Grant Schaffner, Ph.D.

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Grant Schaffner, Ph.D

Assistant Professor,
Department of Aerospace Engineering & Engineering Mechanics
College of Engineering and Applied Science
University of Cincinnati
grant.schaffner@uc.edu

 

 

 

 

 


Brief Biography

Grant Schaffner completed BS and MS degrees in Aeronautics and Astronautics at MIT in 1989 and 1994, respectively. In 2000 he completed a Ph.D. in Medical Engineering in the Harvard-MIT Division of Health Sciences and Technology. He has worked for multiple aerospace and defense companies and also worked at the NASA Johnson Space Center for seven years. He is currently an Assistant Professor in the Department of Aerospace Engineering and Engineering Mechanics within the College of Engineering and Applied Science at the University of Cincinnati.

 

Exoskeleton and Robotic Surgery Projects at the UC Human Systems and Simulation Laboratory

Grant Schaffner, Gaurav Mukherjee, Chris Korte, Evan Sneath

The University of Cincinnati Human Systems and Simulation Laboratory is investigating means to improve the capabilities, efficiency, and human-compatibility of robotic systems in two primary areas: assistive exoskeletons and semi-autonomous robotic surgery. Current efforts are seeking to improve the efficiency of a lower-body assistive exoskeleton system in stand-to-sit and sit-to-stand motions by exploiting the natural energetics of the motion and supplementing, rather than supplanting, the user's efforts. A passive knee-joint exoskeleton has been developed and tested and efforts are underway to add actively controlled actuators. Efforts are also under way to utilize a combination of kinematic measurements, electromyographic signals, and a brain-computer interface to reliably interpret the intent of the user. Semi-autonomous surgical robotics seeks to provide surgical capabilities in situations where a surgeon cannot be present, such as in remote areas or during spaceflight, particularly when telecommunications time-delay prohibits effective teleoperation. The methods being developed seek to utilize machine-learning and intelligent control methods to develop a framework for semi-autonomous surgery that can be applied to a wide range of surgical procedures. The current phase of work intends to demonstrate the capability by performing a semi-autonomous temporal bone drilling procedure, as a precursor to cochlear electrode implantation, on a human tissue phantom.