Find research datasets worth reusing

opencc-by-4.0Feb 2019View details →

Sound source localization with varying amount of visual information in virtual reality [dataset]

This is the dataset that belongs to the publication "Sound source localization with varying amount of visual information in virtual reality". -The file "AVIL_lab.fbx" contains the visual model of the loudspeaker environment that was used throughout the experiment. -The file "localization_task_instruction_final.docx" contains the information sheet that was handed to the subjects before the experiment. -The file "localizationData_public.xlsx" contains the responses from the subjects (column B). The responses in azimuth and elevation (columns F&G) are corrected for the pointing bias (columns H&I).

opencc-by-4.0Jun 2019View details →

Virtual reality bimodal neurofeedback paradigm for fMRI/EEG/MEG/fNIRS (video demo)

To watch the video demo of the software, please download this file: <a href="https://zenodo.org/api/files/28e810fb-4f02-4301-ad77-a8323a81a313/VR_NF_OHBM_demo.mp4?versionId=1aaea91b-984d-4b66-9a61-78eedfba7112">VR_NF_OHBM_demo.mp4</a>

opencc-by-4.0Aug 2019View details →

On the Influence of the Supine Posture onSimulation Sickness in Virtual Reality (Dataset)

The entire experimental data set used for the published work: J. Marengo, P. Lopes, R. Boulic, “On the Influence of the Supine Posture on Simulation Sickness in Virtual Reality“, in Proc. of IEEE Conference on Games (CoG), London, August 20th-23rd 2019 Please cite the above work if this dataset was used within your own work. This data set includes: All scripts used for Data Analysis. Raw data collected during the experiment. All plots used in the paper.

opencc-by-4.0Oct 2024View details →

Virtual Reality Balance Disturbance Dataset

Background and Purpose: There are very few publicly available datasets on real-world falls in scientific literature due to the lack of natural falls and the inherent difficulties in gathering biomechanical and physiological data from young subjects or older adults residing in their communities in a non-intrusive and user-friendly manner. This data gap hindered research on fall prevention strategies. Immersive Virtual Reality (VR) environments provide a unique solution. This dataset supports research in fall prevention by providing an immersive VR setup that simulates diverse ecological environments and randomized visual disturbances, aimed at triggering and analyzing balance-compensatory reactions. The dataset is a unique tool for studying human balance responses to VR-induced perturbations, facilitating research that could inform training programs, wearable assistive technologies, and VR-based rehabilitation methods.   Dataset Content: The dataset includes: <ul> <li>Kinematic Data: Captured using a full-body Xsens MVN Awinda inertial measurement system, providing detailed movement data at 60 Hz.</li> <li>Muscle Activity (EMG): Recorded at 1111 Hz using Delsys Trigno for tracking muscle contractions.</li> <li>Electrodermal Activity (EDA)*: Captured at 100.21 Hz with a Shimmer GSR device on the dominant forearm to record physiological responses to perturbations.</li> <li>Metadata: Includes participant demographics (age, height, weight, gender, dominant hand and foot), trial conditions, and perturbation characteristics (timing and type).</li> </ul> The files are named in the format "ParticipantX_labelled", where X represents the participant's number. Each file is provided in a .mat format, with data already synchronized across different sensor sources. The structure of each file is organized into the following columns: <ul> <li>Column 1: Label indicating the visual perturbation applied. 0 means no visual perturbation.</li> <li>Column 2: Timestamp, providing the precise timing of each recorded data point.</li> <li>Column 3: Frame identifier, which can be cross-referenced with the MVN file for detailed motion analysis.</li> <li>Columns 4 to 985: Xsens motion capture features, exported directly from the MVN file.</li> <li>Columns 986 to 993: EMG data - Tibialis Anterior (R&L), Gastrocnemius Medial Head (R&L), Rectus Femoris (R), Semitendinosus (R), External Oblique (R), Sternocleidomastoid (R).</li> <li>Columns 994 to 1008: Shimmer data: Accelerometer (x,y,z), Gyroscope (x,y,z), Magnetometer (x,y,z), GSR Range, Skin Conductance, Skin Resistance, PPG, Pressure, Temperature.</li> </ul> In addition, we are also releasing the .MVN and .MVNA files for each participant (1 to 10), which provide comprehensive motion capture data and include the participants' body measurements, respectively. This additional data enables precise body modeling and further in-depth biomechanical analysis.   Participants & VR Headset: Twelve healthy young adults (average age: 25.09 ± 2.81 years; height: 167.82 ± 8.40 cm; weight: 64.83 ± 7.77 kg; 6 males, 6 females) participated in this study (Table 1). Participants met the following criteria: i) healthy locomotion, ii) stable postural balance, iii) age ≥ 18 years, and iv) body weight < 135 kg. Participants were excluded if they: i) had any condition affecting locomotion, ii) had epilepsy, vestibular disorders, or other neurological conditions impacting stability, iii) had undergone recent surgeries impacting mobility, iv) were involved in other experimental studies, v) were under judicial protection or guardianship, or vi) experienced complications using VR headsets (e.g., motion sickness). All participants provided written informed consent, adhering to the ethical guidelines set by the University of Minho Ethics Committee (CEICVS 063/2021), in compliance with the Declaration of Helsinki and the Oviedo Convention. To ensure unbiased reactions, participants were kept unaware of the specific protocol details. Visual disturbances were introduced in a random sequence and at various locations, enhancing the unpredictability of the experiment and simulating a naturalistic response. The VR setup involved an HTC Vive Pro headset with two wirelessly synchronized base stations that tracked participants’ head movements within a 5m x 2.5m area. The base stations adjusted the VR environment’s perspective according to head movements, while controllers were used solely for setup purposes.   Table 1 - Participants' demographic information <table> <tbody> <tr> <td>Participant</td> <td>Height (cm)</td> <td>Weight (kg)</td> <td>Age</td> <td>Gender</td> <td>Dom. Hand</td> <td>Dom. Foot</td> </tr> <tr> <td>1</td> <td>159</td> <td>56.5</td> <td>23</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>2</td> <td>157</td> <td>55.3</td> <td>28</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>3</td> <td>174</td> <td>67.1</td> <td>31</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>4</td> <td>176</td> <td>73.8</td> <td>23</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>5</td> <td>158</td> <td>57.3</td> <td>23</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>6</td> <td>181</td> <td>70.9</td> <td>27</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>7</td> <td>171</td> <td>73.3</td> <td>23</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>8</td> <td>159</td> <td>69.2</td> <td>28</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>9</td> <td>177</td> <td>57.3</td> <td>22</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>10</td> <td>171</td> <td>75.5</td> <td>25</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>11</td> <td>163</td> <td>58.1</td> <td>23</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>12</td> <td>168</td> <td>63.7</td> <td>25</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> </tbody> </table>   