Good quality sleep is essential for good health and sleep monitoring becomes a vital research topic. This paper provides a low cost, continuous and contactless WiFi-based vital signs (breathing and heart rate) monitoring method. In particular, we set up the antennas based on Fresnel diffraction model and signal propagation theory, which enhances the detection of weak breathing/heartbeat motion. We implement a prototype system using the off-shelf devices and a real-time processing system to monitor vital signs in real time. The experimental results indicate the accurate breathing rate and heart rate detection performance. To the best of our knowledge, this is the first work to use a pair of WiFi devices and omnidirectional antennas to achieve real-time individual breathing rate and heart rate monitoring in different sleeping postures.

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The ever evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction (HCI) and affective computing. Traditional sensor-based and vision-based user behavior analysis approaches are obtrusive in general, hindering their usage in real world. Therefore, in this article, we first introduce WiFi signal as a new source instead of sensor and vision for unobtrusive user behaviors analysis. Then we design BeSense, a contactless behavior analysis system leveraging signal processing and computational intelligence over WiFi channel state information (CSI). We prototype BeSense on commodity low-cost WiFi devices and evaluate its performance in realworld environments. Experimental results have verified its effectiveness in recognizing user behaviors. their exercise time. Its side effects like sedentary behavior (SB) pose great threats to people's wellness [1]. Therefore, understanding user behavior, like knowing whether the user is working, gaming or surfing and how long s/he has been doing it, emerges as a key to spot such heath risk factors. Moreover, it constitutes a promising enabler to many other fields like human-computer interaction (HCI) and affective computing (AC).In general, there are two types of approaches to understand user behavior. The traditional vision-based approach leverages video as the main source, where cameras are deployed to record and infer user behavior [2]- [4]. The vision-based approach is effective because of the mature Computer Vision (CV) technology. However, it makes users to concern about their privacy. Also, inherent defects of CV like line-of-sight and illumination constraints further jeopardize its usage in practice. Sensor is another typical source, where wearable sensors are attached to the human body to capture body gestures and deduce the corresponding user behavior [5]- [7]. Sensor has a limited sensing range, and thus multiple sensors are needed at different parts of the human body to ensure a complete coverage of user gestures. Unfortunately, such deployment is obtrusive for the user, hindering its practicability in real-world scenarios.In this article, we introduce WiFi signal, which is insensible to users, as an alternative source to vision and sensor for perceiving user behavior. The key reason behind is that the human body reflects or absorbs WiFi signal, and thus changes the WiFi channel state information (CSI)[8]- [10]. The inherent research problem is how to exploit WiFi CSI that contains rich behavior information to retrieve micro-gestures like keystrokes and mouse movements for understanding the corresponding user behavior?Our response to the question is three-fold. Firstly, we explore signal processing to improve the sensing granularity of WiFi CSI. In particular, we build a Fresnel-zone based model to guide the antenna deployment to enhance minor signal changes caused by user's micro-gestures. Then, we design a ligh...

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 "where to look" and "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.