DOI: 10.5281/zenodo.14013467
title: Virtual Reality Balance Disturbance Dataset

description: 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:



Kinematic Data: Captured using a full-body Xsens MVN Awinda inertial measurement system, providing detailed movement data at 60 Hz.

Muscle Activity (EMG): Recorded at 1111 Hz using Delsys Trigno for tracking muscle contractions.

Electrodermal Activity (EDA)*: Captured at 100.21 Hz with a Shimmer GSR device on the dominant forearm to record physiological responses to perturbations.

Metadata: Includes participant demographics (age, height, weight, gender, dominant hand and foot), trial conditions, and perturbation characteristics (timing and type).


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:



Column 1: Label indicating the visual perturbation applied. 0 means no visual perturbation.

Column 2: Timestamp, providing the precise timing of each recorded data point.

Column 3: Frame identifier, which can be cross-referenced with the MVN file for detailed motion analysis.

Columns 4 to 985: Xsens motion capture features, exported directly from the MVN file.

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).

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.


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




Participant
Height (cm)
Weight (kg)
Age
Gender
Dom. Hand
Dom. Foot


1
159
56.5
23
F
Right
Right


2
157
55.3
28
F
Right
Right


3
174
67.1
31
M
Right
Right


4
176
73.8
23
M
Right
Right


5
158
57.3
23
F
Right
Right


6
181
70.9
27
M
Right
Right


7
171
73.3
23
M
Right
Right


8
159
69.2
28
F
Right
Right


9
177
57.3
22
M
Right
Right


10
171
75.5
25
M
Right
Right


11
163
58.1
23
F
Right
Right


12
168
63.7
25
F
Right
Right




 

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.



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.

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.

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.


 

Table 2 - Visual perturbations' name and parameters (L - Lateral; B - Backward; F - Forward; S - Slip; T - Trip; CW- Clockwise; CCW - Counter-Clockwise)




Perturbation [Fall Category]


Parameters



Roll Axis Tilt - CW [L]
[10º, 20º, 30º] during 0.5s


Roll Axis Tilt – CCW [L]
[10º, 20º, 30º] during 0.5s


Support Surface ML Axis Translation - Bidirectional [L]
Discrete Movement (static pauses between movements) – 1 m/s


AP Axis Translation - Front [F]
1 m/s


AP Axis Translation - Backwards [B]
1 m/s


Pitch Axis Tilt [S]
0º-25º, 60º/s


Virtual object with lower height than a real object [T]
Variable object height


Roll-Pitch-Yaw Axis Tilt [Syncope]
Sum of sinusoids drive each axis rotation


Scene Object Movement [L]
Objects fly towards the subject’s head. Variable speeds


Vertigo Sensation [F/L]
Walk at a comfortable speed. With and without avatar. House’s height


Axial Axis Translation [F/B/L]
Free fall




 

Table 3 - Label Encoding




Visual Perturbation
Label
Visual Perturbation
Label
Visual Perturbation
Label


Roll Indoor 1 CW10
1
Roll Indoor 1 CW20
2
Roll Indoor 1 CW30
3


Roll Indoor 1 CCW10
4
Roll Indoor 1 CCW20
5
Roll Indoor 1 CCW30
6


Roll Indoor 2 CW10
7
Roll Indoor 2 CW20
8
Roll Indoor 2 CW30
9


Roll Indoor 2 CCW10
10
Roll Indoor 2 CCW20
11
Roll Indoor 2 CCW30
12


Roll Outdoor CW10
13
Roll Outdoor CW20
14
Roll Outdoor CW30
15


Roll Outdoor CCW10
16
Roll Outdoor CCW20
17
Roll Outdoor CCW30
18


ML-Axis Trans. - Kitchen
19
AP-Axis Trans. - Corridor Forward
20
AP-Axis Trans. - Corridor Backward
21


Pitch Indoor - Bathroom (wet floor)
22
Pitch Indoor - Near Fridge (wet floor)
23
Roof Beam Walking - Vertigo
24


Roof Beam Walking - Vertigo No Avatar
25
Simple Roof - Vertigo
26
Simple Roof - Vertigo No Avatar
27


Pitch Outdoor - Near Car Oil
28
Trip - Sidewalk / Trip Shock
29/290
Bedroom Syncope
30


Garden - Object Avoidance
31
Electricity Pole - Vertigo
32
Electricity Pole - No Avatar
33


Free Fall
34
Climbing Virtual Stairs
35
 
 




 

 

* Some data from Shimmer device was collected but not used or checked by the research team.
description: This dataset captures biomechanical responses to balance disturbances induced within an immersive VR environment. Participants experienced randomized visual perturbations designed to simulate real-world balance challenges, while data on their responses were collected from kinematic, electromyographic, and electrodermal sensors. This dataset addresses gaps in prior research by integrating diverse sensor data across multiple disturbance scenarios, providing a valuable resource for researchers in biomechanics, fall prevention, and VR-based rehabilitation studies.
description: Published Paper ReferenceN. F. Ribeiro, A. Veloso, H. Pires, and C. P. Santos, "Immersive Ecological Virtual Environment for Inducing Balance Disturbances," IEEE Transactions on Visualization and Computer Graphics, 2025, doi: 10.1109/TVCG.2025.3598464.

Researchers using this dataset are kindly requested to cite the above publication.

For those interested in further information about our research, please feel free to reach out to us via email [nuno.fribeiro@dei.uminho.pt or cristina@dei.uminho.pt]. Additional details about our projects and team can be found on our lab’s website: http://birdlab.dei.uminho.pt