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1,596 results for “gait”
Triaxial accelerometer gait dataset: foot and lower back motion during normal and metronome walking
<p><strong>This dataset contains accelerometric data collected from young and older individuals walking in a controlled environment. The data were recorded using two triaxial accelerometers, one attached to the participant's lower back and the other attached to the foot. Participants were instructed to walk back and forth along a 205-meter corridor under two different conditions:</strong></p> <p><strong>Normal walking: </strong>Participants walked at their preferred walking speed, reflecting their natural gait and pace.</p> <p><strong>Metronome walking: </strong>Participants synchronized their walking pace to a metronome set to their preferred walking cadence. This condition introduced a rhythmic element to the walking pattern, allowing for the study of gait changes when adhering to an external tempo.</p> <p><strong>Another condition was also measured to introduce a more variable and dynamic walking pattern that reflects everyday pedestrian movement in a real-world context.</strong></p> <p><strong>Free outdoor walking:</strong> Older participants engaged in approximately 5 minutes of free walking in an urban environment, navigating city streets. During this activity, only the lumbar accelerometer was used to record data. </p>
Smartwatch gait dataset in simulated Parkinson's disease restricted arm swing conditions
<p>Movement data was collected through smartwatches to monitor gait impairments in healthy subjects. </p> <p>The dataset collected for this study consists of triaxial acceleration and triaxial gyroscope data from 24 subjects when performing a set of gait activities while wearing a smartwatch in their preferred wrist. Each participant performed three gait activities twice, 30 meters straight walk while carrying progressively heavier loads (0 kg, 2 kg, and 4 kg) to simulate restricted arm swing. So, considering that there were 24 participants, 3 different activities and each activity performed twice, a total of 144 data files were obtained.</p> <p>Data was collected using a sample rate of 50Hz. Acceleration is expressed in m/s^2 and gyroscope data in rad/s.</p> <p>Check the " Bioclite_Restricted_Arm_Swing_Data_README.txt" file for details about this dataset.</p> <p><strong>Funding:</strong></p> <p>This research was funded by the following projects:</p> <div> <p>(1) Proyectos de Generación de Conocimiento 2021. PID2021-123708OB-I00, funded by MCIN/AEI/10.13039/ 501100011033/ FEDER, EU</p> </div>
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at Lake Garda, GAIT site (Italy)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Lake Garda in Italy (GAIT). It is a subset of the complete data record which consists of the best quality GAIT measurements which could be used for satellite validation. </p><p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p><p>\(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\)</p><p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p><p>For the GAIT site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:</p><p>\(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\)</p><p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p><p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p><p>To obtain this dataset, we start from the full GAIT data record and omit all the data that do not pass all the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p><p>1. The coefficient of variation in water reflectance is below 10% in the 580-600 nm range</p><p>2. The water reflectance (after correction for the NIR similarity) at 500 nm is below 0.1</p><p> </p>
GSPEED - BLE-based gait speed dataset
<p>Bluetooth Low Energy (BLE) database to evaluate the gait speed of individuals in an in-home environment. The database is composed of several BLE RSSI measurements from different wearable devices and different BLE beacons, corresponding to 382 walks performed by 13 actors.</p> <p>Each row represents an RSSI measurement. The structure of the dataset is as follows:</p> <ul> <li> <pre><strong>mac</strong>: The MAC address of the detected beacon. <strong>rssi</strong>: The RSSI value obtained for the beacon. <strong>device</strong>: A four-character descriptor for the smartwatch that performed the scan. <strong>timestamp</strong>: The time stamp at which the scan was received. <strong>user</strong>: The id of the user that was performing the experiment. <strong>direction</strong>: A number (0 or 1) indicating the direction of the walk. <strong>walk_id</strong>: A number that identifies each walk. <strong>speed</strong>: The actual speed of the user, in $m/s$.</pre> </li> </ul>
