Katie Fitzsimons

Robotics and haptics have the potential to enhance human performance and learning as well as provide unique insight into neuromotor function through sensing and quantification of human motion. At the same time, human behavior can inform the development of control strategies for complex tasks and human-robot interactions. The methods used for evaluation of motion greatly influences our ability to recognize the effects of assistance and training from a statistical standpoint, but more importantly, the mathematical structure imposed by unique measures of motion quality has significant impact on the algorithmic tools that are available to manage the interactions between robots and humans. My research aims to study alternatives to traditional measures of motion (e.g., energy or error) for quanitifying motion quality and synthesizing controls during physical human-robot interaction.  To see my full CV, click here

M.S. Mechanical Engineering Northwestern University 2017

B.S. Mechanical Engineering Michigan State University 2013