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18 results for “hand model”
Data for: Geometrical parameters for musculoskeletal modeling of hand
<p>Dataset to be linked with not yet published manuscript "Geometrical parameters for<br> musculoskeletal modeling of hand"</p> <p>In musculoskeletal modelling, parameters identification, such as the exact position and trajectories of muscle attachments, is a crucial issue. The main goal of this study was to calculate the position, attachment dimensions and cross section areas of twenty-five extrinsic and intrinsic hand muscle complexes. We integrated measurements taken from cadaveric preparations, magnetic resonance imaging and mathematical theory. Sixteen cadaveric preparations were dissected to draw up the anatomical maps including the position of muscle attachments, dimensions, shapes, cross section areas and variations. The magnetic resonance imaging of cadaveric upper extremity was performed to reconstruct the geometry of all bones and hand muscles. Using these outcomes, the muscle attachments and cross section areas were extracted and verified using the obtained morphological and morphometric analysis. The exact trajectories of muscle lines of action were computed using the modified weighted k-means method and Hungary algorithm. This work introduces a new approach to acquiring musculoskeletal modelling data in general and contributes extensive dataset to the hand musculature modelling in particular.</p> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the project n. 182 “Obstetrics 2.0 - Virtual models for the prevention of injuries during childbirth” realised within the frame of the Program INTERREG V-A: Cross- border cooperation between the Czech Republic and the Federal State of Germany Bavaria, Aim European Cross-border cooperation 2014-2020. The realisation is supported by financial means of the European Regional Development Fund (85 % of the costs) and the state budget of the Czech Republic (5 %). KI is part-funded by project No. CZ.02.1.01/0.0/0.0/16_019/0000787 “Fighting INfectious Diseases“, awarded by the Ministry of Education, Youth and Sports of the Czech Republic, financed from The European Regional Development Fund.</p>
RPC-Net Dataset. Simultaneous HD-sEMG Recordings on the Forearm and angles of a 29-DOF Hand Kinematic Model
<p>The dataset in this repository comprises data acquired during the doctoral research project of Giovanni Rolandino at the Nuffield Department of Surgical Sciences, University of Oxford. Five sub-datasets make up the repository:</p> <p>DS1: Simultaneous acquisition of high-density surface electromyography (HD-sEMG) signals from the forearm and hand position kinematics. Data were recorded from 12 healthy subjects while they cycled through 16 hand poses. HD-sEMG was acquired with traditional gel electrode arrays.</p> <p>DS2: A similar protocol to DS1 was followed, but the HD-sEMG was acquired using a novel dry-electrode array. This dataset includes 16 subjects. Whereas DS1 included data from a single session for each subject, DS2 includes two sessions, acquired hours to days apart; these sessions are identified as s1 and s2.</p> <p>DS3: This subset consists of two parts. DS3.a repeats the protocol used in DS2 with 4 subjects, introducing repositioning between trials. DS3.b includes the results of the real-time assessment of RPC-Net, a shallow neural network trained with data from DS3.a to estimate hand position from HD-sEMG activity. DS3.b contains the real-time output recorded during prompt-matching tasks and the corresponding targets.</p> <p>DS4: This subset includes data related to the assessment of RFC-Net, a shallow neural network designed to estimate hand position from neck muscle activation. Experiments were performed on 8 healthy participants and 8 participants with tetraplegia. DS4.a includes the data used for training the network, while DS4.b includes data from the testing phase of the algorithm. DS4.b.s1 includes results from a cursor control task, and DS4.b.s2 includes results from a virtual hand control task.</p> <p>AD1: Additional data related to the electrical validation of the dry-electrode array.</p> <p>Code for processing the data in this repository is available on Dropbox:<br>https://www.dropbox.com/scl/fo/nkvbse7evo0k8ou1utn7i/AMDh_MOZQJ6gCwDXGPadmZ0?rlkey=ynoix3anpc81v24hogn3fymb4&st=xrqx07q2&dl=0</p> <p>For additional information, readers are referred to the original papers detailing acquisition protocols and processing procedures:</p> <p>1) G. Rolandino, M. Gagliardi, T. Martins, G. L. Cerone, B. Andrews, J. J. FitzGerald. Developing RPC-Net: Leveraging High-Density Electromyography and Machine Learning for Improved Hand Position Estimation. IEEE Transactions on Biomedical Engineering, 71(5):1617-1627, May 2024. doi:10.1109/TBME.2023.3346192.</p> <p>2) G. Rolandino, C. Zangrandi, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. HDE-Array: Development and Validation of a New Dry Electrode Array Design to Acquire HD-sEMG for Hand Position Estimation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 32:4004-4013, 2024. doi:10.1109/TNSRE.2024.3490796.</p> <p>3) G. Rolandino, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Performance of a ML-Based 3-DoF Kinematic Model in Estimating Hand Position from High-Density EMG. Presented at IFESS Conference, Bath, UK, September 2024.