The virtual environment was kindly provided by VIS Games (http://www.vis-games.de) to conduct research in virtual reality.

The following protocol was conducted in accordance with guidelines approved by the Ethics Commission of ETH Z&#252;rich as part of the proposal EK 2013-N-73.
1. Recruit and Prepare Participants
<ol>
	<li>Select participants with particular demographics (e.g., age, gender, educational background) using a participant recruitment system or mailing list (e.g., UAST; http://www.uast.uzh.ch/).</li>
	<li>Contact selected participants by e-mail. In this e-mail, remind the participants of the session time and requirements. Let the participants know that they must wear a loose-fitting top (for blood pressure monitoring), refrain from alcohol for 12 h before the experiment, and refrain from several other activities (i.e., caffeine, smoking, eating, and exercise) for 3 h before the experiment.</li>
</ol>
2. Prepare the Experiment and Physiological Devices Using EVE
<ol>
	<li>Before each experimental session, start the computer, the experimenter monitor, and the testing monitor.</li>
	<li>Ensure that the room fan, the thermometer and the humidity monitor are on.</li>
	<li>Switch on the machine measuring the electrodermal activity (EDA) and the electrocardiography (ECG; e.g., PowerLab from ADInstruments). See Table of Materials.</li>
	<li>Open the EDA/ECG software (EVE currently supports Labchart from ADInstruments) and create a new settings file. Select a sampling rate of 1,000 Hz and the appropriate number of channels (e.g., one for EDA and one for ECG). Save this settings file and re-save a version with a different name for each experimental session.</li>
	<li>For the EDA electrodes, perform an open-circuit zero (i.e., without the electrodes attached to anything) to obtain a baseline measure of system conductivity.</li>
	<li>Ensure that the control interface (e.g., joystick) is connected to the computer.</li>
	<li>On the experimenter monitor, open the executable Unity file for the experiment.
	<ol>
		<li>Open the &#34;Experiment Settings&#34; menu in EVE, and enter the experiment parameters (e.g., participant ID number, physiological measurement file, experimental condition, room temperature and humidity).</li>
		<li>Click &#34;Start Experiment&#34;.</li>
	</ol>
	</li>
</ol>
3. Experimental Procedure
<ol>
	<li>Introduction and consent procedure
	<ol>
		<li>Pick up the participant at the agreed meeting location and guide him/her to the laboratory.</li>
		<li>Indicate that the session will take approximately 90 min and ask the participant to store his or her watch and/or mobile phone.</li>
		<li>Ask the participant to sit in the experimental chair and explain the experimental procedure according to the prepared verbal script.</li>
		<li>Ask the participant to read the information sheet and sign the informed consent form.</li>
	</ol>
	</li>
	<li>Connection of EDA and ECG sensors
	<ol>
		<li>Clean the index finger and the ring finger of the non-dominant hand with a wet tissue without soap. Ensure that they are dry and connect the two EDA electrodes to the medial phalanges.</li>
		<li>Clean the skin on the chest where the ECG electrodes will be placed with a wet cloth.</li>
		<li>Place the white, black, and red electrodes on the participant&#39;s body between the ribs according to Figure 2. Place the white electrode on the upper right abdomen (UR), and the black electrode on the upper left abdomen (UL). Place the red electrode on the lower left abdomen (LL). Ensure that the three electrodes are not directly over a rib.</li>
		<li>Connect the three color-coded ECG wires to the corresponding electrodes attached to the participant&#39;s body.</li>
	</ol>
	</li>
	<li>Pre-experiment questionnaires
	<ol>
		<li>Provide the participant with a keyboard and a mouse that will be used to answer the questionnaires (e.g., demographic questions, the first part of the Short Stress State Questionnaire, the Santa Barbara Sense of Direction Scale), and inform him or her that they will be asked a series of questions on the computer.</li>
		<li>Inform the participants that they can ask the experimenter questions regarding the questionnaires at any time.</li>
		<li>Close the two side walls of the cubicle while the participant is completing the questionnaires.</li>
	</ol>
	</li>
	<li>Preparations for physiological measurement. These steps can be conducted while the participant is completing the questionnaires.
	<ol>
		<li>Inform the participant that the experimenter will now prepare the physiological devices.</li>
		<li>Check that the electrodes are attached to the correct locations.</li>
		<li>Attach the blood pressure cuff to the non-dominant arm.</li>
