WiFi human sensing is highly regarded for its low-cost and privacy advantages in recognizing human activities. However, its effectiveness is largely confined to controlled, single-user, line-of-sight settings, limited by data collection complexities and the scarcity of labeled datasets. Traditional cross-modal methods, aimed at mitigating these limitations by enabling self-supervised learning without labeled data, struggle to extract meaningful features from amplitude-phase combinations. In response, we introduce AutoSen, an innovative automatic WiFi sensing solution that departs from conventional approaches. AutoSen establishes a direct link between amplitude and phase through automated cross-modal autoencoder learning. This autoencoder efficiently extracts valuable features from unlabeled CSI data, encompassing amplitude and phase information while eliminating their respective unique noises. These features are then leveraged for specific tasks using few-shot learning techniques. AutoSen's performance is rigorously evaluated on a publicly accessible benchmark dataset, demonstrating its exceptional capabilities in automatic WiFi sensing through the extraction of comprehensive cross-modal features.

翻译：WiFi人体感知因其低成本和隐私优势在人体活动识别中备受关注。然而，其有效性主要局限于受控的单用户视距场景，受到数据采集复杂性和标注数据集稀缺的限制。传统跨模态方法旨在通过无需标注数据的自监督学习缓解这些限制，但难以从幅度-相位组合中提取有意义的特征。为此，我们提出AutoSen——一种创新的自动WiFi感知方案，它突破了传统方法的局限。AutoSen通过自动化的跨模态自编码器学习，在幅度与相位之间建立直接联系。该自编码器从无标签的CSI数据中高效提取有价值特征，涵盖幅度和相位信息的同时消除各自特有的噪声。随后，利用少样本学习技术将这些特征用于特定任务。我们在公开基准数据集上严格评估了AutoSen的性能，结果表明，通过提取全面的跨模态特征，AutoSen在自动WiFi感知方面展现出卓越能力。