Name: using a virtual reality walking simulator to investigate pedestrian behavior
Uploaded: 2020-06-09T12:30:00.000+00:00
Duration: 6 min 38 s
Description: Watch this Scientific Journal Video about Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior at JoVE.com

The Korea Institute funded this work for Advancement of Technology and Ministry of Trade, Industry, and Energy (grant number 10044775).

This experimental protocol involves human subjects. The procedure was approved by the Kunsan National University Research Board.
1. Preparation of equipment
NOTE: The equipment includes the following: a personal computer (PC, 3.3 GHz with 8 GM) with a mouse, keyboard, and monitor; Walking Simulator software installed on the desktop PC; a customized treadmill (width: 0.67 m, length: 1.26 m, height: 1.10 m) equipped with handrails, a belt, and a magnetic encoder with a USB cable; and an Oculus Rift virtual reality device (DK1, U.S., 1280 x 800 pixels). The equipment also includes a customized manual treadmill. The treadmill turns via the walking motions of the participants and does not use an internal motor.
<ol>
	<li>Prepare sufficient space for the treadmill and a nearby desk for the PC. A photograph of the experimental setup is shown in Figure 1A.</li>
	<li>Connect the equipment as shown in Figure 2.
	<ol>
		<li>Connect treadmill&#8217;s magnetic encoder to the PC via a USB port.</li>
		<li>Connect the treadmill to a power source.</li>
		<li>Connect the headset to the PC via DVI/HDMI and USB ports.</li>
	</ol>
	</li>
</ol>
2. Preparation of walking simulator configurations
<ol>
	<li>Access the walking simulator directory on the PC and open the &#8220;Config&#8221; directory. 
	NOTE: Each configuration is saved as a text file in the &#8220;Config&#8221; directory with file names of &#8220;config001&#8221;, &#8220;config002&#8221;, etc. Here, 001, 002, etc. are the configuration numbers. Steps 2.2&#8211;2.8 describe how to create the configuration files so that they are readable by the simulator software. A schematic of a two-vehicle crossing situation showing customizable initial distances is shown in Figure 3. An example configuration file with proper formatting is shown in Figure 4. Section headings of the configuration file use square brackets (e.g., &#8220;[WALKER]&#8221;).</li>
	<li>Complete the section [WALKER] containing the parameter regarding the starting point of the participants.
	<ol>
		<li>Set the parameter &#8220;Distance&#8221;, which indicates the starting distance of the participant from the starting point in meters (m).</li>
	</ol>
	</li>
	<li>Complete the section [CAR] containing parameters regarding the first vehicle.
	<ol>
		<li>Set the parameter &#8220;Type&#8221; (which indicates the type of vehicle) to &#8220;1&#8221; for sedan, &#8220;2&#8221; for bus, or &#8220;0&#8221; to remove the vehicle.</li>
		<li>Set the parameter &#8220;Speed&#8221; (which indicates the vehicle speed) to the desired value in km/h.</li>
		<li>Set the parameter &#8220;Distance&#8221; (which indicates initial distance of the vehicle from the crossing point) to the desired value in meters.</li>
	</ol>
	</li>
	<li>Complete the section [SECONDCAR] containing the parameters related to the second vehicle. Parameters are identical to those of [CAR]. 
	NOTE: In two-vehicle studies, the gap is defined as the empty space between the two vehicles. The gap size, defined as the length of time during which the gap is along the participant&#8217;s walking path, is a function of the &#8220;Distance&#8221;, &#8220;Speed&#8221;, and &#8220;Type&#8221; parameters of [CAR] and [SECONDCAR].</li>
	<li>Complete the section [NEXTCAR] containing parameters related to additional vehicles. The parameters are identical to those of [CAR]. 
	NOTE: This option can be used to investigate pedestrian behavior within continuous traffic flow. This option is not discussed in the representative results section.</li>
	<li>Complete the section [ROAD], containing the parameter for lane selection. Set the parameter &#8220;lane&#8221; to &#8220;1&#8221; to use the lane closer to pedestrian&#8217;s starting position, or &#8220;2&#8221; for the lane further away. [OBSTACLE] indicates the parameters that configure a vehicle traveling in the second lane at the same speed as the first vehicle. 
	NOTE: When using the closer lane as the primary lane, this option can be used to place additional vehicles on the farther lane going in the same direction. Hence, it can be used to study the impedance of the view of a vehicle by a parallel vehicle. This section has parameters &#8220;Type&#8221; and &#8220;Distance&#8221; with the same definitions described above. This option is not discussed in the representative results section. All results shown involve two vehicles driving in the lane closer to the pedestrian.</li>
	<li>Complete the section [SAVE], which contains the parameter related to sampling frequency. Set the parameter &#8220;numberpersecond&#8221; to the desired value in Hz.</li>
	<li>Save the configuration file and exit.</li>
	<li>Repeat sections 2.2&#8211;2.8 for all desired configurations and prepare data&#160;sheets with the list of configurations (in a randomized order) to be used in the experiment.</li>
	<li>Prepare three configuration files to be used in the practice trials. 
	NOTE: The first practice configuration should have no vehicles (i.e., all &#8220;Type&#8221; parameters set to &#8220;0&#8221;). The second and third practice configuration files should have vehicles. The third configuration should have lenient crossing conditions. The same configuration may be used for the second and third practice trials, depending on the experimental design.</li>
</ol>
3. Participation screening and preparation
<ol>
	<li>Recruit participants with normal or corrected-to-normal vision. 
	NOTE: All participants should be free of any conditions that prevent normal walking. They should be free of any dizziness while walking, and they should not have any history of serious traffic accidents.</li>
	<li>Ask the participant to sign a written, informed consent form before each experiment.</li>
	<li>Prepare an audio recording with verbal instructions of the task and play the recording to the participant. 
	NOTE: The verbal instructions should narrate the basic procedure described below and give any specific prompts required by the experimental design.</li>
	<li>Encourage the participant to ask any questions about the experiment.</li>
	<li>Lead the participant to stand on the treadmill when ready.</li>
	<li>Harness the stabilizing belt to the participant&#8217;s waist. Instruct the participant to hold the handrails at all times during the experiment.</li>
</ol>
4. Running the practice trials
<ol>
	<li>Instruct the participant to practice walking on the treadmill, with the belt on, while holding the handrails.</li>
	<li>Begin the walking simulator program by double-clicking the executable simulator program once the participant is able to walk on the treadmill comfortably. 
	NOTE: The black and white cartoon crosswalk shown in Figure 1B is displayed between crossing trials. At this point, it should be shown on the PC screen.</li>
	<li>Instruct the participant to wear the headset. Give assistance as needed. Check for both comfort and stability with respect to head turns.</li>
	<li>Calibrate the headset so that the black and white cartoon crosswalk is properly aligned with participant&#8217;s view. 
	NOTE: Sections 4.5&#8211;4.7 describe three practice trials, which are designed to gradually allow the participant to become accustomed to the simulator environment. If the participant fails any trial due to misunderstanding of the instructions, up to two more extra trials should be performed until the participant understands the instructions. Extra trials are not performed in cases of failure to cross for reasons other than misunderstanding the rules (e.g., if a collision occurs).</li>
	<li>Begin the first practice trial. 
	NOTE: The first practice trial should be without any vehicles for the participant to become accustomed to walking in the virtual reality setting.
	<ol>
		<li>Inform the participant that the first practice trial will occur without any vehicles.</li>
		<li>Instruct the participant to look straight ahead.</li>
		<li>Enter the first practice trial&#8217;s configuration number in the text box on the bottom of the screen.</li>
		<li>Click the &#8220;Start&#8221; button at the bottom of the screen. 
		NOTE: The program should display the realistic setting depicted in Figure 1C on the screen.</li>
		<li>Inform the participant to get ready when hearing &#8220;Ready&#8221; and to begin walking when hearing &#8220;Go&#8221;. Give the verbal cues &#8220;Ready&#8221; and &#8220;Go&#8221;.</li>
	</ol>
	</li>
	<li>Second practice trial 
	NOTE: The second practice trial should introduce the vehicles without walking. The direction of the virtual reality view shifts as the participant&#8217;s head is turned.
	<ol>
		<li>Instruct the participant in this trial, at the verbal cue &#8220;Go&#8221;, to look to the left and simultaneously take a small step forward, but not to walk forward any further. The participant should instead watch the vehicles pass by.</li>
		<li>Type the second trial&#8217;s configuration number into the text box and click &#8220;Start&#8221; by providing the verbal cues. 
		NOTE: The vehicles begin moving as the participant begins moving.</li>
	</ol>
	</li>
	<li>Third practice trial 
	NOTE: The third practice trial should be similar to the experimental configurations, but with lenient crossing conditions.
	<ol>
		<li>Inform the participant that 1) the third practice trial will involve two vehicles coming from the left side, and 2) he/she should attempt to cross the road between the two vehicles.</li>
		<li>Enter the third practice trial number in the text box by providing the verbal cue.</li>
		<li>Click the &#8220;Start&#8221; button and begin the trial by providing the verbal cues.</li>
	</ol>
	</li>
</ol>
5. Virtual walking experiment
<ol>
	<li>Confirm that the participant understands the experimental task and is able to perform it.</li>
	<li>When the participant is ready, type in the first configuration number from the data sheet&#160;on the text box and click &#8220;Start&#8221;.</li>
	<li>Perform the simulation as done in the final practice trial. 
	NOTE: At the end of each crossing trial, the program displays &#8220;S&#8221;, &#8220;F&#8221;, or &#8220;C&#8221;, depending on whether the result is a successful crossing (i.e., the participant crosses to the other side of the street with no collisions), no crossing (participant does not cross to the other side), or a collision (participant has contact with a vehicle), respectively.</li>
	<li>Record the result next to the configuration number on the data sheet.</li>
	<li>Repeat for all configurations on the data sheet and complete the experiment.</li>
</ol>
6. Data export and analysis
<ol>
	<li>Retrieve the data files for analysis. The walking simulator software saves each run as a spreadsheet file in the &#8220;Data&#8221; folder.</li>
	<li>Analyze data with the preferred tools. The output data records the positions and velocities of the walker and the vehicles as a time series. Use this data to analyze participant movements and the dependence on traffic conditions.</li>
</ol>

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The authors have nothing to disclose.

Previous studies have used simulators with projected screens16,17, but this protocol improves ecological validity via a fully immersive virtual view (i.e., 360 degrees). In addition, requiring participants to walk on a treadmill enables the examination of how children and young adults calibrate their actions to a changing environment. This experimental design&#8217;s virtual scene changes simultaneously with participant motions, and the vehicles arrive at the pedestrian&#8217;s crossing line at a specific point in time. This prevents participants from delaying their crossing times due to decisions or preparations to move. In this study, participants are already in motion when attempting to cross the road6, so researchers can clearly access the control of locomotion while crossing.
Critical steps include properly setting the parameters to reflect the experimental design, stopping the experiment when motion sickness occurs, and performing the practice trials so that the participants are comfortable with the treadmill environment. A wide range of traffic flows beyond those discussed in the results is configurable with the current software. The software may also be easily extended to include a wider range of crossing situations (i.e., by adding more lanes or more vehicle types).
The protocol allows the investigation of how children and young adults regulate their locomotion according to dynamically changing environments. Specifically, systematically varying the initial starting location allows the examination of velocity adjustments in children and young adults. The protocol also permits the determination of whether changes in gap characteristics lead to specific velocity control patterns in interceptive actions. The results demonstrate that varying initial distances and gap characteristics is important for identifying systematic crossing behavior adaptations that reflect the perception/action type of control in crossing roads. The results indicate interaction effects of initial distance and vehicle size in children; specifically, their velocity adjustments while approaching the interception were affected by gap characteristics.
In contrast to previous findings on the weak effects of vehicle size on adults&#8217; crossing behaviors, this study found that children poorly adjusted their approach velocities according to the initial distance when facing a large vehicle from a close distance. The results suggest that the ability to finely tune motor movements using visual information in complex interception tasks is subject to developmental changes. However, future research should differentiate vehicle types and sizes by using various sizes of the same vehicle type. This setup would allow a more accurate answer for which visual information is used to control crossing actions in a dynamic environment.
Furthermore, manipulating gap size and vehicle size together did not answer to which properties of the dynamic gap environment directly influence movement modulation. The findings suggest that children underestimate a vehicle&#8217;s arrival time and attempt to cross more quickly in front of large vehicles. Notably, children cross the gaps between buses earlier than expected in the 4 s gap. This may be due to a LV&#8217;s closer distance in the 4 s gap. One limitation of this design is that the gap size&#8217;s effects are confounded by the effects of a vehicle&#8217;s outer edges. Future experimental designs may alter gap size without altering a vehicle&#8217;s outer edges.
Compared to previous virtual reality research, this experiment&#8217;s design offers a safe environment to investigate crossing behavior. However, the apparatus causes motion sickness in some participants. The literature on motion sickness reveals a relationship between motion sickness and postural control, so people who have poor balance control should be excluded18,19,20. Additionally, participants hold the handrails during walking, and this may interrupt a natural walking motion, which may be a limitation of the method. In sum, this study contributes to the understanding of children&#8217;s road crossing behavior in relation to a gap&#8217;s temporal and spatial characteristics.

An erratum was issued for:&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/t/61116/using-virtual-reality-walking-simulator-to-investigate-pedestrian">Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior</a>. An&#160;affiliation was updated.
The first affiliation was updated from:
Department of Sports Science, Kunsan National University
to:
Department of Sport and Exercise&#160;Sciences, Kunsan National University

An erratum was issued for:&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/t/61116/using-virtual-reality-walking-simulator-to-investigate-pedestrian">Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior</a>. Author and affiliation information&#160;were&#160;updated.

Erratum: Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Gap crossing, an interceptive behavior, requires moving oneself in relation to a gap between two moving vehicles1,2,3,4. Gap crossing involves perceiving oncoming vehicles and controlling movement in relation to moving traffic. This requires actions to be precisely coupled with perceived information. Many previous studies have examined perceptual judgment and gap-crossing behavior using artificial roads, roadside simulators, and screen projection virtual environments5,6. However, previous road-crossing literature has an incomplete understanding of this behavior, and the ecological validity of these studies has been questioned7,8,9.
This protocol presents a research paradigm for studying gap crossing behavior in virtual reality, thus maximizing ecological validity. A walking simulator is used to examine the perception and actions of gap crossing behavior. The simulator provides a safe walking environment for participants, and the actual walking in the simulated environment allows researchers to fully capture the reciprocal relationship between perception and action. Individuals who actually cross a road are known to judge the time gap more accurately than those who only verbally decide to cross10. The virtual environment is ecologically valid and allows researchers to easily change task-related variables by altering the program&#8217;s parameters.
In this study, a participant&#8217;s initial starting location is manipulated to assess the velocity control while approaching the gap. This protocol allows the investigation of pedestrian locomotion control while intercepting a gap. Analyzing a participant&#8217;s velocity changing over time allows a functional interpretation of velocity adjustments while he or she approaches a gap.
In addition, the spatial and temporal characteristics of intercepted objects specify how a person can move. In a gap crossing environment, changing of the gap size (inter-vehicle distances) and vehicle size should affect how a pedestrian&#8217;s locomotion also changes. Accordingly, manipulating the gap characteristics will likely cause velocity adjustments in the participant&#8217;s approaching behavior. Thus, manipulating gap characteristics (i.e., gap size and vehicle size) provides valuable information for understanding crossing behavior changes according to various gap characteristics. This study examines how children and young adults regulate their velocity when crossing gaps in various crossing environments. The speed regulation profile can be assessed for various gap crossing environments with different starting locations, inter-vehicle distances, and vehicle sizes.

