Leveraging Hypothesis Testing for CSI Based Passive Human Intrusion Direction Detection

Wang, Jiacheng; Tian, Zengshan; Zhou, Mu; Wang, Jie; Yang, Xiaolong; Liu, Xin

doi:10.1109/tvt.2021.3090800

Cited by 12 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, a Wi-Fi CSI based passive human intrusion direction detection system is developed in [ 29 ]. They propose an algorithm to monitor changes in amplitude and angle of arrival distributions of reflections and perform human intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

A Non Intrusive Human Presence Detection Methodology Based on Channel State Information of Wi-Fi Networks

Mesa-Cantillo

Sánchez-Rodríguez

Alonso-González

et al. 2023

Sensors

View full text Add to dashboard Cite

In recent times, we have been witnessing the development of multiple applications and deployment of services through the indoors location of people as it allows the development of services of interest in areas related mainly to security, guiding people, or offering services depending on their localization. On the other hand, at present, the deployment of Wi-Fi networks is so advanced that a network can be found almost anywhere. In addition, security systems are more demanded and are implemented in many buildings. Thus, in order to provide a non intrusive presence detection system, in this manuscript, the development of a methodology is proposed which is able to detect human presence through the channel state information (CSI) of wireless communication networks based on the 802.11n standard. One of the main contributions of this standard is multiple-input multiple-output (MIMO) with orthogonal frequency division multiplexing (OFDM). This makes it possible to obtain channel state information for each subcarrier. In order to implement this methodology, an analysis and feature extraction in time-domain of CSI is carried out, and it is validated using different classification models trained through a series of samples that were captured in two different environments. The experiments show that the methodology presented in this manuscript obtains an average accuracy above 90%.

show abstract

Section: Related Workmentioning

confidence: 99%

A Non Intrusive Human Presence Detection Methodology Based on Channel State Information of Wi-Fi Networks

Mesa-Cantillo

Sánchez-Rodríguez

Alonso-González

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…It can be calculated by Equation (16). The reason why we choose ReLU is that the calculation speed is fast, the calculation amount is small, and the training time is shorter…”

Section: Kurtosismentioning

confidence: 99%

“…At present, human behavior research based on CSI has achieved good results. Currently, CSI can be used in intrusion detection [15][16][17] with gesture recognition [18][19][20]. Moreover, WiFi devices open new paths for gesture recognition due to their simplicity and ease of deployment.…”

Section: Introductionmentioning

confidence: 99%

Air-CSL: Chinese Sign Language Recognition Based on the Commercial WiFi Devices

Hong-hong

Feng

Hao

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Artificial intelligence and Internet of Things (IoT) devices are experiencing explosive growth. Currently, the commonly used gesture recognition methods are difficult to deploy and expensive, so this paper uses the Channel State Information (CSI) for Chinese sign language recognition. Aiming at the problems of current gesture recognition methods, such as strong personnel dependence, high computational resource consumption, and low robustness, we proposed a Chinese sign language gesture recognition method named Air-CSL. In this method, the Local Outlier Factor (LOF) removal algorithm and the Discrete Wavelet Transform (DWT) are used to reduce the noise in the data, and the subcarriers that best represent the gesture data are selected by principal component analysis. After denoising, mathematical statistics were extracted from the gesture waveform as the eigenvalues, and the features were fused by the Deep Restricted Boltzmann Machine (DBM). Finally, the result of gesture classification and recognition is obtained by the Gated Recurrent Unit (GRU). In this way, the prediction model realizes as well as the classification of sign language gestures. The results show that the proposed method can effectively recognize Chinese sign language gestures of different people in different environments and has good robustness.

show abstract

“…Compared with the RSSI, CSI describes the multipath propagation effects of wireless signals to a certain extent and provides more detailed and robust features for advanced environmental perception. Therefore, multiple CSI-based sensing methods have been proposed, including indoor location [4,5], intrusion detection [6,7], behavior classification [8,9], and gesture recognition [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

MoSeFi: Duration Estimation Robust Human Motion Sensing via Commodity WiFi Device

Wang

Zhang

Qin

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Accurate motion interval segmentation is the basic and crucial step in the advanced human perception based on WiFi signals. However, previous works have rarely considered motion duration, which is one of the important parameters for complete description of human motion. On this basis, we deeply investigate the properties of the CSI ratio from the perspective of Mobius transformation and construct a novel motion indicator using its complementary real and imaginary parts. The new indicator can attenuate the impact of motion fragmentation under short-window conditions and significantly reduce the duration error while ensuring detection accuracy. Moreover, we propose a universal subcarrier screening method based on response sensitivity and shape similarity, which provides more accurate information for perception. Furthermore, we present MoSeFi—a duration estimation robust human motion detection system using an existing commercial WiFi device. Detailed experimental results demonstrate that MoSeFi is lightweight yet effective compared to state-of-the-art systems.

show abstract

Leveraging Hypothesis Testing for CSI Based Passive Human Intrusion Direction Detection

Cited by 12 publications

References 39 publications

A Non Intrusive Human Presence Detection Methodology Based on Channel State Information of Wi-Fi Networks

A Non Intrusive Human Presence Detection Methodology Based on Channel State Information of Wi-Fi Networks

Air-CSL: Chinese Sign Language Recognition Based on the Commercial WiFi Devices

MoSeFi: Duration Estimation Robust Human Motion Sensing via Commodity WiFi Device

Contact Info

Product

Resources

About