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 behaviour-based and vehicle-based techniques, but none 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 prevents catastrophic failures and damages machinery; meanwhile, it will enable 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: