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

• Main Authors: Zain Ul Abiden Akhtar, Hongyu Wang
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Sensors
• Subjects: channel state information (csi); wireless sensing; activity recognition; driver distraction; efficient computation; sparse auto-encoder
• Online Access: https://www.mdpi.com/1424-8220/20/5/1381

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&#8217;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 <inline-formula> <math display="inline"> <semantics> <mrow> <mn>93.1</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> 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.