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1,596 results for “gait”
Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium
<p>Dataset includes 2.5 hours real-world data, for one participant from each of the technical validation study cohorts: ongestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), healthy adult (HA), multiple sclerosis (MS), Parkinson’s (PD) and proximal femoral facture (PFF) linked to the manuscript entitled ‘Assessing real-world gait with digital technology? Validation, insights, and recommendations from the Mobilise-D consortium’. The attached documentation has 6 datasets and a README document.</p> <p>The MOBILISE-D project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA).</p> <p>Content on this publication reflects the author’s view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.</p>
Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
Open the record for dataset details and reuse information.
Load position and weight classification during carrying gait using wearable inertial and electromyographic sensors
<p>This repository contains data from our study titled "Load position and weight classification during carrying gait using wearable inertial and electromyographic sensors." The following file types are included:</p> <p>- Basic participant demographics can be found in participants.xls.</p> <p>- README.pdf contains a detailed description of what can be found in each file.</p> <p>- SX_EMG.mat contains the EMG data for participant X. The file consists of EMG data for left and right erector spinae together with the time vector from that participant.</p> <p>- SX_Xsens.rar contains the Xsens data for participant X. This includes all joint angles and gait step time stamps from the sensors.</p> <p> </p>
Gait analysis results for different pediatric prosthetic knee prescription protocols
<p>Traditionally, children who need a prosthetic knee joint are not provided with one in their first prosthesis. An alternative Early Knee protocol provides a flexing knee in the first prosthesis. The purpose of this multi-site study was to examine kinematic outcomes during walking in separate groups of young children in an Early prosthetic knee prescription protocol (EK) versus a traditional prosthetic knee protocol (TK), along with a population of children without lower limb amputations. Eighteen children aged 12 months to five years were recruited for this study at two clinical sites, six in each of the three groups. Children in the two prosthesis groups had unilateral limb loss and had been treated at one site with the TK protocol or at another with the EK protocol. All children walked at self-selected speed while kinematic data were collected using similar Vicon motion analysis systems. Data include joint angles, extrema, and ranges of motion used for the determination of temporal and spatial gait parameters and swing-phase clearance adaptations. </p>
Current Practices in Clinical Gait Analysis in Europe - Survey
<p>This dataset contains anonymous raw data from a questionnaire on the practice of clinical gait analysis in Europe. This work was initiated by ESMAC (European Society for Movement Analysis in Adults and Children). It includes the analysis of 75 questions answered by 97 laboratories.<br>The dataset contains 5 files:<br>- Survey_ESMAC_Questions is a pdf file containing the questions asked. <br>- Survey_ESMAC_Data.xlsx is an Excel file containing the raw data and the data modified for the analysis. The modifications made were notified in two sheets of the file.<br>- Survey_ESMAC_Results.pdf is a file containing the export of the results in PDF format.<br>- Survey_ESMAC_Results.html is a file containing the export of results in HTML format.</p>
Dataset associated with the study entitled "Dopamine lesions alter the striatal encoding of single-limb gait"
<p>There are two compressed <strong>dataset </strong>files. </p> <p>The file <strong>data.zip</strong> contains a dataset consisting of electrophysiological and behavioral measurements in the dorsal striatum of mice freely behaving in a 60 cm x 60 cm open field. Optogenetic tagging was performed to identify D1 or D2 MSNs.</p> <p>There are three experimental groups in the file <strong>data.zip</strong>:</p> <p>1. HL....the healthy group</p> <p>2. PD....the dopamine lesioned group (unilateral 6OHDA injection in the medial forebrain bundle)</p> <p>3. CT....the sham lesioned control group</p> <p>There is one experimental group in the file <strong>D2_stim_gait.zip</strong>:</p> <p>1. Behavioral measurements during optogenetic stimulation of D2 MSNs to assess changes in gait performance.</p> <p><strong>Code </strong>for analyzing the data is available at: https://github.com/LongYang10/Gait-Analysis-of-Freely-Walking-Mouse</p>
