Research

DRiVE Lab 2.0 (Driving Research in Virtual Environments 2.0) is comprised of two virtual reality laboratories housed in the A.A. Thornbrough Building. One of the laboratories contains a full-sized Pontiac G6 OKTAL static car simulator as well as a 12 camera VICON motion capture system.  The other laboratory contains a 6 degree of freedom hexapod robot dynamic heavy equipment/car simulator coupled with a 7 camera VICON motion capture system. Both laboratories incorporate visual as well as haptic feedback. The dynamic simulator includes a pair of haptic controls coupled with a virtual reality head mounted display whereas the OKTAL simulator uses 6 high definition projectors to provide 300 degrees of coverage on the wrap around screens.

In collaboration with fellow DRiVE Lab Co-Directors,  Dr. Lana Trick from Psychology and Dr. Andrew Hamilton-Wright from the School of Computer Science, both facilities can be used to develop and test new in-vehicle devices to make driving safer. The two simulation facilities allow us to simultaneously investigate combinations of biomechanical, physiological, and psychophysical basic and applied research questions under realistic operating conditions. My current research studies driver behaviour in traffic intersections to inform driver training, road design and autonomous vehicle algorithms.

Whole-Body Vibration Mitigation Strategies and Device Development - In collaboration with School of Engineering Colleague, Dr. Marwan Hassan, we are developing new vibration devices and control strategies to reduce whole-body vibration in mobile heavy vehicles. On a related project, which addresses agri-food industry worker shortages, vibration must still be minimized to prevent machine damage so we are developing a machine learning informed, retrofit autonomous tractor kit. 

Development, Validation and Use of Wearables - In collaboration with School of Engineering Colleague, Dr. Karen Gordon and Dr. Anne Agur (University of Toronto), our previous research focused on quantification and measurement methods of material properties of hand and wrist soft tissues. Current work is developing metrics for quantifying local shape changes in the carpal tunnel which will help to interpret how wrist posture as measured by a wearable influences carpal tunnel shape and median nerve compression location. Ongoing work is developing, validating and using novel wrist and knee wearable devices for ergonomic assessment and training of machine learning algorithms for workplace applications.