In recent years, WiFi-based gesture recognition has garnered increasing attention due to its convenience and widespread applicability. However, achieving generalized crossdomain gesture recognition remains a formidable challenge. A prevalent approach to addressing this issue involves using domain labels to help gesture recognition systems acquire domainagnostic features. Nevertheless, we propose that the subjective nature of current domain dividing methods, which are based on human cognition, limits the generalization capabilities of existing methods in WiFi sensing. To overcome this limitation, we propose GESFI, an objective WiFi-based gesture recognition framework. Unlike traditional approaches, GESFI abandons the biased, subjectively defined domain labels and instead identifies latent domains within the WiFi sensing data objectively. The process begins by roughly training a gesture recognition model to understand the data distribution. Then, using a worst-case criterion, latent domains are mined from the model's perspective. Finally, GESFI minimizes the discrepancies between these latent domains to effectively learn domain-agnostic features, enabling generalized gesture recognition. Extensive experiments demonstrate that GESFI significantly outperforms existing state-of-theart methods across various scenarios. The code is available at: https://github.com/purpleleaves007/GesFi.

Recent years have witnessed a growing interest in Wi-Fi-based gesture recognition. However, existing works have predominantly focused on closed-set paradigms, where all testing gestures are predefined during training. This poses a significant challenge in real-world applications, as unseen gestures might be misclassified as known classes during testing. To address this issue, we propose WiOpen, a robust Wi-Fi-based Open-Set Gesture Recognition (OSGR) framework. Implementing OSGR requires addressing challenges caused by the unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data. Consequently, data ambiguity between classes and challenges in defining appropriate decision boundaries to identify unknowns arise. To tackle these challenges, WiOpen adopts a twofold approach to eliminate uncertainty and define precise decision boundaries. Initially, it addresses uncertainty induced by noise during data preprocessing by utilizing the CSI ratio. Next, it designs the OSGR network based on an uncertainty quantification method. Throughout the learning process, this network effectively mitigates uncertainty stemming from domains. Ultimately, the network leverages relationships among samples' neighbors to dynamically define open-set decision boundaries, successfully realizing OSGR. Comprehensive experiments on publicly accessible datasets confirm WiOpen's effectiveness. Code is available at https://github.com/purpleleaves007/WiOpen.

Facial expression recognition (FER) is a challenging job in Computer Vision due to data uncertainties rooted in the ambiguity of facial expressions. As a complement to current FER studies huddling in data-level or feature-level for suppressing such uncertainties,  we propose a simple yet efficient coarse-to-fine learning strategy at task-level inspired by human beings’ emotion cognitive mode. Specifically, a child learns quickly whether his behavior is allowed by reading adults’ facial expressions for coarse attitude like positive or negative, and then adjusts accordingly via further interpreting its fine sentiment meaning like happy or angry. .

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs.

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing enabled gesture recognition due to its inherent merits like device-free, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don’t know “\emph{where to look}’‘ and “\emph{when to look}”. To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi Channel State Information (CSI) via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a Deep Residual Network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.