Intelligent Assistive Technology and Systems Lab - click to go to homepage
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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Room 438

500 University Ave.

Toronto, Canada

P 416.946.8573

F 416.946.8570


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160 - 500 University Ave.

Toronto, ON, M5G 1V7



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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


Bing holds a Masters of Science in Neuroscience from Queen’s University and an Honours Bachelor of Science in Biopsychology from University of Winnipeg. Her master’s thesis focused on neural correlates of selective attention in cognitively normal older adults, patients with mild cognitive impairment and patients with mild Alzheimer’s disease.

Her interests include the study of age-related diseases, such as Alzheimer’s disease, the use of strategies to improve older people’s daily life and the enhancement of older people’s quality of life.

Bing has been involved in various study projects.

Examples of research Bing is involved in include:

Additionally, Bing assists in the preparation and submission of ethics for the lab's projects.


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).