Stairs have always been dangerous and risky for people to maneuver. The U.S. National Electronic Injury Surveillance System estimates that over one million people received hosipital treatment due to stair or step related injuries in the U.S. in 2005. The ultimate goal of this project is to build a system that can watch stairs with only a camera and automatically detect any anomalous (unsafe) events that may occur. The real-world motivation for this is to help further the understanding of the causes and catalysts of injuries on stairs and prevent accidents on stairs. Furthermore, such a system can help promote independence for older adults as their inability to safely traverse stairs greatly impedes their ability to live independently at home. The system makes use of advanced computer vision and machine learning algorithms to distinguish normal stair traversals from anomalous ones. This research is being done at the Intelligent Assistive Technology and Systems Lab at the University of Toronto in conjunction with the Machine Learning Group.