Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study.

ABSTRACT: Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 10² ms for the BRT of the 12 participants. Furthermore, the reg

SUBMITTER: Ogihara T 

PROVIDER: S-EPMC10790849 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature