Convolutional neural network; Hand gesture recognition; FMCW radar micro-Doppler spectrum.


 This study proposes a cross-connection convolutional neural network, namely Cross‑CNN, to recognize hand gestures based on micro-Doppler spectrum data of FMCW (Frequency Modulated Continuous Wave) radar. In addition, different noise levels are added to the dataset for improving the recognition accuracy of the proposed model when predicting gestures in different noise conditions. The experimental results show that the model trained on the dataset with noise gives better recognition accuracy than the model trained on the dataset without noise does. Afterwards, the Cross-CNN model is investigated in changing the structural superparameters for selecting the most suitable parameter set for the proposed problem. Finally, the chosen Cross-CNN model is compared with other existing models in the same dataset and training conditions. As a result, the Cross-CNN network outperforms other models in terms of recognition accuracy, time-consumption and structural capacity thanks to using cross-connections which allow to combine new features with former ones in training process of the network.


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How to Cite

Hải. “HAND GESTURE RECOGNITION USING FMCW RADAR BASED ON CROSS-CONNECTION CONVOLUTIONAL NEURAL NETWORK”. Journal of Military Science and Technology, no. 75, Oct. 2021, pp. 15-22, https://en.jmst.info/index.php/jmst/article/view/261.



Research Articles