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THÖR-MAGNI: A Large-scale Indoor Motion Capture Recording of Human Movement and Interaction
<h1>The THÖR-MAGNI Dataset Tutorials</h1> <p>THÖR-MAGNI datasets is a novel dataset of accurate human and robot navigation and interaction in diverse indoor contexts, building on the previous <a href="https://ieeexplore.ieee.org/abstract/document/8954833/">THÖR dataset protocol</a>. We provide position and head orientation motion capture data, 3D LiDAR scans and gaze tracking. In total, THÖR-MAGNI captures <strong>3.5 hours of motion of 40 participants on 5 recording days</strong>.</p> <p>This data collection is designed around systematic variation of factors in the environment to allow building cue-conditioned models of human motion and verifying hypotheses on factor impact. To that end, THÖR-MAGNI encompasses 5 scenarios, in which some of them have different conditions (i.e., we vary some factor):</p> <ul> <li>Scenario 1 (plus conditions A and B): <ul> <li> Participants move in groups and individually;</li> <li> Robot as static obstacle;</li> <li> Environment with 3 obstacles and lane marking on the floor for <strong>condition B</strong>;</li> </ul> </li> </ul> <ul> <li> Scenario 2: <ul> <li> Participants move in groups, individually and transport objects with variable difficulty (i.e. bucket, boxes and a poster stand);</li> <li> Robot as static obstacle;</li> <li> Environment with 3 obstacles;</li> </ul> </li> </ul> <ul> <li>Scenario 3 (plus conditions A and B): <ul> <li> Participants move in groups, individually and transporting objects with variable difficulty (i.e. bucket, boxes and a poster stand). We denote each role as: <em>Visitors-Alone, Visitors-Group 2, Visitors-Group 3, Carrier-Bucket, Carrier-Box, Carrier-Large Object;</em></li> <li> Teleoperated robot as moving agent: in <strong>condition A</strong>, the robot moves with differential drive; in <strong>condition </strong>B, the robot moves with omni-directional drive;</li> <li> Environment with 2 obstacles;</li> </ul> </li> </ul> <ul> <li>Scenario 4 (plus conditions A and B): <ul> <li> All participants, denoted as <em>Visitors-Alone HRI</em> interacted with the teleoperated mobile robot;</li> <li> Robot interacted in two ways: in <strong>condition A</strong> (Verbal-Only), the Anthropomorphic Robot Mock Driver (ARMoD), a small humanoid NAO robot on top of the mobile platform, only used speech to communicate the next goal point to the participant; in <strong>condition B</strong> the ARMoD used speech, gestures and robotic gaze to convey the same message;</li> <li> Free space environment</li> </ul> </li> </ul> <ul> <li>Scenario 5: <ul> <li> Participants move alone (<em>Visitors-Alone</em>) and one of the participants, denoted as <em>Visitors-Alone HRI</em>, transport objects and interact with the robot;</li> <li> The ARMoD is remotely controlled by an experimenter and proactively offers help;</li> <li> Free space environment;</li> </ul> </li> </ul> <h2>Preliminary steps</h2> <p>Before proceeding, make sure to download the data from ZENODO</p> <h3>1. Directory Structure</h3> <p>├── CLiFF_Maps <- Directory for CLiFF Maps for all files</p> <p> ├── Files <- Directory for the csv files</p> <p> ├── Readme.md</p> <p>├── CSVs_Scenarios <- Directory for aligned data for all scenarios</p> <p> ├── Scenario_1 <- Directory for the csv files for Scenario 1</p> <p> ├── Scenario_2 <- Directory for the csv files for Scenario 2</p> <p> ├── Scenario_3 <- Directory for the csv files for Scenario 3</p> <p> ├── Scenario_4 <- Directory for the csv files for Scenario 4</p> <p> ├── Scenario_5 <- Directory for the csv files for Scenario 5</p> <p>├── docs</p> <p> ├── tutorials.md <- Tutorials document on how to use the data</p> <p>├── Lidar_sample</p> <p> ├── Files <- Directory for sample files</p> <p> ├── 170522_SC3B_1 <- Directory for the pcd files</p> <p> ├── 170522_SC3B_1.csv <- Synchronization file with QTM</p> <p> ├── manual_view_point.json <- json file with manual view point for visualization</p> <p> ├── requirements.txt <- script pip requirements</p> <p> ├── visualize_pcd.py <- script visualize the lidar data</p> <p> ├── Readme.md</p> <p>├── maps <- Directory for maps of the environment (PNG files) and offsets (json file)</p> <p> ├── offsets.json <- Offsets of the map with respect to the global coordinate frame origin</p> <p> ├── {date}_SC{sc_id}_map.png <- Maps for `date` in {1205, 1305, 1705, 1805} and `sc_id` in {1A, 1B, 2, 3}</p> <p> ├── 3009_map.png <- Map for the Scenarios 4A, 4B and 5</p> <p>├── MP4_Videos</p> <p> ├── Files <- Directory for the mp4 files</p> <p> ├── pupil_scene_camera_instrinsics.json <- json file with the intrinsics of pupil camera</p> <p>├── TSVs_RAWET <- Directory for the TSV files for the Raw Eyetracking data for all Scenarios</p> <p> ├── synch_info.csv <- Event markers necessary to align motion capture with eyetracking data</p> <p> ├── Files <- Directory with all the raw eyetracking TSV files</p> <p>├── goals_positions.csv <- File with the goals locations</p> <p> </p> <h3>2. Data Structure and Dataset Files</h3> <p>Withing each Scenario directory, each csv file contains:</p> <p><strong>2.1. Headers</strong></p> <p>The dataset metadata overview contains important information found in the CSV file headers. This reference is designed to help users understand and use the dataset effectively. The headers include details such as FILE_ID, which provides information on the date, scenario, condition, and run associated with each recording. The header of the document includes important quantities such as the number of frames recorded (N_FRAMES_QTM), the count of rigid bodies (N_BODIES), and the total number of markers (N_MARKERS).</p> <p>It also provides information about the order of the contiguous rotation matrix (CONTIGUOUS_ROTATION_MATRIX), modalities measured with units, and specified measurement units. The text presents details on the eyetracking devices used in each recording, including their infrared sensor and scene camera frequencies, as well as an indication of the presence of eyetracking data.</p> <p>The header provides specific information about rigid bodies, including their names (BODY_NAMES), role labels (BODY_ROLES), and the number of markers associated with each rigid body (BODY_NR_MARKERS). Finally, the table lists all marker names used in the file.</p> <p>This metadata provides researchers and practitioners with essential guidance on recording information, data quantities, and specifics about rigid bodies and markers. It is a valuable resource for understanding and effectively using the dataset in the CSV files.</p> <p><strong>2.2. Trajectory Data</strong></p> <p>The remaining portion of the CSV file integrates merged data from the motion capture system and eye tracking devices, organized based on participants' helmet rigid bodies. Columns within the dataset include XYZ coordinates of all markers, spatial centroid coordinates, 6DOF orientation of the object's local coordinate frame, and <em>if available</em> eye tracking data, encompassing 2D/3D gaze coordinates, scene recording frame numbers, eye movement types, and IMU data.</p> <p>Missing data is denoted by "N/A" or an empty cell. Temporal indexing is facilitated by the "Time" or "Frame" column, indicating timestamps or frame numbers. The motion capture system records at 100Hz, Tobii Glasses at 50Hz (Raw); 25 Hz (Camera), and Pupil Glasses at 100Hz (Raw); 30 Hz (Camera). The dataset is structured around motion capture recordings, and for each rigid body, such as "Helmet_1," details per frame include XYZ coordinates of markers, centroid coordinates, and a 9-element rotational matrix describing helmet orientation.</p> <table> <tbody> <tr> <td><strong>Header</strong></td> <td><strong>Explanation</strong></td> </tr> <tr> <td>Helmet_1 - 1 X</td> <td>X-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - 1 Y</td> <td>Y-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - 1 Z</td> <td>Z-Coordinate of Marker Number 1</td> </tr> <tr> <td>Helmet_1 - [...]</td> <td><em>Same for Marker 2 and 3 of Helmet_1</em></td> </tr> <tr> <td>Helmet_1 Centroid_X</td> <td>X-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 Centroid_Y</td> <td>Y-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 Centroid_Z</td> <td>Z-Coordinate of the Centroid</td> </tr> <tr> <td>Helmet_1 R0</td> <td>1st Element of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> <tr> <td>Helmet_1 R[..]</td> <td>Same for R1- R7</td> </tr> <tr> <td>Helmet_1 R8</td> <td>9th Element of the CONTIGUOUS_ROTATION_MATRIX</td> </tr> </tbody> </table> <p> </p> <p><strong>2.3. Eyetracking Data</strong></p> <p>The eye tracking data in the dataset includes 16 participants, providing a comprehensive dataset of over 500 minutes of recorded data across the different activities and scenarios with three different eyetracking devices. Devices are denoted with a special "Tracker_ID" in the dataset, i.e.:</p> <table> <tbody> <tr> <td><strong>Tracker ID</strong></td> <td><strong>Eyetracking Device</strong></td> </tr> <tr> <td>TB2</td> <td>Tobii 2 Glasses</td> </tr> <tr> <td>TB3</td> <td>Tobii 3 Glasses</td> </tr> <tr> <td>PPL</td> <td>Pupil Insivisible Glasses</td> </tr> </tbody> </table> <p>Gaze points are classified into fixations