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6 results for “optical motion capture”
IMU and marker-based optical motion capture from a humanoid robot
<p>The motion capture contains walking trials from the lower body of the humanoid robot Reem-C from Pal Robotics (Barcelona, Spain). Seven IMUs were attached on the foot, lower leg, upper leg and pelvis segments. IMU data was collected at 100 Hz. Moreover, the robot motion was captured with a marker-based optical system (Qualisys AB, Göteborg, Sweden) at 150 Hz. The focus of the dataset was mainly walking. There are three trials, each with a length of about 6.5 minutes.<br>The dataset contains the definition of the skeleton (segment lengths and coordinate locations), the actual IMU readings and the pose or kinematics from the optical system.</p>
Context-Aware Activity Recognition in Logistics (CAARL) – A optical marker-based Motion Capture Dataset
<p><strong>CAARL </strong>is a freely accessible logistics-dataset for human activity recognition, which contains human movement and context information from two subjects. The context information includes the positions of objects such as two picking carts, a packaging table, different racks, a base and three entrances.</p> <p>In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recorded using an optical marker based motion capture system. Each subject and object is equipped with several markers. 140 minutes of human movements have been labelled and categorised into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The labelled human movements are synchronised with the context information. They have exactly the same sampling rate (same start and end).</p> <p>The oMoCap data is in csv format. Further formats (e.g. C3D) are available on request.</p> <p>CAARL is based on the set-up and scenarios of the LARa dataset, which contains only human movements. Information about LARa can be found in the dataset and the associated paper:</p> <ul> <li>Dataset: “Logistic Activity Recognition Challenge (LARa) – A Motion Capture and Inertial Measurement Dataset”, Zenodo 2020, DOI: <a href="https://doi.org/10.5281/zenodo.3862782">10.5281/zenodo.3862782</a></li> <li>Paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes”, Sensors 2020, DOI: <a href="https://doi.org/10.3390/s20154083">10.3390/s20154083</a></li> </ul> <p> </p> <p><strong>If you use the CAARL dataset for research, please cite the following paper: “Context-Aware Human Activity Recognition in Industrial Processes”, Sensors 2021, DOI: <a href="https://doi.org/10.3390/s22010134">10.3390/s22010134</a></strong></p>
Optical motion capturing of change of direction motions reconstructed with inverse kinematics and dynamics and optimal control simulation
<p>This is the data belonging to the publication "Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data".</p> <p>This study investigated the feasibility and accuracy of reconstructing, especially change of direction motions with a 3D full-body musculoskeletal model by tracking marker and ground reaction force (GRF) data in optimal control simulations. We recorded in total 30 trials with optical motion capture. Using this data, we compared inverse methods (inverse kinematics and dynamics) to coordinate tracking simulations and marker tracking simulations.</p> <p>Please see the README and the publication for further details.</p>
Lower-body Inertial Sensor and Optical Motion Capture Recordings of Walking and Running
<pre>This dataset contains lower-body inertial sensor (IMU) data and optical motion capture (OMC) data from ten participants walking and running overground at different speeds. <br><br><br>The data recording is described in this publication: Dorschky, E., Nitschke, M., Seifer, A. K., van den Bogert, A. J., & Eskofier, B. M. (2019). Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models. Journal of biomechanics, 95, 109278. (https://doi.org/10.1016/j.jbiomech.2019.07.022)<br><br>Please look at the README.txt file for further information.</pre>
Kinder-Gator 2.0, Optical motion capture, Dataset, MIG2020
<p>Dataset for the following publication:Adult2child: Motion Style Transfer using CycleGANs</p>
Optical Motion Capture-Assisted Ultrasound for Pediatric ESWL
ClinicalTrials.gov study NCT07299032. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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.