Whole-home gesture recognition using wireless signals

By Qifan Pu, Sidhant Gupta, Shyamnath Gollakota, Shwetak N. Patel. ACM/IEEE International Conference on Mobile Computing and Networking, 2013

1,127 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/3bb76c2989cb4aeae3b20f42e619a862f0d871ca

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Citations in the corpus

Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals (2014, methodology): “This property has been utilized in [15][12][13][14] for device-free localization (i.”
Gesture recognition for human-robot collaboration: A review (2017, background): “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, background): “Due to complexity of human activity, existing approaches extract signal features, either statistical [10], [31], [33], [34], [35], [36], [37] or physical [11], [13], [16], [38], [39], [40], [41], [42], [43] ones, and map them to discrete activities.”
Capturing the human figure through a wall (2015, methodology): “Some of these systems have demonstrated the potential of using RF signals to recognize a handful of forward-backward gestures by matching them against prior training examples [Pu et al. 2013].”
Widar2.0: Passive Human Tracking with a Single Wi-Fi Link (2018, background): “Many innovative applications have been designed, including activity and gesture recognition [20, 30, 34], respiration detection [31] and direction estimation [22, 36], etc.”
A review of hand gesture and sign language recognition techniques (2017, methodology): “A similar research is done in literature [67].”
Interacting with Soli: Exploring Fine-Grained Dynamic Gesture Recognition in the Radio-Frequency Spectrum (2016, background): “Examples include sensing via disturbances of GSM [39] signals, picking-up electromagnetic interference in LCDs [3] or piggybacking onto existing WiFi signals [26].”
UbiBreathe: A Ubiquitous non-Invasive WiFi-based Breathing Estimator (2015, background): “…more detailed channel state information (CSI) in standard WiFi networks, have emerged as ubiquitous solutions for presence detection [1,4,10–12,18,20,34,36,40,44] , tracking individuals [19, 33, 35, 39] , recognition of different human activities [3,8,16,42], and gesture recognition systems [2,32].”
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].”
Device-Free Human Activity Recognition Using Commercial WiFi Devices (2017, methodology): “RECENTLY, multiple WiFi-signal based human activity recognition systems have been proposed [18], [26], [28].”
WiFall: Device-free fall detection by wireless networks (2017, neutral): “Authors in [17] propose a”
Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi (2017, background): “WiSee [10] leverages Doppler effect to enable gesture recognition using specialized hardwares (USRP).”
A Survey on Behavior Recognition Using WiFi Channel State Information (2017, methodology): “IEEE Communications Magazine • October 2017 99 around 17 Hz [2].”
Dynamic-MUSIC: accurate device-free indoor localization (2016, background): “WiSee [23] is able to capture the Doppler shift introduced by a moving object for gesture recognition.”
We Can Hear You with Wi-Fi! (2014, background): “3) WiHear can help millions of disabled people to conduct simple commands to devices with only mouth motions instead of complicated and inconvenient body movements.”
WiGest: A ubiquitous WiFi-based gesture recognition system (2014, background): “With the ubiquity of WiFi-enabled devices and infrastructure, WiFi-based gesture recognition systems, e.g. [8]–[10], have recently been proposed to help overcome the above limitations in addition to enabling users to provide in-air hands-free input to various applications running on mobile devices.”
Wireless Sensing for Human Activity: A Survey (2020, background): “Doppler-based Recognition: As a Doppler-based gesture recognition system, WiSee [45] successfully recognize 9 arm/leg involved gestures based on the unique Doppler shifts”
FingerIO: Using Active Sonar for Fine-Grained Finger Tracking (2016, methodology): “WiSee [32], AllSee [18] and SideSwipe [43] uses Wi-Fi, TV and cellular transmissions respectively to recognize coarse hand, arm and leg gestures.”