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30 results for “Motion Tracking”
More precise tracking of horizontal than vertical target motion with both the eyes and hand
<p>Those files contain individual data from a large cohort of participant (N=62). </p> <p>In the excel file (DATAmain), each sheet presents one set of variables (with individual value for each trial).</p> <p>This file contains information regarding eye and hand tracking performance (distance+lags), as well as smooth pursuit gains. </p> <p>The other files contain data that we used for the detailed analysis of saccades and lags, as well as the scripts that can be run with Perl. One script is for analysing the lag (Danion.pl) and the other one for analysing the saccades (saccades.pl). The other files (.txt and .dat) that were used for these analyses. Note that some library is needed (common_subroutines, draw_figure, and for the anova’s routines_that_use_R), meaning that you need to have R installed. </p> <pre>Regarding data acquisition we employed a program called Docometre that can be uploaded at the following address: http://139.124.68.1/buloup/index.php?selectedMenu=DOCoMETRe&lang=_fr When this program is installed, it needs to be run with BaselineTracking.dcm We also provide .BAS and .T91 files that correspond to the compiled version of each pattern Regarding visual stimuli, another program called ICE needs to be installed on a separate computer that receives information (target+cursor) from docometer, it can be uploaded at : https://trello.com/b/EtNCNrZH/icehttps://trello.com/b/EtNCNrZH/ice ICE needs to be run with Visuomotor.ice Visuomotor.icepro Visuomotor.txt and Visuomotor.icemat in the respective folder (icepro in Protocol folder, icemat and ice in Scenario Folder, and txt in Serie folder) Note that both Docometre and ICE need to be run with similar equipement as our (including Adwin Gold systems, Megatron joystick, video screen, graphic cards, and desktop eyelink providing analog signals to docometre). Adequate numbering of analogic channels needs also to be ensured. </pre>
Foothold selection during locomotion in uneven terrain: Results from the integration of eye tracking, motion capture, and photogrammetry
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Upper-body movements: precise tracking of human motion using inertial sensors
<p>The <em>Upper-body movements: precise tracking of human motion using inertial sensors</em> is a dataset composed of 11 participants' IMU data (5 women + 6 men). This collection is divided into 6 motion sets containing different motions for the upper-body.</p> <p><strong>Folder Structure</strong></p> <p>subject -> set -> IMU position -> file</p> <p>e.g. subject01 -> set6 -> forearm -> Accelerometer.txt</p> <p><strong>IMU placement </strong></p> <p>For data collection participants wore 4 IMUs:</p> <ul> <li>1 on the chest</li> <li>1 on the right arm</li> <li>1 on the right forearm</li> <li>1 on the right hand.</li> </ul> <p><strong>Sets</strong></p> <p>Each set includes:</p> <ul> <li> set1 - flexion/extension of the forearm; abduction/adduction of the arm; anatomical position</li> <li> set2 - flexion/extension of the wrist; radial/ulnar deviation of the wrist; anatomical position</li> <li> set3 - flexion/extension and lateral flexion of the torso; anatomical position</li> <li> set4 - flexion/extension of the arm; flexion/extension of the torso; anatomical position</li> <li> set5 - flexion/extension of the arm; anatomical position; anatomical position</li> <li> set6 - flexion/extension of the torso; flexion/extension of the arm; anatomical position</li> </ul> <p><strong>Annotations</strong></p> <p>This dataset is accompanied by the<em> annotations.csv</em> file.<br> Each file row present "Set,Subject,Category,Segment,Type,Init,End":</p> <ul> <li>Set - sets 1-6</li> <li>Subject - participant ID</li> <li>Category - relative or absolute. Refers to the joint angle.</li> <li>Absolute if the angle is obtained considering an anatomical plane as reference.</li> <li>Relative if the angle is obtained from one segment in relation to another.</li> <li>Type - segment at action (torso; right_arm_forearm; wrist; right_arm_sagittal)</li> <li>Init/End - time in seconds, describing the begin and end of the motion, respectively.</li> </ul> <p> </p>
Multitask Human Navigation in VR with Motion Tracking
<p>Data from human subjects in virtual reality performing some combination of collecting targets, avoiding obstacles, and following a path. Raw data has been parsed into 300 ms samples for use in machine learning algorithms. The data includes object positions in the virtual environment, human position tracking, and task instructions. </p> <p> </p>
Dataset supporting "Violating instructed human agency: an fMRI study on oculomotor tracking of biological and nonbiological motion stimuli."
