Abstract:

In the non-contact respiratory state monitoring based on WiFi Channel State Information （CSI）， respiratory state data are easily affected by environmental interference during acquisition， resulting in degraded signal quality. Meanwhile， due to constrained acquisition conditions and privacy protection， the number of samples is often insufficient， leading to a few-shot problem， and the generated samples usually lack consistency with real samples in key discriminative features， thereby reducing the effectiveness of data augmentation. In addition， the small differences among different respiratory states further increase the difficulty of classification and limit the depth of research on multi-state respiratory recognition. To address these challenges， an SiamGAN-CNNBiLSTM-based respiratory state recognition system was proposed. First， a multi-state respiratory dataset consisting of normal breathing， apnea， and sneezing was constructed， and a systematic CSI signal preprocessing pipeline was designed to improve signal quality. Second， an SiamGAN module integrating Generative Adversarial Network （GAN） and Siamese network was developed， so that through the adversarial mechanism of generator and discriminator， samples with higher realism were generated. At the same time， a Siamese encoder was introduced to extract 128-dimensional intrinsic features of the signals， and a Mean Squared Error （MSE） constraint was imposed in the feature space to enhance the consistency between generated and real samples in key discriminative features. By this strategy， the discriminability and generalization of generated samples were strengthened， and the physical properties of the original signals were preserved， so as to avoid the introduction of classification-irrelevant high-frequency noise. Finally， a deep classification network combining Convolutional Neural Network （CNN） and Bidirectional Long Short-Term Memory （BiLSTM） network was constructed to fully mine the spatio-temporal features of respiratory signals， and achieve high-precision recognition of multiple respiratory states. Experimental results show that in the office scenario， SiamGAN-CNNBiLSTM achieves excellent performance with an accuracy improvement of 2.2 percentage points， compared with CNN-BiLSTM method， and an accuracy improvement of 8.3 percentage points， compared with the Transformer method； in corridor scenario， SiamGAN-CNNBiLSTM achieves an accuracy of 96.7%， representing a decrease of only 1.6 percentage points compared with that in office scenario， indicating the robustness of the proposed method across different environments.

Key words: WiFi Channel State Information (CSI), data augmentation, Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), Generative Adversarial Network (GAN), Siamese network, respiratory state recognition

摘要：

在基于WiFi信道状态信息（CSI）的非接触式呼吸状态监测领域中，呼吸状态数据在采集过程中易受环境干扰，导致信号质量下降；样本数量受限于采集条件与隐私保护，常存在小样本问题，且生成样本通常缺乏与真实样本在关键判别特征上的一致性，降低了数据增强的有效性；此外，不同呼吸状态间信号差异较小，进一步增加了分类识别难度，限制了多状态呼吸识别的研究深度。针对上述挑战，提出一种基于SiamGAN-CNNBiLSTM的呼吸状态识别系统。首先，构建包含正常呼吸、呼吸暂停和打喷嚏等多状态呼吸数据集，并设计系统化的CSI信号预处理流程，提升信号质量。其次，提出融合生成对抗网络（GAN）与孪生网络的SiamGAN模块，通过生成器与判别器的对抗机制生成具有更高真实性的样本。同时，引入孪生编码器提取信号的128维本质特征，并在特征空间施加均方误差（MSE）约束，以强化生成样本与真实样本在关键判别特征上的一致性；该策略既增强生成样本的判别性与泛化性，也有效保留原始信号的物理属性，避免引入与分类无关的高频噪声。最后，设计融合卷积神经网络（CNN）与双向长短期记忆（BiLSTM）网络的深度分类网络，充分挖掘呼吸信号的时空特征，实现多种呼吸状态的高精度识别。实验结果表明，在办公室场景中，SiamGAN-CNNBiLSTM表现优异，与CNN-BiLSTM方法相比，准确率提升了2.2个百分点；与Transformer方法相比，准确率提升了8.3个百分点。在走廊场景中，SiamGAN-CNNBiLSTM的准确率达到96.7%，仅比办公室场景下降了1.6个百分点，显示出所提方法在不同环境下的鲁棒性。

关键词: WiFi信道状态信息, 数据增强, 卷积神经网络, 双向长短期记忆, 生成对抗网络, 孪生网络, 呼吸状态识别