Human Behaviour Recognition Using Wifi Channel State Information

Daanish Ali Khan, Saquib Razak, B. Raj, Rita Singh
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引用次数: 10

Abstract

Device-Free Human Behaviour Recognition is automatically recognizing physical activity from a series of observations, without directly attaching sensors to the subject. Behaviour Recognition has applications in security, health-care, and smart homes. The ubiquity of WiFi devices has generated recent interest in Channel State Information (CSI) that describes the propagation of RF signals for behaviour recognition, leveraging the relationship between body movement and variations in CSI streams. Existing work on CSI based behaviour recognition has established the efficacy of deep neural network classifiers, yielding performance that surpasses traditional techniques. In this paper, we propose a deep Recurrent Neural Network (RNN) model for CSI based Behaviour Recognition that utilizes a Convolutional Neural Network (CNN) feature extractor with stacked Long Short-Term Memory (LSTM) networks for sequence classification. We also examine CSI de-noising techniques that allow faster training and model convergence. Our model has yielded significant improvement in classification accuracy, compared to existing techniques.
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利用Wifi信道状态信息进行人类行为识别
无需设备的人类行为识别是通过一系列观察自动识别身体活动,而无需直接在受试者身上安装传感器。行为识别在安全、医疗保健和智能家居领域都有应用。WiFi设备的无处不在最近引起了人们对信道状态信息(CSI)的兴趣,CSI描述了用于行为识别的射频信号的传播,利用了身体运动和CSI流变化之间的关系。基于CSI的行为识别的现有工作已经建立了深度神经网络分类器的有效性,产生了超越传统技术的性能。在本文中,我们提出了一种深度递归神经网络(RNN)模型用于基于CSI的行为识别,该模型利用卷积神经网络(CNN)特征提取器和堆叠长短期记忆(LSTM)网络进行序列分类。我们还研究了CSI去噪技术,它允许更快的训练和模型收敛。与现有技术相比,我们的模型在分类精度方面取得了显着提高。
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