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

Jia, Weijia; Peng, Hongjian; Ruan, Na; Tang, Zhiqing; Zhao, Wei

doi:10.1109/tbdata.2018.2848969

Cited by 39 publications

(37 citation statements)

References 30 publications

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“…However, in real vehicle scenarios there may exist more than one person which can degrade system performance accordingly by making the recognition much more complex. In general, other vehicles on the road and people outside the vehicle may have a very slight influence [42]. Thus, additional signal processing may overcome these issues which we will consider in future.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, WiFi-based driver fatigue detection and driver activity recognition systems have been investigated with some constraints. In this context, WiFind [42] is suitable only for fatigue detection, while WiDriver [43] leverages a driver's hand movements for better characterization of driver action recognition using WiFi signals. SafeDrive-Fi [44] demonstrated CSI-based dangerous driving recognition through body movements and gestures, using variance of CSI amplitude and phase measurements.…”

Section: Related Workmentioning

confidence: 99%

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WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

Akhtar

Wang

2019

Applied Sciences

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In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

Akhtar

Wang

2019

Applied Sciences

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“…σ is the standard deviation, and µ is the mean. The CV threshold can be used in breath monitoring and motion detection [45].…”

Section: Time-domain Thresholdmentioning

confidence: 99%

Human Activity Sensing with Wireless Signals: A Survey

Liu

Teng

Hong

2020

Sensors

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Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities.

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“…In this context, WiDriver [24] is based on the driver's hand movements to characterize driving actions. WiFind [23] is suitable only for driver fatigue detection. SafeDrive-Fi [25] investigated dangerous driving action recognition using CSI of WiFi signals.…”

Section: Related Workmentioning

confidence: 99%

“…WiFi-based wireless scheme opened a new window for scientists to further investigate device-free activity recognition for the safety benefits of drivers. In this context, several WiFi-based driver monitoring systems have been investigated with good recognition performance [14,[23][24][25][26][27]. Despite all its prospects, the WiFi-based complete description of driver's attention and inattention monitoring has not been deeply investigated thus far and is still a competitive task to solve.…”

Section: Introductionmentioning

confidence: 99%

WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

Akhtar

Wang

2020

Sensors

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Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver’s activity monitoring has emerged with remarkable attention. With substantial progress in WiFi-based device-free localization and activity recognition, radio-image features have achieved better recognition performance using the proficiency of image descriptors. The major drawback of image features is computational complexity, which increases exponentially, with the growth of irrelevant information in an image. It is still unresolved how to choose appropriate radio-image features to alleviate the expensive computational burden. This paper explores a computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information (CSI) of WiFi signals. In this novel research work, we demonstrate an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy. Specifically, we addressed the problem of the computational burden by efficacious use of Gabor filters with gray level statistical features. The presented low-cost solution requires neither sophisticated camera support to capture images nor any special hardware to carry with the user. This novel framework is evaluated in terms of activity recognition accuracy. To ensure the reliability of the suggested scheme, we analyzed the results by adopting different evaluation metrics. Experimental results show that the presented prototype outperforms the traditional methods with an average recognition accuracy of 93.1 % in promising application scenarios. This ubiquitous model leads to improve the system performance significantly for the diverse scale of applications. In the realm of intelligent vehicles and assisted driving systems, the proposed wireless solution can effectively characterize the driving maneuvers, primary tasks, driver distraction, and fatigue by exploiting radio-image descriptors.

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

Cited by 39 publications

References 30 publications

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

Human Activity Sensing with Wireless Signals: A Survey

WiFi-Based Driver’s Activity Monitoring with Efficient Computation of Radio-Image Features

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