<table><tbody><tr><td>Customized treadmill</td><td>Kunsan National University</td><td></td><td>Treadmill built for this study</td></tr><tr><td>Desktop PC</td><td>Multiple companies</td><td></td><td>Standard Desktop PC</td></tr><tr><td>Oculus Rift Development Kit</td><td>Oculus VR, LLC</td><td>DK1</td><td>Virtual reality headset</td></tr><tr><td>Walking Simulator Software</td><td>Kunsan National University</td><td></td><td>Software deloped for this experiment</td></tr></tbody></table>

using a virtual reality walking simulator to investigate pedestrian behavior

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which people walk on a treadmill to intercept gaps between two moving vehicles in an immersive virtual environment. Virtual reality allows for a safe and ecologically varied investigation of gap crossing behavior. Manipulating the initial starting distance can further the understanding of a participant&#8217;s speed regulation while approaching a gap. The speed profile may be assessed for various gap crossing variables, such as initial distance, vehicle size, and gap size. Each walking simulation results in a position/time series that can inform how velocity is adjusted differently depending on the gap characteristics. This methodology can be used by researchers investigating pedestrian behavior and behavioral dynamics while employing human participants in a safe and realistic setting.

The walking simulator can be used to examine a pedestrian&#8217;s crossing behavior while manipulating the initial distance from curb to interception point and the gap characteristics (i.e., gap and vehicle sizes). The virtual environment method allows the manipulation of gap characteristics to understand how dynamically changing crossing environments affect children&#8217;s and young adults&#8217; road-crossing behaviors.
A quantified velocity profile and crossing position within the gap used to compare the crossing behavior of various pedestrian groups. We evalutated time of intercept (TOI) as the instantaneous effect of speed adjustment on participants&#39; position within the gap. These representative results use data from 16 young adults (mean age = 22.75 years, SD = 2.56) and 16 children (mean age = 12.18 years, SD = 0.83). Generally, 12 year-old children undergo developmental changes in the ability to coordinate movements with moving objects3,4,11,12,13,14, so varying the initial distance provided an opportunity to compare functional adjustment of approaching velocity in children vs. young adults. The participants were recruited via a university social media posting. Of the recruited participants, two young adults experienced motion sickness, in which the experiments were immediately stopped, and they were excluded from the study.
The success rate was 98.95% among children and 99.48% among young adults. Only successful trials were included in the analysis. To access the velocity data, a 3 x 2 x 2 x 4 (initial distance [near, intermediate, far]; gap size [3 s, 4 s]; vehicle size [car, bus]; time [3.5 s, 2.5 s, 1.5 s, 0.5 s]) repeated measures ANOVA was performed using initial distance, gap size, vehicle size, and time as within factor variables. Timing data was analyzed by performing a 3 x 2 x 2 (initial distance [near, intermediate, far]; gap size [3 s, 4 s]; vehicle size [car, bus]) repeated measures ANOVA with initial distance, gap size, and vehicle size as within factor variables. To estimate effect size, the partial eta squared (&#951;2p) was used. For all pairwise post-hoc analyses, least square means were used.
Effects of initial distance 
Tested first was the hypothesis that manipulation of the initial distance from the curb to interception point would&#160;affect the approach velocity of participants. The systematic change in initial distance affected both young adults&#8217; and children&#8217;s velocity adjustments: F(2, 30) = 29.62, p &#60; 0.0001, &#951;2 p = .66; and F(2,&#8239;30) = 207.32, p &#60; 0.0001, &#951;2p = .93, respectively.
For young adults, the initial distance and time interaction was significant: F(6, 90) = 11.88, p &#60; 0.0001, &#951;2 p = 0.44. A simple effects test showed a significant effect of time for: near initial distance, F(3, 45) = 140.34, p &#60; 0.0001, &#951;2p = 0.90; intermediate initial distance, F(3, 45) = 29.93, p &#60; 0.0001, &#951;2 p = 0.67; and far initial distance, F(3, 45) = 184.46, p &#60; 0.0001, &#951;2p = 0.93. It was found from the post-hoc analysis that young adults increased in speed throughout the approach (p &#60; 0.0001). However, when the initial distance was short, participants slowed down (p &#60; 0.0001) at the beginning of trials and sped up continuously. This represents the functional adjustment. The mean velocities during approach are plotted across age groups (Figure 5).
For children, initial distance and time interaction was also significant: F(6, 90) = 53.51, p &#60; 0.0001, &#951;2p = 0.78. This interaction effect was captured by the three-way interaction. The vehicle size, initial distance, and time interaction was significant: F(6, 90) = 2.12, p &#60; 0.05, &#951;2p = 0.12. The results indicate that children&#8217;s velocity changes induced by the initial distance were affected by vehicle size.
Effects of vehicle size in children 
Tested next was the hypothesis that manipulation of vehicle size would&#160;affect the velocity profiles and crossing time of children and young adults. It was found that in children, vehicle size affected the velocity profiles and crossing position induced by the initial distance.
In children, vehicle size, initial distance, and time interaction was significant: F(6, 90) = 2.12, p &#60; 0.05, &#951;2p = 0.12. Further analysis revealed that, between the cars, the initial distance x time interaction was significant, F(6, 90) = 33.55, p &#60; 0.0001, &#951;2p = 0.69. A simple effects test showed a significant effect of time for near initial distance, F(3, 45) = 132.54, p &#60; 0.0001, &#951;2p = 0.90; intermediate initial distance, F(3, 45) = 173.83, p &#60; 0.0001, &#951;2p = 0.92; and far initial distance, F(3, 45) = 272.78, p &#60; 0.0001, &#951;2p = 0.95. Post-hoc analysis showed that children sped up throughout the approach (p &#60; .0001); however, when they crossed between the cars, they slowed down at the beginning of the approach for the near initial distance (p &#60; 0.0002),
However, when children crossed between the buses, the initial distance and time interaction was also significant: F(6, 90) = 18.70, p &#60; 0.0001, &#951;2p = 0.55. A simple effects test showed a significant effect of time for the near initial distance: F(3, 45) = 124.41, p &#60; 0.0001, &#951;2p = 0.89; intermediate initial distance, F(3, 45) = 132.79, p &#60; 0.0001, &#951;2p = 0.90; and far initial distance, F(3, 45) = 331.16, p &#60; 0.0001, &#951;2p = 0.96. Post-hoc analysis showed that when children crossed between the buses, their speeds neither increased nor decreased at the beginning of the approach for the near initial distance. The mean velocities during approach are plotted across age groups in Figure 6.
