WiFi-based human activity recognition (HAR) aims to recognize human activities in an off-the-shelf manner that only relies on the commercial Wi-Fi devices already installed in environments. The recent trend in HAR research is to train classifiers on top of statistical or deep neural features extracted from channel state information (CSI) data. Unfortunately, existing methods only take into account the temporal-correlation within each CSI subcarrier, while ignoring the spatial-correlation between different subcarriers. This issue has not been fully exploited yet, resulting a limited performance. To address this issue, we propose WiAReS, a WiFi-based device-free activity recognition system that takes both temporal-correlation and spatial-correlation into account. WiAReS embarks on diversified deep ensemble methods 2̌for single-user activity recognition where one user performs a single activity at a given time. More specifically, it adopts convolutional neural network (CNN) to automatically extract features from CSI measurements with the preservation of the locality of both spatial patterns and temporal patterns. To further improve recognition accuracy upon CNN-extracted features, we propose a novel ensemble architecture that fuses a multiple layer perception (MLP), a random forest (RF) and a support vector machine (SVM). Our system obtains the CSI data in PHY layer of off-the-shelf WiFi devices by installing Atheros-CSI-Tool on AR9590 based WiFi network interface cards (NICs). Comprehensive experiments have been conducted in three real environments with environmental variation to evaluate the performance of the proposed WiAReS. The experimental results demonstrate that the proposed WiARes system significantly outperforms existing methods.

基于WiFi的人类活动识别（HAR）旨在以现成的方式识别人类活动，这种方式仅依赖于已安装在环境中的商用Wi-Fi设备。HAR研究的最新趋势是在从通道状态信息（CSI）数据提取的统计或深度神经特征的基础上训练分类器。不幸的是，现有方法仅考虑每个CSI子载波内的时间相关性，而忽略了不同子载波之间的空间相关性。此问题尚未得到充分利用，从而导致性能受到限制。为了解决这个问题，我们提出了WiAReS，这是一种基于WiFi的无设备活动识别系统，该系统同时考虑了时间相关性和空间相关性。WiAReS着手针对单一用户活动识别的多种深度集成方法2，其中一个用户在给定时间执行单个活动。更具体地说，它采用卷积神经网络（CNN）从CSI测量中自动提取特征，同时保留空间模式和时间模式的局部性。为了进一步提高CNN提取特征的识别准确性，我们提出了一种新颖的集成体系结构，该体系结构融合了多层感知（MLP），随机森林（RF）和支持向量机（SVM）。我们的系统通过在基于AR9590的WiFi网络接口卡（NIC）上安装Atheros-CSI-Tool，来获取现成WiFi设备的PHY层中的CSI数据。已经在具有环境变化的三个实际环境中进行了全面的实验，以评估建议的WiAReS的性能。实验结果表明，所提出的WiARes系统明显优于现有方法。