and saccades using the Tobii I-VT Attention filter, which is specifically optimized for dynamic scenarios with a velocity threshold of 100°. Eyetracking devices were systematically repeated after each 4-minute recording to account for natural variations in participants' eye shapes and to improve the gaze estimation algorithms. In addition, gaze estimation adjustments for the pupil invisible glasses were made after each 4-minute recording to mitigate potential drifts. It's worth noting that the scene cameras of the eye tracking glasses had different fields of view. The scene camera of the Pupil Invisible Glasses had a 1088x1080 image with both horizontal (HFOV) and vertical (VFOV) opening angles of 80°, while the Tobii Glasses provided a 1920x1080 image with different opening angles for Tobii Glasses 3 (HFOV: 95°, VFOV: 63°) and Tobii Glasses 2 (HFOV: 82°, VFOV: 52°).</p> <p><strong>NOTE AS OF 2024:</strong> <strong>Videos are NOW part</strong> of the dataset</p> <p>For one participant, wearing the Tobii Glasses 3 and Helmet_6, the data would be denoted as:</p> <table> <tbody> <tr> <td><strong>Header</strong></td> <td><strong>Explanation</strong></td> </tr> <tr> <td><em>Helmet_6 - [...]</em></td> <td><em>*X,Y,Z Coordinates for 5 markers*</em></td> </tr> <tr> <td><em>Helmet_6 [...]</em></td> <td><em>X,Y,Z Coordinates for 1 Centroid* </em></td> </tr> <tr> <td><em>Helmet_6 R[...]</em></td> <td><em>9 Elements of the CONTIGUOUS_ROTATION_MATRIX</em></td> </tr> <tr> <td> <p>Helmet_6 TB3_Accelerometer_[...]</p> </td> <td>Accelerometer data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_Gyroscope_[...]</td> <td>Gyroscope data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_Magnetometer_[...]</td> <td>Magnetometer data along the X,Y,Z Axis</td> </tr> <tr> <td>Helmet_6 TB3_G2D_[...]</td> <td>2D Eye tracking data (X,Y)</td> </tr> <tr> <td>Helmet_6 TB3_G3D_[...]</td> <td>3D Cyclopic Eye gaze Vector (X,Y,Z)</td> </tr> <tr> <td>Helmet_6 TB3_Movement</td> <td>Eye movement type (N/A, Fixation or Saccade)</td> </tr> <tr> <td>Helmet_6 TB3_SceneFNr</td> <td>Frame number of the scene camera recording </td> </tr> </tbody> </table> <h2>How to use and tools</h2> <p><a href="https://github.com/tmralmeida/magni-dash/tree/dash-public">magni-dash</a></p> <p><a href="https://magni-dash.streamlit.app">This</a> is a dashboard to quickly visualize our data: trajectories, speeds, eye-tracking data and LiDAR visualization (for Scenario 3). If you cannot use the dashboard from the streamlit cloud service, just run it locally by following the <a href="https://github.com/tmralmeida/magni-dash/tree/dash-public">README File</a>.</p> <p><a href="https://github.com/tmralmeida/thor-magni-tools">thor-magni-tools</a></p> <p>To install and use the package, follow the instructions on the <a href="https://github.com/tmralmeida/thor-magni-tools/blob/main/README.md">README file</a> . This package comprises:</p> <ul> <li>3D trajectory restoration: agents in the scene wore an helmet. The helmet is equipped with markers, which are tracked by the Mocap system. 3D trajectory restoration stands for <a href="https://github.com/tmralmeida/thor-magni-tools/blob/main/thor_magni_tools/preprocessing/cfg.yaml#L3">two different ways</a> of aggregating the trackings of the various markers in each helmet: (1) <em>3D-restoration</em> and (2) <em>3D-best marker</em>. The former applies an average over the locations of all visible markers while the latter uses the marker with highest tracking duration.</li> <li>3D pre-processing of restored trajectories: interpolation, downsampling and smoothing. To run the 3D pre-processing, check <a href="https://github.com/tmralmeida/thor-magni-tools?tab=readme-ov-file#preprocessing#preprocessing">this</a>.</li> <li>trajectory analysis: trajectory-related metrics like tracking duration (in seconds), number of 8s <em>tracklets</em>, motion speed, path efficiency score, and minimal distance between people. To run the trajectory analysis, check <a href="https://github.com/tmralmeida/thor-magni-tools?tab=readme-ov-file#preprocessing#analysis">this</a>.</li> </ul>
Dataset of "Towards Artefact Aware Human Motion Capture using Inertial Sensors Integrated into Loose Clothing"
