A New Method of Posture Recognition Based on WiFi Signal

Zuo, Jiancun; Zhu, Xianxun; Yue, Peng; Zhao, Zhiyang; Xiong, Wei; Wang, Xun

doi:10.1109/lcomm.2021.3081135

Cited by 28 publications

(11 citation statements)

References 13 publications

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“…Regardless of the adopted ML algorithms, a set of feature extraction and dimensionality reduction is required for simpler, more robust and high-performance inferences. For example, in an approach of extracting features from the CSI amplitude and phase, the authors in [26] applied a linear discriminant analysis and Softmax regression algorithm to generate a human activity recognition (HAR) model that classifies 4 types of human activities. The linear discriminant analysis and the Softmax regression model do not require any type of DNNs and can achieve an accuracy of classification in the range of 95.4%.…”

Section: Deep Learningmentioning

confidence: 99%

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Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

Hayajneh

Aldalahmeh

Zaidi

et al. 2022

2022 4th IEEE Middle East and North Africa COMMunications Conference (MENACOMM)

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The channel state information (CSI) of the sub-carriers employed in orthogonal frequency division multiplexing (OFDM) systems has been employed traditionally for channel equalisation. However, the CSI intrinsically is a signature of the operational RF environment and can serve as a proxy for certain activities in the operational environment. For instance, the CSI gets influenced by scatterers and therefore can be an indicator of how many scatterers or if there are mobile scatterers etc. The mapping between the activities whose signature CSI encodes and the raw data is not deterministic. Nevertheless, machine learning (ML) based approaches can provide a reliable classification for patterns of life. Most of these approaches have only been implemented in lab environments. This is mainly because the hardware requirements for capturing CSI, processing it and performing signal-processing algorithms are too complex to be implemented in commercial devices. The increased proliferation of IoT sensors and the development of edge-based ML capabilities using the TinyML framework opens up possibilities for the implementation of these techniques at scale on commercial devices. Using RF signature instead of more invasive methods e.g. cameras or wearable devices provide ease of deployment, intrinsic privacy and better usability. The design space of device-free wireless sensing (DFWS) is complex and involves device, firmware and ML considerations. In this article, we present a comprehensive overview and key considerations for the implementation of such solutions. We also demonstrate the viability of these approaches using a simple case study.

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Section: Deep Learningmentioning

confidence: 99%

“…In the context of HAR, there are too many efforts to mention in this conference-style paper. However, many interesting efforts can be found in [26], [40]- [44].…”

Section: A Applications For Harmentioning

confidence: 99%

Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

Hayajneh

Aldalahmeh

Zaidi

et al. 2022

2022 4th IEEE Middle East and North Africa COMMunications Conference (MENACOMM)

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“…DeepSeg [22] and Wihi [23] worked on WiFi-based activity recognition using deep learning approach. During the recent years, WiFi-based posture recognition system has been developed with good recognition results [21]. WiAct [19] proposed a device-free passive activity recognition system exploiting the correlations between WiFi CSI amplitude information and human body movement.…”

Section: Related Workmentioning

confidence: 99%

“…For optimal performance, the sensing system should be noninvasive and able to perform accurately under various driving conditions, e.g., nights, clouds, sunrises, and sunsets. During the recent years, WiFi-based activity and gesture recognition systems have been emerged with remarkable performance [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27], leveraging channel state information (CSI). Motivated by the desire, a WiFi CSI-based wireless sensing framework is presented that is simple yet accurate face localization system to overcome the difficulties of existing methods.…”

Section: Introductionmentioning

confidence: 99%

Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation Internet of Vehicles

Akhtar

Rasool

Asif

et al. 2022

Wireless Communications and Mobile Computing

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Driver’s behavior and gesture recognition are most significant in the emerging next-generation vehicular technology. Driver’s face may provide important cues about his/her attention and fatigue behavior. Therefore, driver’s face pose is one of the key indicators to be considered for automatic driver monitoring system in next-generation Internet of Vehicles (IoV) technology. Driver behavior monitoring is most significant in order to reduce road accidents. This paper aims to address the problem of driver’s attentiveness monitoring using face pose estimation in a nonintrusive manner. The proposed system is based on wireless sensing, leveraging channel state information (CSI) of WiFi signals. In this paper, we present a novel classification algorithm that is based on the combination of support vector machine (SVM) and K nearest neighbor (KNN) to enhance the classification accuracy. Experimental results demonstrate that the proposed device-free wireless implementation can localize a driver’s face very accurately with an average recognition rate of 91.8 % .

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“…Simultaneously, the operation of the sensors in the phone will be affected by its battery capacity. The usage of WiFi devices for human activity recognition has also been successful currently [16]- [19]. WiFi provides new research directions for universal, nonvisual human activity recognition due to its universality, low cost, and contactless operation.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Using Self-Powered Sensors Based on Multilayer Bidirectional Long Short-Term Memory Networks

Liao

Sheng

et al. 2023

IEEE Sensors J.

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A New Method of Posture Recognition Based on WiFi Signal

Cited by 28 publications

References 13 publications

Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

Channel State Information based Device Free Wireless Sensing for IoT Devices Employing TinyML

Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation Internet of Vehicles

Human Activity Recognition Using Self-Powered Sensors Based on Multilayer Bidirectional Long Short-Term Memory Networks

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