Real-Time Radar-Based Hand Motion Recognition on FPGA Using a Hybrid Deep Learning Model

Radar-based hand motion recognition (HMR) presents several challenges, including sensor interference, clutter, and the limitations of small datasets, which collectively hinder the performance and real-time deployment of deep learning (DL) models. To address these issues, this paper introduces a novel real-time HMR framework that integrates advanced signal pre-processing, a hybrid convolutional neural network-support vector machine (CNN-SVM) architecture, and efficient hardware deployment. The pre-processing pipeline applies filtration, squared absolute value computation, and normalization to enhance radar data quality. To improve the robustness of DL models against noise and clutter, time-series radar signals are transformed into binarized images, providing a compact and discriminative representation for learning. A hybrid CNN-SVM model is then utilized for hand motion classification. The proposed model achieves a high classification accuracy of 98.91%, validating the quality of the extracted features and the efficiency of the proposed design. Additionally, it reduces the number of model parameters by approximately 66% relative to the most accurate recurrent baseline (CNN-GRU-SVM) and by up to 86% relative to CNN-BiLSTM-SVM, while achieving the highest SVM test accuracy of 92.79% across all CNN-RNN variants that use the same binarized radar images. For deployment, the model is quantized and implemented on two System-on-Chip (SoC) FPGA platforms-the Xilinx Zynq ZCU102 Evaluation Kit and the Xilinx Kria KR260 Robotics Starter Kit-using the Vitis AI toolchain. The system achieves end-to-end accuracies of 96.13% (ZCU102) and 95.42% (KR260). On the ZCU102, the system achieved a 70% reduction in execution time and a 74% improvement in throughput compared to the PC-based implementation. On the KR260, it achieved a 52% reduction in execution time and a 10% improvement in throughput relative to the same PC baseline. Both implementations exhibited minimal accuracy degradation relative to a PC-based setup-approximately 1% on ZCU102 and 2% on KR260. These results confirm the framework's suitability for real-time, accurate, and resource-efficient radar-based hand motion recognition across diverse embedded environments.

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