基于深度学习和分布式雷达传感器的鲁棒连续人体活动识别

中文摘要 AI 辅助

使用分布式雷达传感器网络进行连续人体活动识别（HAR）具有挑战性，因为多普勒特征强烈依赖于方位角，单个雷达视图的信息量随运动方向变化，且不间断序列中的活动转换往往模糊不清。本文提出了一种ConvNeXt-MHSA-BiGRU框架，用于从五个空间分布的雷达节点获取的多普勒-时间频谱图中进行逐帧连续HAR。一个共享的受ConvNeXt启发的编码器从每个雷达流中提取时频表示，而雷达级多头自注意力（MHSA）建模节点间依赖关系并自适应融合互补视图。RadarDropout和SpecAugment风格的时频掩码对网络进行正则化，以应对不可靠的雷达视图和局部频谱图扰动，而堆叠的双向门控循环单元（BiGRUs）利用时间上下文在活动转换中进行逐帧分类。该框架在14名参与者执行的九项活动上进行了评估，采用留一人法（L1PO）受试者独立测试和五折交叉验证。它在14个留出受试者上实现了87.56%的平均L1PO测试准确率，并优于已发表的CNN-RNN基线。结果表明，结合现代频谱图编码、自适应雷达视图融合和双向时间建模为连续和分布式基于雷达的HAR提供了有效框架。

英文摘要

Continuous human activity recognition (HAR) with distributed radar sensor networks is challenging because Doppler signatures depend strongly on aspect angle, the informativeness of individual radar views varies with motion direction, and activity transitions in uninterrupted sequences are often ambiguous. This paper proposes a ConvNeXt-MHSA-BiGRU framework for frame-wise continuous HAR from Doppler-time spectrograms acquired by five spatially distributed radar nodes. A shared ConvNeXt-inspired encoder extracts time-frequency representations from each radar stream, while radar-wise multi-head self attention (MHSA) models inter-node dependencies and adaptively fuses complementary views. RadarDropout and SpecAugment-style time-frequency masking regularize the network against unreliable radar views and local spectrogram perturbations, whereas stacked bidirectional gated recurrent units (BiGRUs) exploit temporal context for frame-wise classification across activity transitions. The framework is evaluated on nine activities performed by 14 participants using leave-one-person-out (L1PO) subject-independent testing with fivefold cross-validation. It achieves a mean L1PO test accuracy of 87.56% across the 14 held-out subjects and improves recognition performance over the published CNN-RNN baseline. The results demonstrate that combining modern spectrogram encoding, adaptive radar view fusion, and bidirectional temporal modeling provides an effective framework for continuous and distributed radar-based HAR.

发表机构

• Universität Paderborn（帕德博恩大学）

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