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            <header>
                <identifier>oai:data.mendeley.com/ndrczc35bt.2</identifier>
                <datestamp>2021-03-08T13:12:17Z</datestamp>
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            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>Nuzzi, Cristina</dc:creator>
    <dc:title>HANDS: a dataset of static Hand-Gestures for Human-Robot Interaction</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>The HANDS dataset has been created for human-robot interaction research, and it is composed of spatially and temporally aligned RGB and Depth frames. It contains 12 static single-hand gestures performed with both the right-hand and the left-hand, and 3 static two-hands gestures for a total of 29 unique classes.

Five subjects (2 females and 3 males) performed the gestures, each of them with a different background and light conditions. For each subject, 150 RGB frames and their corresponding 150 depth frames per gesture have been collected, for a total of 2400 RGB frames and 2400 depth frames per subject.

Data has been collected using a Kinect v2 camera intrinsically calibrated to spatially align RGB data to depth data. The temporal alignment has been performed offline using MATLAB, aligning frames with a maximum temporal distance of 66 ms. We provide our MATLAB scripts to process similar rosbags and align the streams, elaborate a MATLAB Labeling Session, and create the same Annotation files we provide. For users who want to use the annotated data for research, we also provide a Python script showing how to convert the Annotation files into a TensorFlow record.

The data is valuable for the field of Computer Vision, especially for the tasks of hand-gesture recognition, human-machine interaction, and hand-pose recognition. The dataset can be used to train Deep Learning models to recognize the gestures in the dataset using only a single modality (RGB or depth) or both at the same time. It is also useful as a reference dataset for benchmarking models.

If you use this dataset for your work, please cite the related papers:
- https://doi.org/10.1016/j.dib.2021.106791
- https://doi.org/10.1016/j.rcim.2020.102085</dc:description>
    <dc:subject>Computer Vision</dc:subject>
    <dc:subject>Image Processing</dc:subject>
    <dc:subject>Human-Computer Interaction</dc:subject>
    <dc:subject>Gesture Recognition</dc:subject>
    <dc:contributor>Pasinetti, Simone</dc:contributor>
    <dc:contributor>Pagani, Roberto</dc:contributor>
    <dc:contributor>Coffetti, Gabriele</dc:contributor>
    <dc:contributor>Sansoni, Giovanna</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/ndrczc35bt.2</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/ndrczc35bt.2</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/ndrczc35bt</dc:relation>
    <dc:date>2021-03-08T13:12:17Z</dc:date>
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