@inproceedings{,
    author = {Alirezazad, Keivan; Rhiel, Gregor; Maurer, Linus},
    title = {2D CNN-GRU Model for Multi-Hand Gesture Recognition System Using FMCW Radar},
    editor = {},
    booktitle = {2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) : Quebec City, QC, Canada, 2022},
    series = {},
    journal = {},
    address = {Piscatway, NJ},
    publisher = {IEEE},
    edition = {},
    year = {2022},
    isbn = {},
    volume = {},
    number =  {},
    pages = {158-162},
    url = {},
    doi = {10.1109/NEWCAS52662.2022.9841948},
    keywords = {},
    abstract = {Contactless human hand gesture recognition has received significant attention in the preceding decade. This paper proposes a novel classification approach utilizing an advanced 77-GHz multiple-input-multiple-output (MIMO) frequency modulated continuous wave (FMCW) radar. The pre-processed range-Doppler images (RDIs) and range-angle images (RAIs) of this radar are fed into a dual-stream artificial neural network comprised of 2D convolutional neural network-gated recurrent units (2D CNN-GRU) for human hand gesture classification. According to the conducted experiments, the average accuracy of the proposed classification model with 8-fold cross-validation achieves 92.50%.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Elektrotechnik und Informationstechnik, EIT 4 - Institut für Mikroelektronik und Schaltungstechnik, Professur: Maurer, Linus},
    
    
}