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88 results for “gestures”
Testosterone amplifies the negative valence of an agonistic gestural display by exploiting receiver perceptual bias
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Data from: Biogeography predicts macro-evolutionary patterning of gestural display complexity in a passerine family
Gestural displays are incorporated into the signaling repertoire of numerous animal species. These displays range from complex signals that involve impressive and challenging maneuvers, to simpler displays or no gesture at all. The factors that drive this evolution remain largely unclear, and we therefore investigate this issue in New World blackbirds by testing how factors related to a species' geographical distribution and social mating system predict macro-evolutionary patterns of display elaboration. We report that species inhabiting temperate regions produce more complex displays than species living in tropical regions, and we attribute this to i) ecological factors that increase the competitiveness of the social environment in temperate regions, and ii) different evolutionary and geological contexts under which species in temperate and tropical regions evolved. Meanwhile, we find no evidence that social mating system predicts species differences in display complexity, which is consistent with the idea that gestural displays evolve independently of social mating system. Together, these results offer some of the first insight into the role played by geographic factors and evolutionary context in the evolution of the remarkable physical displays of birds and other vertebrates.
How do students respond to different gestures produced by animated pedagogical agents? An investigation of attention, narrative recall, and knowledge transfer within a personalized learning context
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UMONS-TAICHI: A multimodal motion capture dataset of expertise in Taijiquan gestures
<p><strong>Presentation</strong></p> <p>UMONS-TAICHI is a large 3D motion capture dataset of Taijiquan martial art gestures (n = 2200 samples) that includes 13 classes (relative to Taijiquan techniques) executed by 12 participants of various skill levels. Participants levels were ranked by three experts on a [0-10] scale. The dataset was captured using two motion capture systems simultaneously: 1) Qualisys, a sophisticated motion capture system of 11 Oqus cameras that tracked 68 retroreflective markers at 179 Hz, and 2) Microsoft Kinect V2, a low-cost markerless sensor that tracked 25 locations of a person’s skeleton at 30 Hz. Data from both systems were synchronized manually. Qualisys data were manually corrected, and then processed to complete any missing data. Data were also manually annotated for segmentation. Both segmented and unsegmented data are provided in this database. The data were initially recorded for gesture recognition and skill evaluation, but they are also suited for research on synthesis, segmentation, multi-sensor data comparison and fusion, sports science or more general research on human science or motion capture. A preliminary analysis has been conducted by Tits et al. (2017) on a part of the dataset to extract morphology-independent motion features for gesture skill evaluation and presented in: “Morphology Independent Feature Engineering in Motion Capture Database for Gesture Evaluation” (<a href="https://doi.org/10.1145/3077981.3078037">https://doi.org/10.1145/3077981.3078037</a>).</p> <p><strong>Processing</strong></p> <p><em><strong>Qualisys</strong></em></p> <p>Qualisys data were processed manually with <a href="http://www.qualisys.com/software/qualisys-track-manager/">Qualisys Track Manager</a>.</p> <p>Missing data (occluded markers) were then recovered with an automatic recovery method: <a href="https://github.com/titsitits/MocapRecovery">MocapRecovery</a>.</p> <p>Data were annotated for gesture segmentation, using the <a href="https://github.com/numediart/ofxMotionMachine">MotionMachine</a> framework (C++ <a href="http://openframeworks.cc/">openFrameworks</a> addon). The code for annotation can be found <a href="https://github.com/numediart/ofxMotionMachine/tree/master/mmTutorial_4_Annotation">here</a>. Annotations were saved as ".lab" files (see Download section).</p> <p><em><strong>Kinect</strong></em></p> <p>The Kinect data were recorded with <a href="https://msdn.microsoft.com/en-us/library/hh855389.aspx">Kinect Studio</a>. Skeleton data were then extracted with <a href="https://www.microsoft.com/en-us/download/details.aspx?id=44561">Kinect SDK</a> and saved into “.txt” files which contain several lines corresponding to each captured frame. Each line contains one integer number (ms), relative to the moment when the frame was captured, followed by 3 x 25 float numbers corresponding to the 3-dimentional locations of the 25 body joints.</p> <p>For more information please visit <a href="https://github.com/numediart/UMONS-TAICHI">https://github.com/numediart/UMONS-TAICHI</a></p> <p>PS: All files can be used with the <a href="https://github.com/numediart/ofxMotionMachine">MotionMachine</a> framework. Please use the parser provided in this github repository for kinect (.txt) data.</p> <p> </p>
Hand gesture - thumb left and right
<p>A database of hand gestures(Thumb left and Thumb right) created for the needs of my master's thesis</p>