Kuopio gait dataset: motion capture, inertial measurement and video-based sagittal-plane keypoint data from walking trials
<p>This dataset contains motion capture (3D marker trajectories, ground reaction forces and moments), inertial measurement unit (wearable Movella Xsens MTw Awinda sensors on the pelvis, both thighs, both shanks, and both feet), and sagittal-plane video (anatomical keypoints identified with the OpenPose human pose estimation algorithm) data.<br>The data is from 51 willing participants and collected in the HUMEA laboratory in the University of Eastern Finland, Kuopio, Finland, between 2022 and 2023. All trials were conducted barefoot.</p> <p>The file structure contains an Excel file containing information of the participants, data folders under each subject (numbered 01 to 51), and a MATLAB script.</p> <p>The Excel file has the following data for the participants:</p> <ul> <li><strong>ID</strong>: ID of the participants from 1 to 51</li> <li><strong>Age</strong>: age of the participant in years</li> <li><strong>Gender</strong>: biological sex as M for male, F for female</li> <li><strong>Leg</strong>: the participant's dominant leg, identified by asking which foot the participant would use to kick a football; R for right, L for left</li> <li><strong>Height</strong>: height of the participant in centimeters</li> <li><strong>Invalid_trials</strong>: list of invalid trials in the motion capture data (MOCAP) data, usually classified as such because the participant did not properly step on the middle force plate</li> <li><strong>IAD</strong>: inter-asis distance in millimeters, the distance between palpated left and right anterior superior iliac spine, measured with a caliper</li> <li><strong>Left_knee_width</strong>: width of the left knee from medial epicondyle to lateral epicondyle in millimeters, palpated and measured with a caliper</li> <li><strong>Right_knee_width</strong>: same as above for the right knee</li> <li><strong>Left_ankle width</strong>: width of the left ankle from medial malleolus to lateral malleolus in millimeters, palpated and measured with a caliper</li> <li><strong>Right_ankle_width</strong>: same as above for the right ankle</li> <li><strong>Left_thigh_length</strong>: the distance between the greater trochanter of the left femur and the lateral epicondyle of the left femur in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_thigh_length</strong>: same as above for the right thigh</li> <li><strong>Left_shank_length</strong>: the distance between the medial epicondyle of the femur and the medial malleolus of the tibia in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_shank_length</strong>: same as above for the right shank</li> <li><strong>Mass</strong>: mass in kilograms, measured on a force plate just before the walking measurements</li> <li><strong>ICD</strong>: inter-condylar distance of the knee of the dominant leg, measured from low-field MRI</li> <li><strong>Left_knee_width_mocap</strong>: distance between reflective MOCAP markers on the medial and lateral epicondyles of the knee in millimeters, measured from a static standing trial; -1 for missing (subject did not have those markers)</li> <li><strong>Right_knee_width_mocap</strong>: same as above for the right knee</li> </ul> <p>The folders under each subject (folders numbered 01 to 51) are as follows:</p> <ul> <li><strong>imu</strong>: "Raw" inertial measurement unit (IMU) data files that can be read with Xsens Device API (included in Xsens MT Manager 4.6, which may be unavailable these days, not sure). You won't need this if you use the data in the imu_extracted folder.</li> <li><strong>imu_extracted</strong>: IMU data extracted from those data files using the Xsens Device API, so you don't have to. <ul> <li>The data is saved as MATLAB structs where the fields are named as a sensor ID (e.g., "B42D48"). The sensor IDs and their corresponding IMU locations are as follows: <ul> <li>pelvis IMU: B42DA3</li> <li>right femur IMU: B42DA2</li> <li>left femur IMU: B42D4D</li> <li>right tibia IMU: B42DAE</li> <li>left tibia IMU: B42D53</li> <li>right foot IMU: B42D48</li> <li>left foot IMU: B42D51 (except for subjects 01 and 02, where left foot IMU has the ID B42D4E)</li> </ul> </li> <li>Some of the data are just zeros as they couldn't be read from these sensors, but under each sensor, the fields "calibratedAcceleration", "freeAcceleration", "time", "rotationMatrix", and "quaternion" contain usable data. <ul> <li>time: Contains time stamps of the measurement at each frame recorded at 100 Hz, so if you remove the first value from all values in the time vector and divide the result by 100, you will get the time in seconds from the beginning of the walking trial.</li> <li>calibratedAcceleration and freeAcceleration: Contain triaxial acceleration data from the accelerometers of the IMU. freeAcceleration is just calibratedAcceleration without the effect of Earth's gravitational acceleration.</li> <li>rotationMatrix: Orientations of the IMU as rotation matrices.</li> <li>quaternion: Orientations of the IMU as quaternions.</li> </ul> </li> </ul> </li> <li><strong>openpose</strong>: Trajectories of the keypoints identified from sagittal plane video frames, saved as json files. <ul> <li>The keypoints are from the BODY_25 model of OpenPose (https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_02_output.html).</li> <li>Each frame in the video has its own json file.</li> <li>You can use the function in the script "OpenPose_to_keypoint_table.m" in the root folder to read the keypoint trajectories and confidences of all frames in a walking trial into MATLAB tables. The function takes as argument the path to the folder containing the json files of the walking trial.</li> </ul> </li> <li>Note that some subjects (11, 14, 37, 49) do not have keypoint and IMU data.</li> </ul> <p>The folders under each subject are divided into three ZIP archives with 17 subjects each.</p> <p>The script "OpenPose_to_keypoint_table.m" is a MATLAB script for extracting keypoint trajectories and confidences from JSON files into tables in MATLAB.</p> <p><br><strong>Publication in Data in Brief</strong>: <a href="https://doi.org/10.1016/j.dib.2024.110841" target="_blank" rel="noopener">https://doi.org/10.1016/j.dib.2024.110841</a></p> <p><br><strong>Contact</strong>: Jere Lavikainen, jere.lavikainen@uef.fi</p>
Principles of gait encoding in the subthalamic nucleus of people with Parkinson's disease
<p>Disruption of subthalamic nucleus dynamics in Parkinson’s disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson’s disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson’s disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson’s disease.</p>
Full-Body 3D Human Gait Dataset walking on flat ground
<p>This dataset contains full-body 3D gait data collected from 26 healthy participants (10 males, 16 females) with an average age of 28.19 ± 7.77 years. Data was captured using the Xsens Awinda MTw inertial measurement system, comprising 17 wireless sensors operating at a 60Hz sampling frequency.</p> <p>Key Features:</p> <ul> <li>Full-body motion data using MVN Analyze software's full-body model</li> <li>Anthropometric measurements: height (170.5 ± 8.61 cm), foot length (26.47 ± 1.88 cm), shoulder width (39.32 ± 7.79 cm), and wrist span (131.36 ± 8.85 cm)</li> <li>Four distinct walking paths: Mixed (straight and curved), Circle (3m diameter), Turn (180-degree turns), and Zigzag</li> <li>Total of 1,024,295 frames (17,071.58 seconds) of gait recordings</li> <li>Average of 3,568.97 ± 1,204.26 frames per recording (59.48 ± 20.07 seconds)</li> </ul> <p>The dataset includes various walking patterns designed to capture a wide range of gait characteristics, including straight walks, gentle curves, sharp turns, and zigzag movements. Participants were allowed some freedom in executing turns, particularly in the Zigzag and Mixed paths, to introduce natural variations in gait patterns.</p> <p>This comprehensive dataset is suitable for gait analysis, biomechanics research, and the development of motion synthesis algorithms, particularly those focused on normal walking patterns on a fixed surface with various turning scenarios.</p> <p>Dataset Structure:</p> <ol> <li>'<strong>participants.xlsx</strong>': An Excel file containing participant codes and their anthropometric data.</li> <li>'<strong>data</strong>' folder: Contains subdirectories named with participant codes. <ul> <li>Each participant subdirectory contains CSV files of different gait recordings for that participant.</li> </ul> </li> </ol> <p>This dataset was collected as part of the study:</p> <p><strong>Carneros-Prado, D., Dobrescu, C. C., Cabañero, L., Villa, L., Altamirano-Flores, Y. V., Lopez-Nava, I. H., … & Hervás, R. (2024). Synthetic 3D full-body skeletal motion from 2D paths using RNN with LSTM cells and linear networks. </strong><strong><em>Computers in Biology and Medicine, 180,</em></strong><strong> 108943.</strong></p>
Gait Kinematics Data from 2 minute walk test assessment
<p>The dataset consists of gait parameters collected from a single male participant (age: 29 years, height: 1.72 m, mass: 78.3 kg) at four time points: baseline, post-immobilization (post-IM), post-resistance training (post-RT), and 14 weeks post-RT (post-14). The participant underwent a 14-day single-leg immobilization followed by an 8-week resistance training (RT) program. Gait data were recorded during the Two-Minute Walk Test (2MWT) under two conditions: comfortable (COM) and fast (MAX) walking speeds. The dataset includes kinematic data captured using ten synchronized Opal inertial sensors (APDM Inc.) placed at specific anatomical locations, sampled at 128 Hz. The recorded signals were processed using the Mobility Lab™ software.</p>
Dataset from "Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition"
<p>Written by: Paolo Mistretta<br> Contact information: paolo.mistretta@phd.unipd.it<br> Date: 24/01/2020</p> <p><br> This document contains supplementary material for the article<br> “Collection of kinematic and kinetic data of young & adult, male & female subjects performing periodic and transient gait tasks for gait pattern recognition”<br> (Authors: Paolo Mistretta, Cecilia Marchesini, Andrea Volpini, Luca Tagliapietra, Tommaso Sciarra, Aldo Lazich, Salvatore Forte, Mauro De Matteis, Emanuele Menegatti and Nicola Petrone)<br> presented at the 13th conference of the International Sports Engineering Association, Tokyo, Japan, 22-25 June 2020.</p> <p><br> Data are contained in the file: “database_ISEA2020.mat”</p>
FIGURE2 in Stance and gait in the flesh-eating dinosaur Tyrannosuurus
FIGURE2. The knee-joint of a megalosaurian to show the femoral condyle (indicated by an arrow) inserted between the tibia and fibula. This is similar to the knee-jointof Tyrannosaurus.
Gait Initiation with additional load
<p>Dataset containing raw data from gait initiation with additional load protocol using two force platforms.</p> <p>Each file has force, moments and center of pressure data.</p> <p>Additional load was additioned to waist and shoulder, uniformly and laterally on the stance foot side and swing foot side.</p> <p>The control condition was no load.</p> <p>Each participant performed each condition three times.</p>
An elaborate data set on human gait and the effect of mechanical perturbations
<p>This data set includes measurements during trials of human walking on a treadmill with a movable base intended to provide data rich in content for the identification of the human's control system. The primary experiments were designed to longitudinally perturb the subject at the ground contact by randomly accelerating the belt. The marker locations (treadmill and human), treadmill accelerations, treadmill belt speeds, and the forces and moments from the dual force plates were measured during the trials.</p> <p>PeerJ Article: https://peerj.com/articles/918</p> <p>PeerJ Preprint: https://peerj.com/preprints/700/</p> <p>Paper source repository: https://github.com/csu-hmc/perturbed-data-paper</p>
GSTRIDE: A database of frailty and functional assessments with inertial gait data from elderly fallers and non-fallers populations
<p>The GSTRIDE database contains relevant metrics and motion data of elder people for the assessment of their health status. The data correspond to 163 patients, 45 men and 118 women, between 70 and 98 years old with an average Body Mass Index (BMI) of 26.1±5.0 kg/m<sup>2</sup> and a cognitive deterioration status index between 1 and 7, according to the Global Deterioration Scale (GDS) scale. In this way, we ensure variability among the volunteers in terms of socio-demographic and anatomic parameters and their functional and cognitive capacities. The database files are stored in CSV format to ease their usability with common data processing software.