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51 results for “Human motion”
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>
Data set for "Columnar clusters in the human motion complex reflect consciously perceived motion axis"
<p>Accompanying data for manuscript “Columnar clusters in the human motion complex reflect consciously perceived motion axis” written by Marian Schneider, Valentin Kemper, Thomas Emmerling, Federico De Martino, Rainer Goebel, submitted, November 2018.</p> <p>Imaging files<br> -------------<br> * T1w and PDw images, only acquired in session 1<br> * 2 runs task-MotLoc, only acquired in session 2<br> * 5-6 runs task-ambiguous (called "Experiment 1" in accompanying manuscript, divided across 2 scanning sessions)<br> * 5-6 runs task-unambiguous (called "Experiment 2" in accompanying manuscript, divided across 2 scanning sessions)</p> <p><br> Acquisition details<br> -------------------<br> For visualization of the functional results, we acquired scans with structural information in the first scanning session. At high magnetic fields, MR images exhibit high signal intensity variations that result from heterogeneous RF coil profiles. We therefore acquired both T1w images and PDw images using a magnetization-prepared 3D rapid gradient-echo (3D MPRAGE) sequence (TR: 3100 ms (T1w) or 1440 ms (PDw), voxel size = 0.6 mm isotropic, FOV = 230 x 230 mm2, matrix = 384 x 384, slices = 256, TE = 2.52 ms, FA = 5°). Acquisition time was reduced by using 3× GRAPPA parallel imaging and 6/8 Partial Fourier in phase encoding direction (acquisition time (TA): 8 min 49 s (T1w) and 4 min 6 s (PDw)).</p> <p>To determine our region of interest, we acquired two hMT+ localiser runs. We used a 2D gradient echo (GE) echo planar imaging (EPI) sequence (1.6 mm isotropic nominal resolution; TE/TR = 18/2000 ms; in-plane field of view (FoV) 150×150 mm; matrix size 94 x 94; 28 slices; nominal flip angle (FA) = 69°; echo spacing = 0.71 ms; GRAPPA factor = 2, partial Fourier = 7/8; phase encoding direction head - foot; 240 volumes). We ensured that the area of acquisition had bilateral coverage of the posterior inferior temporal sulci, where we expected the hMT+ areas. Before acquisition of the first functional run, we collected 10 volumes for distortion correction - 5 volumes with the settings specified here and 5 more volumes with identical settings but opposite phase encoding (foot - head), here called "phase1" and "phase2".</p> <p>For the sub-millimetre measurements (Experiments 1: here called "task-ambiguous" and Experiments 2: here called "task-unambiguous"), we used a 2D GE EPI sequence (TE/TR = 25.6/2000 ms; in-plane FoV 148×148 mm; matrix size 186 x 186; slices = 28; nominal FA = 69°; echo spacing = 1.05 ms; GRAPPA factor = 3, partial Fourier = 6/8; phase encoding direction head - foot; 300 volumes), yielding a nominal resolution of 0.8 mm isotropic. Placement of the small functional slab was guided by online analysis of the hMT+ localizer data recorded immediately at the beginning of the first session. This allowed us to ensure bilateral coverage of area hMT+ for every subject. In the second scanning session, the slab was placed using Siemens auto-align functionality and manual corrections. Before acquisition of the first functional run, we collected 10 volumes for distortion correction (5 volumes with opposite phase encoding: foot - head). During acquisition, runs for the ambiguous and unambiguous motion experiments were interleaved.</p>
ROAG (Reaching Over A Grid): Dataset of Human Reaching Motions Over a Discretely Sampled Workspace
<p>Joint absence in people with upper limb differences leads to compensatory motions. Such compensation has long been a topic of study, but typically only for a single object/user layout, which is unlikely to generalise across a workspace.<br><br>To better understand how arm motion and compensatory movements vary over the workspace, we created the <strong>ROAG</strong> dataset. ROAG is pronounced 'Rogue' and stands for <strong>R</strong>eaching <strong>O</strong>ver <strong>A</strong> <strong>G</strong>rid. The dataset was recorded at the GRAB Lab of Yale University (USA) and has since been processed at the Manipulation and Touch Lab of Imperial College London (UK). <br><br>ROAG is a motion capture dataset involving arm and torso pose during reach-to-grasp actions for 49 equally spaced cylindrical targets, orientated horizontally or vertically. The data is collected from seven able-bodied participants and two transradial amputees who use prosthetic devices. In the case of able-bodied participants, different bracing systems were applied to the arm to immobilise wrist joints and simulate transradial limb loss, leading to compensatory motions. In total, the dataset consists of 2450 reaching trajectories. This resource hosts the collected dataset and the related MATLAB analysis files.