NEWS ARTICLE
学术动态：Human Motion Segmentation via Spatiotemporally Dual-Constrained Density Estimation with Commodity Wi-Fi Device
论文标题：Human Motion Segmentation via Spatiotemporally Dual-Constrained Density Estimation with Commodity Wi-Fi Device
发布日期：2026-05-22
作者：Xu Wang, Linghua Zhang, Feng Shu
DOI：10.3390/s26113303
论文摘要：In ubiquitous Wi-Fi sensing, human motion interval segmentation is crucial for applications ranging from basic intrusion detection to advanced activity understanding. Existing methods often treat the Channel State Information (CSI) primarily as time series, overlooking its rich information in the spatial and frequency domains. To address this, we propose a training-free motion segmentation method that exploits the spatiotemporal features of CSI. We first analyze the discriminative spatial distributions of the CSI Ratio on the complex plane and construct a spatiotemporally dual-constrained local density estimator to characterize motion-induced perturbations. To overcome subcarrier selection challenges, we introduce a packet-level asymmetric truncation-based fusion algorithm, which yields a feature representation with a pronounced bimodal histogram. This enables the automatic determination of the optimal segmentation threshold based on the distribution characteristics of the truncated density image. Experiments in typical indoor environments demonstrate that the proposed method achieves high accuracy in both motion event detection and interval localization.
元数据：Crossref 收录的 MDPI Sensors 论文。 DOI: 10.3390/s26113303. Vol. 26, Issue 11. Authors: Xu Wang, Linghua Zhang, Feng Shu.
开放许可：https://creativecommons.org/licenses/by/4.0/
原文链接：https://doi.org/10.3390/s26113303
PDF 链接：https://www.mdpi.com/1424-8220/26/11/3303/pdf