<p>Here we provide fMRI data used for the following project (for details see data description file): Gertz, H., Hilger, M., *Hegele, M., & *Fiehler, K. (2016). Violating instructed human agency: an fMRI study on oculomotor tracking of biological and nonbiological motion stimuli. Neuroimage, doi: 10.1016/j.neuroimage.2016.05.043. (*shared last authorship)</p> <p> </p> <p>Previous studies have shown that beliefs about the human origin of a stimulus are capable of modulating the coupling of perception and action. Such beliefs can be based on top-down recognition of the identity of an actor or bottom-up observation of the behavior of the stimulus. Instructed human agency has been shown to lead to superior tracking performance of a moving dot as compared to instructed computer agency, especially when the dot followed a biological velocity profile and thus matched the predicted movement, whereas a violation of instructed human agency by a nonbiological dot motion impaired oculomotor tracking (Zwickel et al., 2012). This suggests that the instructed agency biases the selection of predictive models on the movement trajectory of the dot motion. The aim of the present fMRI study was to examine the neural correlates of top-down and bottom-up modulations of perception–action couplings by manipulating the instructed agency (human action vs. computer-generated action) and the observable behavior of the stimulus (biological vs. nonbiological velocity profile). To this end, participants performed an oculomotor tracking task in an MRI environment. Oculomotor tracking activated areas of the eye movement network. A right-hemisphere occipito-temporal cluster comprising the motion-sensitive area V5 showed a preference for the biological as compared to the nonbiological velocity profile.Importantly,a mismatch between instructed human agency and a nonbiological velocity profile primarily activated medial-frontal areas comprising the frontal pole, the paracingulate gyrus, and the anterior cingulate gyrus, as well as the cerebellum and the supplementary eye field as part of the eye movement network. This mismatch effect was specific to the instructed human agency and did not occur in conditions with a mismatch between instructed computer agency and a biological velocity profile. Our results support the hypothesis that humans activate a specific predictive model for biological movements based on their own motor expertise. A violation of this predictive model causes costs as the movement needs to be corrected in accordance with incoming (nonbiological) sensory information.</p>
Dataset of Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion
<p>MATLAB Dataset for the paper. </p> <p>Paper Abstract:</p> <p>Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).</p>
Sample tracking - mixed motion
<p>This video shows the output of a stereo mode sample tracking run under mixed motion. Views from port 19 offset at approx. 45 degrees and port 17 in line with the origin of the chamber.</p>
Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets
<p><span>The datasets store both motion and observation information of a single fluorescent sub-diffraction limit-sized particle moving in a three-dimensional confined environment. The confined motion is following a nonlinear model driven by non-Gaussian noise, the observation is formed by engineered Double-helix (DH) point spread function (PSF) and captured by scientific complementary metal-oxide semiconductor (sCMOS) camera. Based on our prior computationally efficient application of Sequential Monte Carlo - Expectation Maximization (SMC-EM), we extended it to handle the DH-PSF for encoding the three-dimensional position of the particle in two-dimensional image plane of the camera. We focus on studying the datasets at low signal and low signal-to-background ratio (SBR). Based on the datasets across different SBR and confinement lengths, a quantitative comparison is conducted to show that in the low signal regime, the SMC-EM approach outperforms the other methods while at higher signal-to-background levels, SMC-EM and the MLE-based methods perform equally well and both are significantly better than fitting to the MSD. In addition, our results indicate that at smaller confinement lengths where the nonlinearities dominate the motion model, the SMC-EM approach is superior to the alternative approaches. </span></p>
Three dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions: Simulation datasets