<p>This dataset was used in the publication:<br> <strong>Towards Artefact Aware Human Motion Capture using Inertial Sensors Integrated into Loose Clothing</strong><br> presented at the IEEE International Conference on Robotics and Automation 2022</p> <p><strong>Abstract:</strong><br> Inertial motion capture has become an attractive alternative to optical motion capture for human joint angle estimation outside the laboratory. Usually inertial sensors are assumed to be tightly fixed to the body segments, which can be cumbersome regarding setup-time and ease-of-use. However, integrating the sensors directly into clothing, usually, results in additional clothing motion relative to the motion of the underlying bones that should be captured.<br> In this work we propose the <em>Difference Mapping</em> distributions approach that corrects the segment orientations of a given inertial motion capture system that assumes tightly coupled sensors.<br> The approach allows to reduce the joint angle errors due to clothing artefacts by at least 77.2 percent for people with similar morphology performing a similar task as seen in the training data, including an ergonomic assessments scenario at work places with 10 participants. <br> Moreover, we show that the uncertainty of the distribution can be used to measure the reliability of the predicted map if e.g. the motion is further away from the training data to allow for an artefact aware inertial motion tracking approach.<br> The experimental data for this study is available online</p> <p> </p> <p><strong>Data structure:</strong><br> The data contains trials of 12 subjects for different motions, wearing at the same time a tight setup with inertial sensors and a loose working suit with integrated inertial sensors. It contains the raw IMU data, raw Magnetometer data and the estimated segment orientations using a Sensor Fusion engine provided by Sci-Track.<br> Please note, that in the publication only the first 10 subjects were used and the upper body information was used only. The Sternum sensor of the tight setup of subjects 11, 12 and 13 tilted slowly during the long-term measurements. For this reason only 10 subjects were included in the study. However all remaining sensor of the tight setup were not tilted during recording. In particular the lower body recordings of all subjects are not corrupted.<br> <br> Code samples, a visualizer and further useful information is provided under the following git repository:<br> https://github.com/lorenzcsunikl/Dataset-of-Artefact-Aware-Human-Motion-Capture-using-Inertial-Sensors-Integrated-into-Loose-Clothing</p>
THÖR-Magni (Demo Subset): a new multi-modal context-rich dataset of human-robot motion
<p>The Magni Human Motion Dataset provides high-quality tracking information from motion capture, eye-gaze trackers, and on-board robot sensors in a semantically rich environment. To induce natural behavior of recorded participants, we utilized loosely scripted task assignment, which induced participants to navigate through a dynamic laboratory environment in a natural and purposeful way. The dataset sets a high-quality standard as realistic and accurate data is enhanced with semantic information, enabling development of new algorithms that rely not only on tracking information but also on contextual cues of moving agents, static and dynamic environments.</p> <p> </p> <p>Link to dashboard that uses the data: https://magni-dash.streamlit.app/</p> <p><br> Here we publish a subset of the final dataset, to accompany the presentation at the 2023 IEEE International Conference on Robotics and Automation (ICRA)</p>
Visual-inertial input datasets for SLAM applications containing extreme and human-like motion patterns
<p>Recorded datasets in compressed rosbag format, which contain visual and IMU sensor information that are bearing high resemblance to the movement of a human player with a handheld AR-capable device.</p> <p>For machine learning training and validation tasks, separate dataset are available containing motion patterns in a wide range from steady camera image to extremely challenging movements.</p>
Dataset for the article "Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion" by Roman Barth and Haitham Shaban
<p>The data set comprises all raw microscopy images and DFCC analyses as presented in </p> <p><strong>Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion</strong></p> <p>by Roman Barth and Haitham Shaban, published in Nucleus (https://doi.org/10.1080/19491034.2022.2088988)</p> <p>There are two folders for RNAPII and DNA each, one for the raw images and one for the processed DFCC data, supplied as .mat files.</p> <p>Every folder contains three sub-folders containing the data for the conditions: +Serum, -Serum, and +DRB.</p>
Dataset: Feedback contribution to surface motion perception in the human early visual cortex
