Intelligent Drowsiness Prognosis Using Chaos Theory

Drowsiness is considered the foremost cause of accidents in the mining industry. Studies estimate that up to 65% of annual accidents in mines are related to drowsiness. The total loss to the company’s assets is around $22.6 billion and operator lives are not replaceable. The lack of real-time drowsiness monitoring and prognosis in the mining industry can cause irrecoverable losses, so a prognosis system is vital. Different drowsiness detection systems were developed, such as behavior-based and vehicle-based techniques, but, none of them are reliable and accurate in mining applications. The proposed project applies nonlinear extraction algorithms to EEG signals to develop a reliable and effective drowsiness prognosis system. This project will benefit Canada’s mining industries, it saves lives and also prevents catastrophic failures and damages machinery meanwhile it enables the Canadian mining industry to become more competitive and efficient.

Faculty Supervisor:

Farid Golnaraghi

Student:

Partner:

DyMoTech

Discipline:

Engineering

Sector:

Mining; Professional, scientific and technical services

University:

Simon Fraser University

Program: