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 ped...

An erratum was issued for:&#160;<a href="https://www.jove.com/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/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...

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 ...

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4.8K Views.  Kunsan National University. 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.

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

Two-photon Calcium Imaging in Mice Navigating a Virtual Reality Environment

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically identified cells during behavior1-6. Here we describe methods to perform two-photon imaging in mouse cortex while the animal navigates a virtual reality environment. We focus on the aspects of the experimental procedures that are key to imaging in a behaving animal in a brightly lit virtual environment. The key problems that arise in this experimental setup that we here address are: minimizing brain motion related artifacts, minimizing light leak from the virtual reality projection system, and minimizing laser induced tissue damage. We also provide sample software to control the virtual reality environment and to do pupil tracking. With these procedures and resources it should be possible to convert a conventional two-photon microscope for use in behaving mice.

Here we describe the experimental procedures involved in two-photon imaging of mouse cortex during behavior in a virtual reality environment.

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically ...

Development of a Virtual Reality Assessment of Everyday Living Skills

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; Treatment studies aimed at cognitive impairment in patients with schizophrenia not only require demonstration of improvements on cognitive tests, but also evidence that any cognitive changes lead to clinically meaningful improvements.&#160; Measures of &#8220;functional capacity&#8221; index the extent to which individuals have the potential to perform skills required for real world functioning. &#160;Current data do not support the recommendation of any single instrument for measurement of functional capacity.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is a novel, interactive gaming based measure of functional capacity that uses a realistic simulated environment to recreate routine activities of daily living. Studies are currently underway to evaluate and establish the VRFCAT&#8217;s sensitivity, reliability, validity, and practicality.&#160;This new measure of functional capacity is practical, relevant, easy to use, and has several features that improve validity and sensitivity of measurement of function in clinical trials of patients with CNS disorders.

A challenge for proving treatment efficacy for cognitive impairments in schizophrenia is finding the optimizing measurement of skills related to everyday functioning.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is an interactive gaming based computerized measure aimed at skills associated with everyday functioning, including baseline impairments and treatment related changes.

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; ...

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular function include transcranial magnetic stimulation, electromyography, and three-dimensional motion capture. The advent of readily available and cost-effective virtual reality solutions has expanded the capabilities of researchers in recreating &#8220;real-world&#8221; environments and movements in a laboratory setting. Naturalistic movement analysis will not only garner a greater understanding of motor control in healthy individuals, but also permit the design of experiments and rehabilitation strategies that target specific motor impairments (e.g. stroke). The combined use of these tools will lead to increasingly deeper understanding of neural mechanisms of motor control. A key requirement when combining these data acquisition systems is fine temporal correspondence between the various data streams. This protocol describes a multifunctional system&#8217;s overall connectivity, intersystem signaling, and the temporal synchronization of recorded data. Synchronization of the component systems is primarily accomplished through the use of a customizable circuit, readily made with off the shelf components and minimal electronics assembly skills.

Transcranial magnetic stimulation, electromyography, and 3D motion capture are commonly used non-invasive techniques for investigating neuromuscular function in humans. In this paper, we describe a protocol that synchronously samples data generated by all three of these tools along with the unique addition of virtual reality stimulus presentation and feedback.

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular ...

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Studies investigating how people represent themselves and their own body often use variants of &#34;ownership illusions&#34;, such as the traditional rubber-hand illusion or the more recently discovered enfacement illusion. However, these examples require rather artificial experimental setups, in which the artificial effector needs to be stroked in synchrony with the participants&#39; real hand or face&#8212;a situation in which participants have no control over the stroking or the movements of their real or artificial effector. Here, we describe a technique to establish ownership illusions in a setup that is more realistic, more intuitive, and of presumably higher ecological validity. It allows creating the virtual-hand illusion by having participants control the movements of a virtual hand presented on a screen or in virtual space in front of them. If the virtual hand moves in synchrony with the participants&#39; own real hand, they tend to perceive the virtual hand as part of their own body. The technique also creates the virtual-face illusion by having participants control the movements of a virtual face in front of them, again with the effect that they tend to perceive the face as their own if it moves in synchrony with their real face. Studying the circumstances that illusions of this sort can be created, increased, or reduced provides important information about how people create and maintain representations of themselves.

Here, we describe virtual-hand and virtual-face illusion paradigms that can be used to study body-related self-perception/-representation. They have already been used in various studies to demonstrate that, under specific conditions, a virtual hand or face can be incorporated into one&#39;s body representation, suggesting that body representations are rather flexible.

Studies investigating how people represent themselves and their own body often use variants of &#34;ownership illusions&#34;, such as the traditional ...

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 ...

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by observation or passive movement of trainee&#39;s hands by a robotic device). This obviously presents a major challenge in the rehabilitation of a paretic limb since voluntary control of physical movement is limited. Here, we describe a novel training scheme we have developed that has the potential to circumvent this major challenge. We exploited the voluntary control of one hand and provided real-time movement-based manipulated sensory feedback as if the other hand is moving. Visual manipulation through virtual reality (VR) was combined with a device that yokes left-hand fingers to passively follow right-hand voluntary finger movements. In healthy subjects, we demonstrate enhanced within-session performance gains of a limb in the absence of voluntary physical training. Results in healthy subjects suggest that training with the unique VR setup might also be beneficial for patients with upper limb hemiparesis by exploiting the voluntary control of their healthy hand to improve rehabilitation of their affected hand.

We describe a novel virtual-reality based setup which exploits voluntary control of one hand to improve motor-skill performance in the other (non-trained) hand. This is achieved by providing real-time movement-based sensory feedback as if the non-trained hand is moving. This new approach may be used to enhance rehabilitation of patients with unilateral hemiparesis.

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by ...

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. Game-based therapy can promote patients&#39; engagement in rehabilitation therapy as a more interesting and a motivating tool. Mobile devices such as smartphones and tablet PCs can provide personalized home-based therapy with interactive communication between patients and clinicians. In this study, a mobile VR upper extremity rehabilitation program using game applications was developed. The findings from the study show that the mobile game-based VR program effectively promotes upper extremity recovery in patients with stroke. In addition, patients completed two weeks of treatment using the program without adverse effects and were generally satisfied with the program. This mobile game-based VR upper extremity rehabilitation program can substitute for some parts of the conventional therapy that are delivered one-on-one by an occupational therapist. This time-efficient, easy to implement, and clinically effective program would be a good candidate tool for tele-rehabilitation for upper extremity recovery in patients with stroke. Patients and therapists can collaborate remotely through these e-health rehabilitation programs while reducing economic and social costs.

Here, we present a protocol to develop and apply a mobile game-based virtual reality program for the recovery of upper limb dysfunction in patients with stroke. The present study shows that the mobile program is feasible and effectively promotes upper limb recovery in stroke patients.

Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. ...

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.