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2,000 results for “Hand”
Bronze Hands #3DST28
Those are the hands of this guy, who lives on Union Sq in NYC https://sketchfab.com/models/17b50148d8fc491083e2bf6d42da4ad2 It's my entry for Hands 3DST http://blog.sketchfab.com/post/104169966674/3d-scanning-thursday-28-hands Source: Objaverse 1.0 / Sketchfab
Fel Embraced One-hand Axe
It's a fel axe empowered with a fel crystal which makes the axe extremly powerful. Even one small cut brings agony and death. The axe pefectly fits to a hand and allows perfect mobility. It's low poly, game ready game asset. Modelled in Autocad Maya, detailed in Pixologic Zbrush and textured in Substance Painter. The the axe was used in my school project and is free downloadable. Source: Objaverse 1.0 / Sketchfab
Hand painted old Woman wooden sculpture
Scanned by [Christer Lindqvist](https://sketchfab.com/Pappac). Basic retopology. Photogrammetry. 178 pictures. Nikon D3400 Hand painted old Woman wooden sculpture. Unknown origin. Check out more at https://www.molithaeus.com Source: Objaverse 1.0 / Sketchfab
Dataset for Dynamic Hand Gesture Recognition Systems
<p>Computer vision systems are commonly used to design touchless human-computer interfaces (HCI) based on dynamic hand gesture recognition (HGR) systems, which have a wide range of applications in several domains, such as gaming, multimedia, automotive, and home automation. However, automatic HGR is still a challenging task, mostly because of the diversity in how people perform the gestures. In addition, the number of publicly available hand gesture datasets is scarce; often, the gestures are not acquired with sufficient image quality, and the gestures are not correctly performed. In this data article, we propose a dataset of 27 dynamic hand gesture types acquired at full HD resolution from 21 different subjects, which were carefully instructed before performing the gestures and monitored when performing the gesture; the subjects had to repeat the movement in case the performed hand gesture was not correct, i.e., the authors of this paper that were observing the gesture found that it did not correspond to the exact expected movement and/or the camera recorded a viewpoint did not allow for a plain visualizing of the gesture. Each subject performed 3 times the 27 hand gestures for a total of 1701 videos collected and corresponding to 204120 video frames.</p> <p> </p> <p>In the following, we discuss the details of the provided datasets.</p> <p><strong>hand_gestures_dataset_videos.zip</strong> - This dataset contains the videos of the recorded hand gestures. The zip contains 27 main folders. Each main folder refers to a hand gesture class, for a total of 27 main folders named “class_xx”, where “xx” identifies the class from 01 to 27. Within each of the class folders, there are 21 sub-folders, one folder for each of the subjects that performed the hand gestures. These folders are named “Useryy_”, where “yy” identifies the user from 01 to 21. Each of the user folders contains three videos (.avi) corresponding to the three hand gestures performed by the user for each hand gesture class. The size of the full dataset is 21.34 GB.</p> <p><strong>HGD_VideoFrames_class_XX.zip</strong> - These datasets contain the video frames extracted from the videos of the recorded hand gestures. Each zip file contains the video frames of a hand gesture class, for a total of 27 zip files named “HGD_VideoFrames_class_XX.zip”, where “xx” identifies the class from 01 to 27. Therefore, each zip file contains one of the 27 class folders. Within each of the class folders, there are 21 sub-folders, one folder for each of the subjects that performed the hand gestures. These folders are named “Useryy_”, where “yy” identifies the user from 01 to 21. Each of the user folders contains, in turn, 3 sub-folders, one folder for each of the three hand gestures performed for each hand gesture class. These sub-folders are named, respectively, “Useryy_1”, “Useryy_2”, and “Useryy_3”, and contain 120 video frames (.png) extracted from the corresponding video. The size of each zip file is about 9 GB.</p> <p><strong>hand_gesture_timing_stats.csv</strong> - This dataset contains timing information regarding the gestures performed by the subjects. The size of this dataset is 36 KB. It has 567 records plus the header. The meaning of the columns is as follows:</p> <ul> <li> <p><em>class</em>: hand gesture class, from 01 to 27.</p> </li> <li> <p><em>user</em>: user who performed the hand gestures, from 01 to 21.</p> </li> <li> <p><em>start_frame_1</em>: starting frame related to the first performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>end_frame_1</em>: ending frame related to the first performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>exec_time_1</em>: execution time (in seconds) related to the first performed hand gesture. It is computed as the difference between the ending frame and the starting frame divided by the 30 fps set for video recording.</p> </li> <li> <p><em>start_frame_2</em>: starting frame related to the second performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>end_frame_2</em>: ending frame related to the second performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>exec_time_2</em>: execution time (in seconds) related to the second performed hand gesture. It is computed as the difference between the ending frame and the starting frame divided by the 30 fps set for video recording.