The Effect of a New Generation of Ankle Foot Orthoses on Sloped Walking in Children with Hemiplegia Using the Gait Real Time Analysis Interactive Lab (GRAIL)
<div>The dataset includes the kinematics and kinetics of gait uphill (+10 deg), level ground (0 deg), and downhill (-5 deg) in children with unilateral cerebral palsy who underwent a single session of walking using immersive virtual reality (GRAIL system by Motek), while wearing traditional ankle-foot orthosis (oldAFOs) and a new generation AFO (newCAMOt1).</div> <div>In column A of the file, you will find the patient ID, the type of orthosis used, and the walking condition (e.g., P01_oldAFO_flat0001 indicates that patient 01 performed the walking trial on level ground with the traditional orthosis).</div> <div>In column B of the file, you will find the laterality (R = right, L = left), the name of the analyzed variable, and the stride number (e.g., L_Moment Ankle Flex_step1 means that we are considering the moment at the left ankle of the first left stride).</div> <div>From column C to column CY, you will find the values at the 101 temporal instants of the analyzed variable.</div>
Whole body gait kinematics in patients with bilateral and chronic unilateral vestibulopathy
<p>This dataset contains whole body kinematics and 3D ground reaction forces and moments from 30 subjects (10 with bilateral vestibulopathy, 10 with unilateral vestibulopathy, and 10 healthy subjects) during gait at three different walking speed: slow, comfortable, and fast. Three repetitions of these gait were performed by each subject. They were instrumented with 35 reflective placed on the whole body according to the Convention Gait Model 1.0. A 12-camera motion capture system (Oqus 7+, Qualisys, Göteborg, Sweden), set at a 100 Hz sampling frequency, was used to track cutaneous reflective markers. The marker trajectories were labeled using Qualisys Tracking Manager software (QTM 2019.3, Qualisys, Göteborg, Sweden) and exported in the C3D file format. Three force plates sampled at 1000 Hz (AMTI Accugait, Watertown, MA, USA) were used to record 3D ground reaction forces and moments. Results of gait standard deviation (GaitSD) and anchoring index (AI), as well as the kinematics data, are included in this dataset in csv file format.</p>
Data and code from: Healthy young adults use distinct gait strategies to enhance stability when walking on mild slopes and when altering arm swing
<p>This repository contains the Julia code, Jupyter notebook, and data used in the study “Healthy young adults use distinct gait strategies to enhance stability when walking on mild slopes and when altering arm swing” by MacDonald et al.</p> <p><strong>Instructions</strong></p> <p>To run this analysis on your computer, both Julia and Jupyter Notebook must be installed. A version of Julia appropriate for your OS can be downloaded from the <a href="https://julialang.org/downloads/">Julia website</a>, and Jupyter can be installed from within Julia (in the REPL) with</p> <pre><code>] add IJulia</code></pre> <p>Alternate instructions for installing Jupyter can be found on the <a href="https://github.com/JuliaLang/IJulia.jl">IJulia github</a> or the <a href="https://jupyter.org/install">Jupyter homepage</a> (not recommended).</p> <p>From within the main repository directory, start Julia and then start Jupyter in the Julia REPL</p> <pre><code>using IJulia notebook(;dir=pwd())</code></pre> <p>or if using a system Jupyter installation, start Jupyter from your favorite available shell (e.g. Powershell on Windows, bash on any *nix variant, etc.). In Jupyter, open the <code>notebooks/analysis.ipynb</code> notebook. Running all cells will reproduce the results for this paper.</p> <p><strong>Description of data</strong></p> <p>The <code>data</code> directory contains all the data used in the production of the results which were statistically tested.</p> <p>Each <code>.mat</code> file contains events and data generated in Visual3D:</p> <ul> <li>Events <ul> <li><code>LTO</code>/<code>RTO</code> (Left/right toe-off)</li> <li><code>LHS</code>/<code>RHS</code> (Left/right heel-strike)</li> <li><code>HIST</code>/<code>HIEN</code> (Hilly start/end)</li> <li><code>ROST</code>/<code>ROEN</code> (Rocky start/end)</li> <li><code>MLST</code>/<code>MLEN</code> (ML translation start/end)</li> </ul> </li> <li>Data <ul> <li><code>LFootPos</code>/<code>RFootPos</code> (Left/right foot COM position)</li> <li><code>TrunkPos/TrunkVel</code>/<code>TrunkAcc</code> (Trunk COM position, velocity, and acceleration)</li> <li><code>HeadPos/HeadVel</code>/<code>HeadAcc</code> (Head COM position, velocity, and acceleration)</li> <li><code>COG</code> (Whole-body COM/COG)</li> </ul> </li> </ul> <p>The <code>.csv</code> files contain system state of the CAREN system produced by D-Flow software, which includes various system and software settings, most pertinent of which is the treadmill speed.</p> <p>The <code>.c3d</code> files contain the raw motion capture data from Vicon Nexus.</p> <ul> </ul> <p>The results of the <code>notebooks/analysis.ipynb</code> notebook are found in the <code>results</code> folder. Please see the paper for a list of the dependent variables and statistical analyses.</p>
Gait data of hemiparetic subjects collected over the GRAIL system
<p>The dataset includes multiple gait data collected with the GRAIL system (Motek) of 6 children/adolescents with hemiparesis. The patients performed a personalized intervention on gait pattern, walking resistance and gross motor function. Each .csv file is related to a patient (Sx) at T0 (pre-assessment) or T1 (post assessment). </p>
Data from: Neurofeedback facilitation enhances post-stroke gait and balance recovery: a randomized trial
<p><strong>Objective:</strong> To test the hypothesis that supplementary motor area (SMA) facilitation with functional near-infrared spectroscopy mediated neurofeedback (fNIRS-NFB) augments post-stroke gait and balance recovery using the 3-meter-Timed Up-and-Go (TUG) test, we conducted this two-center, double-blind, randomized controlled trial involving 54 Japanese patients.</p> <p><strong>Methods:</strong> Patients with subcortical stroke-induced mild-to-moderate gait disturbance more than 12 weeks from onset, underwent 6 sessions of SMA neurofeedback facilitation during gait- and balance-related motor imagery using fNIRS-NFB. Participants were randomly allocated to intervention (REAL: 28 patients) or placebo (SHAM: 26 patients) group. In the REAL group, the fNIRS signal contained participants' cortical activation information. Primary outcome was TUG improvement, 4 weeks post intervention.</p> <p><strong>Results: </strong>The REAL group showed greater improvement in the TUG test (12.84 ± 15.07 s, 95% CI: 7.00-18.68) than the SHAM group (5.51± 7.64 s, 95% CI: 2.43 – 8.60; group difference 7.33 s, 95% CI: 0.83 – 13.83; p = 0.028), even after adjusting for covariates (group × time interaction; F1.23,61.69 = 4.50, p = 0.030, partial η2 = 0.083). Only the REAL group showed significantly increased imagery-related SMA activation and enhancement of resting-state connectivity between SMA and ventrolateral premotor area. Adverse effects associated with fNIRS-mediated neurofeedback intervention were absent.</p> <p><strong>Conclusion:</strong> SMA facilitation during motor imagery using fNIRS neurofeedback may augment post-stroke gait and balance recovery by modulating the SMA and its related network.</p> <p><strong>Classification of Evidence:</strong> This study provides Class III evidence that for patients with gait disturbance from subcortical stroke, SMA neurofeedback facilitation improves TUG time. (UMIN000010723 at UMIN-CTR http://www.umin.ac.jp/english/)</p>
Preliminary Testing of a System for the Multimodal Analysis of Gait Training in a Virtual Reality Environment
<p>The dataset is related to the paper Piazza, C.; Pirovano, I.; Mastropietro, A.; Genova, C.; Gagliardi, C.; Turconi, A.C.; Malerba, G.; Panzeri, D.; Maghini, C.; Reni, G.; Rizzo, G. and Biffi E. Development and Preliminary Testing of a System for the Multimodal Analysis of Gait Training in a Virtual Reality Environment. Electronics 2021, 10, 2838. https://doi.org/10.3390/electronics10222838. </p> <p>It includes multimodal data (EEG, EMG, kinematics and kinetics) of a healthy child (HC) and a child with hemiparesis (CP01) during walking in a virtual environmment (GRAIL, Motek).</p> <p>Data can be opened by using EEGLab and Matlab </p> <p> </p> <p> </p>
Gait behavioral and neuromuscular characterization in response to increasing working memory load while walking under optic flow perturbations in young adults