		<li>Provide instructions to the participant regarding the accurate measurement of blood pressure. Tell the participant to minimize arm and body movements, keep the blood pressure cuff at heart level, and maintain an upright posture with his or her feet flat on the floor.</li>
		<li>Connect the two EDA wires to the electrodes on the fingers.</li>
		<li>Turn off the light above the monitor and dim all other overhead lights to the lowest setting.</li>
		<li>Hand the joystick to the participant and ensure that the mouse is off the screen of the testing monitor.</li>
		<li>Zero the EDA channel in order to obtain a measure of an individual&#39;s starting level of skin conductance.</li>
		<li>In the EDA/ECG software, open the &#34;Bio Amp&#34; dialog box. Choose the signal range in which the heart beat signal covers around one third of the preview window (5 mV in most cases).</li>
		<li>Start recording with the EDA/ECG software, and check whether a signal is visible in the EDA/ECG software window on the experimenter monitor.</li>
		<li>Start blood pressure recording by pressing the appropriate button in the blood pressure machine.</li>
		<li>Switch to the open Unity program, and press &#34;Start Measurements&#34;. A fixation cross should appear.</li>
	</ol>
	</li>
	<li>Joystick training and baseline video
	<ol>
		<li>Ask the participant to watch and follow the training video that instructs him or her how to use the joystick.</li>
		<li>Ask the participants to complete the training maze in order to practice using the joystick. In this training maze, participants are instructed to follow the arrows that indicate a route and collect floating gems.</li>
		<li>If the experiment includes sound, place the headphones on the participant&#39;s head.</li>
		<li>Ask the participant to watch the baseline nature video without moving. This video is used to account for a baseline measurement of the participant&#39;s physiological data during the subsequent analysis.</li>
	</ol>
	</li>
	<li>Navigation task
	<ol>
		<li>Ensure that the participants have read the instructions regarding the to-be-completed navigation task. Inquire as to whether the participant has any questions before the navigation task begins. Tell the participant that they should not ask questions during the navigation task.</li>
		<li>Ask the participant to press the trigger on the joystick when he or she is ready to begin the navigation task.</li>
	</ol>
	</li>
	<li>Final physiological measures and detachment of physiological sensors
	<ol>
		<li>Wait until the system has completed the final blood pressure measurement.</li>
		<li>Stop recording EDA and ECG by pressing the stop button in the EDA/ECG software.</li>
		<li>Remove the blood pressure cuff.</li>
		<li>Remove the EDA electrodes from the participant.</li>
		<li>Ask the participants not to remove the ECG electrodes until the end of the experiment.</li>
		<li>Remove the joystick and headphones.</li>
	</ol>
	</li>
	<li>Post-experiment questionnaires
	<ol>
		<li>Provide the participant with a keyboard and a mouse for the post-experiment questionnaires (e.g., the second part of the Short Stress State Questionnaire, the Self-Assessment Manikin, the Simulator Sickness Questionnaire).</li>
		<li>Inform the participants that they will be asked another series of questions on the computer and that he or she can ask questions if necessary.</li>
	</ol>
	</li>
	<li>End of the experimental session
	<ol>
		<li>Inform the participant that the experimental part is now finished. Thank her or him for participating in the experiment.</li>
		<li>Tell the participant that he/she can now remove the ECG electrodes.</li>
		<li>Pay out the participants and ask them to sign the printed receipt.</li>
		<li>Ask if the participant has any questions regarding the purpose of the experiment, and escort him or her outside of the experimental room.</li>
	</ol>
	</li>
</ol>
4. After Each Experimental Session
<ol>
	<li>Open the &#34;Evaluation&#34; menu in EVE in order to conduct experiment diagnostics (e.g., replay trajectories), and save the physiological measurement files in the EDA/ECG software.</li>
	<li>In the &#34;Evaluation&#34; menu in EVE, press the &#34;Add Event Marker&#34; button to mark events in the physiological measurement files. This step is critical for the analysis of the physiological data in terms of particular experimental phases.</li>
	<li>Save the EDA/ECG file in the physiological measurement file in the EDA/ECG software.</li>
	<li>Export the experimental data for backup using the evertools package.</li>
	<li>Switch off the EDA/ECG machine and clean the EDA electrodes with alcohol pads.</li>
	<li>Mark that the participant showed up in the participant recruitment system.</li>
</ol>

