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16 results for “hand gestures”
UC2017 Static and Dynamic Hand Gestures
<p>We introduce the UC2017 static and dynamic gesture dataset. Most researchers use vision-based systems such as the Microsoft Kinect to acquire and classify hand gesture data. Despite that, we believe that we can achieve more reliable results and allow the use of more complex gestures with wearable systems. There are not many datasets with wearable systems due to the plethora of data gloves in the market and their relative high cost. For these reasons, we opted by creating a new dataset to present and evaluate our gesture recognition framework. The objectives of the dataset are: (1) provide a superset of hand gestures for HRI, (2) have user variability, (3) to be representative of the actual gestures performed in a real-world interaction.</p> <p>We divide the dataset in two types of gestures: SG and DG. SG are described by a single timestep of data, therefore representing a single hand pose and orientation. DGs are variable-length timeseries of poses and orientations with particular meanings. Some of the gestures of the dataset are correlated with a certain meaning in the context of HRI, while others are arbitrary, to enrich the dataset and add complexity to the classification problem.</p> <p>The library is composed of 24 SG classes and 10 DG. The dataset includes SG data from eight subjects with a total of 100 repetitions for each of the 24 classes (2400 samples in total). The DG samples were obtained from six subjects and has cumulatively 131 repetitions of each class (1310 samples in total). All of the subjects are right-handed and performed the gestures with their left hand.</p> <p>We used a data glove (CyberGlove II) and a magnetic tracker (Polhemus Liberty) to capture the hand shape, position and orientation over time. The glove provides digital signals that are proportional to the bending angle of each one of the 22 sensors which are elastically attached to a subset of the hand's joints. In this way we have an approximation of the hand's shape. The tracker's sensor is rigidly attached to the glove on the wrist and measures its position and orientation in respect to a ground-fixed frame. The orientation is the rotation between the fixed frame and the frame of the sensor, given a quaternion (WXYZ). We fuse the sensor data together online since the sensors have slightly different acquisition rates -- 100Hz for the glove and 120Hz for the tracker. The tracker data are under-sampled by gathering only the closest tracker frame in time.</p> <p>The files are in the h5df format. The dimensions are (sample, time, variables).</p>
Leap Motion Hand Gestures for Interaction with 3D Virtual Music Instruments (LMHGIf3DVMI)
<p>The aim of the dataset is to investigate machine learning real-time gesture recognizer captured with a Leap Motion sensor to control the performance of a virtual 3D musical instrument. The dataset includes from 10-15 samples for each of the 8 gesture classes collected from 10 participants (5 female and 5 male) using the Leap Motion sensor.</p> <p> </p>
UC2018 DualMyo Hand Gesture Dataset
<p>This is set of data obtained from two consumer-market EMG sensors (Myo) with a subject performs 8 distinct hand gestures.</p> <p>There are a total of 110 repetitions of each class of gesture obtained across 5 recording sessions.</p> <p>Besides the data set, which is saved in a python pickle file, we include a python test script to load the data, generate random synthetic sequences of gestures and classify them with multiple models.</p> <p><strong>Gesture library:</strong></p> <ol> <li>Rest</li> <li>Closed fist</li> <li>Open hand</li> <li>Wave in</li> <li>Wave out</li> <li>Double-tap</li> <li>Hand down</li> <li>Hand up</li> </ol> <p><strong>Device placement:</strong></p> <ol> <li>The two Myos are placed on the forearm with the usb port pointing outwards, palm and sensor 5 facing upwards.</li> <li>The Myos are next to one another with their middle position close the the thickest section of the forearm.</li> <li>The outwards Myo is rotated slightly so that sensor 5 is aligned with the axis of the palmaris longus tendon.</li> <li>The Myo inside is rotated so that it has an angle of 22.5 degrees with the first Myo, in clockwise direction (subject perspective).</li> </ol> <p><strong>Acquisition protocol:</strong></p> <p>The subjects wear the armbands according to the instructions above. The sensors are run for a few minutes to warm-up.</p> <p>The subjects are requested to hold the positions of the gestures for a few seconds while we record 2 seconds of data. The gestures are repeated in random order in several sessions.</p>