A Computer Graphics Approach to Creating New Method for Generating 3D Gesture Animations in Bangla Sign Language via HamNoSys to SiGML Conversion
<p>To prepare the system, we employed 94 classes of data. In this dataset, there are 13 Bangla numerical data classes, 36 Bangla alphabet data classes, and 41 Bangla word data classes. Every class of data contains different data types like Hand Shape, Hand Orientation, Hand Movement, Notations, etc. These data were created in SiGML tags. Every class of data is unique and different from others. The system was prepared using BdSL. And BdSL is an uncommon and unique sign language, among others. That's why every class of data is unique and created by us. We search HamNoSys notations for Bangla alphabets, words, and numbers in English HamNoSys datasets (almost 6,000 data). However, we find only 20% of the data, which is quite similar to BdSL. We create 80% HamNoSys notation for BdSL and we modify the matching 20% notations. Then, we converted them into SiGML and made the data classes. This was a big challenge in our research. </p>
CCDb Head Gesture (CCDb-HG)
<p><strong>Description</strong></p> <p>The CCDb-HG is a head gesture annotation from the existing Cardiff Conversation Database <a href="https://huggingface.co/datasets/CardiffVisualComputing/CCDb">CCDd</a>. CCDb is a video dataset with natural dyadic conversations including 22 subjects. CCDb-HG contains annotations for each frame with 6 head gestures: Nod, Shake, Tilt, Turn, Up/Down, and Waggle. In total, 115 videos of ~4 minutes (850k frames) are annotated resulting in around 5000 events. For more information please refer to the paper and code </p> <p> </p> <p><strong>Reference</strong></p> <p>If you use this dataset, please cite the following publication:</p> <p>P. Vuillecard, A. Farkhondeh, M. Villamizar and J. -M. Odobez, "CCDb-HG: Novel Annotations and Gaze-Aware Representations for Head Gesture Recognition," <em>2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)</em>, Istanbul, Turkiye, 2024, pp. 1-9</p> <p> </p>
Gesture comprehension and L2 listentening: materials to the avatar study
<p>This dataset contains part of the supplementary materials to my (unpublished) doctoral dissertation: Prové, Valentijn. 2024. Beyond Foreigner Talk: multimodal repertoires in L1-L2 interaction. Leuven: KU Leuven. More specifically, you will find the stimuli images and videos used in the experiment presented in Chapter 6 (pp 115-139, 'Gesture comprehension and L2 listentening').</p>
IBM DVS Gesture dataset pre-processed
<p>A pre-processed version of DVS gesture in npy format</p>
High Dimensional Computing Gesture Recognition
ClinicalTrials.gov study NCT07155460. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Bringing the nonlinearity of the movement system to gestural theories of language use: multifractal structure of spoken English supports the compensation for coarticulation in human speech perception
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Data from: Biogeography predicts macro-evolutionary patterning of gestural display complexity in a passerine family
Open the record for dataset details and reuse information.
Example Stimuli "Neben dem Ball ist eine Kiste" for a Study on Speech Gesture Interfaces
<p>The material contains two videos from a video corpus created for a study on speech gesture interfaces. The videos show a lady speaking the same German sentence: "Neben dem Ball ist eine Kiste" - Besides the ball is a box, but accompanied by two different gestures.</p>
Example Stimuli "Neben dem Ball ist eine Kiste" with Box-like and Roundish Gesture for a Study on Speech Gesture Interfaces
<p>The material contains two videos from a video corpus created for a study on speech gesture interfaces. A special feature of these data is that the same utterance is combined with different gesture occurrences by re-combining head and torso videos. In both videos the same utterance of the German sentence "Neben dem Ball ist eine Kiste" (English: Beside the ball there is a box.) is used. In the first case, a roundish gesture stroke overlaps with "the ball" and in the second case, a box-like gesture stroke overlaps with "is a box."</p>
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>
Study of Gesture and Executive Functions in Children With High Intellectual Potential
ClinicalTrials.gov study NCT03128125. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Effect of Articulatory Gestures on Early Literacy Skills in 4-year Olds
ClinicalTrials.gov study NCT06504264. IPD Sharing: NO. Countries: 1. Publications: 0.
Analysis of the Kinematics of the Shoulder Complex in Sports "Overhead" Gestures Such as Kayaking Polo Throwing (Overhead)
ClinicalTrials.gov study NCT05111470. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Motor Imagery and Action Observation on Electromyographic Activity and Intramuscular Oxygenation in the Hand Gripping Gesture
ClinicalTrials.gov study NCT03324217. IPD Sharing: NO. Countries: 1. Publications: 0.
Neurobiological, Neuropsychological,Linguistic and Gestural Processes and Phenomena in Individuals With Alexithymia
ClinicalTrials.gov study NCT00830752. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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)
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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.