WiFind: Driver Fatigue Detection with Fine-Grained Wi-Fi Signal Features

Peng, Hongjian; Jia, Weijia

doi:10.1109/glocom.2017.8253925

Cited by 10 publications

(6 citation statements)

References 15 publications

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“…In recent decades, WiFi-based driver's in-vehicle activity and gestures recognition systems have been introduced with good recognition performance [50][51][52]. WiFind [36] presented a WiFi CSI-based driver fatigue detection system. This model is based on one-class SVM technique.…”

Section: Related Workmentioning

confidence: 99%

“…In this research work, we propose a novel hybrid classification technique that is based on the combination of sup-port vector machine (SVM) classifier with K nearest neighbor (KNN), to enhance the recognition performance. Both SVM and KNN have been effectively used for various WiFi-based activity and gesture recognition systems [32][33][34][35][36]. The performance of KNN is dependent on the size of the training samples.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Table 1 shows related work and their differences with CarAu system. Daily activity recognition No metal obstacles between the target and signal transmitter and receiver [7,25,26] user authentication [27] AP user authentication [28,29] Activity recognition [30,31] Gesture recognition Vehicle security systems [34] Car user identification Video Privacy considering light limitation [35] Vehicle steering detection Smartphone sensors The support of additional devices [36,37] Vehicle dynamic sensing [38] Driver distraction detection [39][40][41][42][43][44][45] Driver activity detection WiFi signals In-car recognition [46,47] In-car driver authentication [48] Vital sign monitoring [49,50] Traffic monitoring unauthorized users to access confidential information, but also can customize the services for the user. Shi et al [24] leverage users' daily activities, such as walking and stationary ones to authenticate the user.…”

Section: Related Workmentioning

confidence: 99%

“…WiBot [39] presents a gesture based personal assistant system for vehicles, which can detect distracted behaviors and uses a gesture to help user interact with the cars. WiFind [40] proposes a fatigue detection approach, and Muneeba et al [41] detect driver's distracted behavior. WiDriver [42] monitors driver operation on steering wheel using CSI.…”

Section: Csi-based Authentication and Identification Systems User Authentication And Identification System Cannot Only Preventmentioning

confidence: 99%

Intelligent user identity authentication in vehicle security system based on wireless signals

Zhang

Wang

Yan

2021

Complex Intell. Syst.

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Intelligent identity authentication in vehicle security systems, as a vital component in anti-theft system and safety driving assist system, has received wide attention. Current vehicle security systems, however, focus the car security on the car keys security, ignore the owner of car keys. Anyone who owns the car keys can operate the car. This paper introduces an intelligent identity authentication method for vehicle security system based on wireless signals. Unlike past work, our approach combines car security with car owners and car keys. The intuition underlying our design is that when a user walks towards the car, the user’s gait information can be leveraged to identify the user. We capture the user’s gait information using wireless devices which can be deployed in the car, and then extract features from the received wireless signals using convolution kernel and apply artificial neural network to identify the user. We built a prototype and experimental results show that our approach can achieve high accuracy and strong robustness.

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“…Therefore, the amplitude of the CSI signal quantifies the signal power attenuation after the multi-path effect, similar to the received signal strength of the wireless signal. Currently, there has many CSI-based recognition applications, such as indoor human behavior recognition, indoor localization [33]- [41], indoor object state detection [42], fire detection [43], traffic monitoring [44], wheat moisture detection [45], object distinction [46], in-baggage suspicious object detection [47], school violence monitoring [48], and other identification applications in driving scenario (e.g., risky driving behavior detection [49], [50], driver fatigue detection [51], driver's distracted behavior detection [52], in-vehicle behavior and hand gesture recognition [53], [54]).…”

Section: Introductionmentioning

confidence: 99%

A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including patternbased, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions. INDEX TERMS Channel state information (CSI), deep learning, human behavior recognition, model, pattern, WiFi.

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WiFind: Driver Fatigue Detection with Fine-Grained Wi-Fi Signal Features

Cited by 10 publications

References 15 publications

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

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

Intelligent user identity authentication in vehicle security system based on wireless signals

A Survey on Human Behavior Recognition Using Channel State Information

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