Wang Zhijian, Ouyang Lei, Chen Shi, Ding Han, Wang Ge, Wang Fei
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2025 Aug 19;25(16):5151. doi: 10.3390/s25165151.
In recent years, indoor user identification via Wi-Fi signals has emerged as a vibrant research area in smart homes and the Internet of Things, thanks to its privacy preservation, immunity to lighting conditions, and ease of large-scale deployment. Conventional deep-learning classifiers, however, suffer from poor generalization and demand extensive pre-collected data for every new scenario. To overcome these limitations, we introduce SimID, a few-shot Wi-Fi user recognition framework based on identity-similarity learning rather than conventional classification. SimID embeds user-specific signal features into a high-dimensional space, encouraging samples from the same individual to exhibit greater pairwise similarity. Once trained, new users can be recognized simply by comparing their Wi-Fi signal "query" against a small set of stored templates-potentially as few as a single sample-without any additional retraining. This design not only supports few-shot identification of unseen users but also adapts seamlessly to novel movement patterns in unfamiliar environments. On the large-scale XRF55 dataset, SimID achieves average accuracies of 97.53%, 93.37%, 92.38%, and 92.10% in cross-action, cross-person, cross-action-and-person, and cross-person-and-scene few-shot scenarios, respectively. These results demonstrate SimID's promise for robust, data-efficient indoor identity recognition in smart homes, healthcare, security, and beyond.
近年来，通过Wi-Fi信号进行室内用户识别已成为智能家居和物联网领域一个充满活力的研究领域，这得益于其隐私保护、不受光照条件影响以及易于大规模部署的特点。然而，传统的深度学习分类器存在泛化能力差的问题，并且在每个新场景中都需要大量预先收集的数据。为了克服这些限制，我们引入了SimID，这是一种基于身份相似性学习而非传统分类的少样本Wi-Fi用户识别框架。SimID将用户特定的信号特征嵌入到高维空间中，促使来自同一个体的样本表现出更大的成对相似性。一旦训练完成，新用户可以通过将其Wi-Fi信号“查询”与一小部分存储的模板（可能少至单个样本）进行比较来识别，而无需任何额外的再训练。这种设计不仅支持对未见用户的少样本识别，还能无缝适应陌生环境中的新运动模式。在大规模XRF55数据集上，SimID在跨动作、跨人、跨动作与人、跨人和场景的少样本场景中分别实现了97.53%、93.37%、92.38%和92.10%的平均准确率。这些结果证明了SimID在智能家居、医疗保健、安全等领域实现强大、数据高效的室内身份识别的潜力。