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O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Calibrating+Visual+Path+Integration+in+VEs.">Calibrating Visual Path Integration in VEs.</a> Presence: Teleoperators and Virtual Environments. 10 (2), 216-224 (2001).</li><li>Thrash, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+control+interfaces+for+desktop+virtual+environments.">Evaluation of control interfaces for desktop virtual environments.</a> Presence. 24 (4), (2015).</li><li>Kinateder, M., Warren, W. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Social+Influence+on+Evacuation+Behavior+in+Real+and+Virtual+Environments.">Social Influence on Evacuation Behavior in Real and Virtual Environments.</a> Frontiers in Robotics and AI. 3, 43 (2016).</li><li>Loomis, J. M., Blascovich, J. J., Beall, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Immersive+virtual+environment+technology+as+basic+research+tool+in+psychology.">Immersive virtual environment technology as basic research tool in psychology.</a> Behavior Research Methods, Instruments &amp; Computers. 31 (4), 557-564 (1999).</li></ol>

The authors have nothing to disclose.

In the present paper, we described a protocol for conducting experiments in VR with physiological devices using the EVE framework. These types of experiments are unique because of additional hardware considerations (e.g., physiological devices and other peripherals), the preparatory steps for collecting physiological data using VR, and data management requirements. The present protocol provides the necessary steps for experimenters that intend to collect data from multiple peripherals simultaneously. For example...

Understanding how individuals navigate has important implications for several fields, including cognitive science1,2,3, neuroscience4,5, and computer science6,7. Navigation has been investigated in both real and virtual environments. One advantage of real-world experiments is that navigation does not require the mediation of a control interface and thus may produce more realistic spatial behavior. In contrast, virtual reality (VR) experiments allow for more precise measurement of behavioral (e.g., walking trajectories) and physiological (e.g., heart rate) data, as well as more experimental control (i.e., internal validity). In turn, this approach can result in simpler interpretations of the data and thus more robust theories of navigation. In addition, neuroscience can benefit from VR because researchers can investigate the neural correlates of navigation while participants are engaged in the virtual environment but cannot physically move. For computer scientists, navigation in VR requires unique developments in processing power, memory, and computer graphics in order to ensure an immersive experience. Findings from VR experiments can also be applied in architecture and cartography by informing the design of the building layouts8 and map features9 to facilitate real-world navigation. Recently, advances in VR technology combined with a dramatic decrease in its cost have led to an increase in the number of laboratories employing VR for their experimental designs. Because of this growing popularity, researchers need to consider how to streamline the implementation of VR applications and standardize the experiment workflow. This approach will help shift resources from implementation to the development of theory and extend the existing capabilities of VR.
VR setups can range from more to less realistic in terms of displays and controls. More realistic VR setups tend to require additional infrastructure such as large tracking spaces and high-resolution displays10. These systems often employ redirected walking algorithms in order to inject imperceptible rotations and translations into the visual feedback provided to users and effectively enlarge the virtual environment through which participants can move11,12. These algorithms can be generalized in that they do not require the knowledge of environmental structure13 or predictive in that they assume particular paths for the user14. Although most research on redirected walking has used head-mounted displays (HMDs), some researchers employ a version of this technique with walking-in-place as part of a large projection system (e.g., CAVEs)15. While HMDs can be carried on the head of the participant, CAVE displays tend to provide a wider horizontal field of view16,17. However, less infrastructure is needed for VR systems using desktop displays18,19. Neuroscientific research has also employed VR systems in combination with functional magnetic resonance imaging (fMRI) during scanning20, in combination with fMRI after scanning21,22, and in combination with electroencephalography (EEG) during recording23,24. Software frameworks are needed in order to coordinate the variety of displays and controls that are used for navigation research.
Research that incorporates VR and physiological data poses additional challenges such as data acquisition and synchronization. However, physiological data allows for the investigations of implicit processes that may mediate the relationship between navigation potential and spatial behavior. Indeed, the relationship between stress and navigation has been studied using desktop VR and a combination of different physiological sensors (i.e., heart rate, blood pressure, skin conductance, salivary cortisol, and alpha-amylase)25,26,27,28. For example, van Gerven and colleagues29 investigated the impact of stress on navigation strategy and performance using a virtual reality version of a Morris water maze task and several physiological measures (e.g., skin conductance, heart rate, blood pressure). Their results revealed that stress predicted navigation strategy in terms of landmark use (i.e., egocentric versus allocentric) but was not related to navigation performance. In general, findings from previous studies are somewhat inconsistent regarding the effect of stress on navigation performance and spatial memory. This pattern may be attributable to the separation of the stressor (e.g., the cold pressor procedure26, the Star Mirror Tracing Task25) from the actual navigation task, the use of simple maze-like virtual environments (e.g., virtual Morris water maze26, virtual radial arm maze28), and differences in methodological details (e.g., type of stressor, type of physiological data). Differences in the format of collected physiological data can also be problematic for the implementation and analysis of such studies.
The Experiments in Virtual Experiments (EVE) framework facilitates the design, implementation, and analysis of VR experiments, especially those with additional peripheral devices (e.g., eye trackers, physiological devices)30. The EVE framework is freely available as an open-source project on GitHub (https://cog-ethz.github.io/EVE/). This framework is based on the popular Unity 3D game engine (https://unity3d.com/) and the MySQL database management system (https://www.mysql.com/). Researchers can use the EVE framework in order to prepare the various stages of a VR experiment, including pre- and post-study questionnaires, baseline measurements for any physiological data, training with the control interface, the main navigation task, and tests for spatial memory of the navigated environment&#160;(e.g., judgments of relative direction). Experimenters can also control the synchronization of data from different sources and at different levels of aggregation (e.g., across trials, blocks, or sessions). Data sources may be physical (i.e., connected to the user; see Table of Materials) or virtual (i.e., dependent on interactions between the participant&#39;s avatar and the virtual environment). For example, an experiment may require recording heart rate and position/orientation from the participant when that participant&#39;s avatar moves through a particular area of the virtual environment. All of this data is automatically stored in a MySQL database and evaluated with replay functions and the R package evertools (https://github.com/cog-ethz/evertools/). Evertools provides exporting functions, basic descriptive statistics, and diagnostic tools for distributions of data.
The EVE framework may be deployed with a variety of physical infrastructures and VR systems. In the present protocol, we describe one particular implementation at the NeuroLab at ETH Z&#252;rich (Figure 1). The NeuroLab is a 12 m by 6 m room containing an isolated chamber for conducting EEG experiments, a cubicle containing the VR system (2.6 m x 2.0 m), and a curtained area for attaching physiological sensors. The VR system includes a 55&#34; ultra-high definition television display, a high-end gaming computer, a joystick control interface, and several physiological sensors (see Table of Materials). In the following sections, we describe the protocol for conducting a navigation experiment in the NeuroLab using the EVE framework and physiological sensors, present representative results from one study on stress and navigation, and discuss the opportunities and challenges associated with this system.

