Excellent Potential of Geometric Brownian Motion (GBM) as a Random Process Model for Level of Drowsiness Signals

Pouyan Ebrahimbabaie, Jacques G. Verly

2018

Abstract

We show that Geometric Brownian Motion (GBM) appears to be an excellent choice of random process model to describe mathematically the real-life signals that represent the evolution with time of the level of drowsiness (LoD) of an individual, such as a driver. We collected data from thirty (30) healthy participants, who each underwent three tests (either driving in a simulator or performing Psychomotor Vigilance Tests) at successive levels of sleep deprivation. During each test, the LoD was produced by a photooculography (POG) based device designed and built by our team. We so obtained a total of 90 LoD signals. For each, we applied statistical methods to determine whether a GBM was a valid model for it. All 90 signals passed statistical tests of normality and independency, meaning that each can be modeled by GBM, thereby showing the excellent potential of GBM as a random process model for LoD signals. This finding could lead to the development of a number of innovative means for predicting the evolution of the LoD and the occurrence of related events beyond the present moment. The resulting technology should help reduce the number of accidents due to drowsy driving.

Paper Citation

in Harvard Style

Ebrahimbabaie P. and Verly J. (2018). Excellent Potential of Geometric Brownian Motion (GBM) as a Random Process Model for Level of Drowsiness Signals. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS; ISBN 978-989-758-279-0, SciTePress, pages 105-112. DOI: 10.5220/0006545101050112

in Bibtex Style

@conference{biosignals18,
author={Pouyan Ebrahimbabaie and Jacques G. Verly},
title={Excellent Potential of Geometric Brownian Motion (GBM) as a Random Process Model for Level of Drowsiness Signals},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS},
year={2018},
pages={105-112},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006545101050112},
isbn={978-989-758-279-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS
TI - Excellent Potential of Geometric Brownian Motion (GBM) as a Random Process Model for Level of Drowsiness Signals
SN - 978-989-758-279-0
AU - Ebrahimbabaie P.
AU - Verly J.
PY - 2018
SP - 105
EP - 112
DO - 10.5220/0006545101050112
PB - SciTePress