</p> <p>4) G. Rolandino, L. Lion, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Artificial Neural Networks for HD-sEMG-Based Hand Position Estimation: Addressing Inter- and Intra-Subject Variability. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>5) G. Rolandino, G. Parisi, T. Vieira, G. L. Cerone, B. Andrews, J. J. FitzGerald. Real-Time Hand Kinematic Estimation with HD-sEMG and Artificial Neural Networks: Feasibility and Effects of Multi-Subject Training and Visual Feedback. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>6) G. Rolandino, V. Taboni Lisboa, T. Vieira, A. Cliquet Jr., B. Andrews, J. J. FitzGerald. HD-sEMG-Based Control Using Neck Muscles and Shallow Neural Networks: Assessing Performance in Rehabilitation-Oriented Tasks. Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering, July 2025.</p> <p>This dataset benefited from the support of all listed authors and arose from collaborations between the Oxford Neural Interfacing Group; LISiN (Politecnico di Torino, Turin, Italy); the Department of Orthopedics, Rheumatology and Traumatology (University of Campinas, SP, Brazil); and the Oxford Robotics Institute (University of Oxford, Oxford, UK). Part of this work was funded by the John Fell Oxford University Press Research Fund.</p> <p>The corresponding author is available for questions or clarification at g.rolandino@protonmail.com.</p>
Processed data and trained models for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf">https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</a></p> <p>Link to the Arxiv article: <a href="https://arxiv.org/abs/2402.17062">https://arxiv.org/abs/2402.17062</a></p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.</div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/11668766/draft/files/ckpts.zip/content" target="_blank" rel="noopener noreferrer">ckpts.zip</a> - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)</div> <div>├── <a href="../api/records/11668766/draft/files/annotations.zip/content" target="_blank" rel="noopener noreferrer">annotations.zip</a> - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.</div> <div>├── <a href="../api/records/11668766/draft/files/simple_ycb_models.zip/content" target="_blank" rel="noopener noreferrer">simple_ycb_models.zip</a> - Contains the preprocessed YCB objects for batched evaluation.</div> <div>├── <a href="../api/records/11668766/draft/files/test.zip/content" target="_blank" rel="noopener noreferrer">test.zip</a> - Contains the processed SDF files for DexYCB test set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_render_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_render_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: <a href="https://github.com/amathislab/HOISDF">https://github.com/amathislab/HOISDF</a></div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Raw data of: "Controlling Hand Movements Relying on Tactile Illusions: A Model Predictive Control Framework"
<p>in Fig4_a.txt: raw the data for the plot of Fig4_a (x and y of the first simulated trajectory from trajectory 1 to 50)</p> <p>in Fig4_b.txt: raw the data for the plot of Fig4_b </p> <p>in Fig4_c.txt raw the data for the plot of Fig4_b. Each column corresponds to the optimal angle of the plate for each of the 50 trajectories simulated in Fig4_a</p>
Anomaly-free, flavour-dependent U(1) charge assignments for Standard Model/Standard Model plus three right-handed neutrino fermionic content
<p>We present lists of anomaly-free charge combinations up to a maximum magnitude charge Qmax given by the number at the end of the filename. Filenames beginning "SMcharges" are for the Standard Model fermion content, whereas "SMnuRcharges" are for Standard Model plus three right-handed neutrino fermion content. Use the bunzip2 program to unpack the larger files with a bz2 suffix.</p> <p>The files searchU1.cpp and searchU1.h contain C++ files (in the 2014 standard) to produce the solutions. runme.sh is a bash script that compiles the programs and then runs it several times, once to produce each file.</p> <p>filterNeut.cpp contains an example program that reads in one of the solution lists, applies a filter to it, and only prints out solutions that satisfy the filter.</p> <p>These data and programs are based on this paper: https://arxiv.org/abs/1812.04602</p> <p> </p>
IntelliMan_WP5_Grasping, Manipulationand Arm-Hand Coordination_T5.4_Experience-and Model-Based Grasp Synthesis and Manipulation_Pushing_v0
<p>The dataset provides the data recorded during the experiments described in the paper “Costanzo, M.; De Simone, M.; Federico, S.; Natale, C. Non-Prehensile Manipulation Actions and Visual 6D Pose Estimation for Fruit Grasping Based on Tactile Sensing. Robotics 2023, 12, 92. https://doi.org/10.3390/robotics12040092”</p>
Praying Hands 3D Model - Albrecht Durer
More than 450 years have passed. By now, Albrecht Durer's hundreds of masterful portraits, pen and silver-point sketches, water colors, charcoals, woodcuts, and copper engravings hang in every great museum in the world, but the odds are great that you, like most people, are familiar with only one of Albrecht Durer's works. More than merely being familiar with it, you very well may have a reproduction hanging in your home or office. One day, to pay homage to Albert for all that he had sacrificed, Albrecht Durer painstakingly drew his brother's abused hands with palms together and thin fingers stretched skyward. He called his powerful drawing simply "Hands," but the entire world almost immediately opened their hearts to his great masterpiece and renamed his tribute of love "The Praying Hands." Source: Objaverse 1.0 / Sketchfab
