Cross-Domain CSI-Based Activity Recognition via Iterative Soft Labeling and Domain Alignment

The development of wireless technology has triggered wireless sensing. Most WiFi sensing methods are data driven and learning based. They face two major drawbacks: 1) a large number of labeled samples are required to train the sensing model; and 2) the sensing model depends on the training environment and degrades dramatically in a different environment. To mitigate these problems, we propose a domain adaptation method to achieve environment robustness for channel-state-information-based activity recognition with sparsely labeled samples. The method, named cross-domain activity recognition (CDAR), consists of iterative soft labeling, domain alignment, and activity classification. To reduce the number of labeled samples, CDAR adopts dynamic time warping to measure the similarity between the samples, based on which the unlabeled samples are pseudo-labeled iteratively and progressively. To tolerate false labels, the pseudo-labels take the form of soft labels. To reduce the data distribution discrepancy, the domains are aligned by minimizing the intraclass distance and maximizing the interclass distance, using maximum mean discrepancy as the metric. The activities are finally classified by integrating convolutional neural network and bidirectional long short-term memory. Extensive experiments demonstrate the effectiveness of the method CDAR on activity recognition across people, locations, environmental dynamics, and rooms.