</p> </li> <li> <p><em>start_frame_3</em>: starting frame related to the third performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>end_frame_3</em>: ending frame related to the third performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>exec_time_3</em>: execution time (in seconds) related to the third performed hand gesture. It is computed as the difference between the ending frame and the starting frame divided by the 30 fps set for video recording.</p> </li> <li> <p><em>mean_exec_time</em>: mean execution time (in seconds) for that related user and hand gesture. It is computed as the mean of the execution times computed for the three hand gestures performed by that user for that class.</p> </li> <li> <p><em>std_dev_exec_time</em>: standard deviation of the three execution times (in seconds) computed for the three hand gestures performed by that user for that class.</p> </li> <li> <p><em>total_mean_exec_time</em>: total mean execution time (in seconds) for that class. It is computed as the mean of all the execution times computed for the three hand gestures performed by all the users for that class.</p> </li> <li> <p><em>total_std_dev_exec_time</em>: total standard deviation of all the execution times (in seconds) for that class. It is computed as the standard deviation of all the execution times computed for the three hand gestures performed by all the users for that class. Note that in this case, the standard deviation has been computed by dividing by (N-1) as the entire population is considered.</p> </li> </ul> <p> </p> <p dir="ltr"><strong>If you make use of this dataset, please consider citing the following publication:</strong></p> <p dir="ltr">Fronteddu, G., Porcu, S., Floris, A., & Atzori, L. (2022). A dynamic hand gesture recognition dataset for human-computer interfaces. Computer Networks, 205, 108781.</p> <p dir="ltr">BibTex format:</p> <p>@article{fronteddu2022dynamic, title={A dynamic hand gesture recognition dataset for human-computer interfaces}, author={Fronteddu, Graziano and Porcu, Simone and Floris, Alessandro and Atzori, Luigi}, journal={Computer Networks}, volume={205}, pages={108781}, year={2022}, publisher={Elsevier}, doi = {https://doi.org/10.1016/j.comnet.2022.108781} }</p>
The Relationship Between Hand Grip Strength, Trunk Control and Functional Performance in Individuals With Mild Mental Retardation
ClinicalTrials.gov study NCT06934850. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Safety and Efficacy of Adipose-Derived Regenerative Cells (ADRCs) for Improving Hand Dysfunction in Systemic Sclerosis
ClinicalTrials.gov study NCT07360808. IPD Sharing: NO. Countries: 1. Publications: 0.
Nylon Versus Chromic Gut Sutures for Minor Hand Surgery
ClinicalTrials.gov study NCT03407820. IPD Sharing: NO. Countries: 1. Publications: 0.
Alitretinoin in the Treatment of Chronic Hand Eczema
ClinicalTrials.gov study NCT00519675. IPD Sharing: Not stated. Countries: 2. Publications: 0.
Effect of Using Adaptive Seating Equipment on Hand Function
ClinicalTrials.gov study NCT04651283. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effectiveness Allopurinol Topical Agent Prevention Capecitabine-induced Hand-foot Syndrome
ClinicalTrials.gov study NCT01609166. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Hand Assisted Versus "Pure" Laparoscopic Assisted Proctectomy for Rectal Cancer
ClinicalTrials.gov study NCT00651677. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Performance and Tolerance of Cyto-selective Difluoroethane-based Cryotherapy in th Treatment of Brown Spots (Solar and Senile Lentigines, Post-Inflammatory Hyperpigmentation) on the Face and Hands of
ClinicalTrials.gov study NCT05886010. IPD Sharing: Not stated. Countries: 1. Publications: 0.
An Edge AI-based AR System for Hand Hygiene Training in Undergraduates of Health Related Disciplines
ClinicalTrials.gov study NCT06211660. IPD Sharing: YES. Countries: 1. Publications: 0.
Targeted Noninvasive Brain Stimulation to Improve Hand Dexterity in Stroke Patients
ClinicalTrials.gov study NCT04058288. IPD Sharing: NO. Countries: 1. Publications: 0.
A Comparison Between a PC and a CC Approach for the Placement of Infraclavicular Perineural Catheters in Hand Surgery
ClinicalTrials.gov study NCT04960046. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Vascularized Composite Allotransplantation of the Hand
ClinicalTrials.gov study NCT00711373. IPD Sharing: NO. Countries: 1. Publications: 0.
Hand & Wrist Pyrocarbon Implants Outcomes Clinical Study
ClinicalTrials.gov study NCT04137237. IPD Sharing: NO. Countries: 2. Publications: 0.
Occupational Therapy-led Services for Adults Presenting Acutely With a Hand Condition
ClinicalTrials.gov study NCT06399614. IPD Sharing: NO. Countries: 1. Publications: 0.
Hand Position for Stylet Removal During Tracheal Intubation
ClinicalTrials.gov study NCT04782479. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of the Efficacy and Safety of PiQo4 for the Treatment of Hand Pigmentation Using 1064nm PSL and 532nm PSL
ClinicalTrials.gov study NCT04539561. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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
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DANDI Archive for NWB datasets
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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.