Date of Award
10-1-2021
Publication Type
Thesis
Degree Name
M.A.Sc.
Department
Electrical and Computer Engineering
Keywords
Automotive, Driver monitoring, Heart rate variability, Physiological measures
Supervisor
B. Balasingam
Supervisor
S. Das
Rights
info:eu-repo/semantics/embargoedAccess
Abstract
The society of automotive engineers define six levels of automation in vehicles from Level-0 (no automation) to Level-5 (full automation). Until the Level-5 automation is achieved, driver monitoring systems play a major role in road safety in partially automated vehicles. A driver monitoring system uses sensors to extract various psychophysiological measurements from the driver in order to monitor their readiness to safely operate the vehicle. Some driver monitoring systems use webcam type cameras to extract various features related to the alertness of the driver, such as, head-pose patterns, eye-closing patterns, and facial features. The use of physiological features such as heart rate and pupil size are also studies in many of these applications application. The focus of this thesis is on utilizing heart rate and heart rate variability metrics in driver monitoring systems. By analyzing data collected from 16 participants this thesis presents a study exploring the feasibility of using heart rate variability measures in driver monitoring systems.
Recommended Citation
Kavousi, Safoura, "Cognitive Load Estimation Using Heart Rate Variability Measures for Driver Monitoring Systems" (2021). Electronic Theses and Dissertations. 8769.
https://scholar.uwindsor.ca/etd/8769