</p> <p>We provide socio-demographic data, anatomical, functional and cognitive variables, and the outcome measurements from test commonly performed for the evaluation of elder people. The evaluation tests carried out to obtain these data are the Gait Speed Test (4-metre), the Hand Grip Strength, the Short Physical Performance Battery (SPPB), the Timed up and go (TUG) and the Short Falls Efficacy Scale International (FES-I). We also include the outcomes of the GDS questionnaire, the Frailty assessment and the information about falls during the last year prior to the tests.</p> <p>These data are complemented with the gait parameters of a walking test recorded by an Inertial Measurement Unit (IMU) placed on the foot. Inertial data from foot-mounted IMUs (acceleration (m/s2), angular velocity (rad/s) and timestamps (s)) are included in the database in .csv files for each participant.</p> <p>The current version includes a new gait analysis processing conducted following the methodology described in [1].</p> <p>The complete gait analysis is included for each participant and trial in .csv files, including the gait parameters estimated for all individual steps and the gait segmentation events. The gait parameters included are: cycle duration (CD) (s), cadence (steps/min), stride length (SL) (m), path length 3D (%SL), path length 2D (%SL), stride velocity (m/s), percentage of swing (%CD), percentage of stance (%CD), percentage of stance subphases (loading, foot-flat, and pushing) (%stance), heel strike pitch (degrees), toe-off pitch (degrees), peak angle velocity (degrees/s), turning angle (degrees), and heel range of motion (ROM) (degrees). </p> <p>GSTRIDE is specially focused on, but not limited to, the study of faller and non-faller elder people. The main aim of this dataset is the availability of study these different populations. By including the results of the health evaluation tests and questionnaires and the inertial and spatio-temporal data, researchers can analyze different techniques for the identification of fallers. Moreover, this database allows the analysis of cognitive deterioration and frailty parameters of patients by the research community.</p> <p>[1] L. Ruiz-Ruiz, J. J. García-Domínguez and A. R. Jiménez, "A Novel Foot-Forward Segmentation Algorithm for Improving IMU-Based Gait Analysis," in <em>IEEE Transactions on Instrumentation and Measurement</em>, vol. 73, pp. 1-13, 2024, Art no. 4010513, doi: 10.1109/TIM.2024.3449951.</p>
Wrist and Tibia/Shoe Mounted IMU Measurement Results for Gait Analysis
<p>This document describes a dataset of measurement results collected using wrist, tibia and shoe mounted inertial sensors. The main purpose of the dataset was to test signal translation algorithms converting signals registered using the wrist-worn sensor e.g. a smartwatch to signals which would be measured with a shoe or tibia - mounted device. The dataset includes tri-axial acceleration and angular velocity registered during several walks.</p> <p>More details are included in the dataset_description pdf file.</p> <p>When using the data, please consider also citing the original paper, for which it was collected:</p> <p>Kolakowski, M.; Djaja-Josko, V.; Kolakowski, J.; Cichocki, J. Wrist-to-Tibia/Shoe Inertial Measurement Results Translation Using Neural Networks. <em>Sensors</em> <strong>2024</strong>, <em>24</em>, 293. <a href="https://doi.org/10.3390/s24010293" target="_blank" rel="noopener">https://doi.org/10.3390/s24010293</a></p> <p><strong>The dataset will be gradually updated as new data are gathered.</strong></p>
Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
<p>Energy harvesting from flow induced vibrations (FIV) in flexible bodies offer opportunities for power generation in biomimicking robotic devices and is an active area of research. The focus of this study is on investigating the underlying physics and qualitatively analysing the energy extraction scenarios in similar structural systems, comprising of a flexible piezoelectric flapper in a low Reynolds number flow regime. A high-fidelity three-way fully coupled fluid-structure-electric energy solver is developed in-house to study the energy harvesting capabilities of such a flapper, its hydrodynamic characteristics and the associated unsteady flow-field. The results indicate that the flapper deformation profiles at the most efficient harvesting regimes, resemble the propulsion gaits of natural swimmers. Investigations on the effects of a sinusoidal heaving actuation reveal no significant impact on the harvested power at the high yield (high power output) regime, identified under the passive condition showing biomimetic gait. This study provides mechanics based insights that is expected to be useful for bio-inspired designs of FIV based harvesters.</p>
