Embedded mmWave Radar-Based Hand Gesture Recognition Using a Dual-Stream LSTM and Attention-BiGRU Network

Millimeter-wave (mmWave) radar-based gesture recognition has attracted increasing attention for real-time human–computer interaction owing to its robustness to illumination changes, privacy-preserving sensing capability, and suitability for embedded deployment. However, existing single-stream models often couple heterogeneous point-cloud and temporal statistical features, which may limit their ability to capture fine-grained motion patterns and key action frames. To address this problem, this paper proposes a dual-stream long short-term memory network (LSTM) and a bidirectional gated recurrent unit (BiGRU) with an attention mechanism (Attention-BiGRU) network for mmWave radar-based hand gesture recognition, termed as DSTG-Net. In the proposed DSTG-Net framework, an LSTM branch is used to process radar point-cloud sequences and extract fine-grained spatio-temporal features, while an Attention-BiGRU branch models global motion trends from statistical and temporal-difference features. The attention mechanism in the Attention-BiGRU branch is introduced to emphasize discriminative frames during gesture transitions, and the complementary features from the two branches are fused through feature concatenation for final classification. The proposed method is evaluated on a public mmWave radar gesture dataset to verify its recognition performance, and an additional self-built near-field dataset is used to test its effectiveness under a constrained acquisition scene. The proposed method achieves recognition accuracies of 97.40% and 98.75% on the two datasets, respectively, outperforming several baseline models. The Raspberry Pi-based implementation with a TI IWR1642 radar confirmed the functional feasibility of the proposed pipeline.