Keystroke Recognition Using WiFi Signals

Ali, Kamran; Liu, Alex X.; Wang, Wei; Shahzad, Muhammad

doi:10.1145/2789168.2790109

Cited by 439 publications

(232 citation statements)

References 23 publications

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“…Past work leverages this phenomenon to sense human motion: it transmits an RF signal and analyzes its reflections to track user locations, 5 gestures, 4,8,38,43 a The ECG-based system and EQ-Radio use exactly the same classification features but differ in how they obtain the heartbeat series.…”

Section: Rf-based Sensingmentioning

confidence: 99%

Emotion recognition using wireless signals

2018

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This paper demonstrates a new technology that can infer a person's emotions from RF signals reflected off his body. EQ-Radio transmits an RF signal and analyzes its reflections off a person's body to recognize his emotional state (happy, sad, etc.). The key enabler underlying EQ-Radio is a new algorithm for extracting the individual heartbeats from the wireless signal at an accuracy comparable to on-body ECG monitors. The resulting beats are then used to compute emotion-dependent features which feed a machine-learning emotion classifier. We describe the design and implementation of EQ-Radio, and demonstrate through a user study that its emotion recognition accuracy is on par with state-of-theart emotion recognition systems that require a person to be hooked to an ECG monitor.

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Section: Rf-based Sensingmentioning

confidence: 99%

Emotion recognition using wireless signals

2018

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“…Since RSS signal is too sensitive to the slight variation of the environment, a robust device-free localization (DFL) scheme based on differential RSS is proposed [22], 2 Journal of Sensors which can eliminate the need of acquiring reference RSS measurements as well as overcome the negative effect environment induced. Since RSS schemes suffer from coarse granularity of 1 dB and limited accuracy, researchers begin to pay more attention to CSI-based schemes owing to its frequency diversity [11].…”

Section: Related Workmentioning

confidence: 99%

“…Due to the recent development in wireless network techniques and smart devices, a range of applications have been explored, including motion detection [3][4][5][6], gesture recognition [7], indoor localization [8,9], and activity identification [10][11][12]. We can use pervasive WiFi signals to realize passive detection of moving targets by detecting whether there exists any wireless content changes in the surrounding environment without attaching any proposed device to users.…”

Section: Introductionmentioning

confidence: 99%

A Robust Passive Intrusion Detection System with Commodity WiFi Devices

Ding

Zhao

et al. 2018

Journal of Sensors

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In recent years, due to the rapidly growing capacities of physical layer, device-free passive detection holds great importance for a broad range of application. Most recent works focus on motion detection, intrusion detection, and vital sign with commodity WiFi devices in the indoor environment. Conventional device-free motion detection techniques, which utilize received signal strength (RSS), may suffer from coarse granularity and high variability problems. In resorting to the finer-grained channel state information (CSI), we propose PhaseMode, a novel approach for device-free motion detection leveraging CSI phase difference data between adjacent antenna pairs. We implement our approach on commercial WiFi devices and validate its performance. We conduct experiments in different test periods of three indoor environments; the results show that the proposed scheme achieves an average accuracy over 99.4% of motion detection in different scenarios.

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“…WifiU [21] extracts the spectrogram signatures to describe the detailed gait patterns for person identification. WiKey [22] exploits the micro-movements of fingers while typing to extract the individual keystrokes. PreFi [23] leverages CSI to analyze customer's product preference.…”

Section: Wireless Signal Based Activity Sensingmentioning

confidence: 99%

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

Pang

Zhu

et al. 2017

MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)

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Abstract-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.

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Keystroke Recognition Using WiFi Signals

Cited by 439 publications

References 23 publications

Emotion recognition using wireless signals

Emotion recognition using wireless signals

A Robust Passive Intrusion Detection System with Commodity WiFi Devices

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

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