Error Related Potential at the start of the gait with a lower limb exoskeleton
<h2>Description</h2> <p>This dataset contains EEG signals from experiments designed to evoke Error Related Potentials (ErrP) at the onset of gait using a Brain-Computer Interface (BCI) to control a lower limb exoskeleton. The ErrP is elicited using three different stimuli: Tactile, Visual, and VisuoTactile.</p> <p>During the experiment, participants remain stationary and engage in two mental tasks: Relax (R) and Motor Imagery (I) of walking to activate the exoskeleton. These tasks can be executed correctly (RC, IC) or incorrectly (RE, IE). For example, during RC (Relax Correct), the subject maintains an idle state, whereas during RE (Relax Error), the exoskeleton activates unexpectedly. Conversely, in IC, the subject imagines the sensation of starting to walk in their muscles, and the exoskeleton activates, but during IE, the exoskeleton does not move despite the motor imagery. When the exoskeleton activates before starting to walk, the stimulus remains active for 2 seconds to alert the subject about the impending movement. Therefore, ErrP is elicited by the stimuli in RE and can be compared with the absence of ErrP in IC, where the stimulus activates but should not evoke an error.</p> <p>Each subject participates in three sessions, one for each stimulus, consisting of 12 trials. In each trial, 10 mental tasks are performed, 5 Relax and 5 Imagination, interleaved. Since the subject is never in control of the system, tasks are correctly performed 70% of the time (RC, IC), while the remaining 30% are incorrect (RE, IE). In an exception, subject R01_VisuoTactile performed 7 trials of 20 mental tasks each, 10 of each type. However, due to the extended duration of the trials and resulting fatigue, they were later split for subsequent sessions.</p> <p> </p> <h2>Data information</h2> <p>A trial consists of a Matlab structure that stores all information related to the trial experiment. </p> <ul> <li><em>data_EEG</em>: Original EEG signals recorded with a sampling rate of 250Hz, where each row is a channel (1-28 EEG, 29-32 EOG, 33-35 inertial electrodes).</li> <li><em>data_preprocessed_EEG</em>: Matrix that contains the preprocessed signals for each channel. Rows 1-35 are the original signals and then, the preprocessed signals in blocks of 35. Find the indexes of each filter in <em>session.conf.info.preprocessingSteps.ListPreprocessingSteps</em>.</li> <li><em>trigger_EEG</em>: Information related to signal quality and missing data while recording. </li> <li><em>data_EXO</em>: Exoskeleton recorded data with a sampling rate of 250Hz.</li> <li><em>data_preprocessed_EXO: </em>The same data recorded by the exoskeleton in <em>data_EXO</em>, since it does not require the application of any filter.</li> <li><em>trigger_EXO</em>: Empty vector. </li> <li><em>data_Actuators</em>: Arduino response when activates (1) and deactivates (-1) the feedback. </li> <li><em>data_preprocessed_Actuators: </em>The same Arduino resposes recorded in <em>data_Actuators</em>, because it does not require any filter application. </li> <li><em>trigger_Actuators</em>: Empty vector. </li> <li><em>task_EEG</em>: Vector that associates a task to each signal sample.</li> <li><em>task_index_EEG</em>: Zero vector with negative peaks at the samples indicating the start of a task. Each peak decrements by one unit with each task. </li> <li><em>task_order_EEG</em>: Vector that increments a unit with each task change. </li> <li><em>event_EEG</em>: Vector of commands to activate (1) and deactivate (-1) the feedback in Arduino. </li> <li><em>conf</em>: Configuration employed for data acquisition and preprocessing. <ul> <li><em>acquisition</em>: User and signals acquisition information. <ul> <li><em>user_code</em>: User code name.</li> <li><em>feedback</em>: Trial in openloop (User do not have control of the system).</li> <li><em>feedbackErrP</em>: Feedback type employed during the trial.</li> <li><em>readfile</em>: Path to read files after its acquisition.</li> <li><em>saveSession_Script</em>: Script used to save the recorded data.</li> <li><em>writeResults</em>: Path to save the recorded data.