<p><strong>Dataset</strong></p> <p>Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (<a href="https://doi.org/10.1101/653626">biorxiv</a>).</p> <p><strong>Description</strong></p> <p>fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.</p> <p>├── sub-01<br> │ ├── anat<br> │ │ └── ...<br> │ ├── func<br> │ │ └── ...<br> │ ├── func_se<br> │ │ └── ...<br> │ └── func_se_op<br> │ └── ...</p> <p>The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.</p> <p>Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.</p> <p>The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on <a href="https://github.com/ingo-m/pyprf">github</a>.</p> <p>For example videos of the main experimental stimuli, see <a href="https://doi.org/10.5281/zenodo.2583017">zenodo.2583017</a>. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on <a href="https://github.com/ingo-m/PacMan/tree/master/stimuli/experiment">github</a>.</p> <p>The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on <a href="https://github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata">github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata</a>.</p> <p><strong>Analysis</strong></p> <p>The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on <a href="https://github.com/ingo-m/PacMan/tree/master/docker">github</a>.</p> <p>If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_PacMan_Image_Jessie.txt">here</a> and <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_CBS_Image.txt">here</a>.</p> <p>Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. <a href="http://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_01.sh">~/analysis/20180118/metascript_01.sh</a> for subject 20180118) and a separate script for the depth sampling (e.g. <a href="https://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_03.sh">~/analysis/20180118/metascript_03.sh</a>). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).</p> <p>Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">py_depthsampling</a>.</p> <p><strong>Further resources</strong></p> <p>Please refer to the research paper for more details: <a href="https://doi.org/10.1101/653626">https://doi.org/10.1101/653626</a></p> <p>The analysis pipeline can be found on <a href="https://github.com/ingo-m/PacMan">https://github.com/ingo-m/PacMan</a></p> <p>A separate repository contains the code used for visualisation of depth-sampling results: <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">https://github.com/ingo-m/py_depthsampling/tree/PacMan</a></p> <p>Free & open source software package for population receptive field mapping: <a href="https://github.com/ingo-m/pyprf">https://github.com/ingo-m/pyprf</a></p> <p> </p>
Evaluating 3D Human Motion Capture using Apple ARKit against the Vicon System: A Dataset
<p><strong>A journal paper which was published in Applied Sciences gives detailed information about the dataset.</strong></p> <p>Reimer, L.M.; Kapsecker, M.; Fukushima, T.; Jonas, S.M. Evaluating 3D Human Motion Capture on Mobile Devices. Appl. Sci. <em>(2022)</em></p> <p><a href="https://www.mdpi.com/2076-3417/12/10/4806">https://www.mdpi.com/2076-3417/12/10/4806</a></p> <p>Please cite the corresponding paper when using the dataset.</p> <p> </p> <p>A dataset containing anonymized exercise data for eight exercises from ten subject. The exercise data was recorded with two iPads 11" (2021 version, Apple Inc., Cupertino, CA, USA) and a Vicon system. The two iPads were positioned frontal and in a 30° angle to the left side of the subject. The Vicon system used 14 cameras and captured the motion using the Full-body Plug-in-gait model.</p> <p>The dataset contains 220 files, 22 per subject. The structure of the dataset contains 10 folders, one per subject. Each folder contains two subfolders: ARKit and Vicon. Each ARKit folder holds two CSV files. Each Vicon folder holds 16 files, two per exercise: a .csv file with the motion data and a .xcp file containing meta data about the recording, including the camera setup and start/stop timestamps.</p> <p>Du to export problems, the ARKit files for the Side View do not always contain all joint data. The upper body joints are only available for three out of the ten subjects for the Side View.</p>
Dataset of Human Hand Motion Planning
<p>This dataset contains 544 human hand motion trajectories in a point-to-point reaching experiment. The purpose of this dataset is to provide human demonstrations for imitation-learning- and reinforcement-learning -based robot motion planning. Refer to 'ReadMe.md' in the zip file for details about the format and usage of the dataset.</p>
Fig. 14 in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 14. Grasping pattern of the hand. A. Grasping a bough of the tree; B. Holding a piece of fruit. Sketches are drawn imitating the hand motion shown in movies of NHK BS TV program (Yamagiwa, 2012).