<p>The precise role of cognitive control on optic flow processing during locomotion has rarely been investigated. Therefore, this study aimed to determine whether coping with unreliable visual inputs during walking requires attentional resources. Twenty-four healthy young adults walked on an instrumented treadmill in a virtual environment under two optic flow conditions: normal (NOF) and perturbed (POF, continuous mediolateral pseudo-random oscillations). Each condition was performed under single-task and dual-task conditions of increasing difficulty (1-, 2-, 3-back). In all conditions, subjective mental workload was estimated (raw NASA-TLX). For kinematic variables, mean, standard deviation, statistical persistence and step-to-step error correction were computed from gait time series in mediolateral and anteroposterior directions. For EMG variables of soleus and gluteus medius, the full width at half maximum and the variance ratio were calculated. Performance on N-back tasks was assessed using mean reaction time and d-prime. Cognitive performance was not affected by simultaneous walking in any optic flow condition. Gait variability was altered under POF compared to NOF, so that young adults sought to counteract those perturbations by adopting an effortful gait control strategy, independently of concurrent working memory (WM) load. Increasing WM load led changes first at the neuromuscular level, then at the behavioral level, with a prioritization of gait control in the mediolateral direction. Interestingly, dual-tasking lowered the effects of POF but in the anteroposterior direction only. These findings and their theoretical implications provide valuable insight into the complex interaction effects of cognitive and visual constraints on gait control during treadmill walking.</p>
Outliers and Missing Gait_Human, IDS, JavaScript vulnerability Datasets
<p>Human Gait Dataset (CASIA-A) [1] is available at: http://www.cbsr.ia.ac.cn/english/Gait\%20Databases.asp, JavaScript vulnerability<br>dataset is publicly available at [2] and KDD CUP 99 dataset is publicly available at [3].</p> <p> </p> <p> </p> <p>[1] Wang L, Tan T, Ning H, Hu W. Silhouette analysis-based gait recognition for<br>human identification. IEEE transactions on pattern analysis and machine<br>intelligence. 2003;25(12):1505–1518</p> <p>[2] Ferenc R, Heged ̋us P, Gyimesi P, Antal G, B ́an D, Gyim ́othy T. Challenging<br>machine learning algorithms in predicting vulnerable javascript functions. In:<br>2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence<br>Synergies in Software Engineering (RAISE). IEEE; 2019. p. 8–14.</p> <p>[3] Tavallaee M, Bagheri E, Lu W, Ghorbani AA. A detailed analysis of the KDD<br>CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for<br>security and defense applications. Ieee; 2009. p. 1–6.</p> <p> </p>
The effect of stride length on lower extremity joint kinetics at various gait speeds
<p>Dataset linked with the manuscript "The effect of stride length on lower extremity joint kinetics at various gait speeds". Please see attached readme document for details</p>
A multifaceted hybrid ES-robotic device for gait training in individuals with neurological disorders
<p>Data files acquired during tests and Matlab code necessary for analyzing data and generating the images of the paper </p>
Magnetic Distance Estimation Data from Gait Experiments with Magnetoelectric Sensors
<h2>Overview</h2> <p><br>This is the "Magnetic Distance Estimation Data from Gait Experiemtns with Magnetoelectric Sensors" dataset. <br>It represents a pilot study on magnetic motion tracking with novel magnetoelectric sensors during treadmill walking.<br>Therefore, it contains both technical (calibration) data and clinical (gait) data of five healthy participants.</p> <p>Example scripts for loading and processing data are available in the linked respository.</p> <p>The dataset is formatted according to the Brain Imaging Data Structure. See the `dataset_description.json` file for the specific version used.</p> <p>The work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through the Collaborative Research Center CRC 1261 Magnetoelectric Sensors: From Composite Materials to Biomagnetic Diagnostics. The data was recorded in the project B9 on "Magnetoelectric Sensors for Movement Detection and Analysis".</p> <p>All measurements were approved by the ethics committee of Kiel University (File number: A122/20) and conducted in accordance with the Declaration of Helsinki.