Evidently, vehicle size influenced children&#8217;s crossing behavior as induced by initial distance. The children&#8217;s crossing times deviated systematically from the gap center depending on the initial distance at which they crossed between the small vehicles. However, children did not deviate based on the initial distance when they crossed between the large vehicles.
The vehicle size also significantly affected the children&#8217;s crossing position within the gap induced by initial distance. The vehicle size and initial distance interaction was significant: F(2, 30) = 18.13, p &#60; 0.0001, &#951;2p = 0.55. A simple effects test showed a significant effect of initial distance between cars, F(2, 30) = 62.30, p &#60; 0.0001, &#951;2p = 0.81, and between buses, F(2, 30) = 6.15, p &#60; 0.005, &#951;2p = 0.30. It was found that children&#39;s times of intercept increased significantly (p &#60; 0.0001) as the initial distance increased from near to far initial distances. However, when crossing between buses, children&#39;s times of interception were not significantly different between near and intermediate initial distances. The mean crossing position during approach are plotted across age groups (Figure 7).
Interaction effects of vehicle size and gap size in children 
Finally, the interaction effects of vehicle size and gap size in children were examined. The vehicle size and gap size interaction was significant: F(1, 15) = 4.26, p &#60; 0.05, &#951;2p = 0.22. A simple effects test showed a significant effect of gap size between the cars: F(1, 15) = 7.42, p &#60; .02, &#951;2p = 0.33; and between the buses, F(1, 15) = 35.93, p &#60; 0.001, &#951;2p = 0.71. Post-hoc analysis showed that when crossing between the cars, children crossed the gap significantly further ahead of the gap center in the 4 s gap than the 3 s gap (p &#60; 0.01). When crossing between the buses, children also crossed the gap significantly earlier in the 4 s gap than the 3 s gap (p &#60; 0.0001). Children crossed the gap further ahead of the gap center in the 4 s gap than the 3 s gap, regardless of vehicle size (Table 1).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61116/61116fig01.jpg" /> 
Figure 1: Images depicting the walking simulation experiment.&#160;(A) Photograph of a participant walking on the treadmill and an experimenter viewing the walking simulator program. (B) Image of the cartoon crosswalk displayed before the configuration is loaded. (C) Image of the realistic virtual environment in which the simulation takes place. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61116/61116fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61116/61116fig02.jpg" /> 
Figure 2: Experimental setup diagram.&#160;The components of the experimental setup and their connections are illustrated. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61116/61116fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61116/61116fig03.jpg" /> 
Figure 3: Diagram of the crossing situation.&#160;Distance parameters that can be configured for each experiment are shown. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61116/61116fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61116/61116fig04.jpg" /> 
Figure 4: Configuration file example.&#160;Example of a properly formatted configuration text file for the simulation program. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61116/61116fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61116/61116fig5v2.jpg" /> 
Figure 5: Velocity dependence on initial distance.&#160;Mean velocities for each initial distance in children and young adults (near, intermediate, and far defined as 3.5 m, 4.5 m, and 5.5 m from the interception point) as a function of time before reaching the interception point. The approaching velocity was averaged into 1 s intervals (-3.5 s, -2.5 s, -1.5 s, and -0.5 s), counting backwards from the interception point. Asterisks represent statistically significant inter-mean differences for initial distances at each timepoint. One asterisk represents one inter-mean difference, and two asterisks represent two or more inter-mean differences. Error bars indicate SD. This figure has been reprinted with permission from Chung et al.15. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61116/61116fig5v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/61116/61116fig6v2.jpg" /> 
Figure 6: Children&#8217;s velocity dependence on initial distance based on two different vehicle sizes.&#160;Children&#8217;s mean velocity profiles before reaching the interception point for each initial distance are plotted for cars (top) and buses (bottom). The approach velocity was averaged into 1 s intervals, counting backwards from the interception point. Asterisks represent statistically significant inter-mean differences for initial distances at each timepoint. One asterisk represents one inter-mean difference, and two asterisks represent two or more inter-mean differences. Error bars indicate SD. This figure was reprinted with permission from Chung et al.15. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61116/61116fig6v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/61116/61116fig7v2.jpg" /> 
Figure 7: Effect of vehicle size on children&#8217;s TOI.&#160;The children group&#8217;s mean TOI for each initial distance is shown as a function of vehicle size (car, bus). TOI refers to the temporal distance relative to the gap center at the moment of crossing, such that 0.2 s refers to 1.6 m when the vehicle speed is 30 km/h (8.3 m/s). Asterisks represent statistically significant inter-mean differences for vehicles at each initial distances. One asterisk represents one inter-mean difference, and two asterisks represent two or more inter-mean differences. Error bars indicate SD. This figure was reprinted with permission from Chung et al.15. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61116/61116fig7v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always" fo:text-align="center">
	<tbody>
		<tr>
			<td>Vehicle Size</td>
			<td colspan="2" style="text-align: center;">Gap Size</td>
		</tr>
		<tr>
			<td></td>
			<td>3-s</td>
			<td>4-s</td>
		</tr>
		<tr>
			<td>Car</td>
			<td>0.06 (0.07)</td>
			<td>-0.14 (0.07)</td>
		</tr>
		<tr>
			<td>Bus</td>
			<td>0.12 (0.04)</td>
			<td>-0.12 (0.04)</td>
		</tr>
	</tbody>
</table>
Table 1: Interaction effects of vehicle size and gap size in children.&#160;Children&#8217;s mean TOI as function of vehicle size and gap size Note. Values are given in means(Standard deviations).&#160;.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61116/Table 1 (1).xlsx" target="_blank">Please click here to download this table.</a>

