This paper is focused to develop a wearable glove system to detect driver stress events in real time. The driver's stress is estimated by the use of physiological signals and steering wheel motion analysis. The steering wheel motion is analyzed by driver's hand moving characteristic. Principally, the sensors on the glove gathered the photoplethysmogram signal via fingertip, and hand motion signal via inertial motion unit. The sensor module readings are transmitted to an end terminal application via a Bluetooth low energy transmission module to compute the driver stress index. The studies are carried out in a simulated driving, which is composed of three distinct driving scenarios to study the subjects' behaviors that correlate with stress. Twenty-eight subjects are requested to perform three different driving sessions with random scenarios generated while performing various driving maneuvers to assess the dynamic of mental workloads. The stress assessments of driving test subjects are self-reported at pre-and post-stimulus as well as observed through facial expression recorded throughout the whole experiments. Moreover, this paper also aimed to investigate the correlation of stress events with different driving tasks. Stress index is computed by a support vector machine pattern classifier with extracted features from sensors reading. Notably, stress index differences were found among three driving scenarios and driving maneuvers. Results revealed the true accuracy of stress detection is greater than 95% in average.
- Driver behavior
- mobile application
- pattern classifier
ASJC Scopus subject areas
- Electrical and Electronic Engineering