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    Item type:Publication, Access status: without files ,
    Light-weight and person-independent radar-based hand gesture recognition for classification and regression of continuous gestures
    (IEEE, 2024-05-01)
    Stadelmayer, Thomas
    ;
    Hassab, Youcef
    ;
    Servadei, Lorenzo
    ;
    Santra, Avik
    ;
    Weigel, Robert
    This paper proposes a novel pre-processing technique for radar-based short-range gesture sensing using a frequency modulated continuous wave (FMCW) radar. The pre-processing is light-weight and works without Fourier transformation. The signal after pre-processing represents the backscattering central dynamics of the hand as a complex-valued time signal of a point target. It is shown that the proposed processing provides competitive classification results compared to conventional frequency domain-based solutions, while being less computationally intensive and having better generalization performance. The pre-processed time domain signal preserves a high temporal resolution of the hand movement. Due to this fact, it is possible to integrate a periodic control gesture into the system. In doing so, the system not only detects that a gesture is performed continuously and periodically, but also estimates its speed. This is an essential property for controlling scalable parameters such as brightness or volume at different speeds. The real-time capability was proven on a Raspberry Pi 3B with an ARM Cortex-A53 CPU. The proposed processing causes a CPU utilization of only 6%. The nn inference is done within 75 ms with a classification accuracy of 96.7%.
    Publicationtype: Journal Article
      38
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    Item type:Publication, Access status: without files ,
    Physically-Interpretable Data Augmentation for Multi-Range Hand Gesture Recognition Using FMCW Radar Time Series
    (IEEE, 2023)
    Hassab, Youcef
    ;
    Stadelmayer, Thomas
    ;
    Lurz, Fabian
    In this paper, a robust ac hgr system using a ac fmcw radar and InceptionTime networks on data augmented time series is implemented. The paper proposes multiple data augmentation techniques for radar-based ac hgr. Since a realistic manipulation of raw radar data frames or even range-Doppler maps is a very complex challenge, we instead propose data manipulation on physically interpretable time series of range, azimuth and elevation angles extracted from the data. Due to working on physically interpretable time series data, we can on the one hand make use of well explored existing augmentation techniques for time series and on the other hand do use-case specific interpretable augmentations, such as simulating a different aspect angle or range. To investigate the system, a data recording process covering multiple ranges, angles and types of gestures is carried out. The gain in accuracy from the proposed data augmentation scheme amounts to more than 4 percentage points to reach a global prediction accuracy higher than 95% for a very diverse dataset.
    Publicationtype: Journal Article
      33