</li> <li><em>device</em>: List of connected devices during the trial and their related information, such as name, sampling rate, connection order, etc. </li> <li><em>task</em>: Information about tasks occurring during the trial. <ul> <li><em>task_list</em>: Decodes tasks numbers. The first number is the global task/mental activity, the second one is the physiological state of the user, and the third one indicates the task version (preparation or basic task).</li> <li><em>sequence_tasks</em>: List of tasks in order of execution.</li> <li><em>sequence_times</em>: List with the duration of each task in the sequence.</li> </ul> </li> <li><em>deviceOutput</em>: List of devices that receive commands to execute orders, such as the exoskeleton for walking and stopping and the VibroLed for turning feeedback on and off.</li> <li><em>eye_index</em>: Indexes of EOG electrodes.</li> <li><em>EEG_index</em>: Indexes of EEG electrodes.</li> <li><em>inertial_index</em>: Indexes of inertial electrodes.</li> <li><em>file_name</em>: Trial name.</li> <li><em>num_epochs</em>: Number of epochs within a trial. An epoch is the half of sampling rate (250Hz), this means that an epoch has a duration of 0.5s and 125 samples. </li> </ul> </li> <li><em>preadjustment</em>: Empty list. </li> <li><em>preprocessing</em>: Information of the preprocessing filters, parameters and order of application.</li> <li><em>processing</em>: Not necessary for this analysis. </li> <li><em>static</em>: Information used internally by the architecture for its correct operation.</li> <li><em>info</em>: Important information about filters, their order and indexes in <em>data_processed_EEG</em>.</li> </ul> </li> <li><em>times</em>: Struct with information of the devices synchronization and preprocessing times.</li> <li><em>times_processing</em>: Processing duration times. </li> </ul>
Health&Gait: a dataset for gait-based analysis
<p>This repository contains the <strong>Health&Gait</strong> dataset, the first that enables gait analysis using visual information without specific sensors, relying solely on cameras. The dataset includes multimodal features extracted from videos, and gait parameters and anthropometric measurements from each participant. This dataset is intended for use in health, sports and gait analysis research.</p> <p>Health&Gait consists of 1,564 videos of 398 participants walking in a controlled closed environment, where each video has associated the following information:</p> <ul> <li>2D pose estimation of their joints by <a href="https://github.com/MVIG-SJTU/AlphaPose"><strong>AlphaPose</strong></a> (JSON format files).</li> <li>Semantic segmentation by <a href="https://github.com/facebookresearch/detectron2/tree/main/projects/DensePose"><strong>DensePose</strong></a> (PNG images).</li> <li>Optical flow by <a href="https://docs.opencv.org/3.4/dc/d47/classcv_1_1DualTVL1OpticalFlow.html" rel="nofollow"><strong>TVL1</strong></a> and <a href="https://github.com/haofeixu/gmflow"><strong>GMFlow</strong></a> (PNG images).</li> <li>Silhouette by <a href="https://github.com/ultralytics/ultralytics"><strong>YOLOV8</strong></a> (JPEG images).</li> </ul> <p>Moreover, for each subject, the following data has been recorded:</p> <ul> <li>Anthropometric measurements.</li> <li>Gait parameters obtained from OptoGait and MuscleLAB.</li> <li>Gait parameters estimated from pose information.</li> </ul>
Dataset for train and test BRITTANY (Biometric RecognITion Through gAit aNalYsis)
<p>This dataset can be used train and test the BRITTANY tool. Information contained in the dataset is especially suitable to be used as train and test data for neural network-based classifiers.</p> <p>This dataset contains 198 Rosbag files, of 5 seconds duration, recorded in different locations (kitchen, livingroom-window and livingroom-door) with Orbi-One robot stood still. Two sorts of Rosbag files have been recorded. In 90 Rosbag files (train*.bag), data recorded correspond to a person walking in a straight line in front of the robot. Data from five different people have been recorded. For each location and person, six Rosbag files have been recorded.</p> <p>In 108 Rosbag files (test*.bag), data recorded correspond to a person walking in a straight line in front of the robot. Data from six different people have been recorded. Five of those six people are the same as in the other rosbags and the other one is not registered in the system to evaluate the false-positive cases in the system.</p>
Data/Code: Objective monitoring of functional recovery after total knee and hip arthroplasty using sensor-derived gait measures
