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88 results for “gestures”
EMG and Video Dataset for sensor fusion based hand gestures recognition
<p>This dataset contains data for hand gesture recognition recorded with 3 different sensors. </p> <p>sEMG: recorded via the Myo armband that is composed of 8 equally spaced non-invasive sEMG sensors that can be placed approximately around the middle of the forearm. The sampling frequency of Myo is 200 Hz. The output of the Myo is a.u </p> <p>DVS: Dynamic Video Sensor which is a very low power event-based camera with 128x128 resolution</p> <p>DAVIS: Dynamic Video Sensor which is a very low power event-based camera with 240x180 resolution that also acquires APS frames.</p> <p>The dataset contains recordings of 21 subjects. Each subject performed 3 sessions, where each of the 5 hand gesture was recorded 5 times, each lasting for 2s. Between the gestures a relaxing phase of 1s is present where the muscles could go to the rest position, removing any residual muscular activation.</p> <p> </p> <p>Note: All the information for the DVS sensor has been extracted and can be found in the *.npy files. In case the raw data (.aedat) was needed please contact</p> <p> </p> <p>enea.ceolini@ini.uzh.ch</p> <p>elisa@ini.uzh.ch</p> <p>==== README ====</p> <p> </p> <p>DATASET STRUCTURE:</p> <p>EMG, DVS and APS recordings</p> <p>21 subjects</p> <p>3 sessions for each subject</p> <p>5 gestures in each session ('pinky', 'elle', 'yo', 'index', 'thumb')</p> <p> </p> <p>SINGLE DATASETS:</p> <p>- relax21_raw_emg.zip: contains raw sEMG and annotations (ground truth of gestures) in the format `subjectXX_sessionYY_ZZZ` with `XX` subject ID (01 to 21), `YY` session ID (01-03) and `ZZZ` that can be ‘emg’ or ‘ann’.</p> <p> </p> <p>- relax21_raw_dvs.zip: contains the full-frame dvs events in an array with dimensions 0 -> addr_x, 1 -> addr_y, 2 -> timestamp, 3 -> polarity. The timestamps are in seconds and synchronized with the Myo. Each file is in the format `subjectXX_sessionYY_dvs` with `XX` subject ID (01 to 21), `YY` session ID (01-03).</p> <p> </p> <p>- relax21_cropped_aps.zip: contains the 40x40 pixel aps frames for all subjects and trials in the format `subjectXX_sessionYY_Z_W_K` with `XX` subject ID (01 to 21), `YY` session ID (01-03), Z gesture ('pinky', 'elle', 'yo', 'index', 'thumb’), W trial ID (1-5), `K` frame index.</p> <p> </p> <p>- relax21_cropped_dvs_emg_spikes.pkl: spiking dataset that can be used to reproduce the results in the paper. The dataset is a dictionary with the following keys:</p> <ul> <li><strong>- </strong><strong>y</strong>: array of size 1xN with the class (0->4).</li> <li><strong>- </strong><strong>sub</strong>: array of size 1xN with the subject id (1->10).</li> <li><strong>- </strong><strong>sess</strong>: array of size 1xN with the session id (1->3).</li> <li><strong>- </strong><strong>dvs</strong>: list of length N, each object in the list is a 2d array of size 4xT_n where T_n is the number of events in the trial and the 4 dimensions rappresent: 0 -> addr_x, 1 -> addr_y, 2 -> timestamp, 3 -> polarity .</li> <li><strong>- </strong><strong>emg</strong>: list of length N, each object in the list is a 2d array of size 3xT_n where T_n is the number of events in the trial and the 3 dimensions rappresent: 0 -> addr, 1 -> timestamp, 3 -> polarity.</li> </ul> <p> </p> <p> </p>
Database of Head- and Hearing-Aid-Related Impulse Responses in the Gesture Lab
<p>Head- and hearing-aid-related impulse responses (HRIRs) were measured in the Gesture Lab at the Carl von Ossietzky University of Oldenburg (Hendrikse, 2018).</p>
Introductory gestures before songbird vocal displays are shaped by learning and biological predispositions
