Related Experiment Video
Updated: Aug 1, 2025
Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
Published on: June 9, 2020
Dataset of 3D gait analysis in typically developing children walking at three different speeds on an instrumented
Rachel Senden1, Rik Marcellis1, Kenneth Meijer2
1Department of Physical Therapy, Maastricht University Medical Center, Postbus 5800 AZ, Maastricht 6020, the Netherlands.
Insights
This study shares publicly available gait data from typically developing children walking at various speeds. The dataset includes raw and processed information to aid research in pediatric biomechanics and gait analysis.
Area of Science:
- Biomechanics
- Pediatric Gait Analysis
- Human Movement Science
Background:
- Understanding typical gait development in children is crucial for identifying developmental abnormalities.
- Publicly accessible, comprehensive gait datasets are essential for advancing research and clinical applications.
- Standardized gait analysis protocols are needed for reliable data collection and comparison.
Purpose of the Study:
- To publicly share a detailed dataset of gait data from typically developing children.
- To provide raw and processed kinematic and kinetic data for various walking speeds.
- To facilitate research in pediatric biomechanics and the development of clinical diagnostic tools.
Main Methods:
- Gait data collected from typically developing children using the Computer Assisted Rehabilitation Environment (CAREN) system.
- Children walked at comfortable, 30% slower, and 30% faster speeds on a treadmill in a virtual environment.
- Data processed using custom Matlab algorithms, including spatiotemporal parameters, joint angles, ground reaction forces, and joint moments.
Main Results:
- A comprehensive dataset including raw and processed gait parameters for individual children and age groups is now publicly available.
- Data encompasses kinematic and kinetic variables for each step of both legs across different walking speeds.
- Demographic and physical examination data are provided to enable matched group selection for comparative studies.
Conclusions:
- This dataset provides a valuable resource for researchers studying pediatric gait and biomechanics.
- The availability of detailed gait data supports the development of normative gait profiles for clinical use.
- The study facilitates further investigation into factors influencing gait patterns in children.
Abstract:
In this article, gait data of typically developing (TD) children (24 boys/31 girls, mean (95% confidence interval) age 9.38 (8.51 - 10.25) years, body mass 35.67 (31.40 - 39.94) kg, leg length 0.73 (0.70 - 0.76) m, and height 1.41 (1.35 - 1.46) m) walking at different walking speeds is shared publicly. Raw and processed data is presented for each child separately and includes data of each single step of both legs. Beside, the subject demographics and the results from the physical examination are presented allowing to select TD children from the database to create a matched group, based on specific parameters (e.g. sex and body weight). For clinical application, gait data is also presented per age group, which provides quick insight into the normal gait pattern of TD children of varying age. Gait analysis was performed during treadmill walking in a virtual environment using the Computer Assisted Rehabilitation Environment (CAREN). The human body lower limb model with trunk markers (HBM2) was used as biomechanical model. Children walked at comfortable walking speed, 30% slower and 30% faster (random sequence) while wearing gymnastic shoes and a safety harness to prevent falling. For each speed condition, 250 steps were recorded. Data quality check, step detection and the calculation of gait parameters was done by custom made Matlab algorithms. Raw data files are provided per walking speed, for each child separately. The raw data is exported from the CAREN software (D-flow) and is provided in .mox and .txt files. It includes the output from the models such as subject data, marker and force data, kinematic data (joint angles), kinetic data (joint moments, GRFs, joint powers), as well as CoM data and EMG data (the last two are not described in this manuscript), for each speed condition and each child. Unfiltered and filtered data are included. C3D files with raw marker and GRF data were recorded in Nexus (Vicon software) and are available upon request. After analyzing the raw data into Matlab (R2016a, Mathworks) using custom made Matlab algorithms, processed data is obtained. The processed data is provided in .xls files and is also presented for each child separately. It contains spatiotemporal parameters, 3D joint angles, anterior-posterior and vertical ground reaction forces (GRF), 3D joint moments and sagittal joint power of each step of the left and right leg. In addition to each individual's data, overview files (.xls) are created per walking speed condition. These overviews present the averaged gait parameter (e.g. joint angle), calculated over all valid steps, of each child.
