AI 中文总结
针对现有无线传感数据集模态与轨迹局限，本文提出XGait多模态数据集，采用Wi-Fi与声学同步采集，构建统一多普勒频谱图表示，验证了两种传感模态的互补优势，为相关研究提供支撑。
AI 中文摘要
无线传感已成为使用商用物联网设备进行跟踪和识别的有前景方法，但从单一无线模态提取的特征往往对环境布局和行走轨迹的变化脆弱。此外，大多数现有研究基于在特定场景收集的数据集，其轨迹多样性和传感模态有限，无法对系统泛化性进行稳健评估。为解决这一差距，我们引入XGait，这是一个多模态无线传感数据集，可在三个室内场景中使用Wi-Fi和声学收发器同步捕获人体行走，基于视觉的测量作为真值。具体而言，XGait包含来自27名参与者的超过22000个行走样本，涵盖不同方向和轨迹，以支持室内跟踪和身份识别。为弥合无线传感模态的异质性，我们提出一种统一的多普勒频谱图表示，将Wi-Fi和声学信号映射到共享时频空间，同时提出标准化基准流程用于预处理、时间对齐和特征构建，实现可复现的评估和系统的跨模态分析。广泛的评估表明，Wi-Fi和声学传感展现出互补优势，尤其在复杂轨迹和挑战性传播条件下，从而为多模态无线传感领域的新研究铺平道路。该数据集和代码可在此URL获取。
英文摘要
Wireless sensing has emerged as a promising approach for tracking and identification using commodity Internet of Things devices. However, the features derived from a single wireless modality are often fragile to variations in environmental layouts and walking trajectories. Furthermore, most existing studies are based on datasets collected in specific scenarios with limited trajectory diversity and sensing modalities, preventing a robust evaluation of system generalization. \textcolor{blue}{To address this gap, we introduce \textbf{XGait}, a multi-modality wireless sensing dataset that synchronously captures human walking using Wi-Fi and acoustic transceivers across three indoor scenarios, with vision-based measurements serving as ground truth. Specifically, XGait contains more than 22K walking samples from 27 participants, covering diverse directions and trajectories to support both indoor tracking and identity recognition. To bridge the heterogeneity of wireless sensing modalities, we propose a unified Doppler spectrogram representation that maps Wi-Fi and acoustic signals into a shared time--frequency space, along with a standardized benchmark pipeline for pre-processing, temporal alignment, and feature construction, enabling reproducible evaluation and systematic cross-modal analysis. Extensive evaluations demonstrate that Wi-Fi and acoustic sensing exhibit complementary strengths, particularly under complex trajectories and challenging propagation conditions, thereby paving the way for novel research in the field of multi-modality wireless sensing.} The dataset and code are available at https://github.com/warrior-087/XGait.