Data Collection Methodology: The experimental protocol was designed to integrate four essential components: (i) precise control over stimuli, (ii) high reproducibility of the experimental conditions, (iii) preservation of ecological validity, and (iv) promotion of real-world learning transfer. <ul> <li>Participant Instructions and Familiarization Trial: Before starting, participants were given specific instructions to (i) seek assistance if they experienced motion sickness, (ii) adjust the VR headset for comfort by modifying the lens distance and headset fit, (iii) stay within the defined virtual play area demarcated by a blue boundary, and (iv) complete a familiarization trial. During this trial, participants were encouraged to explore various virtual environments while performing a sequence of three key movements—walking forward, turning around, and returning to the initial location—without any visual perturbations. This familiarization phase helped participants acclimate to the virtual space in a controlled setting.</li> <li>Experimental Protocol and Visual Perturbations: Participants were exposed to 11 different types of visual perturbations as outlined in Table 2, applied across a total of 35 unique perturbation variants (Table 3). Each variant involved the same type of perturbation, such as a clockwise Roll Axis Tilt, but varied in intensity (e.g., rotation speed) and was presented in randomized virtual locations. The selection of perturbation types was grounded in existing literature on visual disturbances. This design ensured that participants experienced a diverse range of visual effects in a manner that maintained ecological validity, supporting the potential for generalization to real-world scenarios where visual perturbations might occur spontaneously.</li> <li>Protocol Flow and Randomized Presentation: Throughout the experimental protocol, each visual perturbation variant was presented three times, and participants engaged repeatedly in the familiarization activities over a nearly one-hour period. These activities—walking forward, turning around, and returning to the starting point—took place in a 5m x 2.5m physical space mirrored in VR, allowing participants to take 7–10 steps before turning. Participants were not informed of the timing or nature of any perturbations, which could occur unpredictably during their forward walk, adding a realistic element of surprise. After each return to the starting point, participants were relocated to a random position within the virtual environment, with the sequence of positions determined by a randomized, computer-generated order.</li> </ul>   Table 2 - Visual perturbations' name and parameters (L - Lateral; B - Backward; F - Forward; S - Slip; T - Trip; CW- Clockwise; CCW - Counter-Clockwise) <table> <tbody> <tr> <td>Perturbation [Fall Category]</td> <td> Parameters </td> </tr> <tr> <td>Roll Axis Tilt - CW [L]</td> <td>[10º, 20º, 30º] during 0.5s</td> </tr> <tr> <td>Roll Axis Tilt – CCW [L]</td> <td>[10º, 20º, 30º] during 0.5s</td> </tr> <tr> <td>Support Surface ML Axis Translation - Bidirectional [L]</td> <td>Discrete Movement (static pauses between movements) – 1 m/s</td> </tr> <tr> <td>AP Axis Translation - Front [F]</td> <td>1 m/s</td> </tr> <tr> <td>AP Axis Translation - Backwards [B]</td> <td>1 m/s</td> </tr> <tr> <td>Pitch Axis Tilt [S]</td> <td>0º-25º, 60º/s</td> </tr> <tr> <td>Virtual object with lower height than a real object [T]</td> <td>Variable object height</td> </tr> <tr> <td>Roll-Pitch-Yaw Axis Tilt [Syncope]</td> <td>Sum of sinusoids drive each axis rotation</td> </tr> <tr> <td>Scene Object Movement [L]</td> <td>Objects fly towards the subject’s head. Variable speeds</td> </tr> <tr> <td>Vertigo Sensation [F/L]</td> <td>Walk at a comfortable speed. With and without avatar. House’s height</td> </tr> <tr> <td>Axial Axis Translation [F/B/L]</td> <td>Free fall</td> </tr> </tbody> </table>   Table 3 - Label Encoding <table> <tbody> <tr> <td>Visual Perturbation</td> <td>Label</td> <td>Visual Perturbation</td> <td>Label</td> <td>Visual Perturbation</td> <td>Label</td> </tr> <tr> <td>Roll Indoor 1 CW10</td> <td>1</td> <td>Roll Indoor 1 CW20</td> <td>2</td> <td>Roll Indoor 1 CW30</td> <td>3</td> </tr> <tr> <td>Roll Indoor 1 CCW10</td> <td>4</td> <td>Roll Indoor 1 CCW20</td> <td>5</td> <td>Roll Indoor 1 CCW30</td> <td>6</td> </tr> <tr> <td>Roll Indoor 2 CW10</td> <td>7</td> <td>Roll Indoor 2 CW20</td> <td>8</td> <td>Roll Indoor 2 CW30</td> <td>9</td> </tr> <tr> <td>Roll Indoor 2 CCW10</td> <td>10</td> <td>Roll Indoor 2 CCW20</td> <td>11</td> <td>Roll Indoor 2 CCW30</td> <td>12</td> </tr> <tr> <td>Roll Outdoor CW10</td> <td>13</td> <td>Roll Outdoor CW20</td> <td>14</td> <td>Roll Outdoor CW30</td> <td>15</td> </tr> <tr> <td>Roll Outdoor CCW10</td> <td>16</td> <td>Roll Outdoor CCW20</td> <td>17</td> <td>Roll Outdoor CCW30</td> <td>18</td> </tr> <tr> <td>ML-Axis Trans. - Kitchen</td> <td>19</td> <td>AP-Axis Trans. - Corridor Forward</td> <td>20</td> <td>AP-Axis Trans. - Corridor Backward</td> <td>21</td> </tr> <tr> <td>Pitch Indoor - Bathroom (wet floor)</td> <td>22</td> <td>Pitch Indoor - Near Fridge (wet floor)</td> <td>23</td> <td>Roof Beam Walking - Vertigo</td> <td>24</td> </tr> <tr> <td>Roof Beam Walking - Vertigo No Avatar</td> <td>25</td> <td>Simple Roof - Vertigo</td> <td>26</td> <td>Simple Roof - Vertigo No Avatar</td> <td>27</td> </tr> <tr> <td>Pitch Outdoor - Near Car Oil</td> <td>28</td> <td>Trip - Sidewalk / Trip Shock</td> <td>29/290</td> <td>Bedroom Syncope</td> <td>30</td> </tr> <tr> <td>Garden - Object Avoidance</td> <td>31</td> <td>Electricity Pole - Vertigo</td> <td>32</td> <td>Electricity Pole - No Avatar</td> <td>33</td> </tr> <tr> <td>Free Fall</td> <td>34</td> <td>Climbing Virtual Stairs</td> <td>35</td> <td> </td> <td> </td> </tr> </tbody> </table>     * Some data from Shimmer device was collected but not used or checked by the research team.

