WiFuse：一种融合CSI幅度与延迟-多普勒信道特征的人体活动识别注意力机制
WiFuse: An Attention Mechanism for Human Activity Recognition using Fused CSI Amplitude and Delay-Doppler Channel Features
AI总结：
本文提出WiFuse框架，以双流CSI融合幅度与延迟-多普勒表征，结合ResNet-TCN架构及迁移学习，在公开HAR数据集上取得95.28%至98.20%的识别准确率，提升了复杂环境下的活动识别性能。
AI中文摘要：
近期，Wi-Fi传感在人体活动识别（Human Activity Recognition, HAR）中发挥了重要作用，其仅通过Wi-Fi信号即可检测各类活动，兼具隐私保护性且对用户非侵入。然而，反射面、硬件偏移等环境特性及其他物理损伤会影响神经网络的识别效果，进而引发错误并大幅降低模型精度。为解决该问题，本文提出WiFuse框架，这是一种用于人体活动识别（HAR）的双流信道状态信息（Channel State Information, CSI）框架，它将去噪后的时域幅度变化与经2D-FFT从净化后的信道相位计算得到的二维延迟-多普勒运动表征相结合。融合后的表征输入至混合ResNet-时间卷积网络（Temporal Convolutional Network, TCN）神经架构，该架构通过通道注意力和时空注意力进行增强；其中ResNet用于提取空间-光谱特征，TCN用于建模长程时间依赖关系；本文采用解耦的两阶段迁移学习策略以提升优化稳定性与特征复用性。我们在两个公开数据集上开展了大量实验，包括与现有最优方法及替代混合架构的对比、 ablation研究，以及跨数据集和域适应评估。所提框架在XRF55数据集的四个环境中达到最高95.28%的整体准确率，在多用户Wi-MIR数据集上达到98.20%的准确率。总体而言，结果表明，在双流策略中结合幅度与延迟-多普勒表征，并通过迁移学习增强，可改善深度神经网络在类别重叠、多径传播、噪声及干扰等通常会降低其性能的条件下的识别表现。
英文摘要：
Recently, Wi-Fi sensing has played a significant role in Human Activity Recognition (HAR), as it enables the detection of various activities using only Wi-Fi signals, ensuring privacy and remaining non-intrusive for the user. However, environmental characteristics such as reflective surfaces, hardware offsets, and other physical impairments affect recognition by the neural network, subsequently causing errors and significantly reducing model accuracy. To overcome this problem we present the WiFuse framework, a dual-stream Channel State Information (CSI) framework for human activity recognition (HAR) that pairs denoised time-domain amplitude variations with 2D-FFT-derived Delay-Doppler motion representations computed from the sanitized channel phase. The fused representation feeds a hybrid ResNet-Temporal Convolutional Network (TCN) neural architecture augmented with channel and spatio-temporal attention, where the ResNet extracts spatial-spectral features and the TCN models long-range temporal dependencies; a decoupled two-stage transfer learning strategy is employed to improve optimization stability and feature reuse. We conduct extensive experiments on two public datasets, including comparisons against state-of-the-art methods and alternative hybrid architectures, ablation studies, and cross-dataset and domain-adaptation evaluations. The proposed framework reaches an overall accuracy of up to 95.28% across the four environments of the XRF55 dataset and up to 98.20% on the multi-user Wi-MIR dataset. Overall, the results indicate that combining amplitude and Delay-Doppler representations within a dual-stream strategy, enhanced by transfer learning, improves recognition performance under conditions that typically degrade deep neural networks, such as class overlap, multipath propagation, noise, and interference.
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