<p>Numerous animal displays begin with introductory gestures. For example, lizards start their head-bobbing displays with introductory push-ups and many songbirds begin their vocal displays by repeating introductory notes (INs) before producing their learned song. Among songbirds, the acoustic structure and the number of INs produced before song vary considerably between individuals in a species. While similar variation in songs between individuals is a result of learning, whether variation in INs are also due to learning remains poorly understood. Here, using natural and experimental tutoring with male zebra finches, we show that mean IN number and IN acoustic structure are learned from a tutor. Interestingly, IN properties and how well INs were learned, was not correlated with the accuracy of song imitation and only weakly correlated with some features of songs that followed. Finally, birds artificially tutored with songs lacking INs still repeated vocalizations that resembled INs, before their songs, suggesting biological predispositions in IN production. These results demonstrate that INs, just like song elements, are shaped both by learning and biological predispositions. More generally, our results suggest mechanisms for generating variation in introductory gestures between individuals while still maintaining the species-specific structure of complex displays like birdsong.</p>
Priming gestures with sounds - dataset
<p>These are the data of the corresponding manuscript: "Priming gestures with sounds" by Guillaume Lemaitre, Laurie M. Heller, Nicolas Zuñiga-Peñaranda and Nicole Navolio.</p> <p>There are five directories corresponding to the five experiments reported in the manuscript. These are Matlab .mat files. Each file corresponds to one participant. There are four variables:</p> <p>- Accuracy: 0 = incorrect answer; 1= correct answer</p> <p>- Time: response time in ms. </p> <p>- Congruency: type of trial. 1 = incongruent, 2=congruent, 3=nosound</p> <p>- Order: trial order.</p> <p>Each variable is Nx2 matrix. N is the number of trials. First line is left gesture, second line is right gesture. </p>
Modulating the assessment of semantic speech–gesture relatedness via transcranial direct current stimulation of the left frontal cortex
<p>Raw data related to the publication:</p> <p>Schülke, R., & <strong>Straube, B.</strong> (accepted). Modulating the assessment of semantic speech-gesture relatedness via transcranial direct current stimulation of the left frontal cortex. Brain Stimulation. DOI: 10.1016/j.brs.2016.10.012.</p> <p> </p> <p>Statistical software: SPSS</p> <p>Variables:</p> <p>Subject<br> Stimulus<br> SessionNr<br> Stimulation<br> Localisation - frontal/parietal/frontoparietal<br> Polarisation - anode left/right<br> Relatedness - related/unrelated<br> Gesture_type - iconic/metaphoric<br> Reaction_time - in milliseconds<br> Rating - on a scale from 1-7</p>
Superior Temporal Sulcus Disconnectivity During Processing of Metaphoric Gestures in Schizophrenia
<p><strong><em>Data related to the following publication:</em></strong></p> <p>Straube, B., Green, A., Sass, K., & Kircher, T. (2014). Superior Temporal Sulcus Disconnectivity During Processing of Metaphoric Gestures in Schizophrenia. <em>Schizophrenia Bulletin</em>, <em>40</em>(4), 936–944. http://doi.org/10.1093/schbul/sbt110</p> <p> </p>
A brief self-rating scale for the assessment of individual differences in gesture perception and production
<p>Data related to the following publication:</p> <p>Nagels, A., <strong>Kircher, T.</strong>, Steines, M., Grosvald, M., & <strong>Straube, B.</strong> (2015). A brief self-rating scale for the assessment of individual differences in gesture perception and production. <em>Learning and Individual Differences, 39</em>, 73–80.</p> <p> </p> <p>The Statistical Package for Social Sciences (SPSS) software was applied to perform a Principal Component Analysis (PCA) with varimax rotation.</p> <p>SPSS file: BAG_all_data_220_online.sav includes all relevant data of the E-scale (EP1-EP25) and the BAG scale (GE1-GE12).</p> <p>SPSS script: Syntax_BAG_analyses.sps includes the relevant scripts.</p> <p> </p>
Buddha Earth Touching Gesture
Buddha in the earth-touching gesture, Thailand, 1500, National Museum (Copenhagen, Denmark). Made with Memento Beta Source: Objaverse 1.0 / Sketchfab
Offering and showing gestures in 12- to 15-month-old infants in natural contexts: A corpus-based study
<p>Supplementary material for an original article titled "Offering and showing gestures in 12- to 15-month-old infants in natural contexts: A corpus-based study", submitted to the European Journal of Developmental Psychology.</p>
Links between Gestures and Multisensory Processing: Individual Differences Suggest a Compensation Mechanism
