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

Sensors
Zain Ul Abiden Akhtar, Hongyu Wang

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 a...Continue Reading

References

Dec 27, 2017·Accident; Analysis and Prevention·John G GasparJulie Kang
Feb 8, 2018·Applied Ergonomics·Katie J ParnellKatherine L Plant
May 4, 2018·Accident; Analysis and Prevention·C OrtizC Salas
Nov 13, 2018·Computer Methods and Programs in Biomedicine·Yawen XiaoXiaodong Zhao
May 24, 2019·Sensors·Tao WangShugong Xu
Jun 27, 2019·Journal of Safety Research·Lingqiao QinDavid A Noyce

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

WiVi
Fi
GC
SAE
MATLAB
SafeDrive
Ubuntu
WiDrive
WiDriver
TW

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