Watch this Scientific Journal Video about Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior at JoVE.com

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

This protocol describes use of a walking simulator that serves as a safe and ecologically valid method to study pedestrian behavior in the presence of moving traffic.

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which ...

using-virtual-reality-walking-simulator-to-investigate-pedestrian

Nanyang Technological University

Research

JoVE Journal

Behavior

4.8K Views.  Kunsan National University. Dit zeer meeslepende virtual reality protocol stelt ons in staat om te onderzoeken hoe mensen hun emotie aanpassen tijdens het oversteken. Virtueel lopen op een loopband maakt het mogelijk om de wederzijdse relatie tussen waarneming en actie volledig vast te leggen. Deze boodschap die inzicht kan geven in het gebied van gedragsecologie en onderzoekers in staat stelt om onze passies in voetgangersveiligheid en autonome voertuigontwikkeling te verkennen.Het is nuttig om diagrammen te geb...

Video: Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

For slender and lightweight structures, vibration serviceability is a matter of growing concern, often constituting the critical design requirement. With designs governed by the dynamic performance under human-induced loads, a strong demand exists for the verification and refinement of currently available load models. The present contribution uses a 3D inertial motion tracking technique for the characterization of the in-field pedestrian behavior. The technique is first tested in laboratory experiments with simultaneous registration of the corresponding ground reaction forces. The experiments include walking persons as well as rhythmical human activities such as jumping and bobbing. It is shown that the registered motion allows for the identification of the time variant pacing rate of the activity. Together with the weight of the person and the application of generalized force models available in literature, the identified time-variant pacing rate allows to characterize the human-induced loads. In addition, time synchronization among the wireless motion trackers allows identifying the synchronization rate among the participants. Subsequently, the technique is used on a real footbridge where both the motion of the persons and the induced structural vibrations are registered. It is shown how the characterized in-field pedestrian behavior can be applied to simulate the induced structural response. It is demonstrated that the in situ identified pacing rate and synchronization rate constitute an essential input for the simulation and verification of the human-induced loads. The main potential applications of the proposed methodology are the estimation of human-structure interaction phenomena and the development of suitable models for the correlation among pedestrians in real traffic conditions.

A protocol is presented for the characterization of the in-field pedestrian behavior and the simulation of the resulting structural response. Field-tests demonstrate that the in situ identified pacing rate and synchronization rate among the participants constitute an essential input for the simulation and verification of the human-induced loads.

For slender and lightweight structures, vibration serviceability is a matter of growing concern, often constituting the critical design requirement. With ...

Engineering

Using a Virtual Store As a Research Tool to Investigate Consumer In-store Behavior

People&#39;s responses to products and/or choice environments are crucial to understanding in-store consumer behaviors. Currently, there are various approaches (e.g., surveys or laboratory settings) to study in-store behaviors, but the external validity of these is limited by their poor capability to resemble realistic choice environments. In addition, building a real store to meet experimental conditions while controlling for undesirable effects is costly and highly difficult. A virtual store developed by virtual reality techniques potentially transcends these limitations by offering the simulation of a 3D virtual store environment in a realistic, flexible, and cost-efficient way. In particular, a virtual store interactively allows consumers (participants) to experience and interact with objects in a tightly controlled yet realistic setting. This paper presents the key elements of using a desktop virtual store to study in-store consumer behavior. Descriptions of the protocol steps to: 1) build the experimental store, 2) prepare the data management program, 3) run the virtual store experiment, and 4) organize and export data from the data management program are presented. The virtual store enables participants to navigate through the store, choose a product from alternatives, and select or return products. Moreover, consumer-related shopping behaviors (e.g., shopping time, walking speed, and number and type of products examined and bought) can also be collected. The protocol is illustrated with an example of a store layout experiment showing that shelf length and shelf orientation influence shopping- and movement-related behaviors. This demonstrates that the use of a virtual store facilitates the study of consumer responses. The virtual store can be especially helpful when examining factors that are costly or difficult to change in real life (e.g., overall store layout), products that are not presently available in the market, and routinized behaviors in familiar environments.

This paper describes the use of a desktop virtual store to create virtual shopping environments to investigate in-store consumer behavior. A description of the protocol to build and run experiments, example results from an experiment concerning store layout, and important considerations when conducting virtual store experiments are presented.

