MAR 20, 2014 12:00 PM PDT

The potential of robotic technology as a next generation technology for neurological assessment

Presented At Neuroscience
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  • Professor Department of Biomedical and Molecular Sciences, Queens University School of Medicine
      Dr. Stephen Scott is a professor in the Department of Biomedical and Molecular Sciences at Queen's University. He is also a member of the Centre for Neuroscience Studies and the CIHR Group in Sensory-Motor Systems. He graduated from the University of Waterloo in Systems Designs Engineering for his undergraduate degree and a M.A.Sc. with Dr. D.A. Winter. He then did a Ph.D. with Dr. Gerry Loeb at Queens University in the Department of Physiology. After that he went to the Universit de Montral for his postdoctoral training in the Department of Physiology with Dr. John Kalaska from 1993 to 1995. His first faculty position was as a chercheur adjoint in the Department of Physiology at Universit de Montral in 1995. He moved his lab to Queen's University in 1997.


    Assessment of sensorimotor and cognitive function plays a crucial role in all facets of patient care, from diagnosing the specific disease or injury, to management and monitoring of rehabilitation strategies to ameliorate dysfunction. Most assessment scales for sensorimotor function are subjective in nature with relatively coarse rating systems, reflecting that it is difficult for even an experienced observer to discriminate small changes in performance using only the naked eye. Robotic technologies have had a profound impact in basic research to understand fundamental properties of sensorimotor control due to their ability to control the position or forces applied to the limb and their inherent ability to objectively quantify motor behavior. Our general hypothesis is that these same attributes make robotic technologies ideal for creating a new approach to neurological assessment. I will discuss a number of novel robot-based tasks weve developed to assess brain function in subjects with stroke, highlighting the complex patterns of sensory, motor and cognitive deficits that can be quantified with this technology.

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