Search

Contactless WiFi based human action recognition exhibits remarkable prospects in the fields such as human-computer interaction and smart home. However, domain dependency restricts its generalization into the real-world deployment. Since it is expensive to label enough new data for retaining a model, it is beneficial to explore few-shot learning for cross-domain sensing with limited target labels. Nevertheless, there are two challenges to be addressed. The first challenge is how to select a suitable dataset from a series of available source domains to prevent negative transfer. The second is to mine action-related characteristics by the feature learning model for the following effective knowledge transfer. In order to tackle the above challenges, we present a cross-domain sensing framework named CDFi, which consists of Nearest Neighbor based Domain Selector (NNDS) and Fine-to-Coarse-Grained Transformer Network (FCGTN). NNDS is proposed to evaluate the source-target domain similarities by measurements among local and global feature distributions. Besides, FCGTN embeds convolution map based hierarchical transformer structures and the modified linear layer into an end-to-end deep network, which can quickly adapt to the unseen domain by few samples. Comprehensive experiments show that CDFi can effectively realize cross-domain action recognition, and achieve about 4% and 10% improvement on cross-user and cross-scene cases, respectively, compared to the state-of-the-art.

Human identification is essential for current intelligent applications. Traditional human identification methods usually require dedicated collection equipment or user cooperation, which limits the application scope of human identification. Recent research works have shown that Wi-Fi signals can support fine-grained sensing for human respiration. In this article, we leverage Wi-Fi signals to collect and recognize a unique physiological feature of humans, i.e., the respiration, for device-free and nonintrusive human identification. We propose a respiration sensing-based human identification system, namely BreathID. BreathID explores the nature of the amplitude and phase data in the CSI ratio model through vector analysis. By jointly using the discrete wavelet transform (DWT) and fake peak removal method, we accurately calculate the respiration rate and extract the breathing feature. We also propose a weighted multidimensional dynamic time warping (WMD-DTW) algorithm to realize the human identification based on multiple input multiple output (MIMO) technology. We implement the BreathID system on commercial Wi-Fi devices. The results show that BreathID can identify 11 users in three typical scenarios in daily life with an average accuracy of 97.52%. In particular, the average absolute error of the respiration rate in BreathID is about 0.1 bpm, which is better than the state-of-the-art algorithms.

With the widespread deployment of commercial wireless devices, researchers begin to focus on device-free sensing tasks. In the field of action perception, existing WiFi-based sensing works mostly follow the framework in which action instances of channel state information (CSI) are first extracted and then classified. As for the part of human action detection, a majority of works adopt threshold based sliding window or frame-by-frame detection methods. However, it is hard for the former approach to set a reasonable threshold for all samples. As for the latter, it costs a relatively substantial amount of labor to label each moment of the time sequences. In order to overcome the above problems, we design an end-to-end context-aware faster region-based convolutional neural networks (RCNN) framework named Wisense to simultaneously detect the temporal boundaries as well as classify the actions. More specifically, Wisense consists of backbone net, region proposal net (RPN), pooling layer, and the prediction net, which directly regresses the action location along the time axis and classifies the action types. For the sake of wireless signal temporal detection, we transform the input into 1-D feature map and extract multiscale 1-D anchors. Besides, in order to sufficiently mine the context information, we extend the boundaries of region proposals and further establish the temporal pyramid features. Experimental results conducted in three indoor scenes validate the effectiveness of our proposed Wisense.