Device-Free Wireless Sensing for Human Detection: The Deep Learning Perspective

Zhang, Ronghui; Jing, Xiaojun; Wu, Sheng; Jiang, Chunxiao; Mu, Junsheng; Yu, F. Richard

doi:10.1109/jiot.2020.3024234

Cited by 97 publications

(35 citation statements)

References 141 publications

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“…Ordinary cameras are inexpensive and easy to deploy, but have a high risk of privacy leakage. Compared with the sensing techniques mentioned above, wireless sensing does not require special sensor equipment, can control privacy disclosure to a low level, can function normally in smoky or dark environments, and represents important technical support for achieving ubiquitous sensing [ 1 ]. Currently, researchers from industry and academia are actively promoting wireless sensing technologies for human identification [ 3 , 4 , 5 , 6 , 14 ].…”

Section: Related Workmentioning

confidence: 99%

“…Multiwireless link sensing enhances spatial resolution, and the spatial information is hidden in the high-dimensional CSI data. By performing the convolution operation (operated by multiple filters) of CNN [ 1 ] on a spectrogram, spatial features can be obtained. Additionally, different frequency components and other signatures in the spectrogram contribute differently to the maximization of recognition performance.…”

Section: Wirelessidmentioning

confidence: 99%

“…Presently, developments in wireless sensing technologies have shown that wireless signals can be deployed to transmit information between wireless communication devices and are also able to realize object wireless sensing [ 1 ]. Movements of individuals within the coverage of wireless signals will inevitably impact signal propagation.…”

Section: Introductionmentioning

confidence: 99%

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Device-Free Human Identification Using Behavior Signatures in WiFi Sensing

Zhang

Jing

2021

Sensors

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Wireless sensing can be used for human identification by mining and quantifying individual behavior effects on wireless signal propagation. This work proposes a novel device-free biometric (DFB) system, WirelessID, that explores the joint human fine-grained behavior and body physical signatures embedded in channel state information (CSI) by extracting spatiotemporal features. In addition, the signal fluctuations corresponding to different parts of the body contribute differently to the identification performance. Inspired by the success of the attention mechanism in computer vision (CV), thus, to extract more robust features, we introduce the spatiotemporal attention function into our system. To evaluate the performance, commercial WiFi devices are used for prototyping WirelessID in a real laboratory environment with an average accuracy of 93.14% and a best accuracy of 97.72% for five individuals.

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Section: Related Workmentioning

confidence: 99%

Section: Wirelessidmentioning

confidence: 99%

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Device-Free Human Identification Using Behavior Signatures in WiFi Sensing

Zhang

Jing

2021

Sensors

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“…Recently, Wi-Fi-based human activity recognition has attracted extensive attention in both academia and industry, becoming one of the most popular device-free sensing (DFS) technologies [7,8]. Compared with the other wireless signals, such as Frequency Modulated Continuous Wave (FMCW) [9,10], millimeter-wave (MMW) [11,12], and Ultra Wide Band (UWB) [13][14][15][16][17], Wi-Fi possesses the most prominent and potential advantage, which is that it is ubiquitous in people's daily lives.…”

Section: Introductionmentioning

confidence: 99%

Wi-Fi-Based Location-Independent Human Activity Recognition via Meta Learning

Ding

Jiang

Zhong

et al. 2021

Sensors

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Wi-Fi-based device-free human activity recognition has recently become a vital underpinning for various emerging applications, ranging from the Internet of Things (IoT) to Human–Computer Interaction (HCI). Although this technology has been successfully demonstrated for location-dependent sensing, it relies on sufficient data samples for large-scale sensing, which is enormously labor-intensive and time-consuming. However, in real-world applications, location-independent sensing is crucial and indispensable. Therefore, how to alleviate adverse effects on recognition accuracy caused by location variations with the limited dataset is still an open question. To address this concern, we present a location-independent human activity recognition system based on Wi-Fi named WiLiMetaSensing. Specifically, we first leverage a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) feature representation method to focus on location-independent characteristics. Then, in order to well transfer the model across different positions with limited data samples, a metric learning-based activity recognition method is proposed. Consequently, not only the generalization ability but also the transferable capability of the model would be significantly promoted. To fully validate the feasibility of the presented approach, extensive experiments have been conducted in an office with 24 testing locations. The evaluation results demonstrate that our method can achieve more than 90% in location-independent human activity recognition accuracy. More importantly, it can adapt well to the data samples with a small number of subcarriers and a low sampling rate.

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“…Numerous researchers have tried to find a method that can efficiently and accurately identify human activities [3,4]. Based on the support of various software and hardware, there have been many good research results in the field of human activity recognition, but the recognition effect still needs to be improved, and with the continuous development of technology and continuous research of various theories, it is necessary to continuously carry out new exploration and research in the field of human activity recognition in order to propose an efficient and accurate method of human activity recognition in the future [5,6].…”

Section: Introductionmentioning

confidence: 99%

Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

Cao

Lin

Wang

et al. 2021

Mobile Information Systems

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The field of activity recognition has evolved relatively early and has attracted countless researchers. With the continuous development of science and technology, people’s research on human activity recognition is also deepening and becoming richer. Nowadays, whether it is medicine, education, sports, or smart home, various fields have developed a strong interest in activity recognition, and a series of research results have also been put into people’s real production and life. Nowadays, smart phones have become quite popular, and the technology is becoming more and more mature, and various sensors have emerged at the historic moment, so the related research on activity recognition based on mobile phone sensors has its necessity and possibility. This article will use an Android smartphone to collect the data of six basic behaviors of human, which are walking, running, standing, sitting, going upstairs, and going downstairs, through its acceleration sensor, and use the classic model of deep learning CNN (convolutional neural network) to fuse those multidimensional mobile data, using TensorFlow for model training and test evaluation. The generated model is finally transplanted to an Android phone to complete the mobile-end activity recognition system.

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Device-Free Wireless Sensing for Human Detection: The Deep Learning Perspective

Cited by 97 publications

References 141 publications

Device-Free Human Identification Using Behavior Signatures in WiFi Sensing

Device-Free Human Identification Using Behavior Signatures in WiFi Sensing

Wi-Fi-Based Location-Independent Human Activity Recognition via Meta Learning

Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

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