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18 results for “hand grasps”
Hand-selective visual regions represent how to grasp 3D tools for use: brain decoding during real actions
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Acquired data necessary to perform the control algorithm introduced in the scientific paper: "Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention" (Advances in Mechanical Engineering, 2016, vol. 8, pp. 1-13)
<p>Acquired data necessary to perform the control algorithm introduced in this paper.</p> <p>a) Figure 6: Calibration data for the three FSRs placed on the prosthetic hand and covered with silicon caps.<br> b) Figure 9: Data for the cost during the learning of two grasping tasks of an egg: bi-digital grasp and tri-digital grasp.<br> c) Figure 10 and Figure 11: Data for the experimental results with the plastic cup and with the highlighter shown in the paper.<br> </p> <p> </p>
Present day human hand grasping the same artifact by hand and hafted
<p><em>Examples of a present day human hand demonstrating a precision grip (top left) when grasping an artifact by hand and a power "squeeze" grip (top right) when grasping a hafted artifact (both palmar view). In turquoise (first metacarpal) and purple (trapezium) are the present day human and Neanderthal bones forming the trapeziometacarpal complex at the base of the thumb and responsible for its movements. </em></p>
SYNTHETIC dataset attached to the paper "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis"
<p>SYNTHETIC dataset to replicate the results in "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis", accepted to IEEE/RSJ IROS 2022.</p> <p>In order to fully reproduce the experiments, download also the REAL dataset. </p> <p>To automatically download the REAL and SYNTHETIC dataset, run the script provided at the link below.</p> <p>Code to replicate the results available at: https://github.com/hsp-iit/prosthetic-grasping-experiments</p>
REAL dataset attached to the paper "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis"
<p>REAL dataset to replicate the results in "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis", accepted to IEEE/RSJ IROS 2022.</p> <p>In order to fully reproduce the experiments, download also the SYNTHETIC dataset. </p> <p>To automatically download the REAL and SYNTHETIC dataset, run the script provided at the link below.</p> <p>Code to replicate the results available at: https://github.com/hsp-iit/prosthetic-grasping-experiments</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>
IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_DataFusion and Sensing Technology_characterization of sensing system for grippers_v0
<p>The dataset contains data related to the simulations and experiments presented in the publication:<br>G. Laudante, O. Pennacchio, and S. Pirozzi, “Multiphysics simulation for the optimization of an optoelectronic-based tactile sensor,” in Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, 2023, pp. 101–110. (DOI: 10.5220/0012166900003543)</p>
IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data Fusion and Sensing Technology_sensing system design for grippers_v0
<p><span>The dataset includes pr</span><span>ecisely made Computer-Aided Design (CAD) models in .step format, representing essential components such as metallic frame, silicone pad, plastic case, plastic grid, and tactile board with integrated proximity sensor. Additionally, the dataset provides a complete assembly of multi-modal sensor model in .f3z format.</span></p> <p><span>Furthermore, the dataset includes essential design files for the electronic infrastructure, including a precisely engineered circuit schematic design file in .sch format and a printed circuit board layout design file in .brd format. Additionally, the dataset offers visual aids in the form of digital images (.png) showcasing top view, PCB model, ToF module, and sensor assembly configuration. These resources enable researchers to leverage the dataset's comprehensive capabilities for advanced investigations and practical implementations in sensor technology and design.</span></p>
IntelliMan_WP5_Grasping, Manipulation and Arm-Hand Coordination_T5.1_Data Fusion and Sensing Technology_characterization of sensing system for grippers_v0
<p><span>The dataset contain the data acquired from the multi-sensorized fingers developed in T5.1 and integrated into grippers used in IntelliMan UC3 and UC4. The data contain tactile data, proximity data and endoscopic camera data for the evaluation of sensor performance with respect to IntelliMan use cases requirements.</span></p>
Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps
<p>Dataset analyzed in the study "Toward Early and Objective Hand Osteoarthritis Detection by using EMG during grasps". Use of the data requires proper reference to [1].</p> <p>Dataset contains Electromyographic data from forearm, recorded with an 8-channel sEMG Biometrics Ltd. device. The fields contained in the structure are those detailed in the following scheme::</p> <ul> <li>Group: 0 for healthy subjects; 1 for HOA patients</li> <li>Subject: subject ID;</li> <li>Grasp: grasp ID, according to Figure 1 [1];</li> <li>Raw EMG data (7 columns): Raw sEMG data without any filter and not resampled, for the seven representative spot areas according to [2].</li> </ul> <p>[1] Jarque-Bou, N.J.; Gracia-Ibáñez, V.; Roda-Sales, A.; Bayarri-Porcar, V.; Sancho-Bru, J.L.; Vergara, M. Toward Early and Objective Hand Osteoarthritis Detection by Using EMG during Grasps. <em>Sensors</em> <strong>2023</strong>, <em>23</em>, 2413. https://doi.org/10.3390/s23052413</p> <p>[2] Jarque-Bou, N. J., Vergara, M., Sancho-Bru, J. L., Alba, R.-S. & Gracia-Ibáñez, V. Identification of forearm skin zones with similar muscle activation patterns during activities of daily living. <em>J. NeuroEngineering Rehabil. </em>(2018).</p> <p> </p>
Improving Grasp Function in People With Sensorimotor Impairments by Combining Electrical Stimulation With a Robotic Hand Orthosis
ClinicalTrials.gov study NCT05976087. IPD Sharing: NO. Countries: 1. Publications: 15.
Neural Stimulation for Hand Grasp in People With Tetraplegia
ClinicalTrials.gov study NCT05555914. IPD Sharing: NO. Countries: 1. Publications: 1.
Neural Stimulation for Hand Grasp
ClinicalTrials.gov study NCT04306328. IPD Sharing: NO. Countries: 1. Publications: 1.
Robotic Hand Orthosis Providing Grasp Assistance for Patients With Brachial Plexus Injuries
ClinicalTrials.gov study NCT04939233. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Transfer of Grasp Control Across Hands After Stroke
ClinicalTrials.gov study NCT00589368. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Hand Grasp Function After Spinal Cord Injury
ClinicalTrials.gov study NCT05128994. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Hand Grasping Techniques on Eliciting the Grasp Reflex in Patients With Dementia
ClinicalTrials.gov study NCT02925273. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Retracted: Biomechanical characteristics of hand coordination in grasping activities of daily living
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International Brain Laboratory public data
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OpenNeuro
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