Green-computing technology and energy-saving design have become the focus of research in various fields in recent years. As a ubiquitously deployed infrastructure, WiFi can be considered as a platform for green sensing, and a plethora of efforts have been made in WiFi-based passive detection recently. However, little work has been done on the exercise activity recognition. In this paper, we propose SEARE, a novel energy-efficient solution using WiFi for exercise activity recognition. It is prototyped by fine-grained CSI extracted from existing commercial WiFi devices. Different from traditional features like mean or max value exploited in previous activity recognition works, involving either time or frequency information, we select CSI-waveform shape as activity feature, which contains the information from both of these two domains. A series of de-noise methods are designed, including low-pass, PCA, and median filtering, where PCA can remove the in-band noise that traditional low-pass filters fail to do. Finally the evaluation of activities quality can be made. Extensive experimental result validates the great performance of SEARE in both LOS and NLOS scenarios, with average recognition accuracies of 97.8 and 91.2 percent respectively.

中文翻译：

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SEARE：基于绿色感知的运动活动识别和质量评估系统

近年来，绿色计算技术和节能设计成为各领域研究的热点。作为一种无处不在的基础设施，WiFi 可以被视为绿色感知的平台，并且最近在基于 WiFi 的被动检测方面做出了大量努力。然而，关于运动活动识别的工作很少。在本文中，我们提出了 SEARE，这是一种使用 WiFi 进行运动活动识别的新型节能解决方案。它的原型是从现有的商业 WiFi 设备中提取的细粒度 CSI。与以往活动识别工作中利用的平均值或最大值等传统特征不同，涉及时间或频率信息，我们选择 CSI 波形作为活动特征，其中包含来自这两个域的信息。设计了一系列去噪方法，包括低通、PCA和中值滤波，PCA可以去除传统低通滤波器无法做到的带内噪声。最后可以对活动质量进行评价。大量的实验结果验证了 SEARE 在 LOS 和 NLOS 场景中的出色性能，平均识别准确率分别为 97.8% 和 91.2%。