People&#39;s responses to products and/or choice environments are crucial to understanding in-store consumer behaviors. Currently, there are various ...

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based assessments of IADL are easy to use but prone to subjective bias. Here, we describe a novel virtual reality (VR) test to assess two complex IADL tasks: handling financial transactions and using public transportation. While a participant performs the tasks in a VR setting, a motion capture system traces the position and orientation of the dominant hand and head in a three-dimensional Cartesian coordinate system. Kinematic raw data are collected and converted into &#39;kinematic performance measures,&#39; i.e., motion trajectory, moving distance, and time to completion. Motion trajectory is the path of a particular body part (e.g., dominant hand or head) in space. Moving distance refers to the total distance of the trajectory, and time to completion is how long it took to complete an IADL task. These kinematic measures could discriminate patients with cognitive impairment from healthy controls. The development of this kinematic measuring protocol allows detection of early IADL-related cognitive impairments.

We designed a virtual reality test to assess instrumental activities of daily living (IADL) with a motion capture system. We propose a detailed kinematic analysis to interpret the participant&#39;s various movements, including trajectory, moving distance, and time to completion to evaluate IADL capabilities.

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based ...

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

This paper describes a method for conducting multi-user experiments on decision-making and navigation using a networked computer laboratory.

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and ...

Virtual Reality Experiments with Physiological Measures

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and laboratory experiments, respectively. VR is especially useful for geographic visualizations and investigations of spatial behavior. In spatial behavior research, VR provides a platform for studying the relationship between navigation and physiological measures (e.g., skin conductance, heart rate, blood pressure). Specifically, physiological measures allow researchers to address novel questions and constrain previous theories of spatial abilities, strategies, and performance. For example, individual differences in navigation performance may be explained by the extent to which changes in arousal mediate the effects of task difficulty. However, the complexities in the design and implementation of VR experiments can distract experimenters from their primary research goals and introduce irregularities in data collection and analysis. To address these challenges, the Experiments in Virtual Environments (EVE) framework includes standardized modules such as participant training with the control interface, data collection using questionnaires, the synchronization of physiological measurements, and data storage. EVE also provides the necessary infrastructure for data management, visualization, and evaluation. The present paper describes a protocol that employs the EVE framework to conduct navigation experiments in VR with physiological sensors. The protocol lists the steps necessary for recruiting participants, attaching the physiological sensors, administering the experiment using EVE, and assessing the collected data with EVE evaluation tools. Overall, this protocol will facilitate future research by streamlining the design and implementation of VR experiments with physiological sensors.

Virtual reality (VR) experiments can be difficult to implement and require meticulous planning. This protocol describes a method for the design and implementation of VR experiments that collect physiological data from human participants. The Experiments in Virtual Environments (EVE) framework is employed to accelerate this process.

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and ...

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of tactile warnings in reducing driver&#8217;s brake response time. At the same time, tactile warnings have been proved effective in take-over request (TOR) for partially autonomous vehicles.
How the performance of tactile warnings can be optimized is an ongoing hot research topic in this field. Thus, the presented low-cost driving simulation software and methods are introduced to attract more researchers to take part in the investigation. The presented protocol has been divided into five sections: 1) participants, 2) driving simulation software configuration, 3) driving simulator preparation, 4) vibrating toolkit configuration and preparation, and 5) conducting the experiment.
In the exemplar study, participants wore the tactile vibrating toolkit and performed an established car-following task using the customized driving simulation software. The front vehicle braked intermittently, and vibrating warnings were delivered whenever the front vehicle was braking. Participants were instructed to respond as quickly as possible to the sudden brakes of the front vehicle. Driving dynamics, such as the brake response time and brake response rate, were recorded by the simulation software for data analysis.
The presented protocol offers insight into the exploration of the effectiveness of tactile warnings on different body locations. In addition to the car-following task that is demonstrated in the exemplar experiment, this protocol also provides options&#160;to apply other paradigms to the driving simulation studies by making simple software configuration without any code development. However, it is important to note that due to its affordable price, the driving simulation software and hardware introduced here may not be able to fully compete with other high-fidelity commercial driving simulators. Nevertheless, this protocol can act as an affordable and user-friendly alternative to the general high-fidelity commercial driving simulators.

This protocol describes a driving simulation platform and a tactile vibrating toolkit for the investigation of driving-related research. An exemplar experiment exploring the effectiveness of tactile warnings is also presented.&#160;

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of ...

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Efficient and economical performance evaluation of pilots has become critical to the aviation industry. With the development of virtual reality (VR) and the combination of eye-tracking technology, solutions to meet these needs are becoming a reality. Previous studies have explored VR-based flight simulators, focusing mainly on technology validation and flight training. The current study developed a new VR flight simulator to evaluate pilots' flight performance based on eye movement and flight indicators in a 3D immersive scene. During the experiment, 46 participants were recruited: 23 professional pilots and 23 college students without flight experience. The experiment results showed significant differences in flight performance between participants with and without flight experience, the former being higher than the latter. In contrast, those with flight experience showed more structured and efficient eye-movement patterns. These results of the differentiation of flight performance demonstrate the validity of the current VR flight simulator as a flight performance assessment method. The different eye-movement patterns with flight experience provide the basis for future flight selection. However, this VR-based flight simulator has shortcomings like motion feedback compared to traditional flight simulators. This flight simulator platform is highly flexible except for the apparent low cost. It can meet the diverse needs of researchers (e.g., measuring situation awareness, VR sickness, and workload by adding relevant scales).

