Graduate Student Seminar Series – Shaghayegh Chavoshian

Graduate Student Seminar Series
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Location: HS610 – 155 College St, Room 610
Presentation Title: Addressing Slips and Falls with AI: Evaluating Footwear Slipperiness
Abstract: Slips and falls are a significant concern in Canada, leading to numerous hospitalizations and substantial healthcare costs. The World Health Organization stated that falls account for the second leading cause of injury-related deaths. In the United States, approximately 32,000 deaths were estimated out of 3 million emergency department visits due to falls. Up to 50% of falls are due to slips. Slips occur when the shear forces generated from the interaction of the shoe and floor exceed the frictional limits. Slips can cause back injuries, strains, sprains, turned ankles, or other painful injuries due to jerking and twisting while attempting to prevent the fall. The data also indicate that the impact of slips and falls is particularly severe in specific populations, such as in workplaces and among the aging population. In addition, the global aging population is rapidly increasing. By 2050, the number of people over 65 is expected to reach 88.5 million in the United States, and the proportion of those over 85 in Canada is projected to be 2.7 million. The rapid aging of the population is one of the key factors contributing to slips and falls becoming major global public health issues. Footwear is the main contact between the body and the walking surface. Consequently, one key factor for preventing slip and fall incidents is slip-resistant footwear. Therefore, it is crucial to understand how the footwear and outsole features perform on slippery surfaces to minimize slip-related injury risks. While methods exist to evaluate slip resistance by measuring the Coefficient of Friction between footwear and flooring surfaces, further studies are needed to determine how accurately these methods replicate real-world conditions and assess the reliability of their results. Additionally, footwear manufacturers require a reliable tool to predict a shoe’s slip resistance and streamline the development process. This research proposal aims to address these gaps by identifying AI-based solutions to prevent falls, reduce workplace costs, and enhance safety.
Supervisor Name: Dr. Atena Roshan Fekr
Year of Study: 4
Program of Study: PhD
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