Human-computer interaction using radar has gained a lot of attention. Gesture recognition with a single Frequency Modulated Continuous Wave (FMCW) radar has been investigated by many researchers, and several promising results have been achieved. However, the stability of such a system still needs to be improved. This paper proposes a signal-processing approach for gesture recognition based on multiple FMCW radars.VGG16 pre-trained model is employed to extract features from the dataset, after which gesture recognition is performed using a Support Vector Machine (SVM) classifier and an XGBoost classifier, respectively. Moreover, the effect of the incomplete dataset in case of failure of one of the radars on the accuracy of gesture recognition is also analysed. The experimental results show that the SVM classifier in the multiple radar scenario obtains up to 96.67% accuracy on the test set, which is 1.78% to 5.95% higher than the single radar scenario. In the multi-radar scenario, when the extracted dataset is incomplete, the SVM classifier achieves up to 95% recognition accuracy. This proves that multi-radar is more stable than the single radar scheme. This system can be applied in smart homes, in-car entertainment systems or smart factories.
Gesture Recognition Using Multiple mmWave FMCW Radars
2023-10-10
3076425 byte
Conference paper
Electronic Resource
English
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