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Dataset for Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality
<p>Distributed Virtual Reality (DVR) systems enable geographically dispersed users to interact in a shared virtual environment. The realism of the interaction is crucial to increase the feeling of co-presence. Latency, produced either by hard- or software components of DVR applications, impedes reaching high realism levels of the DVR experience. For example, the time delay between the user's motion and the corresponding display rendering of the DVR system might lead to adverse effects such as a reduced sense of presence or motion sickness. One way of minimizing the latency is to predict user's motion and thus compensate for the inherent latency in the system. In order to address this problem, we propose a neural network 3D pose tracking and prediction system with latency guarantees for end-to-end avatar reconstruction. We evaluate and compare our system against multiple traditional methods and provide a thorough analysis on real-world human motion data. Datasets used in the paper experiments. Datasets used in paper experiments.</p>
Motion Tracking Data - Pro-supination Forearm
<p>Within the scope of the SNF Project for the study of the instability of the distal radioulnar joint of the forearm (DRUJ), we conducted different experiments to record the anatomical changes in the pro-supination motion of the forearm, while subsequently resecting the surrounding soft tissue. </p> <p>The structures of interest were the interosseous membrane, the TFCC complex and the distal and proximal forearm joints. </p> <p>This dataset is divided into 5 different folders, corresponding to 5 different cadaveric forearms. The motion movement for each forearm was passively generated by a custom-made device, and the motion was recorded using 4 different IR markers and the Atracsys motion system.</p> <p>The protocol of the experiments as well as the recorded motion data are provided, together with the 3D models of each forearm.</p> <p>Additionally we provide the internship report of the student working on the data curation and preparation for the upcoming publication (see "InternshipReportSM")</p> <p>In case of questions or inquiries about the data, please feel free to contact us</p>
Datsets for Publication "In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation"
<p>This dataset contains Magnetic Particle Imaging and Intravascular optical coherence tomography data for the profiles</p> <ul> <li>Standard Profile (3x)</li> <li>Bending Profile (3x)</li> <li>Heart Beat Profile (3x)</li> </ul> <p>used in the publication "In-Vitro MPI-Guided IVOCT Catheter Tracking in Real Time for Motion Artifact Compensation".</p>
Influence of Couch Tracking Motion
ClinicalTrials.gov study NCT02820532. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Feasibility of Using Ultrasound to Track Respiration Motion
ClinicalTrials.gov study NCT02173353. IPD Sharing: NO. Countries: 1. Publications: 1.
Identification of Motor Symptoms Related to Parkinson's Disease Using Motion Tracking Sensors at Home
ClinicalTrials.gov study NCT03366558. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.
Evaluation of the Impact of Integrating Dynamic Patient-Specific Motion and Implementing a Jaw Tracking System in the Attribution of the Occlusion in Full-Arch Restorations Through a Digital Workflow:
ClinicalTrials.gov study NCT06773728. IPD Sharing: NO. Countries: 1. Publications: 4.
Synthetic and real datasets for "Seismic source tracking with six degree-of-freedom ground motion observations"
<p>Synthetic datasets for the 2D and 3D rupture tracking and real datasets for the traffic noise tracking used in the manuscript "Seismic source tracking with six degree-of-freedom ground motion observations". The README file describes the data structures.</p>
Smart-gloves Hand Motion Tracking for Live Endoscopic Submucosal Dissection Training
ClinicalTrials.gov study NCT06683326. IPD Sharing: NO. Countries: 0. Publications: 14.
Examining the Use of a Novel Immersive Motion Tracking Upper Extremity Exercise Program for Acute Hospitalized Patients
ClinicalTrials.gov study NCT06222554. IPD Sharing: YES. Countries: 1. Publications: 0.
Efficacy of Using 3D Motion Tracking Toothbrush in Dental Plaque Control
ClinicalTrials.gov study NCT04627324. IPD Sharing: Not stated. Countries: 0. Publications: 2.
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.