Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

Yang, Jianfei; Chen, Xinyan; Wang, Dazhuo; Zhao, Han; Lu, Chris Xiaoxuan; Sun, Sumei; Xie, Lihua

doi:10.48550/arxiv.2207.07859

Cited by 4 publications

(3 citation statements)

References 84 publications

(156 reference statements)

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“…In the simplest form, fully RNN is an MLP with the previous set of hidden unit activations feeding back into the network along with the inputs (Roy et al, 2019). Additionally, the LSTM, GRU, BiLSTM, and LSTM-Attention are able to overcome RNN's vanishing gradient problem which happens when RNN learns long-range dependencies of inputs (Yang et al, 2022). Therefore, the ability of short-and long-range interaction in these considered networks performs similarly, as the results above.…”

Section: Discussionmentioning

confidence: 59%

An end-to-end LSTM-Attention based framework for quasi-steady-state CEST prediction

Yang,

Zou,

Zhang

et al. 2024

Front. Neurosci.

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Chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) often takes prolonged saturation duration (Ts) and relaxation delay (Td) to reach the steady state, and yet the insufficiently long Ts and Td in actual experiments may underestimate the CEST measurement. In this study, we aimed to develop a deep learning-based model for quasi-steady-state (QUASS) prediction from non-steady-state CEST acquired in experiments, therefore overcoming the limitation of the CEST effect which needs prolonged saturation time to reach a steady state. To support network training, a multi-pool Bloch-McConnell equation was designed to derive wide-ranging simulated Z-spectra, so as to solve the problem of time and labor consumption in manual annotation work. Following this, we formulated a hybrid architecture of long short-term memory (LSTM)-Attention to improve the predictive ability. The multilayer perceptron, recurrent neural network, LSTM, gated recurrent unit, BiLSTM, and LSTM-Attention were included in comparative experiments of QUASS CEST prediction, and the best performance was obtained by the proposed LSTM-Attention model. In terms of the linear regression analysis, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE), the results of LSTM-Attention demonstrate that the coefficient of determination in the linear regression analysis was at least R2 = 0.9748 for six different representative frequency offsets, the mean values of prediction accuracies in terms of SSIM, PSNR and MSE were 0.9991, 49.6714, and 1.68 × 10−4 for all frequency offsets. It was concluded that the LSTM-Attention model enabled high-quality QUASS CEST prediction.

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Section: Discussionmentioning

confidence: 59%

An end-to-end LSTM-Attention based framework for quasi-steady-state CEST prediction

Yang,

Zou,

Zhang

et al. 2024

Front. Neurosci.

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“…Motion of humans and objects during HAR and HAP are sensed by tracking and analyzing the changes in the reflected signal caused by variation in wave propagation paths due to the movement. The data gathered using Wi-Fi transmission show high quality and are used in machine learning algorithms [46][47][48]. Data are organized in the form of channel state information (CSI) matrix, which holds information on properties of the communication signal such as phase shifts, power decay, etc.…”

Section: Wi-fi Devicesmentioning

confidence: 99%

Predicting Human Activity – State of the Art

Awong¹,

Zielińska²

2023

PAR

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Predicting human actions is a very actual research field. Artificial intelligence methods are commonly used here. They enable early recognition and classification of human activities. Such knowledge is extremely needed in the work on robots and other interactive systems that communicate and cooperate with people. This ensures early reactions of such devices and proper planning of their future actions. However, due to the complexity of human actions, predicting them is a difficult task. In this article, we review state-of-the-art methods and summarize recent advances in predicting human activity. We focus in particular on four approaches using machine learning methods, namely methods using: artificial neural networks, support vector machines, probabilistic models and decision trees. We discuss the advantages and disadvantages of these approaches, as well as current challenges related to predicting human activity. In addition, we describe the types of sensors and data sets commonly used in research on predicting and recognizing human actions. We analyze the quality of the methods used, based on the prediction accuracy reported in scientific articles. We describe the importance of the data type and the parameters of machine learning models. Finally, we summarize the latest research trends. The article is intended to help in choosing the right method of predicting human activity, along with an indication of the tools and resources necessary to effectively achieve this goal.

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“…The WiFi-based gait recognition method uses RF signals from WiFi-enabled devices to determine human identity. The transmitter emits WiFi signals, which are reflected by different body parts of the walking subject and then recorded by CSI data at the receiver [19], which has empowered many applications including occupancy detection [20], crowd counting [21], [22], human activity recognition [23], [24], [25], [26], [27], person identification [8], [28], vital sign detection [29], pose estimation [30] and gesture recognition [31], [32], [33]. To use WiFi sensing in the real world, current research aims at efficient communication [16], model security [34] and dataefficient training [35].…”

Section: Introductionmentioning

confidence: 99%

GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning

Deng

Yang

Yuan

et al. 2023

IEEE Internet Things J.

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As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications. At present, most research works are based on cameras and computer vision techniques to perform gait recognition. However, vision-based methods are not reliable when confronting poor illuminations, leading to degrading performances. In this paper, we propose a novel multimodal gait recognition method, namely GaitFi, which leverages WiFi signals and videos for human identification. In GaitFi, Channel State Information (CSI) that reflects the multi-path propagation of WiFi is collected to capture human gaits, while videos are captured by cameras. To learn robust gait information, we propose a Lightweight Residual Convolution Network (LRCN) as the backbone network, and further propose the two-stream GaitFi by integrating WiFi and vision features for the gait retrieval task. The GaitFi is trained by the triplet loss and classification loss on different levels of features. Extensive experiments are conducted in the real world, which demonstrates that the GaitFi outperforms state-of-the-art gait recognition methods based on single WiFi or camera, achieving 94.2% for human identification tasks of 12 subjects.

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Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

Cited by 4 publications

References 84 publications

An end-to-end LSTM-Attention based framework for quasi-steady-state CEST prediction

An end-to-end LSTM-Attention based framework for quasi-steady-state CEST prediction

Predicting Human Activity – State of the Art

GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning

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