<br><br>The dataset has been the basis of the following publications:</p> <ul> <li>A. J. Spiers, Y. Gloumakov and A. M. Dollar, "Transradial Amputee Reaching: Compensatory Motion Quantification Versus Unaffected Individuals Including Bracing," in <em>IEEE Transactions on Medical Robotics and Bionics</em>, vol. 6, no. 2, pp. 706-717, May 2024, <a href="https://doi.org/10.1109/TMRB.2024.3381339" target="_blank" rel="noopener">https://doi.org/10.1109/TMRB.2024.3381339</a></li> <li>Qihan Yang, Yuri Gloumakov, and Adam J. Spiers. "Multi-feature Compensatory Motion Analysis for Reaching Motions Over a Discretely Sampled Workspace." in <em>IEEE RAS EMBS 10th International Conference on Biomedical Robotics and Biomechatronics (BioRob 2024),</em> <a href="https://doi.org/10.48550/arXiv.2409.05871" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2409.05871</a></li> <li>Adam J Spiers, Yuri Gloumakov, Aaron M Dollar, "Examining the impact of wrist mobility on reaching motion compensation across a discretely sampled workspace", in IEEE <em>7th International Conference on Biomedical Robotics and Biomechatronics (BioRob 2018), </em><a href="https://doi.org/10.1109/BIOROB.2018.8487871" target="_blank" rel="noopener">https://doi.org/10.1109/BIOROB.2018.8487871</a><br><br></li> </ul>
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>
Perceiving tempo in incongruent audiovisual presentation of human motion: Evidence for a visual driving effect
<p>The video set includes 81 audiovisual stimuli which served in the bisection experiment of the study "Perceiving tempo in incongruent audiovisual presentation of human motion: Evidence for a visual driving effect". The auditory sound tracks (bass drum) and visual stimuli (point-light displays of biological motions) each cover the tempo range from 60 to 180BPM, 15BPM per step. Coupling of the stimuli from both modalities results in 81 stimuli in total. </p>
Dataset for: Spinal motion and muscle activity during active trunk movements - comparing sheep and humans adopting upright and quadrupedal postures
<p>Datasets used for the manuscript "Spinal motion and muscle activity during active trunk movements - comparing sheep and humans adopting upright and quadrupedal postures", accepted in PLOS ONE</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>
Perceiving Tempo in Incongruent Audiovisual Presentations of Human Motion: Evidence for a Visual Driving Effect
<p>Data set from the study published in Timing & Time Perception.</p>
Supplemental Figure 1: Metachrony in motion: Video of human bronchi.
<p><span>Supplemental Figure 1: Metachrony in motion: Video of human bronchi. </span><span>(A</span><span>)</span><span> Example of human bronchi in </span><span>metachrony</span><span> in real time, acquisition speed was 40 frames per second. (B) Same ROI as presented in </span><span>A, but</span><span> played at 10% speed. Note the white flash of cilia traveling across the plane. (C) Corresponding ribbon diagram to B, demonstrating the line of synchrony and plane of </span><span>metachrony</span><span>. </span></p>
Raw data of human stepping motion
<p>Data necessary to support the findings in this study. </p> <p>Raw data of foot motion trajectories of pedestrians in each experiment run.</p>
Dataset: Imaging the columnar functional organization of human area MT+ to axis-of-motion stimuli using VASO at 7 Tesla
<p>First release upon acceptance of the paper. </p>
CARRT - Motion Capture Data for Robotic Human Upper Body Model
<p>As advancements in the study of human activity have progressed in recent years, researchers have directed their attention towards analyzing human daily activities to investigate a diverse range of performance metrics unconsciously optimized by individuals while engaged in specific tasks. To replicate these movements in robotic systems based on human models, researchers have developed a framework for robot motion planning capable of utilizing various optimization methods to reproduce such motions through human demonstrations. In this process, capturing the movements of the human body and the objects involved in the demonstrations is imperative, as they provide essential information for the motion planning procedure. The objective of this dataset is to present human motion data while performing activities of daily living. This dataset encompasses comprehensive and precise whole-body motion data of individuals collected using a Vicon motion capture system, which facilitated the development of a full-body model integrated into OpenSim and MATLAB. The dataset comprises nine different daily living activities and eight Range of Motion activities performed by ten healthy participants. A publicly accessible whole-body human motion database has been established, encompassing raw motion data in .c3d format, motion data in .csv format for the OpenSim model, and post-processed motion data for the MATLAB model.</p>
Sequential human assembly and disassembly motions in human-robot coexisting environments
Open the record for dataset details and reuse information.
Data from: Mechanism for analogous illusory motion perception in flies and humans.
