploration. To achieve this, the authors plan to examine user preferences within different design settings and identify the effects of changes in design features (e.g., geometry, lighting, layout, and etc.) on user behavior and preference (e.g., spatial configurations, lighting, noise, etc.). With the collected preference, behavioral, and interaction data, the authors plan to create bottom-up rule-sets in order to include user-related data in an automated and or semi-automated design process to improve building design performance and occupant satisfaction. It must be highlighted that the authors hope to improve the design phases as a means to improve upon overall building performance in a lifecycle sense. In other words if decision making during the design


phase can be improved, it can safely be assumed that it will be possible to further optimize building performance in the operation phase of buildings and especially through a truly coupled and symbiotic interactions of users and buildings. The authors also plan to explore the use of IVEs in construction safety and training as well as real-time sensor data visualization and interaction during the operation phase of the buildings.

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thank the Worldviz LLC on their support for providing feedback for improving the 3D models and assisting with the software and equipment setup. Special thanks also to all of the participants and the researchers that contributed to this project, specifically to Alexander Coco and Samantha Kaplan for their contributions in recruiting participants and running the experiments. References

7. Conclusions The research presented in this paper prefaces the need for engaging with end-users in the earliest stages of design and planning as a means to achieve higher performing designs with an increased certainty for end-user satisfaction. To reach this goal, the authors explored whether the use of IVEs can be an efficient approach. This paper summarized the author's investigation on user performance of everyday officerelated tasks in an IVE compared to a benchmarked physical environment. The authors explored the use of IVE for an office space, evaluated the end-user's sense of presence within the IVE through a questionnaire, and compared user performance on a set of identical tasks in an IVE and a physical environment with same architectural settings. The data analyzed from 112 participants showed that there exists a difference between participants' performances between dark and bright conditions in the physical environment, as well as the IVE; yet this difference is almost equal between the two environments, physical and virtual. These results indicate that IVE is a satisfactory representation of the physical environment based on the performance measures and questionnaire data and there are no significant differences in performances on the authors' measures of everyday office-related tasks in a physical environment and in an IVE. This study thus provides evidence that IVEs can be an effective tool to study user behavior and measure user performance. More specifically, IVEs can be used to acquire essential information about the end-users' preferences during the design phase of buildings which will lead to enhanced design decision making and the ability to test an increased set of design alternatives normally limited given the constraints of time, and resources such as fee and costs. In addition, the questionnaire data shows that the participants' ability to control the events happening in the IVE was strongly similar to those in the physical environment and their interaction within the IVE felt moderately natural. This finding suggests that participants feel a sense of presence within the IVE. It further suggests that IVE could be used as an effective tool to examine user behavior in different design alternatives, which could include spatial configurations, tectonic, and material and color differences all of which impact the measured environmental factors. With performance being similar in these environments based on the experiment's measures, IVEs could be used as an approach to explore end-user preferences and behavioral changes within various design alternatives. Such findings suggest that IVEs have the potential to be used as tools during the construction phase of buildings in order to train construction personnel about safety, identify user and environment triggered safety hazards using real-time interactions and simulations, and identify the reasons behind user decisions and behavior. This approach could reduce possible risks on jobsites. During the operation phase of buildings, facility managers can also use IVEs not only for training purposes but also to understand and analyze decisionmaking effectiveness. Providing means to interact with sensors and visualize sensor data remotely could enrich the experience of facility personnel. Acknowledgments This project is partly supported by the National Science Foundation funding under the contract 1231001. Any discussion, procedure, result, and conclusion discussed in this paper are the authors' views and do not reflect the views of the National Science Foundation. It is important to

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