Towards safe human-to-robot handovers of unknown containers: pre-trained models and 3D hand keypoints annotations
<p>This repository contains additional data to be used with the implementation of the real-to-simulation framework of the paper <em>Towards safe human-to-robot handovers of unknown containers</em>. The data include pre-trained models and annotations of the 3D hand poses for selected recordings from the public training and testing sets of <a href="http://corsmal.eecs.qmul.ac.uk/containers_manip.html">CORSMAL Container Manipulation (CCM) dataset</a>. The pre-trained models are used for classifying the filling type and filling level of a container. 3D hand poses are annotated as 21 keypoints based on the <a href="https://github.com/CMU-Perceptual-Computing-Lab/openpose">OpenPose</a> format.</p>
Data from: A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams
Open the record for dataset details and reuse information.
Three-dimensional surface models of hand bones (individual 15-06)
<p><b>Objectives</b>: Cuncaicha, a rockshelter site in the southern Peruvian Andes, has yielded archaeological evidence for human occupation at high elevation (4480 masl) during the Terminal Pleistocene (12,500 to 11,200 cal BP), Early Holocene (9500-9000 cal BP), and later periods. One of the excavated human burials (Feature 15-06), corresponding to a middle-aged female dated to ~8500 cal BP, exhibits skeletal osteoarthritic lesions previously proposed to reflect habitual loading and specialized crafting labor. Three small tools found in association with this burial are hypothesized to be associated with precise manual dexterity.</p> <p><b>Materials and Methods:</b> Here, we tested this functional hypothesis through the application of a novel multivariate methodology for the three-dimensional analysis of muscle attachment surfaces (entheses). This original approach has been recently validated on both lifelong-documented anthropological samples as well as experimental studies in non-human laboratory samples. Additionally, we analyzed the three-dimensional entheseal shape and resulting moment arms for muscle <i>opponens pollicis.</i></p> <p><b>Results: </b>Results show that Cuncaicha individual 15-06 shows a distinctive entheseal pattern associated with habitual precision grasping via thumb-index finger coordination, which is shared exclusively with documented long-term precision workers from recent historical collections. The separate geometric morphometric analysis revealed that the individual's <i>opponens pollicis</i> enthesis presents a highly projecting morphology, which was found to strongly correlate with long joint moment arms (a fundamental component of force-producing capacity), closely resembling the form of Paleolithic hunter-gatherers from diverse geo-chronological contexts of Eurasia and North Africa.</p> <p><b>Discussion:</b> Overall, our findings provide the first bio-cultural evidence to confirm that the lifestyle of some of the earliest Andean inhabitants relied on habitual and forceful precision grasping tasks.</p>
Data from: Forward modeling the rubber hand: illusion of ownership modifies motor-sensory predictions by the brain
Open the record for dataset details and reuse information.
Three-dimensional surface models of hand bones (individual 15-06)
Open the record for dataset details and reuse information.
Comparison of the pathogenicity of CA10 with CA16 and EV71 that cause hand, foot and mouth disease in a mouse model
GEO Series GSE237009. Mus musculus. 80 samples. Type: Expression profiling by high throughput sequencing.
Open source code for Brain-inspired bodily self-perception model for robot rubber hand illusion
<p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi">RHI</a></p> <p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi_matlab">RHI_Matlab</a></p> <p>This file is the open-source code for 'Brain-inspired bodily self-perception model for robot rubber hand illusion', mainly used in simulation environments, and can reproduce various rubber hand illusion experiments.</p> <p><a href="https://github.com/Brain-Cog-Lab/RHI#rhi_braincog">RHI_BrainCog</a></p> <p>We are building an open source spiking neural network based brain-inspired cognitive intelligence engine for Brain-inspired Artificial Intelligence and brain simulation. Therefore, we also implemented the core Proprioceptive drift experiment of the rubber hand illusion experiment using Braincog.</p> <p><a href="https://github.com/BrainCog-X/Brain-Cog/tree/main/examples/Embodied_Cognition/RHI">The open source code built by BrainCog</a></p> <p>BrainCog provides essential and fundamental components to model biological and artificial intelligence. The current version of BrainCog contains at least 18 functional spiking neural network algorithms (including but not limited to perception and learning, decision making, knowledge representation and reasoning, motor control, social cognition, etc.) built based on BrainCog infrastructures, and BrainCog also provide brain simulations to drosophila, rodent, monkey, and human brains at multiple scales based on spiking neural networks at multiple scales.</p>
Using Deep Learning Methods to Analyze Automated Breast Ultrasound and Hand-held Ultrasound Images, to Establish a Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer.