<p>Dataset associated with the following publication:</p> <p>Schmalenbach, S.B., Billino, J., Kircher, T., van Kemenade, B.M.*, Straube, B*. (2017). Links between Gestures and Multisensory Processing: Individual Differences Suggest a Compensation Mechanism. <em>Frontiers in Psychology</em> 8:1828.</p>
Dataset for Evaluating Pedalling Techniques Recognition Using Gesture Data
<p>With the help of a dedicated measurement system (see reference for the details of the system), the pedalling gestures and the piano sound can be synchronously recorded at an audio sampling rate and a high resolution. The measurement system was deployed on the sustain pedal of a Yamaha baby grand piano situated in the studios at Queen Mary University of London. Ten well known passages of Chopin's piano music were selected to form this dataset. Therefore the dataset consists of:</p> <p>- <strong>audio-data.zip</strong>: piano sound of the ten passages recorded at 44.1kHz, each saved as "<strong>PASSAGE.wav</strong>".</p> <p>- <strong>pedal-data.zip</strong>: associated gesture data recorded at 22.05kHz, each saved as "<strong>PASSAGE.npy</strong>". The gesture data correspond to the movement trajectory of the sustain pedal.</p> <p>- <strong>pedal-label.zip</strong>: label the "continuous" gesture data by "discrete" pedalling techniques at every 0.02 second. Label 0-4 represents none, 1/4, 1/2, 3/4 and full pedalling technique, respectively. Labels for gesture data "<strong>PASSAGE.npy</strong>" are saved in "<strong>PASSAGE-label.npy</strong>".</p> <p>- <strong>passage.zip</strong>: music scores of the ten passages, each saved as "<strong>PASSAGE.pdf</strong>". They were annotated with pedalling techniques by the experimenter in advance and then performed by a pianist, who was asked to follow the annotated scores. The resulting audio recording and gesture data formed the above "<strong>PASSAGE.wav</strong>" and the "<strong>PASSAGE.npy</strong>". The annotated score guided the labelling process and formed the "<strong>PASSAGE-label.npy</strong>".</p>
Hand gestures raw images
<p>This dataset includes 4 categories of hand gestures: open hand, fist, right hand, left hand + one negative class</p>
RIS Based Hand Gesture Recognition Dataset
<h1><strong>RIS Based Hand Gesture Recognition Dataset</strong></h1> <h2><strong>Overview</strong></h2> <div>This dataset contains images for gesture recognition, divided into two main sets: dataset0608 and data_synthetic_variab. The data was collected using a wooden hand. </div> <div> </div> <h3>dataset0608</h3> <div>This dataset consists of two modes: ris_random and ris_optimized. The main difference between the two subfolders is the configuration of the RIS (random or optimized).</div> <div> </div> <div>This dataset consists of four subfolders: ris_random, ris_random2, ris_optimized, and ris_optimized2. The main difference between the subfolders is the format of the data:</div> <div>- ris_random and ris_optimized: Data is stored in individual files for each frame, named as 'frame_{i}{posture}{n_med}' </div> <div>- ris_random2 and ris_optimized2: Data has already been processed and combined into single files for all frames using the compact_files_frames.txt function, named as 'all_frames_{posture}_{n_med}' </div> <div> </div> <div>For each gestures = {close, two, open}, we have n_med values from 0 to 114 and 10 frames. Therefore, the ris_random and ris_optimized folders contain 10 frames × 115 measurements × 3 gestures = 3450 files, while the ris_random2 and ris_optimized2 folders contain 1 × 115 measurements × 3 gestures = 345 files.</div> <div> </div> <h3>data_synthetic_variab</h3> <div>This dataset consists of two modes: ris_random and ris_optimized. The main difference between the two subfolders is the configuration of the RIS (random or optimized). </div> <div> </div> <div>This dataset consists of four subfolders: ris_random, ris_random2, ris_optimized, and ris_optimized2. The main difference between the subfolders is the format of the data:</div> <div>- ris_random and ris_optimized: Data is stored in individual files for each frame, named as 'frame_{i}{posture}{n_med}' </div> <div>- ris_random2 and ris_optimized2: Data has already been processed and combined into single files for all frames using the compact_files_frames.txt function, named as 'all_frames_{posture}_{n_med}' </div> <div> </div> <div>For each gestures = {close, two, open}, we have n_med values from 0 to 8 and 10 frames. This dataset provides additional synthetic data with variations in hand position to increase the dataset's diversity. Each gesture is represented by 8 different ways, where the hand position was slightly modified between each sample. These real data were used as a basis for generating synthetic data. By using the functions in the files "multiply_files.txt" and "add_gaussian_noise.txt," the dataset was expanded and made more realistic by adding Gaussian noise to the images.