Prof. Neville Hogan
Professor of Mechanical Engineering
Massachusetts Institute of Technology
Neville Hogan is Sun Jae Professor of Mechanical Engineering and Professor of Brain and Cognitive Sciences at the Massachusetts Institute of Technology. He earned a Diploma in Engineering (with distinction) from Dublin Institute of Technology and M.S., Mechanical Engineer and Ph.D. degrees from MIT. He joined MIT’s faculty in 1979 and presently Directs the Newman Laboratory for Biomechanics and Human Rehabilitation. He co-founded Interactive Motion Technologies, now part of Bionik Laboratories. His research includes robotics, motor neuroscience, and rehabilitation engineering, emphasizing the control of physical contact and dynamic interaction. Awards include: the Silver Medal of the Royal Academy of Medicine in Ireland; the Henry M. Paynter Outstanding Investigator Award; the Rufus T. Oldenburger Medal; and Honorary Doctorates from Dublin Institute of Technology and Delft University of Technology.
Theme: Basic science and new technology
A scientific basis for robot-enabled neuro-recovery
The emergence of therapeutic and assistive robotics promises new approaches to aiding recovery after neurological injury. To fully realize that promise, we need a quantitative mathematical model of the recovery process. A theory of how motor behavior is recovered should be able to account for at least the main features of unimpaired motor behavior. Despite much slower actuators (muscles), communication (neural transmission) and computation (neural processing) than contemporary robots, humans exhibit remarkably superior dexterity and agility. In consequence, they also exhibit surprising limitations: I will review evidence that moving slowly and smoothly is hard for humans. These observations suggest that human motor control is based on dynamic primitives, including at least three classes: submovements, oscillations and mechanical impedances. I will review evidence that stereotyped submovements are present in the earliest movements made by persons recovering after stroke. The re-organization of submovements serves to quantify the progress of recovery. Conversely, I will review evidence that learning based on rhythmic performance transfers poorly to more general actions. This may account, in part, for the surprising difficulty of technology-assisted locomotor rehabilitation. I will also articulate how dynamic primitives may account for some striking features of recovery: abnormal synergies may emerge as a consequence of abnormal muscle mechanical impedance; and saltatory progress—plateaus of performance followed by subsequent periods of further improvement—may emerge as a consequence of grouping submovements.