opencc-by-4.0May 2019View details →

Improving neuropsychiatric symptoms following stroke using virtual reality: A case report

Post-stroke cognitive impairment occurs frequently in patients with stroke, with a 20% to 80% prevalence. Anxiety is common after stroke, and is associated with a poorer quality of life. The use of standard relaxation techniques in treating anxiety in patients undergoing post-stroke rehabilitation have shown some positive effects, whereas virtual reality seems to have a role in the treatment of anxiety disorders, especially when associated to neurological damage. A 50-year-old woman, smokers, affected by hypertension and right ischemic stroke in the chronic phase (i.e., after 12 months by cerebrovascular event), came to our observation for a severe anxiety state and a mild cognitive deficit, mainly involving attention and visuo-executive processes, besides a mild left hemiparesis. Anxiety in a patient with ischemic stroke. Standard relaxation techniques alone in a common clinical setting or the same psychological approach in an immersive virtual environment (i.e., Computer Assisted Rehabilitation Environment – CAREN). The patient's cognitive and psychological profile, with regard to attention processes, mood, anxiety, and coping strategies, were evaluated before and after the 2 different trainings. A significant improvement in the functional and behavioral outcomes were observed only at the end of the combined approach.The immersive virtual reality environment CAREN might be useful to improve cognitive and psychological status, with regard to anxiety symptoms, in post-stroke individuals.

opencc-by-4.0Dec 2018View details →

A novel use of virtual reality in the treatment of cognitive and motor deficit in spinal cord injury

Aim of this study is to evaluate the cognitive and motor outcomes after a combined rehabilitative training using a standard cognitive approach and virtual reality (VR), in a patient with spinal cord injury (SCI). A 60-year-old right-handed man, affected by incomplete cervical SCI, came to our observation for a moderate tetraparesis, mainly involving the left side, after about 6-months from the acute event. The neurological examination showed imbalance with upper limb incoordination, besides the paresis mainly involving the left side. At a neuropsychological evaluation, he presented important impairment in cognitive and behavioural status, with temporal and spatial disorientation, a reduction of attention and memory process, deficit of executive function and a severe depression of mood, which was not detected during the previous recovery. Motor and cognitive deficits in SCI. The patient was 1st submitted to standard cognitive training and traditional physiotherapy, and then to a combined therapeutic approach, in which virtual reality training was provided by means of the virtual reality rehabilitation system (VRRS, Khymeia, Italy). After the combined therapeutic approach with the VRRS training, we observed a significant improvement in different cognitive domains, a notable reduction of anxiety and depressive symptoms, as well as motor performance, and balance improvement. Virtual reality can be considered a promising tool for the rehabilitation of different neurological disorders, including patients with both motor and cognitive deficits following SCI.

dryad36/100

Data from: Familiar size affects perception differently in virtual reality and the real world

The promise of virtual reality as a tool for perceptual and cognitive research rests on the assumption that perception in virtual environments generalizes to the real world. Here, we conducted two experiments to compare size and distance perception between virtual reality and physical reality (Maltz et al., 2021). In Experiment 1, we used virtual reality (VR) to present dice and Rubik's cubes at their typical sizes or reversed sizes at distances that maintained a constant visual angle. After viewing the stimuli binocularly (to provide vergence and disparity information) or monocularly, participants manually estimated perceived size and distance. Unlike physical reality, where participants relied less on familiar size and more on presented size during binocular vs. monocular viewing, in VR participants relied heavily on familiar size regardless of the availability of binocular cues. In Experiment 2, we demonstrated that the effects in VR generalized to other stimuli and to a higher-quality VR headset. These results suggest that the utility of binocular cues and familiar size differs substantially between virtual and physical reality. A deeper understanding of perceptual differences is necessary before assuming that research outcomes from VR will generalize to the real world.

