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RTI Goes Wild: Radio Tomographic Imaging for Outdoor People Detection and Localization
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Article WiFi-Based Real-Time Calibration-Free Passive Human Motion Detection †
"... Abstract: With the rapid development of WLAN technology, wireless device-free passive human detection becomes a newly-developing technique and holds more potential to worldwide and ubiquitous smart applications. Recently, indoor fine-grained device-free passive human motion detection based on the PH ..."
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Abstract: With the rapid development of WLAN technology, wireless device-free passive human detection becomes a newly-developing technique and holds more potential to worldwide and ubiquitous smart applications. Recently, indoor fine-grained device-free passive human motion detection based on the PHY layer information is rapidly developed. Previous wireless device-free passive human detection systems either rely on deploying specialized systems with dense transmitter-receiver links or elaborate off-line training process, which blocks rapid deployment and weakens system robustness. In the paper, we explore to research a novel fine-grained real-time calibration-free device-free passive human motion via physical layer information, which is independent of indoor scenarios and needs no prior-calibration and normal profile. We investigate sensitivities of amplitude and phase to human motion, and discover that phase feature is more sensitive to human motion, especially to slow human motion. Aiming at lightweight and robust device-free passive human motion detection, we develop two novel and practical schemes: short-term averaged variance ratio (SVR) and long-term averaged variance ratio (LVR). We realize system design with commercial WiFi devices and evaluate it in typical multipath-rich indoor scenarios. As demonstrated in the experiments, our approach can achieve a high detection rate and low false positive rate.
Article Energy-Efficient Privacy Protection for Smart Home Environments Using Behavioral Semantics
, 2014
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E-eyes: Device-free Location-oriented Activity Identification Using Fine-grained WiFi Signatures
"... Activity monitoring in home environments has become increas-ingly important and has the potential to support a broad array of ap-plications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. ..."
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Activity monitoring in home environments has become increas-ingly important and has the potential to support a broad array of ap-plications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, ther-mostats, refrigerators, smartTVs, laptops). Our low-cost system takes advantage of the ever more complex web of WiFi links be-tween such devices and the increasingly fine-grained channel state information that can be extracted from such links. It examines channel features and can uniquely identify both in-place activi-ties and walking movements across a home by comparing them against signal profiles. Signal profiles construction can be semi-supervised and the profiles can be adaptively updated to accom-modate the movement of the mobile devices and day-to-day signal calibration. Our experimental evaluation in two apartments of dif-ferent size demonstrates that our approach can achieve over 96% average true positive rate and less than 1 % average false positive rate to distinguish a set of in-place and walking activities with only a single WiFi access point. Our prototype also shows that our sys-tem can work with wider signal band (802.11ac) with even higher accuracy.
Article Nonlinear Optimization-Based Device-Free Localization with
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1Track Estimation Using Link Line Crossing Information in Wireless Networks
"... Abstract—Device-free or non-cooperative localization uses the changes in signal strength measured on links in a wireless network to estimate a person’s position in the network area. Existing methods provide an instantaneous coordinate estimate via radio tomographic imaging or location fingerprinting ..."
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Abstract—Device-free or non-cooperative localization uses the changes in signal strength measured on links in a wireless network to estimate a person’s position in the network area. Existing methods provide an instantaneous coordinate estimate via radio tomographic imaging or location fingerprinting. In this paper, we explore the problem of, after a person has exited the area of the network, how can we estimate their path through the area? We present two methods which use recent line crossings detected by the network’s links to estimate the person’s path through the area. We assume that the person took a linear path and estimate the path’s parameters. One method formulates path estimation as a line stabbing problem, and another method is a linear regression formulation. Through simulation we show that the line stabbing approach is more robust to false detections, but in the absence of false detections, the linear regression method provides superior performance. I.
