Classifying the Cognitive Performance of Drivers While Talking on Hands-Free Mobile Phone Based on Innovative Sensors and Intelligent Approach

Abstract

The use of mobile phones while driving is restricted to hands-free mode. But even in the hands-free mode, the use of mobile phones while driving causes cognitive distraction due to the diverted attention of the driver. By employing innovative machine-learning approaches to drivers’ physiological signals, namely electroencephalogram (EEG), heart rate (HR), and blood pressure (BP), the impact of talking on hands-free mobile phones in real time has been investigated in this study. The cognitive impact was measured using EEG, HR, and BP data. The authors developed an intelligent model that classified the cognitive performance of drivers using physiological signals that were measured while drivers were driving and reverse bay parking in real time and talking on hands-free mobile phones, considering all driver ages as a complete cohort. Participants completed two numerical tasks varying in difficulty while driving and reverse bay parking. The results show that when participants did the hard tasks, their theta and lower alpha EEG frequency bands increased and exceeded those when they did the easy tasks. The results also show that the BP and HR under phone condition were higher than the BP and HR under no-phone condition. Participants’ cognitive performance was classified using a feedforward neural network, and 97% accuracy was achieved. According to qualitative results, participants experienced significant cognitive impacts during the task completion.

Keywords

• hands-free mobiles phones
• wearable sensors
• real-time driving
• cognitive load
• machine learning