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		<tr height="21">
			<td height="21" style="height:21px;width:253px;">Alienware Area 51 Base</td>
			<td style="width:143px;">Dell&#160;</td>
			<td style="width:113px;">210-ADHC</td>
			<td style="width:223px;">Computation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">138cm 4K Ultra-HD LED-TV</td>
			<td>Samsung</td>
			<td>UE55JU6470U</td>
			<td>Display</td>
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			<td height="21" style="height:21px;">SureSigns VS2+</td>
			<td>Philips Healthcare</td>
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			<td>Blood Pressure</td>
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			<td height="21" style="height:21px;">PowerLab 8/35</td>
			<td>AD Instruments</td>
			<td>PL3508</td>
			<td>Skin Conductance</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">PowerLab 26T (LTS)</td>
			<td>AD Instruments</td>
			<td>ML4856</td>
			<td>Heart Rate</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Extreme 3D Pro Joystick</td>
			<td>Logitech</td>
			<td>963290-0403</td>
			<td>HID</td>
		</tr>
	</tbody>
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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.

From each participant in the NeuroLab, we typically collect physiological data (e.g., ECG), questionnaire data (e.g., the Santa Barbara Sense of Direction Scale or SBSOD31), and navigation data (e.g., paths through the virtual environment). For example, changes in heart rate (derived from ECG data) have been associated with changes in stress states in combination with other physiological32 and self-report measures<...

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Virtual Reality Experiments with Physiological Measures

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 ...

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12.6K Views.  ETH Zürich. 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.

Video: Virtual Reality Experiments with Physiological Measures

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 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 ...

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Virtual reality (VR) has shown great educational potential because it makes it possible to simulate any desired situation or event, thus playing an important role in addressing current educational challenges. Despite the unlimited learning possibilities that VR may offer, unless users are willing to apply virtual devices to education, the investment of time, money, and effort will be fruitless. It is therefore crucial to assess the educational interest of the first generation of VR users and to identify their current needs. To this end, in this study we designed an online questionnaire and applied it through the SaaS (Software as a service) of a private server. The sample consisted of 117 early VR adopters recruited via a main portal of communication and information technologies in Spain. In order to engage participants, we posted a thread in the main forum, which is dedicated to the advances and potential uses of VR. Once the responses were gathered, we analyzed the relationship between 12 variables (mean contrasts with Snedecor&#39;s F, and contingency analysis with chi-square and Sommer&#39;s d). The results showed that the current profile of a VR user is a male over 35 years old, with university studies, and who has purchased his viewer recently (&#60;1 year). As for the learning and teaching applications that these users were interested in, only 13.7% of the participants in this study use VR for educational purposes, although 28.2% were interested, indicating that perhaps the lack of applications or learning experiences may be hampering the use of VR within education. Almost half of the early adopters surveyed would like to learn using VR technology and are somehow optimistic about the relationship between VR and education, particularly those who are younger.

This article describes the profile of Spanish early adopters of virtual reality and their interests and preferences regarding learning and educational applications for this technology. To this aim, we designed an online questionnaire and interviewed 117 users of the main virtual reality forum on the Internet.

Virtual reality (VR) has shown great educational potential because it makes it possible to simulate any desired situation or event, thus playing an ...

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.

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 ...