PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices

Wang, Xuyu; Yang, Chao; Mao, Shiwen

doi:10.1109/icdcs.2017.206

Cited by 243 publications

(131 citation statements)

References 22 publications

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“…These copies can help us to develop more reliable detection algorithms that work based on combined metrics rather than a single one. An example of such a system is PhaseBeat, where CSI phase difference data was used to monitor breathing and heartbeat with commodity WiFi devices [49]. More recently, the multiple antenna approach has also been used in the development of a non-contact respiratory rhythm-detection system using S-band sensing techniques [50].…”

Section: Discussionmentioning

confidence: 99%

A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection Systems

Borhani

Pätzold

2018

IEEE Trans. Wireless Commun.

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The emerging non-wearable fall detection systems rely on processing radio waves reflected off the body of the home user who has no active interaction with the system, increasing the user privacy and acceptability. This paper proposes a nonstationary channel model that is important for the development of such systems. A three-dimensional stochastic trajectory model is designed to capture targeted mobility patterns of the home user. The model is featured with a forward fall mechanism, which is actuated at a random point along the path. A transmitter emits radio waves throughout an indoor propagation environment, while a receiver collects fingerprints of the scattering objects on the emitted waves. The corresponding radio channel is modelled by a process capturing the time-variant Doppler effect caused by the home occupant. The time-frequency behaviour of the non-stationary channel is studied by computing the Doppler power spectral density and by performing spectrogram analysis. The instantaneous mean Doppler shift and Doppler spread are derived and simulated. The model is confirmed with experimental results performed at 5.9 GHz. The results are insightful for developing reliable fall detection algorithms, while the model is useful for studying the impact of different walking/falling patterns on the overall fall detection system performance.

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Section: Discussionmentioning

confidence: 99%

A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection Systems

Borhani

Pätzold

2018

IEEE Trans. Wireless Commun.

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“…The sampling frequency and the bandwidth were set to 1 KHz and 20 MHz, respectively. TV phase distortions exist due to carrier frequency offset [21][22][23], sampling frequency offset [24][25][26], and packet boundary delay [27,28]. These TV phase distortions were eliminated by using a B2B connection between the transmitter station and the receiver station as described in [35].…”

Section: Capturing Csi Datamentioning

confidence: 99%

“…An overview of studies on signal recognition, action recognition, and activity recognition by utilizing the amplitude of the measured CSI can be found in [20]. One of the challenges faced while processing CSI data to extract the micro-Doppler signatures is that the phases of such data are distorted as the transmitter and receiver are not clock synchronized [21][22][23][24][25][26][27][28]. To overcome this issue, one of the attempts is to apply the principle component analysis (PCA) [29][30][31][32] on the magnitude of the complex CSI data to denoise it, then to apply a one-sided spectrogram on the denoised data to reveal the positive frequency components of the spectrogram.…”

Section: Introductionmentioning

confidence: 99%

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Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

Abdelgawwad

Borhani

Pätzold

2020

Sensors

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This paper is about designing a 3D no n-stationary wideband indoor channel model for radio-frequency sensing. The proposed channel model allows for simulating the time-variant (TV) characteristics of the received signal of indoor channel in the presence of a moving object. The moving object is modelled by a point scatterer which travels along a trajectory. The trajectory is described by the object’s TV speed, TV horizontal angle of motion, and TV vertical angle of motion. An expression of the TV Doppler frequency caused by the moving scatterer is derived. Furthermore, an expression of the TV complex channel transfer function (CTF) of the received signal is provided, which accounts for the influence of a moving object and fixed objects, such as walls, ceiling, and furniture. An approximate analytical solution of the spectrogram of the CTF is derived. The proposed channel model is confirmed by measurements obtained from a pendulum experiment. In the pendulum experiment, the trajectory of the pendulum has been measured by using an inertial-measurement unit (IMU) and simultaneously collecting CSI data. For validation, we have compared the spectrogram of the proposed channel model fed with IMU data with the spectrogram characteristics of the measured CSI data. The proposed channel model paves the way towards designing simulation-based activity recognition systems.

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“…Some research works also use phase information for radio based device-free applications [15,23,39,40], but they either use conventional classification methods or see phase as additional and independent information. As mentioned in Section 1, our work differs from earlier work on integrating complex-valued CSI into the SRC method for activity recognition in that we take RFI into consideration while earlier work did not.…”

Section: :31mentioning

confidence: 99%

From Real to Complex

Wei

Yang

et al. 2019

ACM Trans. Sen. Netw.

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Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern compared with camera-based solutions, and subjects do not have to carry a device on them. It has been shown channel state information(CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference(RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier, and activity recognition also becomes harder. Our extensive experiments show that the performance may degrade significantly with RFI. We then propose a number of countermeasures to mitigate the impact of RFI and improve the performance. We are also the first to use complex-valued CSI along with the state-of-the-art Sparse Representation Classification method to enhance the performance in the environment with RFI.

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PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices

Cited by 243 publications

References 22 publications

A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection Systems

A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection Systems

Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

From Real to Complex

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