DGCN-MFW: A Lightweight Human Action Recognition Network for Millimeter-Wave Radar 3D Point Clouds

DING Xuanyu; JIN Biao; ZHANG Zhenkai

doi:10.11999/JEIT251087

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DING Xuanyu, JIN Biao, ZHANG Zhenkai. DGCN-MFW: A Lightweight Human Action Recognition Network for Millimeter-Wave Radar 3D Point Clouds[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251087

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DING Xuanyu, JIN Biao, ZHANG Zhenkai. DGCN-MFW: A Lightweight Human Action Recognition Network for Millimeter-Wave Radar 3D Point Clouds[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251087

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DGCN-MFW: A Lightweight Human Action Recognition Network for Millimeter-Wave Radar 3D Point Clouds

doi: 10.11999/JEIT251087 cstr: 32379.14.JEIT251087

Jiangsu University of Science and Technology, Ocean College, Zhenjiang 212003, China

Funds: The National Natural Science Foundation of China (62571220), Key Research and Development Project of Henan Province (241111212500), Science and Technology Plan (Basic Research) Project of Zhenjiang City ( JC2025026), 2025 Jiangsu Provincial Postgraduate Practice & Innovation Program (SJCX25_2502)

Received Date: 2025-10-13
Accepted Date: 2026-03-03
Rev Recd Date: 2026-02-15

Available Online: 2026-03-15

Abstract

Abstract

Objective Millimeter-wave radar 3D point clouds provide important spatial cues for human action recognition. However, their inherent disorder complicates feature extraction, and actions rely on temporal correlations across multiple frames, which makes single-frame analysis prone to error. In this paper, a dynamic graph convolutional network is proposed for long 3D point-cloud sequences to improve recognition performance and efficiency through multi-scale feature fusion, adaptive frame weighting, and cross-attention. Methods A dynamic graph convolutional network solution, DGCN-MFW, is proposed with three core components: dynamic graph convolution feature extraction, multi-scale feature fusion, and adaptive temporal frame weighting. In Step 1, dynamic graph convolution is used to automatically construct spatial geometry through local directed neighborhood graphs, and the neighborhoods are updated online. This design avoids manual graph construction and improves feature robustness. In Step 2, multi-scale feature fusion is applied to jointly extract and integrate point-cloud features across spatial and temporal dimensions, thereby capturing local details and global semantics. In Step 3, adaptive frame weighting is introduced to learn the importance of each frame, emphasize discriminative key frames, and suppress noisy or unimportant frames. Cross-attention is further used to enable information exchange between the center frame and its context, compensating for the limitations of single-frame analysis caused by motion blur, occlusion, or pose ambiguity. Results and Discussions The proposed network extracts features through dynamic graph convolution, performs multi-scale feature fusion and adaptive frame weighting, and ultimately completes human action recognition. It achieves strong performance on the public TI and Vayyar millimeter-wave radar point-cloud datasets. With only 2.06M parameters and 4.51 GFLOPs, it outperforms existing methods (Tables 2, 3, and 4). Ablation experiments confirm that both core modules substantially improve recognition accuracy (Table 1). The confusion matrices indicate accuracy above 99% for most actions on the two datasets, demonstrating superior recognition performance (Figs. 10 and 11). However, its scalability, parameter efficiency, and processing efficiency for large-scale data still require improvement. Future work will therefore focus on further lightweight design and architectural optimization to improve efficiency. Conclusions To address the two main challenges in mmWave radar 3D point-cloud-based human action recognition, an action recognition algorithm based on a dynamic graph convolutional network and multi-feature fusion is proposed. A multi-scale feature fusion module and cross-scale interaction are used to extract local and global features, which improves spatial representation. An adaptive frame-weighting module and a cross-attention mechanism are adopted to capture the temporal evolution of actions. The method achieves accuracies of 98.32% and 99.48% on two datasets with 2.06M parameters and 4.51 GFLOPs, outperforming mainstream models. It provides a new solution for high-precision, low-resource mmWave radar action recognition and is suitable for real-time scenarios such as industrial human-machine interaction, intelligent security, and healthcare.
- Millimeter-wave radar 3D point cloud,
- Human action recognition,
- Graph convolutional network,
- Multi-scale feature fusion,
- Adaptive frame weighting

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References(26)

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