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57 results for “Motion Capture”
Assessment Effectiveness of the Leap Motion Capture® System on the Functionality of the Upper Limb in Acquired Brain Injury
ClinicalTrials.gov study NCT04166617. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Beneficial wake-capture effect for forward propulsion with a restrained wing-pitch motion of a butterfly
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Kinder-Gator 2.0, Optical motion capture, Dataset, MIG2020
<p>Dataset for the following publication:Adult2child: Motion Style Transfer using CycleGANs</p>
UMONS-TAICHI: A multimodal motion capture dataset of expertise in Taijiquan gestures
<p><strong>Presentation</strong></p> <p>UMONS-TAICHI is a large 3D motion capture dataset of Taijiquan martial art gestures (n = 2200 samples) that includes 13 classes (relative to Taijiquan techniques) executed by 12 participants of various skill levels. Participants levels were ranked by three experts on a [0-10] scale. The dataset was captured using two motion capture systems simultaneously: 1) Qualisys, a sophisticated motion capture system of 11 Oqus cameras that tracked 68 retroreflective markers at 179 Hz, and 2) Microsoft Kinect V2, a low-cost markerless sensor that tracked 25 locations of a person’s skeleton at 30 Hz. Data from both systems were synchronized manually. Qualisys data were manually corrected, and then processed to complete any missing data. Data were also manually annotated for segmentation. Both segmented and unsegmented data are provided in this database. The data were initially recorded for gesture recognition and skill evaluation, but they are also suited for research on synthesis, segmentation, multi-sensor data comparison and fusion, sports science or more general research on human science or motion capture. A preliminary analysis has been conducted by Tits et al. (2017) on a part of the dataset to extract morphology-independent motion features for gesture skill evaluation and presented in: “Morphology Independent Feature Engineering in Motion Capture Database for Gesture Evaluation” (<a href="https://doi.org/10.1145/3077981.3078037">https://doi.org/10.1145/3077981.3078037</a>).</p> <p><strong>Processing</strong></p> <p><em><strong>Qualisys</strong></em></p> <p>Qualisys data were processed manually with <a href="http://www.qualisys.com/software/qualisys-track-manager/">Qualisys Track Manager</a>.</p> <p>Missing data (occluded markers) were then recovered with an automatic recovery method: <a href="https://github.com/titsitits/MocapRecovery">MocapRecovery</a>.</p> <p>Data were annotated for gesture segmentation, using the <a href="https://github.com/numediart/ofxMotionMachine">MotionMachine</a> framework (C++ <a href="http://openframeworks.cc/">openFrameworks</a> addon). The code for annotation can be found <a href="https://github.com/numediart/ofxMotionMachine/tree/master/mmTutorial_4_Annotation">here</a>. Annotations were saved as ".lab" files (see Download section).</p> <p><em><strong>Kinect</strong></em></p> <p>The Kinect data were recorded with <a href="https://msdn.microsoft.com/en-us/library/hh855389.aspx">Kinect Studio</a>. Skeleton data were then extracted with <a href="https://www.microsoft.com/en-us/download/details.aspx?id=44561">Kinect SDK</a> and saved into “.txt” files which contain several lines corresponding to each captured frame. Each line contains one integer number (ms), relative to the moment when the frame was captured, followed by 3 x 25 float numbers corresponding to the 3-dimentional locations of the 25 body joints.</p> <p>For more information please visit <a href="https://github.com/numediart/UMONS-TAICHI">https://github.com/numediart/UMONS-TAICHI</a></p> <p>PS: All files can be used with the <a href="https://github.com/numediart/ofxMotionMachine">MotionMachine</a> framework. Please use the parser provided in this github repository for kinect (.txt) data.</p> <p> </p>
Data from: Drone‐based structure‐from‐motion photogrammetry captures grassland sward height variability
Grasslands deliver a range of ecosystem services, including the provision of food and biodiversity, and regulation of soil carbon storage and hydrology. Monitoring schemes are needed to quantify spatial changes in these multiple functions alongside ecosystem degradation. Sward height is widely recognised as a key spatial variable in the provision of these services. Current manual monitoring approaches are labour intensive, and often fail to capture spatial patterns of important features, including sward height. Proximal sensing from small aerial drones carrying lightweight cameras can be transformed into surface height models using image‐based structure‐from‐motion and Multi‐View Stereo‐based approaches; this presents a new opportunity for monitoring the spatial structure of grassland sward height. We combined aerial photographs with field survey data and an open‐source image‐based modelling‐processing workflow to generate sward height measurements for a field comprising mainly Lolium perenne (perennial ryegrass) and Trifolium pratense (red clover). We compared the derived measurements with in situ data captured on the same day using traditional agronomic sward height techniques to determine the quality of the drone‐derived surface model product for sward characterisation. The SfM and Multi‐View Stereo‐based surface model had a mean absolute sward height measurement error of between 3.7 and 4.2 cm. To produce field observations with equivalent quality would require up to 550 sward height measurements for the study site (area: 8,059 m2), which is not feasible over larger extents required for conservation of key species or agronomic purposes. Synthesis and applications. We demonstrate how the collection of precise and detailed information on the spatial structure of grasslands can be made over management‐relevant extents. Aerial digital photographs can be transformed into surface models using an image‐based modelling approach: structure‐from‐motion and Multi‐View Stereo techniques. Image‐based measurements of sward heights were compared with manual sward height data captured on the same day. This novel source of vegetation spatial information could improve sward management for conservation and agronomy applications. The approach supports frequent surveys, at user‐controlled revisit times, and delivers data for spatial monitoring of key grassland functions and services.
