统一无线感知数据标准，提升多任务研究可复现性
A Sensing Dataset Protocol for Benchmarking and Multi-Task Wireless Sensing
- 定义协议级数据规范，统一异构信号为感知数据块
- 跨用户测试中方差降低约88%，精度与延迟表现稳定
- 适合多模态、多任务无线感知研究者使用
无线感知已成为智能环境的基础技术，支持人体检测、活动识别、定位和生命体征监测等应用。尽管进展迅速，现有数据集与处理流程在不同感知模态间仍碎片化，阻碍公平比较、迁移与复现。本文提出传感数据集协议（SDP），一个面向大规模无线感知的协议级规范与基准框架。SDP通过轻量同步、频时对齐与重采样，将异构无线信号映射为统一感知数据块；采用经典多线性交替最小二乘（CP-ALS）池化阶段，保留多径、频谱与时间结构的任务无关表示。基于该协议，构建了检测、识别与生命体征估计的一致预处理、训练与评估基准。跨用户划分实验表明，SDP将种子间方差降低约88%，同时保持竞争力的准确率与延迟，验证其作为多模态、多任务感知研究可复现基础的价值。
原文摘要 · Abstract (English)
Wireless sensing has become a fundamental enabler for intelligent environments, supporting applications such as human detection, activity recognition, localization, and vital sign monitoring. Despite rapid advances, existing datasets and pipelines remain fragmented across sensing modalities, hindering fair comparison, transfer, and reproducibility. We propose the Sensing Dataset Protocol (SDP), a protocol-level specification and benchmark framework for large-scale wireless sensing. SDP defines how heterogeneous wireless signals are mapped into a unified perception data-block schema through lightweight synchronization, frequency-time alignment, and resampling, while a Canonical Polyadic-Alternating Least Squares (CP-ALS) pooling stage provides a task-agnostic representation that preserves multipath, spectral, and temporal structures. Built upon this protocol, a unified benchmark is established for detection, recognition, and vital-sign estimation with consistent preprocessing, training, and evaluation. Experiments under the cross-user split demonstrate that SDP significantly reduces variance (approximately 88%) across seeds while maintaining competitive accuracy and latency, confirming its value as a reproducible foundation for multi-modal and multitask sensing research.
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