Predicting the Health Impacts of Commuting Using EEG Signal Based on Intelligent Approach

Abstract

Commuting to work is an everyday activity for many which can have a significant effect on our health. Commuting on regular basis can be a cause of chronic stress which is linked to poor mental health, high blood pressure, heart rate, and exhaustion. This research investigates the neurophysiological and psychological impact of commuting in real-time, by analyzing brain waves and applying machine learning. The participants were healthy volunteers with mean age of 30 years. Portable electroencephalogram (EEG) data were acquired as a measure of stress level. EEG data were acquired from each participant using non-invasive NeuroSky MindWave headset for 5 continuous activities during their commute to work. This approach allowed effects to be measured during and following the period of commuting. The results indicate that whether the duration of commute was low or large, when participants were in a calm or relaxed state the bio-signal alpha band exceeded beta band whereas beta band was higher than alpha band when participants were stressed due to their commute. Very promising results have been achieved with an accuracy of 97.5% using Feed-forward neural network. This work focuses on the development of an intelligent model that helps to predict the impact of commuting on participants. In addition, the result obtained from the Positive and Negative Affect Schedule also suggests that participants experience a considerable rise in stress after their commute. For modelling of cognitive and semantic processes underlying social behavior, the most of the recent research projects are still based on individuals, while our research focuses on approaches addressing groups as a complete cohort. This study recorded the experience of commuters with a special focus on the use and limitation of emerging computing technologies in telehealth sensors.