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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 , Out-of-distribution detection for radar-based gesture recognition using metric-learningThe paper addresses the question how and to what extent metric learning can be beneficial for reducing the false alarm rate in radar-based hand gesture recognition systems. To this end, we evaluate different metric learning approaches for out-of-distribution or unknown motion detection. We found that metric learning can help to significantly increase the out-of-distribution capabilities of the network. We further investigated what conditions must be met for metric learning to work well, and found that the composition of the data set for known gestures has a large influence on the out-of-distribution detection rate.Publicationtype: Conference Paper52 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Radar-Based Gesture Recognition Using a Variational Autoencoder With Deep Statistical Metric Learning(2022-11-01); ; ; Radar-based gesture recognition systems are a promising concept for new human–machine interfaces. However, the systems are sensitive to user-dependent gesture patterns, sensor noise characteristics, background environments, and unknown gestures or background motions. To overcome such challenges, we propose a variational autoencoder architecture-based deep metric learning, which is optimized using a novel loss function combining a statistical distance triplet loss and the center loss. By learning the statistical distance between distributions, the proposed solution, compared with triplet loss, is able to better learn the nonlinear characteristics in the data, is less sensitivity to training strategy, and is capable of creating close knit embedding clusters. Moreover, it is experimentally demonstrated that the proposed gesture recognition system using radar spectrograms has improved classification accuracy and random gesture rejection accuracy compared with the state-of-the-art deep metric learning approaches. Furthermore, we proof the real-world capability of the system by implementing a real-time demonstrator, which allows playing “Tetris” with hand gestures.Publicationtype: Journal Article74