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23 results for “walking behavior”
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 2. Eye-tracking glasses image showing the gaze location during stair ascent
<p>At each data collection, participants walked the same U-shaped staircase descending a 22- treads (riser: 0.16 m, run: 0.33 m, and width: 1.15 m), making a short U-turn downstairs and ascending back the staircase, one tread at a time (Figure 1). Only the data of stair walking were taken for further analysis. The staircase was equipped with a handrail on one side but none of the participants used it. To monitor the gaze a SensoMotoric Instruments (SMI) eye-tracking glasses (ETG) system (SMI, Inc.) at a frequency of 60 frames per second and 1280x960 pixel picture was used. Calibration was performed using a matrix of 3 points placed on a board in different highs and different horizontal placement. Mean gaze vectors of the right eye (x, y, z) for stair descent and stair ascent were obtained for each data collection session. Gaze vector x, y, z starts at the eye and heads off in mediolateral, up and down, and anterior-posterior direction, respectively (Figure 2) (Haffegee, Alexandrov, & Barrow, 2007; Scheel, & Staadt, 2015).</p>
Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 1. A simplified representation of the analysed staircase path
<p>At each data collection, participants walked the same U-shaped staircase descending a 22- treads (riser: 0.16 m, run: 0.33 m, and width: 1.15 m), making a short U-turn downstairs and ascending back the staircase, one tread at a time (Figure 1). Only the data of stair walking were taken for further analysis. The staircase was equipped with a handrail on one side but none of the participants used it. To monitor the gaze a SensoMotoric Instruments (SMI) eye-tracking glasses (ETG) system (SMI, Inc.) at a frequency of 60 frames per second and 1280x960 pixel picture was used. Calibration was performed using a matrix of 3 points placed on a board in different highs and different horizontal placement. Mean gaze vectors of the right eye (x, y, z) for stair descent and stair ascent were obtained for each data collection session. Gaze vector x, y, z starts at the eye and heads off in mediolateral, up and down, and anterior-posterior direction, respectively (Figure 2) (Haffegee, Alexandrov, & Barrow, 2007; Scheel, & Staadt, 2015).</p>
DS6.SSSA-02. Human_Walking_Dataset_at_SSSA. Dataset for characterizing the walking behavior of subjects and identification of changes in the motion patterns, based on RGB-D cameras.
<p>This dataset is used for characterizing the wakling behavior of subjects. It is based on RGB-D camerasand obtained through data collection experiments at the premises of the Percro Labotory, TeCIP Intitute, Scuola Superiore Sant'Anna (Pisa, Italy). Data are collected for the gait patterns of 9 healthy participants.</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>
Odor plume tracking behavior of walking and flying insects
<p><span></span></p> <p>Many animals locate food, mates, and territories by following plumes of attractive odors. There are clear differences in the structure of this plume tracking behavior depending on whether an animal is flying, swimming, walking, or crawling. These differences could arise from different control rules used by the central nervous system during these different modes of locomotion or one set of rules interacting with the different environments encountered by animals suspended in flow or moving across the ground. Flow speeds and turbulence that characterize the environments where walking and flying insects track plumes may alter the structure of odor plumes in an environment-specific way that results in the same control rules generating behaviors that appear quite different. We tested these ideas by challenging walking male cockroaches, <em>Periplaneta</em> <em>americana</em>, and flying male moths, <em>Manduca</em> <em>sexta</em>, to track plumes of their species' sex-pheromones in low wind speeds characteristic of cockroach experimental environments, higher wind speeds characteristic of moth experimental environments, and conditions ranging from low to high turbulence. Introducing a turbulence-generating structure into the flow significantly improved the flying plume tracker's ability to locate the odor source, and changed the structure of the behavior of both flying and walking plume trackers. Specifically, the walking and flying plume trackers located the odor source more often in experimental conditions characteristic of environments in which they live suggesting differing reliance on spatial and temporal measurements of the odor plume.</p>
MoST-Influenced Behavioral Intervention for Walking
ClinicalTrials.gov study NCT05425641. IPD Sharing: YES. Countries: 1. Publications: 1.
