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IATSL develops assistive technology that is adaptive, flexible, and intelligent, enabling users to participate fully in their daily lives. Learn more about our research

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Toronto, Canada

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F 416.946.8570


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Bing Ye

Photo of BingResearch Manager

M.Sc. (Neuroscience, Queen's University)

B.Sc.(Honours) (Biopsychology, University of Winnipeg)

(416) 597-3422 x 7910


  • Project Management in Research
  • Ethics
  • Literature Search
  • User-Centered Design


Bing holds a Masters of Science in Neuroscience from Queen’s University and an Honours Bachelor of Science in Biopsychology from University of Winnipeg. During the two years of the life of her masters, she worked as a psychometric rater in a geriatric clinic where she received great experience in performing various tests assessing the patients and extensive knowledge in understanding dementia. By interacting with patients and their caregivers, she gained a deep understanding of what was like to live with the disease and how it may impact on the lives of the family members. Never take life for granted. Be thankful and appreciate what you have.

Her research interests include age-related diseases (i.e., dementia), quality of life of older adults, people with dementia and their caregivers, assistive technologies and human-centred design. Specifically, she is interested in how technology can help people age gracefully and assist caregivers in supporting care; How technology can be developed/designed that reflect user's needs and requirements.

Bing has been involved in various study projects.

Examples of research Bing is currently involved include:

As a lab manager, Bing manages the lab on a daily basis, such as lab resources and personnels. Additionally, Bing provides guidance for writing ethics documents, such as protocol and the consent forms. She assists in the preparation and submission of ethics for the lab's projects.


Khan, K. S., Ye, B., Taati, B., and Mihailidis, A. (2018). Detecting Agitation and Aggression in People with Dementia using Sensors - A Systematic Review. Alzheimer's & Dementia, Accepted, 2018.

Khan, K. S., Zhu, T., Ye, B., Mihailidis, A., Iaboni, A., Newman, K., Wang, H. A., Martin, S. L. (2017). DAAD: A Framework for Detecting Agitation and Aggression in People Living with Dementia using a Novel Multi-Modal Sensor Network. Paper presented at 1st Workshop on Data Mining for Aging, Rehabilitation, and Independent Assistive Living, in conjunction with IEEE International Conference on Data Mining, New Orleans, USA.

Chikhaoui, B., Ye, B., and Mihailidis, A. (2017). Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization. Journal of Ambient Intelligence and Humanized Computing, 1 - 15.

Dolatabadi, E., Zhi, X. Y., Ye, B., Coahran, M., Lupinacci, G., Mihailidis, A., Wang, R., and Taati, B. (2017). The Toronto Rehab Stroke Pose Dataset to detect compensation during stroke rehabilitation therapy. Pervasive Health, Barcelona, Spain.

Chikhaoui, B., Ye, B., and Mihailidis, A. (2016). Feature-level combination of skeleton joints and body parts for accurate aggressive and agitated behavior recognition. Journal of Ambient Intelligence and Humanized Computing, 7(36): 1 - 20.

Chikhaoui, B., Ye, B., and Mihailidis, A. (2016). Ensemble Learning-based Algorithms For Aggressive and Agitated Behavior Recognition. The 10th international Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI, 2016).