WarnFi: Non-invasive wifi-based abnormal activity sensing using non-parametric model

© 2017 IEEE. Abnormal activity sensing has attracted increasing research attention in military surveillance, patient monitoring, and health care of children and elderly, etc. Researchers have exploited the characteristics of wireless signals to sense 'keystrokes' and 'human talks', relieving the privacy invasion concern caused by mounting the surveillance cameras or wearing the smart devices. However, existing technologies usually require some specialized hardware, and can only sense a fixed set of pre-defined activities through a supervised learning from those wireless signals patterns. In this paper, we propose WarnFi, a non-invasive abnormal activity sensing system with only two commodity off-the-shelf (COTS) WiFi devices. The intuition of WarnFi is that whenever the human body occludes the wireless signal transmitting from the access point to the receiver, the time-series of Channel State Information (CSI) will experience a unique variation. By using a non-parametric model, WarnFi can dynamically cluster the human body activities for abnormal sensing. Extensive experiments in various scenarios demonstrate the satisfactory performance of WarnFi

Winect: 3D Human Pose Tracking for Free-form Activity Using Commodity WiFi

WiFi human sensing has become increasingly attractive in enabling emerging human-computer interaction applications. The corresponding technique has gradually evolved from the classification of multiple activity types to more fine-grained tracking of 3D human poses. However, existing WiFi-based 3D human pose tracking is limited to a set of predefined activities. In this work, we present Winect, a 3D human pose tracking system for free-form activity using commodity WiFi devices. Our system tracks free-form activity by estimating a 3D skeleton pose that consists of a set of joints of the human body. In particular, we combine signal separation and joint movement modeling to achieve free-form activity tracking. Our system first identifies the moving limbs by leveraging the two-dimensional angle of arrival of the signals reflected off the human body and separates the entangled signals for each limb. Then, it tracks each limb and constructs a 3D skeleton of the body by modeling the inherent relationship between the movements of the limb and the corresponding joints. Our evaluation results show that Winect is environment-independent and achieves centimeter-level accuracy for free-form activity tracking under various challenging environments including the none-line-of-sight (NLoS) scenarios

FarSense: pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas

International audienceThe past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense-the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. 1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications

WiMorse: a contactless Morse code text input system using ambient WiFi signals

International audienceRecent years have witnessed advances of Internet of Things (IoT) technologies and their applications to enable contactless sensing and human-computer interaction in smart homes. For people with Motor Neurone Disease (MND), their motion capabilities are severely impaired and they have difficulties interacting with IoT devices and even communicating with other people. As the disease progresses, most patients lose their speech function eventually which makes the widely adopted voice-based solutions fail. In contrast, most patients can still move their fingers slightly even after they have lost the control of their arms and hands. Thus we propose to develop a Morse code based text input system, called WiMorse, which allows patients with minimal single-finger control to input and communicate with other people without attaching any sensor to their fingers. WiMorse leverages ubiquitous commodity WiFi devices to track subtle finger movements contactlessly and encode them as Morse code input. In order to sense the very subtle finger movements, we propose to employ the ratio of the Channel State Information (CSI) between two antennas to enhance the Signal to Noise Ratio. To address the severe location dependency issue in wireless sensing with accurate theoretical underpinning and experiments, we propose a signal transformation mechanism to automatically convert signals based on the input position, achieving stable sensing performance. Comprehensive experiments demonstrate that WiMorse can achieve higher than 95% recognition accuracy for finger generated Morse code, and is robust against input position, environment changes, and user diversity