Odor plume tracking behavior of walking and flying insects
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FIG. 10. Carollia perspicillata walking trial plaster Cast 1 in Terrestrial behavior and trackway morphology of Neotropical bats
FIG. 10. Carollia perspicillata walking trial plaster Cast 1, indicating locations of arcuate traces, clustered pedal tracks, and manus-dominated trackway. Forward progress of bat from top to bottom of figure
Step up to Health, Nigeria! Impact of World Cancer Day Walk on Health Behaviors Among Nigerians
ClinicalTrials.gov study NCT04248881. IPD Sharing: NO. Countries: 1. Publications: 7.
Inspiratory Muscle Training and Behavioral Support to Alleviate Dyspnea and Promote Walking in Lung Cancer Survivors: A Pilot Study
ClinicalTrials.gov study NCT05059132. IPD Sharing: NO. Countries: 1. Publications: 1.
Step up to Health, Nigeria! Impact of 2022 World Cancer Day Walk on Health Behaviors Among Nigerians
ClinicalTrials.gov study NCT05239325. IPD Sharing: NO. Countries: 1. Publications: 5.
America Walks: An Observational Study of Walking Behavior Based on Passive Step Tracking
ClinicalTrials.gov study NCT02792439. IPD Sharing: YES. Countries: 1. Publications: 4.
Pedestrian Behavior Following Implementation of a Walking School Bus
ClinicalTrials.gov study NCT00402701. IPD Sharing: Not stated. Countries: 1. Publications: 1.
SOC-based Behavioral Intervention to Promote Walking
ClinicalTrials.gov study NCT07312331. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Dynamic structure of locomotor behavior in walking fruit flies
Open the record for dataset details and reuse information.
Walking behavior of flies (Drosophila melanogaster)
<h3>Dataset description</h3> <p>The dataset consists of walking behaviors of flies, <em>Drosophila melanogaster</em>, that were placed in an acrylic arena that constrained them to move in a 2D plane. The flies were part of a genetic screen (not wild-type) but were examined during behavior capture and were morphologically and behaviorally indistinguishable from wild-type flies.</p> <p>We placed three female flies in one arena simultaneously and filmed them from below for 3 seconds (frame rate 80Hz). This was repeated several times for different sets of flies. To extract each fly from the video separately, we tracked the centroid of each fly using Tracktor (Sridhar et al. 2019), cropped out the flies in each frame, and aligned them to point in the same direction. We then tracked 32 body parts (four joints per leg, as well as head features, thorax, abdomen, and wings), each with x- and y-directions using DeepLabCut (Mathis et al. 2018). This resulted in time series with 64 feature dimensions. We then smoothed the extracted time series using a Savitzky-Golay-Filter (Savitzky and Golay, 1964).</p> <h3>Dataset dimensions</h3> <div> <div> <table> <tbody> <tr> <td>number of sequences</td> <td>28059</td> </tr> <tr> <td>sequence length</td> <td>234</td> </tr> <tr> <td>keypoints (2 per keypoint)</td> <td>64</td> </tr> </tbody> </table> <p> </p> </div> </div> <p>frame rate: 80 Hz </p> <h3>Loading the data in Python</h3> <p> </p> <pre><code>import h5py with h5py.File('Fly_DLC_behavior_tracking.h5', 'r') as f: data = f['data'][:]</code></pre>
Biological and Behavioral Outcomes of Community Nature Walks
ClinicalTrials.gov study NCT06056375. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Learning Effects of Attentional Strategy on Dual-task Walking in Patients With Parkinson's Disease: Behavioral Performance and Neural Plasticity
ClinicalTrials.gov study NCT03895125. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Exploring Locomotion Behavior of Chronic Non-Specific Low Back Pain (cNSLBP) Patients While Walking Through Apertures in Different Configurations: Influence of Environmental and Social Factors.
ClinicalTrials.gov study NCT06644053. IPD Sharing: NO. Countries: 1. Publications: 0.
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.