<p>Visual motion detection is one of the most important computations performed by visual circuits. Yet, we perceive vivid illusory motion in stationary, periodic luminance gradients that contain no true motion. This illusion is shared by diverse vertebrate species, but theories proposed to explain this illusion have remained difficult to test. Here, we demonstrate that in the fruit fly <i>Drosophila</i>, the illusory motion percept is generated by unbalanced contributions of direction-selective neurons' responses to stationary edges. First, we found that flies, like humans, perceive sustained motion in the stationary gradients. The percept was abolished when the elementary motion detector neurons, T4 and T5, were silenced. In vivo calcium imaging revealed that T4 and T5 neurons encode the location and polarity of stationary edges. Furthermore, our proposed mechanistic model allowed us to predictably manipulate both the magnitude and direction of the fly's illusory percept by selectively silencing either T4 or T5 neurons. Interestingly, human brains possess the same mechanistic ingredients that drive our model in flies. When we adapted human observers to moving light edges or dark edges, we could manipulate the magnitude and direction of their percepts as well, suggesting that mechanisms similar to the fly's may also underlie this illusion in humans. By taking a comparative approach that exploits <i>Drosophila</i> neurogenetics, our results provide a causal, mechanistic account for a long-known visual illusion. These results argue that this illusion arises from <a>architectures</a> for motion detection that are shared across phyla.</p>
MRI data of 3D radial multi-echo spin-echo acquisition of the human brain at 3T with and without deliberate motion
<p>Radial 3D multi-echo spin echo acquisition of a human brain acquired with a house made sequence on a Prisma Siemens 3T MRI scanner. </p> <p>The data have been converted in ISMRMRD format. </p> <p>Two raw datasets are available, one with deliberate motion and one without. </p> <p>The sequence allows for retrospective motion correction based on self-navigation. </p> <p>The images can be reconstructed with the code available on Github: https://github.com/nadegecorbin/Reco_MESE_RAD.git</p> <p>Reconstructed images and associated T2 maps are also provided in the folder "Reconstruction".</p> <p>For the motion case, intermediate images are stored in "Motion/Moco/RecoTRImages", the corrected echoes are in "Motion/Moco/RecoEchoes". Echo images without correction are in "Motion/Nomoco/RecoEchoes". </p> <p>For the No motion case, only data without motion correction are reconstructed and are located in "Nomotion/Nomoco/Recoechoes"</p> <p>A manuscript describing the method is under submission. </p> <p> </p>
Data from Schmitt et al. 2020: A causal role of area hMST for self-motion perception in humans. Cerebral Cortex Communications
<p><strong>Dataset associated with the following publication:</strong></p> <p>Constanze Schmitt, Bianca R Baltaretu, J Douglas Crawford, Frank Bremmer, A Causal Role of Area hMST for Self-Motion Perception in Humans, <em>Cerebral Cortex Communications</em>, Volume 1, Issue 1, 2020, tgaa042, <a href="https://doi.org/10.1093/texcom/tgaa042">https://doi.org/10.1093/texcom/tgaa042</a> Published 2020 July 20.</p> <p><strong>Description of dataset:</strong></p> <p>In our study we presented an optic flow stimulus simulating forward self-motion across a ground plane in one of three directions (30° to the left, straight ahead, 30° to the right) to eight human participants. In 57% of all trials TMS pulses were applied either to right hemisphere hMST or a control area while the optic flow stimulus was presented. Participants indicated the perceived heading of the self-motion stimulus.</p> <p>The dataset contains the responses (perceived headings) of all eight participants (s1 to s8). The responses are reported here in degrees with 0 degrees representing a movement straight ahead, negative values describing movements forward to the left and positive values describing movements forward to the right.</p> <p> </p>
Data from Schmitt et al. 2021: Preattentive processing of visually guided self-motion in humans and monkeys. Progress in Neurobiology
<p><strong>Dataset associated with the following publication:</strong></p> <p>Constanze Schmitt, Jakob C.B. Schwenk, Adrian Schütz, Jan Churan, André Kaminiarz, Frank Bremmer.<br> Preattentive processing of visually guided self-motion in humans and monkeys. Progress in Neurobiology,<br> 2021,102117. https://doi.org/10.1016/j.pneurobio.2021.102117. Published 2021 July 2.</p> <p><strong>Description of dataset:</strong></p> <p>In our study we presented an optic flow stimulus simulating forward self-motion across a ground plane in an oddball EEG paradigm to 12 human participants and 2 macaque monkeys. We simulated two different headings (forward-left vs. forward-right) presented either as standard or deviant trials and tested for the occurrence of a visual mismatch negativity (vMMN) by comparing the visual-evoked potentials (VEPs). </p> <p>Human data:</p> <p>The dataset contains the preprocessed VEPs of all 12 human participants already averaged over all trials per participant. It contains data from all electrodes used for analysis (P1, P2, O1, O2, PO3, PO4, CP1, CP2) divided into data recordings in standard ('_std') or deviant ('_odd') trials. Each of these files consists of four subfiles representing the presented combinations of heading (forward to the right: '_r' or forward to the left: '_l') and attention condition (attention towards fixation target: 'fix' or attention towards the ground plane: 'plane'). The data matrices in these subfiles are sorted as [participants x time-points].</p> <p>Monkey data:</p> <p>Each .mat file contains the preprocessed VEPs from one monkey ('data'), the corresponding electrode labels ('elec') and the common time vector in seconds ('time'). The data struct contains VEPs for left- and rightwards heading in separate subfields, which again contain standard ('stan') and deviant ('odd') presentations of that heading. Within each subcondition, the actual data matrices are sorted as [electrodes x time-points x trials]. Here, the numbering of electrodes corresponds to the electrode labels contained in the 'elec' variable.</p>
Bibliometric Analysis of Data Mining Techniques in Human Motion Research: Future Directions
<p>bibliometric dataset</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.