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BeAware: Convolutional Neural Network(CNN) based User Behavior Understanding through WiFi Channel State Information
Neurocomputing ( IF 6 ) Pub Date : 2020-07-01 , DOI: 10.1016/j.neucom.2019.09.111
Leyuan Jia , Yu Gu , Ken Cheng , Huan Yan , Fuji Ren

Abstract In modern informatics society, human beings are becoming more and more attached to the computer. Therefore, understanding user behavior is critical to various application fields like sedentary analysis, human-computer interaction, and affective computing. Current sensor-based and vision-based user behavior understanding approaches are either contact or obtrusive to user s, jeopardizing their availability and practicality. To this end, we present BeAware, a contactless Radio Frequency (RF) based user behavior understanding system leveraging the WiFi Channel State Information (CSI). The key idea is to visualize the channel data affected by human movements into time -series heat-map images, which are processed by a Convolutional Neural Network (CNN) to understand the corresponding user behaviors. We prototype BeAware on commodity low-cost WiFi devices and evaluate its performance in real-world environments. Experimental results have verified its effectiveness in recognizing user behaviors.

中文翻译:

BeAware:基于卷积神经网络 (CNN) 的用户行为理解通过 WiFi 信道状态信息

摘要 在现代信息学社会中,人类对计算机的依恋程度越来越高。因此,了解用户行为对于久坐分析、人机交互和情感计算等各种应用领域至关重要。当前基于传感器和基于视觉的用户行为理解方法对用户来说要么是接触性的,要么是突兀的,危及它们的可用性和实用性。为此,我们提出了 BeAware,这是一种利用 WiFi 信道状态信息 (CSI) 的基于非接触式射频 (RF) 的用户行为理解系统。关键思想是将受人体运动影响的通道数据可视化为时间序列热图图像,由卷积神经网络 (CNN) 处理以了解相应的用户行为。我们在商用低成本 WiFi 设备上对 BeAware 进行原型设计,并评估其在现实环境中的性能。实验结果验证了其在识别用户行为方面的有效性。
更新日期:2020-07-01
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