使用DTW特征和机器学习的基于Wi-Fi csi的人类存在检测

J. Soto, Iandra Galdino, Brenda G. Gouveia, Egberto Caballero, Vinicius C. Ferreira, D. Muchaluat-Saade, C. Albuquerque
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引用次数: 2

摘要

随着智能设备的发展,人体检测和定位已成为安全、医疗监控、娱乐等多个应用的重要任务。现有的基于信号的检测系统主要集中在检测人类活动并通过机器学习(ML)方法对其进行分类,如支持向量机(SVM)和随机森林(RF)。本文主要研究无设备状态检测。我们提出了一种特定的设置,用于收集基于Wi-Fi的信道状态信息(CSI)数据,以检测人类的存在。该方案包括应用动态时间翘曲(DTW)算法的特征来比较空房间和满房间之间的差异。与现有技术相比,所提出的体系结构和方法具有相当的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wi-Fi CSI-based Human Presence Detection Using DTW Features and Machine Learning
With the development of smart devices, human detection and localization became important tasks for several applications including security, healthcare monitoring, entertainment, and so on. Existing signal-based detection systems, mostly focus on detecting human activities and classifying them by Machine Learning (ML) methods, like Support Vector Machine (SVM) and Random Forest (RF). This paper focuses on device-free presence detection. We propose a specific setup for collecting Wi-Fi based Channel State Information (CSI) data for detecting human presence. The proposal includes the application of Dynamic Time Warping (DTW) algorithm features to compare the differences between empty rooms and filled rooms. The proposed architecture and approach achieves competitive accuracy when compared to the existing technologies.
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