TW-See: Human Activity Recognition Through the Wall With Commodity Wi-Fi Devices

Wu, Xuangou; Chu, Zhaobin; Yang, Panlong; Xiang, Chaocan; Zheng, Xiao; Huang, Wenchao

doi:10.1109/tvt.2018.2878754

Cited by 97 publications

(48 citation statements)

References 25 publications

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“…However, this system requires a specialized receiver to deal with Orthogonal Frequency Division Multiplexing (OFDM) technique. TW-See [50] demonstrated opposite-robust PCA (Or-PCA) technique for passive human activity recognition through the wall with commodity WiFi devices. In recent years, WiFi-based radio-image processing [28] has achieved valuable activity recognition performance using Gabor filters.…”

Section: Related Workmentioning

confidence: 99%

WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

Akhtar

Wang

2020

Sensors

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Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver’s activity monitoring has emerged with remarkable attention. With substantial progress in WiFi-based device-free localization and activity recognition, radio-image features have achieved better recognition performance using the proficiency of image descriptors. The major drawback of image features is computational complexity, which increases exponentially, with the growth of irrelevant information in an image. It is still unresolved how to choose appropriate radio-image features to alleviate the expensive computational burden. This paper explores a computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information (CSI) of WiFi signals. In this novel research work, we demonstrate an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy. Specifically, we addressed the problem of the computational burden by efficacious use of Gabor filters with gray level statistical features. The presented low-cost solution requires neither sophisticated camera support to capture images nor any special hardware to carry with the user. This novel framework is evaluated in terms of activity recognition accuracy. To ensure the reliability of the suggested scheme, we analyzed the results by adopting different evaluation metrics. Experimental results show that the presented prototype outperforms the traditional methods with an average recognition accuracy of 93.1 % in promising application scenarios. This ubiquitous model leads to improve the system performance significantly for the diverse scale of applications. In the realm of intelligent vehicles and assisted driving systems, the proposed wireless solution can effectively characterize the driving maneuvers, primary tasks, driver distraction, and fatigue by exploiting radio-image descriptors.

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Section: Related Workmentioning

confidence: 99%

WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

Akhtar

Wang

2020

Sensors

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“…This section presents the review of the literature, in context with the existing work done in this field till date. Wu et al [9] presented a system for device-free activity monitoring, TW-SEE, that recognizes human activities based on Wi-Fi signals. It uses two techniques named robust PCA and sliding window algorithm to segment activities.…”

Section: Related Workmentioning

confidence: 99%

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

Grewal

Kaur

Park

2019

Wireless Communications and Mobile Computing

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With the revolution in smart infrastructure in the recent past, the smart healthcare system has been paid more considerable attention. The continuous upgradation of electricity meters to smart electricity devices has probed into a new market of intelligent data analysis services, providing aid to the health care systems. This paper presents a unified framework for extracting user behaviour patterns from home-based smart electricity meter data. The structure allows exploration and integration of frequent pattern growth algorithm for pattern mining and application of a variety of machine learning algorithms for categorizing the activities into manually labelled classes along with the implementation of Local Outlier Factor method for detection of an abnormal pattern of the inhabitant of smart homes. To evaluate the proposed framework, the work is implemented on the smart electricity dataset from the United Kingdom by separating the data into four distinct data files meant for the morning, afternoon, evening, and night energy utilization records. The results show a remarkable performance of Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers with kappa statics greater than 0.95 for all time slots data. The resultant frequent device utilization patterns with anomaly score more than the threshold value, reflecting abnormal activity patterns, are found more in evening time data in comparison to other time slots, requiring the immediate attention of concerned healthcare authorities.

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“…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. Another attempt has been applied by using a phase sanitization technique [32][33][34] by employing linear transformation operation on the distorted phases of the 30 subcarriers.…”

Section: Introductionmentioning

confidence: 99%

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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TW-See: Human Activity Recognition Through the Wall With Commodity Wi-Fi Devices

Cited by 97 publications

References 25 publications

WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

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

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