Impairment screening utilizing biophysiological measurements and machine learning algorithms

In this project, a comprehensive testing station for impairment screening will be implemented. The station includes an eye testing goggle, movement detectors, biophysiological measurement sensors, and an integration algorithm to integrate the result of measurement to diagnose the status and type of impairment. The hardware technology resides at the industry partner while this project is focused on implementation of data gathering and data storage platforms, feature extraction and selection algorithms and machine learning algorithms to quantify levels of impairment. The project involves comprehensive analysis of multiple available data types and their correlation to the impairment level, and implementation of classification algorithms (supervised/unsupervised) and ensemble methods to perform screening based on type of impairment. The results of this project can be used for automation in roadside sobriety testing procedures. TO BE CONT’D

Faculty Supervisor:

Edward J Park

Student:

Saboora Mohammadian

Partner:

CannSight Technologies Inc

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

Simon Fraser University