Fig. 13. Knuckle walk. A in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 13. Knuckle walk. A. The trunk tilts backward because of longer forelimbs; B. The knuckle walk of gorillas is walking with MP joints in extension and PIP joints in deep flexion bearing the weight on the dorsal aspects of the middle phalanges of the hands. Sketches are drawn imitating the walking style shown in movies of NHK BS TV program (Yamagiwa, 2012).
Fig. 12 in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 12. Dorsal aspect of the metacarpophalangeal joint. A. A shallow groove (white arrows) is seen along the dorsal margin of the articular cartilage of the metacarpal head; B. The dorsal edge of the proximal phalanx base sits in the groove by extending the MP joint passively; C. Grooves are deep enough at the 3rd and 4th metacarpals (white arrows), but not at the 2nd and 5th in the skeletal specimen of another gorilla stored in National Museum of Nature and Science.
Fig. 11. Extensor apparatus and retaining ligaments. A in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 11. Extensor apparatus and retaining ligaments. A. Extensor apparatus consists of two components: extrinsic compornent (dark green) and intrinsic component (light green). Ligamentous structures are shown in orange color. The interosseous hood is the proximal hood-like portion of the aponeurotic expansion of the interosseous tendon (a thick arrow). (modified from Tubiana and Valentin, 1964); B. On the radial side of the middle finger, the interosseous hood is shown. Adherent to the proximal margin of this interosseous hood, a sagittal band expands between DTML and EDC tendon; C. The oblique retinacular ligament (ORL) is well developed at the radial aspect.
Fig. 9. Underdeveloped EIP. A in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 9. Underdeveloped EIP. A small slender muscle sitting at the dorsoradial aspect of the ulna is judged to be underdeveloped EIP muscle although its tendon attaches to the dorsal aspect of the wrist (a white block arrow).
Fig. 7 in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 7. Innervation of intrinsic muscles by the radial bundle of UABM. The radial bundle first gives off branches to the hypothenar muscles and to the lumbrical muscles of the little and ring fingers (marked with asterisks), and then to interossei (4th DI, 3rd PI and 3rd DI, 2nd PI).
Fig. 15 in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 15. The mechanism to block hyperextension of the MP joint with the function of the sagittal band. A. When the finger is flexed, the sagittal band passes over the joint axis of the metacarpal head in coordination with the distalward movement of the extensor tendon; B. When the MP joint is extended and surpasses the range of extension, the traction force of the finger extensor is blocked with the tightened sagittal band that transmits the force palmarward so that the proximal phalanx base does not move further to hyperextension.
Fig. 5 in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 5. Palmar aspect of the proximal phalanx. An osseous crest is seen along each edge of the palmar aspect of the phalanx and the groove is formed between those crests. The head of the proximal phalanx slants palmarward.
Fig. 3. Underdeveloped thumb flexor. A in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 3. Underdeveloped thumb flexor. A. The tendon of the thumb flexor is much thinner than that of FDS or FDP; B. Its muscle belly (uFPL) bifurcates from the distal portion of FDP muscle of the index finger; C. Pulling FDP index finger at its myotendinous junction with a retractor revealed that the thumb and the index finger were flexed simultaneously.
Fig. 4. Finger flexor sheath and pulley system. A in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 4. Finger flexor sheath and pulley system. A. The entire sheath of the finger flexor of the ring finger is shown with the names of individual pulleys. The very thick and wide A2′ pulley (a white thick arrow) is located between A2 and C1; B. The inner surface of the sheath showing that A2′ pulley is twice as wide as A3.
Fig. 2 in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 2. The subcutaneous structures of the palm. A. The palmar aponeurosis is absent except for its most distal parts: natatory ligaments (white arrows); B. The ligamentous fibers (white arrows) arising from the distal margin of the flexor retinaculum; C. Ligamentous septum (a white arrow) along the flexor tendon formed with those ligamentous fibers.
Fig. 10 in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 10. The insertions of the first dorsal interosseous muscle. A. The distal portion of the 1st DI muscle is voluminously muscular and passes into the tendinous portion of the interosseous hood at the level of the proximal portion of the proximal phalanx; B. The tendinous insertion to the proximal phalanx base is exposed by reflect- ing the extensor apparatus. The tendon has been divided to explore the deeper structure and then re-sutured.
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.