Portrait of Jose Zariffa

José Zariffa

Associate Professor

BEng (McGill), MASc (Toronto), PhD (Toronto), PEng

Research Stream: Clinical Engineering

Laboratory Website:

Email: jose.zariffa@utoronto.ca | Tel: 416 597-3422 x7915 | Office: Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, #12-102, Toronto, Ontario, M5G 2A2 Canada

Main Appointments

  • Scientist, Toronto Rehabilitation Institute (TRI), University Health Network (UHN)
  • Institute of Biomaterials & Biomedical Engineering

Additional Appointments

  • Edward S. Rogers Sr. Department of Electrical & Computer Engineering
  • Rehabilitation Science Institute

Research Interests

Central nervous system disorders such as spinal cord injury, stroke, traumatic brain injury, multiple sclerosis, and Parkinson’s disease can drastically limit an individual’s independence, mobility, and quality of life. This has dramatic consequences for completing activities of daily living, integrating within the community, and finding opportunities for employment, as well as a considerable economic impact on affected individuals, their families and the health care system. Our research seeks to address these issues by developing technology that can assist functional recovery after damage to the nervous system.

Our approach is to develop rehabilitation technology that can adapt to short- and long-term changes in the user’s neuromuscular system. In order to this, it is necessary to improve our ability to extract information from the nervous system. We conduct projects ranging from direct interfaces with peripheral nerves for monitoring neural signals, to wearable sensors that can capture a person’s interactions with the world around them. We aim to develop tools that can provide detailed information about movement control and performance in varied contexts from the clinic to the community, with areas of application including: neuroprostheses (such as implanted and non-invasive closed-loop functional electrical stimulation systems), the evaluation and optimization of neurorehabilitation interventions (including pharmacological agents, rehabilitation robotics, and clinical programs), and basic neuroscience.

Stories & Media

Cover image of research gallery of 2019

Research Gallery 2019 

  • Zariffa and colleagues are applying computer vision algorithms on videos captured from wearable camera to monitor patient hand recovery after spinal cord injury. This can provide more effective clinical evaluations of new interventions through precise outcome measurements.