Data from: Drone‐based structure‐from‐motion photogrammetry captures grassland sward height variability
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Data from: Collector motion affects particle capture in physical models and in wind pollination
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Modifiable motion graphics for capturing sensations
<p>Data set for the study "Modifiable motion graphics for capturing sensations", accepted for publication in the journal Plos One.</p>
Using Kinect Motion Capture to Measure Shoulder Motion in Patients Undergoing Capsular Distension for Adhesive Capsulitis
ClinicalTrials.gov study NCT04413162. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Real-time Motion Capture and Visual Feedback for Amputation Gait Training
ClinicalTrials.gov study NCT07049198. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Motion Capture Analysis of Sacroiliac Joint Motion After Manipulation
ClinicalTrials.gov study NCT00620906. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Motion Capture and Analysis of NYIT Athletes
ClinicalTrials.gov study NCT02727049. IPD Sharing: NO. Countries: 1. Publications: 0.
Accuracy Comparison: Optoelectronic Motion Capture and Markerless System
ClinicalTrials.gov study NCT06544824. IPD Sharing: NO. Countries: 1. Publications: 0.
Optical Motion Capture-Assisted Ultrasound for Pediatric ESWL
ClinicalTrials.gov study NCT07299032. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Spine Deformity Patients With Optoelectronic Motion Capture
ClinicalTrials.gov study NCT06223737. IPD Sharing: Not stated. Countries: 1. Publications: 0.
C3D Motion Capture Data of Hand Movements
<p>This C3D motion capture data supplements the article <em>"Electromyography-Based Validation of a Musculoskeletal Hand Model"</em> from Melzner and Engelhardt et al. (2021) published at the Journal of Biomechanical Engineering (https://doi.org/10.1115/1.4052115). The following manuscript describes the exact experimental set-up of the data acquisition as well as the gained motion capture data provided as C3D-files.</p> <p>If you are using the provided dataset, please reference the corresponding paper in the Journal of Biomechanical Engineering.</p>
Wing Markers from Motion Capture of Harris Hawk Flight
<p>Data from wing markers during flight by a Harris' hawk (Toothless) flying between two perches. Perches are 5m or 12m apart. Three markers per wing (two on handwing, one on arm wing) and two on the tail.</p> <p>Contents:</p> <p><strong>XYZ</strong> -- The wing and tail markers using the room coordinate system, where the origin is the perch. The floor is 1.25m below the perch.</p> <p><strong>backpack_XYZ</strong> -- the backpack (centre of the bird) position using the room coordinate system. One backpack position per frame.</p> <p><strong>body_pitch</strong> -- the body pitch angle of the hawk measured from a vector drawn from the backpack to the tailpack along the body of the hawk. In degrees.</p> <p><strong>xyz</strong> -- The wing and tail markers with backpack_XYZ as the origin.</p> <p><strong>rot_xyz</strong> -- The wing and tail markers with backpack_XYZ as the origin and rotated such that the body pitch is removed.</p> <p><strong>time</strong> -- 0 estimated from the hawk's starting jump.</p> <p><strong>frame</strong> -- frame number from the original motion capture data set</p> <p><strong>MarkerName</strong> -- the label for the marker. "Wingtip" and "primary" are on the hand wing, "secondary" is along the bottom of the arm wing. "Tailtip" on the outermost feathers of the tail. Also left and right are described.</p> <p><strong>HorzDistance</strong> -- horizontal distance to the perch.</p> <p><strong>FlightPhase</strong> -- estimated phase of flight based on horizontal distance to the perch.</p> <ol> <li>High amplitude flapping</li> <li>Medium amplitude flapping</li> <li>Low amplitude flapping</li> <li>Gliding/unpowered flight</li> </ol> <p><strong>seqID, frameID, markerID </strong>-- These IDs are just used to subset data.</p> <p><strong>sampleRate</strong> -- framerate of the camera</p> <p> </p> <p> </p> <p> </p>
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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.