Dr. Taati's research focuses on the application of computer vision and machine learning techniques in solving real-world rehabilitation and assistive technology challenges and in health and safety monitoring. Examples include real-time upper-limb motion assessment during post-stroke rehabilitation therapy and the analysis of gait and joint movements to monitor the rehabilitation process in the home. A major focus of Dr. Taati's is to move away from the laboratory and contrived situations towards the development of computer vision algorithms and systems that work reliably in natural settings, such as in the home.
Investigating the feasibility and acceptability of real-time visual feedback in reducing compensatory motions during self-administered stroke rehabilitation exercises: A pilot study with chronic stroke survivors.
J Rehabil Assist Technol Eng. 2019 Jan-Dec;6:2055668319831631
The feasibility of a vision-based sensor for longitudinal monitoring of mobility in older adults with dementia.
Arch Gerontol Geriatr. 2019 May - Jun;82:200-206
J Neuroeng Rehabil. 2018 Nov 06;15(1):97
Dement Geriatr Cogn Disord. 2018 Jul 24;45(5-6):353-367
Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features.
Parkinsonism Relat Disord. 2018 May 05;:
Alzheimers Dement. 2018 Mar 20;:
IEEE J Transl Eng Health Med. 2018;6:2100107
Conf Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:3377-3380
Video analysis of "YouTube funnies" to aid the study of human gait and falls - preliminary results and proof of concept.
Conf Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:1178-1181
IEEE Trans Neural Syst Rehabil Eng. 2017 Aug 07;:
Scientist, KITE (TRI)
Assistant Professor, Institute of Biomaterials and Biomedical Engineering, University of Toronto
Assistant Professor (status only), Department of Computer Science, University of Toronto