TMU researcher receives funding to develop data-driven methodologies for search-and-rescue operations.
To help Canadians in distress, search and rescue personnel often must navigate expansive terrain, extreme weather, and remote regions. However, little is known about each task's impact on the responders. Toronto Metropolitan University (TMU) professor Sharareh Taghipour aims to support the well-being and efficiency of search and rescue teams through an interdisciplinary project that will bridge psychology, signal processing, machine learning, modelling and visualization, and operations research. The project, “End-to-end solution for dynamic tasks allocation in search and rescue missions,” recently received $252,000 from the Search and Rescue New Initiatives Fund, managed by Public Safety Canada.
A professor in TMU’s Department of Mechanical, Industrial, and Mechatronics Engineering and a Canada Research Chair in Physical Asset Management, professor Taghipour is taking a human-centred approach to data-driven methodologies. Working with volunteer participants from the Search & Rescue Volunteer Association of Canada Inc., and the Ontario Search and Rescue Volunteer Association, professor Taghipour’s project team will use virtual mission simulations to capture participants’ physiological and behavioural measurements. This data will be used to develop models for estimating the dynamic cognitive and physical fatigue states of participants. These states will then be used within an optimization model for the real-time dynamic re-assignment of team tasks in uncertain environments.
“Professor Taghipour's commitment to advancing modelling, optimization methodologies, and systems thinking to benefit people, and the communities they live and work in, is truly inspiring. Her interdisciplinary research project, supported by Public Safety Canada, reflects the innovation and collaboration we aim to foster at the Faculty of Engineering and Architectural Science.”
Historically, models developed to support task allocation in time-critical operations have relied on limited static task sets and variables. Professor Taghipour’s model will aim to integrate dynamic characteristics, including the combined effects of influential factors on an individual’s performance, to more accurately inform resource allocation to improve team safety, efficiency, and mission success. To develop this unique methodology, professor Taghipour will work with a range of experts, including TMU Faculty of Science professor Alexander Ferworn and his Computational Public Safety Lab (external link) team, as well as researchers from her TMU Reliability, Risk, and Maintenance Research Laboratory (RRMR Lab).
“It’s important for us to look at task allocation as it relates to real people executing these time-critical missions. By assessing dynamic changes in physical and mental fatigue, we can better manage fatigue and stress and help improve human performance, human-to-human collaboration, and even human-to-robot collaboration.”