Hand gesture dataset based on sEMG data captured from the Technaid human-robot interaction system
<p>Two files with a dataset of five different/independent hand gestures are provided. The data were generated in a sEMG system with two bracelets (eight sEMG sensors and six sEMG sensors) worn in the right forearm of a human. The Technaid human-robot interaction system was used to captured the data. The file "datasetForSegmentation.mat" was used to train a classifier whose purpose is the execution of Segmentation process. On the other hand, the file "datasetForRecognition.mat" was used to train a classifier whose purpose is the execution of gesture Recognition process.</p> <p> </p>
VRGestures: Controller and Hand Gesture Datasets for Virtual Reality
<p>15 VR Controller gestures</p> <p>11 one-handed VR Hand Gestures for each hand</p> <p>2 two-handed VR Hand Gestures </p>
Supplementary materials to: Hand gestures and the oratorical taskscape of Late Republican and Augustan Forum Romanum in Rome
<p>Supplementary materials to the article: "Hand gestures and the oratorical taskscape of Late Republican and Augustan Forum Romanum in Rome"<br><br>S1 - Virtual 3d Reconstruction of Forum Romanum, ca. 54 BCE used in the study</p> <p>S2 - Virtual 3d Reconstruction of Forum Romanum, ca. 27 BCE used in the study</p> <p>S3 - Virtual 3d Reconstruction of Forum Romanum, ca. 14 CE used in the study</p> <p>S4 - Parameters used in the Viewshed function in Exploratory 3D Analysis in the ArcGIS Pro 2.9</p> <div></div>
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>
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>
Thermal Video Dataset of Hand Gestures
<p>Our dataset is captured with the FLIR Lepton 2.5 thermal camera, which comes with a radiometric shutter and a focal length of 80X60 pixels. The camera's spectral range is 8 µm to 14 µm, and it has a 63.5 degrees of diagonal field of view and a 50 degrees of nominal horizontal field of view. The camera uses a progressive scan array format of 80X60 pixels.</p> <p>The dataset contains 990 sequences of thermal videos each made up of 15/10/5 frames. It's built up of 9 hand gestures, each with 10 classes within. We had eleven individuals do the gestures to ensure a variety of hand sizes, shapes, and temperatures. </p>
User Study Data from "HaptiGlow: Helping Users Position their Hands for Better Mid-Air Gestures and Ultrasound Haptic Feedback"
<p>This dataset contains user study data from the experiment reported in our IEEE World Haptics Conference 2019 paper.</p>
EMG from forearm datasets for hand gestures recognition
<p>This dataset contains 2 sets of sEMG recordings: a set containing <strong>PINCH</strong> movements (4 pinches between thumb and index/middle/ring/pinky finger) and a set containing <strong>ROSHAMBO</strong> movements (3 movements: rock, paper, scissors). Both sets have been recorded with the Myo armband. The Myo 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.. The <strong>PINCH </strong>set contains recordings of 22 subjects whilst the <strong>ROSHAMBO</strong> set contains recordings of 10 subjects. Each subject performed 3 sessions, where each 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. Full details for the <strong>ROSHAMBO</strong> set can be found in:</p> <p>Donati, Elisa, et al. "Processing EMG signals using reservoir computing on an event-based neuromorphic system." <em>2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)</em>. IEEE, 2018.</p> <p>For each session, the dataset contains 2 <strong>*.npy</strong> files one specifying the EMG data (<strong>*_emg.npy</strong>) the other one (<strong>*_ann.npy</strong>) specifying the corresponding gestures along the sampled EMG. The data can be easily loaded in python with numpy.</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>
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>
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.
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