See through walls with WiFi!

By Fadel M. Adib, Dina Katabi. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2013

855 citations recorded by the source corpus.

Wi-Fi signals are typically information carriers between a transmitter and a receiver. In this paper, we show that Wi-Fi can also extend our senses, enabling us to see moving objects through walls and behind closed doors. In particular, we can use such signals to identify the number of people in a closed room and their relative locations. We can also identify simple gestures made behind a wall, and combine a sequence of gestures to communicate messages to a wireless receiver without carrying any transmitting device. The paper introduces two main innovations. First, it shows how one can use MIMO interference nulling to eliminate reflections off static objects and focus the receiver on a moving target. Second, it shows how one can track a human by treating the motion of a human body as an antenna array and tracking the resulting RF beam. We demonstrate the validity of our design by building it into USRP software radios and testing it in office buildings.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/62ba7b66f31f5f05dbb73b9fa9a199b64ef6f2cc

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Citations in the corpus

WiGest: A ubiquitous WiFi-based gesture recognition system (2014, methodology): “WiVi [9] uses an inverse synthetic aperture radar (ISAR) technique by treating the motion of a human body as an antenna array to track the resulting RF beam, thus enabling radar-like vision and simple through-wall gesture-based communication.”
mD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking (2018, background): “Wi-Vi [5] employs a signal nulling technique to cancel signals from static objects and then estimate the AoA of the signal reflected off the human, which can track the direction of human’s movement, but no location information can be obtained.”
Widar2.0: Passive Human Tracking with a Single Wi-Fi Link (2018, background): “Various hardware [2–4 , 11] are manufactured manufactured to Since dedicated hardware are difficult to be generalized, re-searchesareshiftedtoubiquitousCOTSRFdevices,suchasRFID [26, 27, 33, 42], millimetre wave [35, 45] and Wi-Fi [21, 32].”
Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi (2019, methodology): “On the signal side, existing approaches extract various parameters of signals reflected or shadowed by human, including DFS [26, 32, 44], ToF [2–4, 21], AoA/AoD [2, 5, 21, 25] and attenuation [7, 41].”
Multi-Person Localization via RF Body Reflections (2015, background): “0 is also related to non-localization systems that employ RF reflections off the human body [4, 20, 33, 35].”
Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals (2014, methodology): “In [13], a device called WiVi is proposed, which uses Wi-Fi OFDM signals in the ISM band (at 2.”
3D Tracking via Body Radio Reflections (2014, methodology): “See through-wall & gesture recognition using WiFi: WiTrack is motivated by recent research that used WiFi signals to detect users through walls and identify some of their gestures [10, 21, 13].”
Gesture recognition for human-robot collaboration: A review (2017, methodology): “Despite understanding human body gestures, the skeleton model approach can also monitor human movements, which provides a secure environment for the HRC team.”
Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi (2021, methodology): “On the signal side, existing approaches extract various parameters of signals reﬂected by human or emitted by portable devices, including DFS [11], [17], [20], ToF [21], [22], [23], [24], AoA/AoD [23], [24], [25], [26], ACF [27], and attenuation [28], [29].”
Capturing the human figure through a wall (2015, methodology): “Speciﬁcally, either they abstract the whole human body as a single-point reﬂector, which they track [Adib et al. 2014; Adib and Katabi 2013; Seifeldin et al. 2013; Nannuru et al. 2013], or they classify a handful of forward-backward gestures by matching them against prior training examples [Pu et…”
E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures (2014, background): “The granularity of the monitoring ranges from coarser movements (such as Wi-Vi [5]) to ﬁne-grained gestures (such as WiSee [25] and WiTrack [4]).”
Gait recognition using wifi signals (2016, background): “As WiFi signals can penetrate through obstacles such as furniture, wooden doors, and walls [3], the burglar only needs to passively measure the CSI of the signal outside the house without decoding the content of the WiFi packets.”
Wireless Sensing for Human Activity: A Survey (2020, background): “Adib and Katabi [30] later improve the through-the-wall system by using MIMO interference nulling to eliminate the flash effect of Doppler shifts and render more accu-”
Emotion recognition using wireless signals (2016, background): “Past proposals also differ in the transmitted RF signals: Doppler radar [19, 20], FMCW [5, 7], and WiFi [6, 50].”
Phaser: enabling phased array signal processing on commodity WiFi access points (2014, methodology): “Recent examples include systems that localize RFID tags [27] and WiFi devices [22, 30], enhance WiFi security [31], and passive radar systems that pinpoint human movements [1, 2, 17].”
Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi (2017, background): “Various approaches such as WiVi [3], E-eyes [19], CARM [18] have been proposed for human detection, activity classification, gesture recognition, etc.”
Electronic frog eye: Counting crowd using WiFi (2014, methodology): “One of the main trends recently is to explore the fine-grained physical layer information to locate or count people in multipath environments [13], [14], which are usually based on USRP.”
WiFall: Device-free fall detection by wireless networks (2017, background): “In [14], the authors propose an innovative approach called Wi-Vi.”