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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); ; ; ; 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 Article37 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Bayesradar : Bayesian Metric-Kalman filter learning for improved and reliable radar target classification(IEEE, 2021-10-25); ; ; ; Publicationtype: Conference Paper81 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-driven Radar Processing Using a Parametric Convolutional Neural Network for Human Activity Classification(2021-09-01); ; ; The paper proposes a data-driven pre-processing optimization for radar data using a parametric convolutional neural network. The proposed method is applied on human activity classification as a use case. Present radar-based activity recognition system exploit micro-Doppler signature by generating Doppler spectrograms or a temporal series of range-Doppler maps, followed by deep neural networks or machine learning approaches for classification. Those radar data representations are typically generated on the basis of short-time Fourier transformations. A Fourier transformation equally resolves the frequency space, which may be sub-optimal in some applications. Although deep convolutional neural networks (DCNN) have been shown to implicitly learn features from raw sensor data in other fields, such as speech recognition, yet, for the case of radar-based DCNNs, pre-processing is required to develop a scalable and robust classification or regression application. In this paper, we propose a parametric convolutional neural network that mimics the radar pre-processing across fast-time and slow-time radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for classification of various human activities. During training only the filter parameters of the 2D sinc filters or 2D wavelets are learned, leading to optimized feature representation for the classification task. It is demonstrated that our proposed solution shows improved results compared to equivalent DCNN architectures that rely on Doppler spectrograms or radar data cubes as input data.Publicationtype: Journal Article84