University of Toronto
(416) 597-3422 x 7911
- Geriatric Functional Assessment
- Internet of things
- Machine learning
- Sensor-based In-home Frailty Assessment
- Smart Home for Health Monitoring
- Entrepreneurship in Technology and Aging
Chao Bian is a Ph.D. Candidate in Biomedical Engineering at the University of Toronto (U of T) under the supervision of Dr. Alex Mihailidis. He has been conducting research in the AI and Robotics team at the University of Toronto and Toronto Rehabilitation Institute since 2016. His PhD research focuses on using connected sensors and AI to assess and predict frailty in home settings for older adults.
His past research and work experience have all been focusing on the use of sensors and AI to discover what is undiscovered. He developed and optimized a Radio Frequency Identification (RFID) system for tracking patients and doctors in an Emergency Room during his master’s study. This experience earned him a visiting graduate student fellowship at the Auto-ID Lab of MIT where he continued the RFID tracking study. His supervisor at MIT was Dr. Sanjay Sarma.
Chao was the lead developer for an award-winning IoT-based roadside train monitoring system called TRAINFO™ before his PhD study. He has also worked as an IoT and AI developer at IBM Canada in 2018. In 2019, he co-founded Nightingale.ai Corp., a mobile health startup that uses AI for automating physical function assessment for older adults. Nightingale won the runner-up prize in the 2018 AGE-WELL National Impact Challenge competition. Chao himself won the Emerging Entrepreneur Award from AGE-WELL NCE. He also won the RBC Graduate Fellowship for Entrepreneurship at U of T.
Bian, C., Ye, B., Chu, C., McGilton, K., Mihailidis, A. (in press). Technology for Home-based Frailty Assessment and Prediction: A Systematic Review. Gerontechnology.
Bian, C., Khan, S.S., Mihailidis, A. (2018). Infusing Domain Knowledge to Improve the Detection of Alzheimer’s Disease from Everyday Motion Behaviour. In: Bagheri E., Cheung J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science, vol 10832. Springer, Cham.
Ternowetsky, N., & Bian, C. (2016). Probe Data: Opening the Black Box. In North America Travel Monitoring Exposition and Conference, Miami, Florida, USA.
Bian, C., Peng, Q., & Zhang, G. (2013). Improvement of RFID Accuracy for a Product Tracking System. In Volume 4: 18th Design for Manufacturing and the Life Cycle Conference; 2013 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications (p. V004T05A014). ASME
C. Bian, P. Tanugraha, C. Chu: AI-Based Physical Function Assessment System, U.S. Patent Pending (Application No. 16/995,523, Filing Date: August 17, 2020)
G. Rempel, N. Ternowetsky, M. Reimer, C. Bian: System to Provide Real-Time Railroad Grade Crossing Information To Support Traffic Management Decision-Making, U.S. Patent Pending, (Application Number: 15/146,391, Filing Date: May 7, 2015)