<p>Abstract</p> <p>Background: Inertial sensors hold the promise to objectively measure functional recovery after total knee (TKA) and hip arthroplasty (THA), but their value in addition to patient-reported outcome measures (PROMs) has yet to be demonstrated. This study investigated recovery of gait after TKA and THA using inertial sensors, and compared results to recovery of self-reported scores of pain and function.</p> <p>Methods: PROMs and gait parameters were assessed before and at two and fifteen months after TKA (n=24) and THA (n=24). Gait parameters were compared with healthy individuals (n=27) of similar age. Gait data were collected using inertial sensors on the feet, lower back, and trunk. Participants walked for two minutes back and forth over a 6m walkway with 180° turns. PROMs were obtained using the Knee Injury and Osteoarthritis Outcome Scores and Hip Disability and Osteoarthritis Outcome Score.</p> <p>Results: Gait parameters recovered to the level of healthy controls after both TKA and THA. Early improvements were found in gait-related trunk kinematics, while spatiotemporal gait parameters mainly improved between two and fifteen months after TKA and THA. Compared to the large and early improvements found in of PROMs, these gait parameters showed a different trajectory, with a marked discordance between the outcome of both methods at two months post-operatively.</p> <p>Conclusion: Sensor-derived gait parameters were responsive to TKA and THA, showing different recovery trajectories for spatiotemporal gait parameters and gait-related trunk kinematics. Fifteen months after TKA and THA, there were no remaining gait differences with respect to healthy controls. Given the discordance in recovery trajectories between gait parameters and PROMs, sensor-derived gait parameters seem to carry relevant information for evaluation of physical function that is not captured by self-reported scores.</p>
Synchronously recorded gait kinematic data with Inertial Measurement Units and a photogrammetry system for a validation assessment
<h3>Description</h3> <p>A gait database of 32 healthy adult subjects was built , volunteers were between 20 and 63 y.o. (33.64 ± 12.44) and 71.88% were females. Every individual underwent a barefoot walking test recorded simultaneously with Inertial Measurement Units (IMUs) and the photogrammetry system Vicon. The dataset contains the kinematic gait information of the hip, knee, and ankle joints in the three planes of motion: sagittal, frontal, and transversal. </p> <p>The signals recorded by the IMUs are referred to as I(t) and were captured with a sampling frequency of 50 Hz, and those recorded by the photogrammetry system are called V(t) and were captured with a sampling frequency of 100 Hz. To perform a comparative study of both systems, the V(t) signals must be resampled to 50 Hz. Then, the delay between the two signals must be corrected to align them. Finally, gait cycles can be extracted for each pair of trials following the data information provided, obtaining a pair of waveforms for each gait cycle [I(t), V(t)]. A total of 268 synchronous gait cycles [I(t), V(t)] can be recovered and analyzed in the three planes of motion per limb.</p> <h3>Data information</h3> <ul> <li><em>raw_data</em>: folder containing the 32 subjects raw kinematic signals recorded with IMUs (sampling frequency 50 Hz) and photogrammetry system (sampling frequency 100 Hz) synchronously.<br> <ul> <li>For IMUs records: <ul> <li>Z: sagittal plane.</li> <li>X: frontal plane.</li> <li>Y: transversal plane.</li> </ul> </li> <li>For photogrammetry system records: <ul> <li>X: sagittal plane.</li> <li>Y: frontal plane.</li> <li>Z: transversal plane.</li> </ul> </li> </ul> </li> </ul> <ul> <li><em>captures_information.xlsx</em>: table containing the delay correction and the samples corresponding to the events of the gait cycles. The delay correction is the number of samples for which each photogrammetry signal V(t), after being resampled to 50 Hz, must be moved to be completely aligned with its synchronous IMUs signal couple I(t). <ul> <li>If the delay is positive (+) the V(t) signal must be delayed by adding zeros at the beginning.</li> <li>If the delay is negative (-) the V(t) signal must be moved forward by removing zeros at the beginning.</li> </ul> </li> </ul>
ScienceDex guides
Understand access before you commit
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.