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Photo of Chao BianChao Bian

PhD Candidate

IBBME (University of Toronto)

(416) 597-3422 x 7911




  • Frailty
  • Dementia
  • Sensors
  • Embedded systems
  • Internet of things
  • Machine learning

Research Interest

  • Internet of Things and Artificial Intelligence for Home-based Frailty Assessment
  • Machine Learning for Early Detection of Dementia
  • 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 Toronto Rehabilitation Institute since 2016.

His past research and work experience have been focusing on the use of Internet of Things and Artificial Intelligence (IoT and AI) to discover what is undiscovered. He developed and optimized a Radio Frequency Identification (RFID) system for discovering the patient location in the 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’s featured work experience includes

  1. Worked as a lead system developer and developed an award-winning IoT-based roadside train monitoring system called “TRAINFO™” to discover railroad crossing blockage pattern for better traffic management.
  2. Worked as an IoT and AI developer intern at IBM Canada in 2018. At U of T and Toronto Rehab, he is using IoT and AI to discover health issues of the aging population. His Ph.D. thesis is to investigate the use of IoT and AI-based technology to discover and continuously monitor the frailty status of older adults in home settings.

Chao is also a Highly Qualified Personnel of AGE-WELL NCE. His transdisciplinary team called was one of the eight finalists in the 2018 AGE-WELL Impact Challenge competition.


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


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)