</p> <h2><br>Details about the experiment</h2> <p><br>Magnetic motion tracking enables a relative tracking, in which the distance between each sensor and actuator node can be estimated. <br>The full setup contains two actuator nodes (a0, a1) and four sensor nodes (s0, s1, s2, s3).<br>Each actuator-sensor pair produces nine magnetic signals (x,y,z by x,y,z) as well as three magnetic dipole moment signals that represent the currents through the coils (actuators).<br>Additionally, each node was tracked with an optical motion capture (OMC) system. The resulting position and orientation data of the attached rigid body act as a reference (ground truth) to evaluate the magnetic estimation. <br>Subfolders sub-01 to sub-08 each contain up to three calibration tasks which each contain between 60 and 120s of arbitrary movement of one coil (wand-mounted) around one stationary sensor (base).<br>Subfolders sub-09 to sub-13 each contain two 120s walking tasks (0.5 and 1 m/s) of five subjects in total with the full sensor and actuator setup. Two actuators were mounted to the shanks, two sensors to the thighs and two sensors were placed stationary next to the threadmill. <br>The dataset also contains a folder with derived data, which contains calibration parameters for each actuator-sensor pair. See the provided matlab script for details on how to load, visualize, and compare the results.</p>
Dataset for article: Gait Speed Assessment in the 10-meter Walk Test for Older Adults Using a Computer Vision-based System: A Cross-sectional Study on Validity, Reliability, and Usability
<p>This dataset provides the Validity, Reliability, and Usability for an assessment of gait speed detection system in the 10-meter Walk Test for Older Adults.</p> <p>The dataset is formatted for easy import into microsoft excel software consist of:<br>Supplementary1.xlsx - Validity <br>Supplementary2.xlsx - Reliability<br>Supplementary3.xlsx - Usability test</p>
Machine-learning-based validation of Microsoft Azure Kinect in measuring gait profiles
<p>The dataset was used to validate Microsoft Azure Kinect in measuring gait profiles, employing machine learning techniques to investigate the impact of residual errors due to environmental, methodological, and processing factors on the accuracy of gait profile assessments. Data were collected from healthy and post-stroke subjects using a motion capture system and a 3D camera-based system with MAK, and corresponding gait profiles were estimated and compiled into a dataset. The estimated gait profiles include spatiotemporal, asymmetry, and body center of mass parameters to capture various normal and pathological gait characteristics.</p> <p>Contact e-mail:</p> <p>claudia.ferraris@cnr.it (Claudia Ferraris)</p> <p>lucavisma@hotmail.com (Luca Vismara)</p> <p>veronica.cimolin@polimi.it (Veronica Cimolin)</p>
The validation of new phase-dependent gait stability measures: a modelling approach
<p><span><span><span><span><span><span><span><span><span><span><span>Identification of individuals at risk of falling is important when designing fall prevention methods. Current stability measures that estimate gait stability and robustness appear limited in predicting falls in older adults. Inspired by recent findings of phase-dependent local stability changes within a gait cycle, we used compass-walker models to test several phase-dependent stability metrics for their usefulness to predict gait robustness. These metrics are closely related to the often-employed maximum finite-time Lyapunov exponent and maximum Floquet multiplier. They entail linearizing the system in a rotating hypersurface orthogonal to the period-one solution, and estimating the local divergence rate of the swing phases and the foot strikes. We correlated the metrics with the gait robustness of two compass walker models with either point or circular feet to estimate their prediction accuracy. To also test for the metrics' invariance under coordinate transform, we represented the point-feet walker in both Euler-Lagrange and Hamiltonian canonical form. Our simulations revealed that for most of the metrics, correlations differ between models and also change under coordinate transforms, severely limiting the prediction accuracy of gait robustness. The only exception that consistently correlated with gait robustness is the divergence of foot strikes. These results admit challenges of using phase-dependent stability metrics as objective measure to quantify gait robustness.</span></span></span></span></span></span></span></span></span></span></span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.