opencc-zeroNov 2022View details →

opencc-by-4.0Dec 2022View details →

Advancing Forest Monitoring and Assessment Through Immersive Virtual Reality

The materials that enable the development of the Forest VR Application and the datasets along with the analysis code that support the findings of this study are openly available. The dataset include a Digital Terrain Model, Pano Images, Pano Videos, Sounds, LAS-format data, as well as R Scripts for analysis of the included questionnaire, respectively raw excel sheets. The Application is not standalone (Unity bound) and a prototype.

opencc-by-4.0Mar 2023View details →

Supporting data for: Immersive information seeking - a scoping review of information seeking in virtual reality environments

Dataset that contains of all reviewed research items obtained for the Scoping Literature Review Immersive information seeking - a scoping review of information seeking in virtual reality environments.

opencc-by-4.0Dec 2022View details →

Virtual Reality Gesture Recognition Dataset

This dataset provides valuable insights into hand gestures and their associated measurements. Hand gestures play a significant role in human communication, and understanding their patterns and characteristics can be enabled various applications, such as gesture recognition systems, sign language interpretation, and human-computer interaction. This dataset was carefully collected by a specialist who captured snapshots of individuals making different hand gestures and measured specific distances between the fingers and the palm. The dataset offers a comprehensive view of these measurements, allowing for further analysis and exploration of the relationships between different gestures and their corresponding hand measurements. The dataset's potential applications are wide-ranging. For instance, it can be used to develop gesture recognition systems that can identify and interpret hand movements accurately. By training machine learning models on this dataset, it is possible to create algorithms capable of recognizing specific hand gestures based on the measured distances. This can enable intuitive human-machine interaction and interfacing, particularly in domains such as virtual reality, augmented reality, and smart devices. Moreover, researchers interested in the biomechanics of hand movements or exploring the cultural significance of specific gestures can leverage this dataset to gain insights into the physical aspects of hand gestures and their variations across different individuals.

opencc-by-4.0Jun 2023View details →

Remapping the Peripersonal Space in Virtual Reality

This is the dataset related to the article : "Remapping the Peripersonal Space in Virtual Reality" The file contains participants' data for the 2 experiments Each .csv file contains the data for one condition of one subject. The condition is indicated in the name of the .csv. Each row of the file is a trial of the experimental block. Relevant columns of the file are: - B: which avatar was shooting the bubble (0 = central, 1 = left, 2 = right) - C: distance of the bubble at which the vibration was delivered - H: Reaction Time to the vibration

Virtual Reality to Reduce Delirium

ClinicalTrials.gov study NCT04498585. IPD Sharing: YES. Countries: 1. Publications: 14.

Stanford Tobacco Treatment Services - Virtual Reality Treatment for Smoking Cessation

ClinicalTrials.gov study NCT05220254. IPD Sharing: NO. Countries: 1. Publications: 1.

Immersive Functional Virtual Reality in People With Acquired Brain Injury and Unilateral Spatial Neglect

ClinicalTrials.gov study NCT07017140. IPD Sharing: NO. Countries: 1. Publications: 5.

Virtual Reality Application for Assessing Neck Movement and Position Sense

ClinicalTrials.gov study NCT07190014. IPD Sharing: NO. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

Using Virtual Reality to Train Children in Pedestrian Safety

ClinicalTrials.gov study NCT00850759. IPD Sharing: Not stated. Countries: 1. Publications: 2.

Immersive Virtual Reality Exposure for Reducing Preoperative Anxiety in Children

ClinicalTrials.gov study NCT07018999. IPD Sharing: YES. Countries: 1. Publications: 16.

Virtual Reality vs Technical Video in Surgical Training

ClinicalTrials.gov study NCT04404010. IPD Sharing: NO. Countries: 1. Publications: 1.

Immersive Virtual Reality (VR) at the Time of Clinical Evaluation to Improve Psychological Distress and Anxiety in Primary Brain Tumor (PBT) Patients

ClinicalTrials.gov study NCT04301089. IPD Sharing: YES. Countries: 1. Publications: 7.

Virtual Reality Rehabilitation for Cognitive, Emotional, and Motor Recovery in Neurological Disorders

ClinicalTrials.gov study NCT06838975. IPD Sharing: YES. Countries: 1. Publications: 3.