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

Abstract

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.

Wearable biosensors for human fatigue diagnosis: A review

“…Typically, a pair of conductive silver/ silver chloride electrodes are integrated into flexible three-electrode system to prepare wearable "tattoos" and "patches" based on screen printing technology. Then, wireless transmission devices, such as WiFi, 139 Bluetooth, 27 and NFC module, 140 been applied in glucose 74 and lactic acid detection. 143 The first wearable current biosensor was used for glucose detection.…”

“…Typically, a pair of conductive silver/silver chloride electrodes are integrated into flexible three‐electrode system to prepare wearable “tattoos” and “patches” based on screen printing technology. Then, wireless transmission devices, such as WiFi, 139 Bluetooth, 27 and NFC module, 140 are integrated and transmit signals to the terminals (e.g., laptops, computers, and mobile phones) for continuous tracking and monitoring changes in electrical signals. According to the principle of signal change, the electrochemical response signal includes current, potential, and electrical impedance.…”

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

“…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.…”

“…In our presented framework, all experiments are performed with 802.11n enabled WiFi devices as described in [50][51][52]. Specifically, a laptop is used as a receiver that is equipped with Intel 5300 NIC and three receiving antennas, i.e., ℕ Rx = 3.…”

Inattentive Driving Detection Using Body-Worn Sensors: Feasibility Study

“…Inattention detection systems can be classified into two categories according to the types of their input signals. One category uses vehicle signals such as the speed and steering angle [ 2 , 3 , 4 , 5 , 6 , 7 ]. Kume et al [ 3 ] reported that the sensitivity of detecting an inattentive state using in-vehicle sensors was 53% with a fixed threshold, and 82% with a manually set threshold.…”