A new virtual reality flight simulator was built, which enables efficient and low-cost evaluation of flight performance and eye movement patterns. It also provides a high-potential research tool for ergonomics and other research.

Efficient and economical performance evaluation of pilots has become critical to the aviation industry. With the development of virtual reality (VR) and ...

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

A decline in the performance of instrumental activities of daily living (IADLs) has been proposed as a prodromal marker of neurological disease. Existing clinical and performance-based IADL assessments are not feasible for integration into clinical medicine. Virtual reality (VR) is a powerful yet underutilized tool that could advance the diagnosis and treatment of neurological disease. An impediment to the adoption and scaling of VR in clinical neurology is VR-related sickness resulting from sensory inconsistencies between the visual and vestibular systems (i.e., locomotion problem).
The Cleveland Clinic Virtual Reality Shopping (CC-VRS) platform attempts to solve the locomotion problem by coupling an omnidirectional treadmill with high-resolution VR content, enabling the user to physically navigate a virtual grocery store to simulate shopping. The CC-VRS consists of Basic and Complex shopping experiences; both require walking 150 m and retrieving five items. The Complex experience has additional scenarios that increase the cognitive and motor demands of the task to better represent the continuum of activities associated with real-world shopping. The CC-VRS platform provides objective and quantitative biomechanical and cognitive outcomes related to the user&#39;s IADL performance. Initial data indicate that the CC-VRS results in minimal VR-sickness and is feasible and tolerable for older adults and patients with Parkinson&#39;s disease (PD). The considerations underlying the development, design, and hardware and software technology are reviewed, and initial models of integration into primary care and neurology are provided.

Virtual reality (VR) is a powerful yet underutilized approach to advance the diagnosis and treatment of neurological disease. The Cleveland Clinic Virtual Reality Shopping platform combines state-of-the-art VR content with an omnidirectional treadmill to quantify instrumental activities of daily living-a proposed prodromal marker of neurological disease.

A decline in the performance of instrumental activities of daily living (IADLs) has been proposed as a prodromal marker of neurological disease. Existing ...

Game-Based Exploration: Unlocking Human Spatial Navigation Abilities

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Spatial navigation (SN) is the ability to locomote through the environment, which requires an understanding of where one is located in time and space. This capacity is known to rely on the sequential firing of place cells within the hippocampus. SN is an important behavior to investigate as this process deteriorates with age, especially in neurodegenerative disorders. However, the investigation of SN is limited by the lack of sophisticated behavioral techniques to assess this hippocampal-dependent task. Therefore, the goal of this protocol was to develop a novel, real-world approach to studying SN in humans. Specifically, an active virtual SN task was developed using a cross-platform game engine. During the encoding phase, participants navigated their way through a virtual city to locate landmarks. During the remembering phase, participants remembered where these reward locations were and delivered items to these locations. Time to find each location was captured and episodic memory was assessed by a free recall phase, including aspects of place, order, item, and association. Movement behavior (x, y, and z coordinates) was assessed through an asset available in the game engine. Importantly, results from this task demonstrate that it accurately captures both spatial learning and memory abilities as well as episodic memory. Further, findings indicate that this task is sensitive to exercise, which improves hippocampal functioning. Overall, the findings suggest a novel way to track human hippocampal functioning over the course of time, with this behavior being sensitive to physical activity training paradigms.

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Here, we present a novel, brief, and active spatial navigation task that assesses both spatial navigation and episodic memory ability. Importantly, spatial navigation and episodic memory were associated with one another, and this task demonstrated sensitivity to exercise.

Spatial navigation (SN) is the ability to locomote through the environment, which requires an understanding of where one is located in time and space. ...

Behavioral Training Procedures for Head-fixed Virtual Reality in Mice

Virtual reality (VR) combined with head-fixation is increasingly being utilized in behavioral neuroscience studies as it allows complex behavioral assays to be performed in head-fixed mice. This enables precise behavioral recordings while incorporating various neurophysiological techniques that require head-fixation to minimize movement-related signal noise during neural recordings. However, despite the growing use of VR, there is little published data on the detailed methodology of how to implement it. In this study, a training protocol is developed whereby male and female C57B16/J mice are trained to run down a virtual linear corridor, the length of which is increased from 1-3 m over multiple training sessions. Building upon this foundation, this study investigated the feasibility of mice performing complex behaviors within VR using a Y-maze paradigm. The task required navigating to the arm with black walls from the choice point in the Y-maze. After reaching a criterion of two consecutive days equal to or greater than 70% correct, the mice progressed to increasingly difficult sensory discrimination. The findings provide important details on the methodologies useful for the successful training of mice in VR and demonstrate that mice exhibit learning capabilities in navigating the Y-maze. The methodology presented not only offers insights into training duration in VR-based assays but also underscores the potential for probing intricate behaviors in mice, opening avenues for more comprehensive neuroscience investigations.

The article describes the experimental procedures for the commonly used linear track virtual reality (VR) paradigm in mice as well as determining the feasibility of running complex VR tasks by testing a Y-shaped signal discrimination task.

Virtual reality (VR) combined with head-fixation is increasingly being utilized in behavioral neuroscience studies as it allows complex behavioral assays ...

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

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