</div> <div> </div> <div>Therefore, the ris_random and ris_optimized folders contain 10 frames × 8 measurements × 3 gestures = 240 files, while the ris_random2 and ris_optimized2 folders contain 1 × 8 measurements × 3 gestures = 24 files.</div> <div> </div> <h3>Functions</h3> <div>* **add_gaussian_noise.txt:** This script adds Gaussian noise to the images to simulate real-world conditions and improve the robustness of the model.</div> <div>* **compact_files_frames.txt:** This script combines multiple frames into a single image, which can be useful for certain types of analysis.</div>
EMG hand gesture dataset
<p>This dataset contains surface electromyography (sEMG) data of 5 different hand gestures performed by eight subjects. The data is store in a folder for each one of the subjects, each folder, contains 5 files, one for each gesture. The files contain the sEMG data of 4 sEMG channels placed on the forearm. The gestures are: open hand, closed hand, lateral pinch, signaling sign, rock sign. A file with a demo Recurrent Neural Network is also included.</p> <p>The reader may refer to the following articles for more information about the dataset: <a href="https://doi.org/10.3390/app12199700">LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals</a> and <a href="https://doi.org/10.3390/biomimetics8010029">A Proposal of Bioinspired Soft Active Hand Prosthesis</a>.</p>
Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned~Dataset
<p>This refers to dataset of the paper Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned to appear in IEEE WoWMoM 2023. Those files labeled with "beamsnr" correspond to 60 GHz while others with "data_processed" correspond to CSI at 5GHz. "ENV1" and "ENV2" represent two environments. "orient90" means user performs gestures 90 degree with respect to LoS.</p> <p> </p> <p>Abstract:</p> <p>In recent years, channel state information (CSI) at sub-6 GHz has been widely exploited for Wi-Fi sensing, particularly for activity and gesture recognition. In this work, we instead explore mmWave (60 GHz) Wi-Fi signals for gesture recognition/pose estimation. Our focus is on the mmWave WiFi signals so that they can be used not only for high data rate communication but also for improved sensing e.g., for extended reality (XR) applications. For this reason, we extract spatial beam signal-to-noise ratios (SNRs) from the periodic beam training employed by IEEE 802.11ad devices. We consider a set of 10 gestures/poses motivated by XR applications. We conduct experiments in two environments and with three people. As a comparison, we also collect CSI from IEEE 802.11ac devices. To extract features from the CSI and the beam SNR, we leverage a deep neural network (DNN). The DNN classifier achieves promising results on the beam SNR task with stateof-the-art 96.7% accuracy in a single environment, even with a limited dataset. We also investigate the robustness of the beam SNR against CSI across different environments. Our experiments reveal that features from the CSI generalize without additional re-training, while those from beam SNRs do not. Therefore, retraining is required in the latter case.</p>
Finger gesture recognition with smart skin technology and deep learning
