A Wi-Fi-based Approach for Recognizing Human-Human Interactions

R. Alazrai, A. Awad, B. Alsaify, M. Daoud
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引用次数: 4

Abstract

This paper presents a new approach for recognizing human activities that involve two humans, referred to as human-human interactions, using Wi-Fi signals. The proposed approach utilizes the Channel State Information (CSI) metric of the Wi-Fi signals to characterize the performed interactions in indoor environment. Specifically, the proposed approach analyzes the CSI data and extracts a set of time-domain and frequency-domain features that comprise salient information to distinguish between the performed interactions. The extracted features are used to construct a multi-class support vector machine classifier that can recognize the classes of the interactions comprised within the CSI data. The performance of the proposed approach was evaluated using our publicly available human-human interaction CSI dataset that contains the CSI data recorded for 40 pairs of participants while performing 13 interactions. The experimental results indicate that our proposed approach achieved an average recognition accuracy of 69.78% computed overall the 13 interactions. The reported results for each pair of participants demonstrate the feasibility of our proposed approach to recognize human-human interactions using the CSI metric of the Wi-Fi signals.
基于wi - fi的人机交互识别方法
本文提出了一种使用Wi-Fi信号识别涉及两个人的人类活动的新方法,称为人与人之间的互动。该方法利用Wi-Fi信号的信道状态信息(CSI)度量来表征室内环境中所执行的交互。具体来说,提出的方法分析CSI数据并提取一组时域和频域特征,这些特征包含显著信息,以区分所执行的交互。提取的特征用于构建一个多类支持向量机分类器,该分类器可以识别CSI数据中包含的交互的类别。我们使用公开可用的人机交互CSI数据集对所提出方法的性能进行了评估,该数据集包含40对参与者在执行13次交互时记录的CSI数据。实验结果表明,该方法在13种交互作用下的平均识别准确率为69.78%。报告的每对参与者的结果证明了我们提出的方法的可行性,即使用Wi-Fi信号的CSI度量来识别人与人之间的互动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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