ClinicalTrials.gov study NCT04270032. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
3D Personalized Modelization of the Hand Using EOS Imaging System
ClinicalTrials.gov study NCT04895891. IPD Sharing: NO. Countries: 1. Publications: 0.
Early Sensory Re-education of the Hand With a Sensor Glove Model
ClinicalTrials.gov study NCT03191032. IPD Sharing: NO. Countries: 0. Publications: 0.
Dataset related to article "Robot-assisted rehabilitation of hand function after stroke: Development of prediction models for reference to therapy"
<p>DATASET #1</p> <p>Il data set è composto da 174 osservazioni riferite ad un campione di n=174 pazienti.</p> <p>Le variabili prese in considerazione per lo studio del data set sono 21:</p> <ul> <li> <p>ID_Pazient: variabile quantitativa continua, indica il numero di identificazione del paziente</p> </li> <li> <p>Sex: variabile dicotomica, indica il sesso del paziente (Maschio=0, Femmina=1)</p> </li> <li> <p>Age: variabile quantitativa continua, indica l'età del paziente nel momento in cui è stata effettuata la valutazione</p> </li> <li> <p>EMG_Control: variabile dicotomica, indica la capacità (Si=1) o meno (No=0) del soggetto di controllare il dispositivo con i propri segnali elettromiografici</p> </li> <li> <p>Force_Control: variabile dicotomica, indica la capacità (Si=1) o meno (No=0) del paziente di controllare il dispositivo con la propria forza</p> </li> <li> <p>Month_Injury: variabile quantitativa continua, indica i mesi trascorsi dalla data in cui è avvenuto l'ictus</p> </li> <li> <p>Diagnosis: variabile dicotomica, indica la tipologia di ictus: (Ischemico=0, Emorragico =1)</p> </li> <li> <p>Hemisphere: variabile dicotomica, indica quale emisfero cerebrale è stato colpito dall'ictus (Destro=0, Sinistro=1)</p> </li> <li> <p>FM_UE: variabile quantitativa discreta, indica la misura della funzione motoria dell'arto superiore determinata somministrando la scala Fugl-Meyer Upper Extremity</p> </li> <li> <p>Sensitivity: variabile quantitativa discreta, indica la sezione per la misura della sensibilità della scala Fugl-Meyer</p> </li> <li> <p>Pain_ROM: variabile quantitativa discreta, indica la sezione per la misura di articolarità e dolore della scala Fugl-Meyer</p> </li> <li> <p>FIM: variabile quantitativa discreta, indica la misura di autonomia della persona nelle attività della vita quotidiana, determinata dalla somministrazione della scala Functional Independence Measure</p> </li> <li> <p>RPS: variabile quantitativa discreta, indica la misura della funzione di raggiungimento di un oggetto</p> </li> <li> <p>Peg_Sec: variabile quantitativa continua, indica la misura della destrezza manuale fine e coincide con il rapporto tra il numero di pioli e i secondi impiegati per inserirli in uno specifico supporto</p> </li> <li> <p>PectMaj: variabile qualitativa ordinata, indica la misura della spasticità del pettorale, secondo la Modified Ashworth Scale</p> </li> <li> <p>BicBrach: variabile qualitativa ordinata, indica la misura della spasticità del bicipite, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexCarp: variabile qualitativa ordinata, indica la misura della spasticità del flessore del carpo, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexProfDig: variabile qualitativa ordinata, indica la misura della spasticità del flessore profondo delle dita, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexSupDig: variabile qualitativa ordinata, indica la misura della spasticità del flessore superficiale delle dita, secondo la Modified Ashworth Scale</p> </li> <li> <p>Ashworth_TOT: variabile quantitativa discreta, indica la misura totale della Modified Ashworth Scale, data dalla somma delle 5 variabili precedenti</p> </li> <li> <p>BB_par: variabile quantitativa discreta, indica la misura della destrezza manuale grossolana dell'arto paretico</p> </li> </ul>
ScienceDex guides
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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.