摘要 : Wireless local-area network (WLAN) sensing offers advantages over other approaches to human activity recognition (HAR) for Internet of Things (IoT) applications, including privacy as well as adaptability to NLoS scenarios. This is... 展开 Wireless local-area network (WLAN) sensing offers advantages over other approaches to human activity recognition (HAR) for Internet of Things (IoT) applications, including privacy as well as adaptability to NLoS scenarios. This is why HAR plays an important role in the upcoming IEEE 802.11bf Wi-Fi standard, which aims to bring the adoption of WLAN sensing to a much larger scale. In this article, we propose CapsHAR, a model based on capsule networks, which uses channel state information (CSI) from Wi-Fi signals to accurately perform HAR. We evaluate the capability of the model on a variety of data sets, including large and small-scale gestures, as well as compare its performance to a variety of models and approaches. We then extend the CapsHAR model into a distributed architecture in order to eliminate the communication overhead of sending CSI data from multiple access points (APs) to a single server. We propose the use of edge computing to run CapsHAR at each AP separately, then combine the outputs of the models through a Fresnel zone-based voting scheme which makes more efficient use of spatial diversity. Overall, the CapsHAR architecture consistently achieves classification accuracy surpassing that of the state-of-the-art models, demonstrating the viability of capsule networks for reliable HAR in Wi-Fi-based IoT applications. 收起