<p class="p1">Finger gesture recognition was extensively studied in recent years for a wide range of human-machine interface applications. Surface electromyography (sEMG), in particular, is an attractive, enabling technique in the realm of finger gesture recognition, and both low and high-density sEMG were previously studied. Despite the clear potential, cumbersome electrode wiring and electronic instrumentation render contemporary sEMG-based finger gestures recognition to be performed under unnatural conditions. Recent developments in smart skin technology provide an opportunity to collect sEMG data in more natural conditions. Here we report on a novel approach based on a soft 16-electrode array, a miniature and wireless data acquisition unit and neural network analysis, in order to achieve gesture recognition under natural conditions. Finger gesture recognition accuracy values, as high as 93.1%, were achieved for 8 gestures when the training and test data were from the same session. For the first time, high accuracy values are also reported for training and test data from different sessions for three different hand positions. These results demonstrate an important step towards sEMG-based gesture recognition in non-laboratory settings, such as in gaming or Metaverse.</p>
Video instructions for study "Mid-air gestural interaction with large fogscreen"
<p>This is a video instruction that participants viewed when receiving the gesture explanation and is mentioned in the manuscript.</p>
Virtual Reality Gesture Recognition Dataset
<p>This dataset provides valuable insights into hand gestures and their associated measurements. Hand gestures play a significant role in human communication, and understanding their patterns and characteristics can be enabled various applications, such as gesture recognition systems, sign language interpretation, and human-computer interaction. This dataset was carefully collected by a specialist who captured snapshots of individuals making different hand gestures and measured specific distances between the fingers and the palm. The dataset offers a comprehensive view of these measurements, allowing for further analysis and exploration of the relationships between different gestures and their corresponding hand measurements.</p> <p>The dataset's potential applications are wide-ranging. For instance, it can be used to develop gesture recognition systems that can identify and interpret hand movements accurately. By training machine learning models on this dataset, it is possible to create algorithms capable of recognizing specific hand gestures based on the measured distances. This can enable intuitive human-machine interaction and interfacing, particularly in domains such as virtual reality, augmented reality, and smart devices. Moreover, researchers interested in the biomechanics of hand movements or exploring the cultural significance of specific gestures can leverage this dataset to gain insights into the physical aspects of hand gestures and their variations across different individuals.</p>
Intentional gestural communication in Hanuman langur
<p>Contrary to previous beliefs, intentional gestural communication (IGC) is not exclusive to the hominoid lineage but is also present in other non-human primates. Here, we report the presence of IGC among free-ranging Hanuman langur troop in Dakshineswar, West Bengal, India. These langurs exhibit a food-requesting behaviour wherein they use several gestures to communicate with the humans nearby. Moreover, they can also assess the recipient's mental state and persistently check if the signal (food request) has been received, waiting until they receive the desired food item. We have identified eight begging gestures used by langurs of all ages, except infants. The most common gesture is by holding cloth (BGc), but provocation-initiated begging (BGpi) and begging by embracing legs (BGe) efficiently direct these events to its success. The frequency of successful begging events is higher in the evening due to increased human interactions. Our findings suggest that ontogenetic ritualization might be at play here among these troop members as this gestural communication has been learned through imitation and reinforced by the reward of receiving food. Moreover, these successful begging events serve as an effective foraging strategy for urban-adapted langurs, allowing them to acquire high-calorie processed food items within a human-modified urban ecosystem. </p>
Evaluation of Meal Gesture Dosing in Adults With Type 1 Diabetes
ClinicalTrials.gov study NCT04964128. IPD Sharing: NO. Countries: 1. Publications: 1.
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.