With the emergence of tools for extracting CSI data from commercial WiFi devices, CSI-based device-free activity recognition technology has developed rapidly and has been widely used in security monitoring, smart home, medical monitoring, and other fields. However, the existing CSI-based activity recognition algorithms need a large number of training samples to obtain the ideal recognition accuracy. To solve the problem, an attention-based bidirectional LSTM method using multidimensional features (called MF-ABLSTM method) is proposed. In this method, the signal preprocessing and continuous wavelet transform algorithms are used to construct time-frequency matrix, the sample entropy is used to characterize the statistical feature of CSI amplitudes, the energy difference at a fixed time interval is used to characterize the time-domain feature of activities, and the energy distribution of different frequency components is used to characterize the frequency-domain feature of activities. By expanding the training samples with the proposed tensor prediction algorithm, the accurate activity recognition can be realized with only a few samples. A large number of experiments verify the good performance of MF-ABLSTM method.

中文翻译：

---

基于小 CSI 样本的活动识别：一种使用多维特征的深度学习方法

随着商用WiFi设备CSI数据提取工具的出现，基于CSI的无设备活动识别技术发展迅速，并已广泛应用于安防监控、智能家居、医疗监控等领域。然而，现有的基于CSI的活动识别算法需要大量的训练样本才能获得理想的识别精度。为了解决这个问题，提出了一种使用多维特征的基于注意力的双向 LSTM 方法（称为 MF-ABLSTM 方法）。该方法利用信号预处理和连续小波变换算法构建时频矩阵，利用样本熵表征CSI幅度的统计特征，固定时间间隔的能量差用于表征活动的时域特征，不同频率分量的能量分布用于表征活动的频域特征。通过使用所提出的张量预测算法扩展训练样本，只需少量样本即可实现准确的活动识别。大量实验验证了MF-ABLSTM方法的良好性能。