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Katie FitzsimonsPhD StudentAdviser: Todd Murphey

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.

MS Mechanical Engineering, Northwestern University, 2017

BS Mechanical Engineering, Michigan State University, 2013

TA for Active Learning in Robotics (ME495), Spring 2018

Grader for Machine Dynamics (ME 314), Fall 2015, Fall 2016, Fall 2017, Fall 2018

Grader for Mechanics of Sports (ME 360) Spring 2017, Spring 2018

Berrueta, T., A. Pervan, K. Fitzsimons, and T. D. Murphey, "Dynamical System Segmentation for Information Measures in Motion", IEEE Robotics and Automation Letters, vol. 4, issue 1, pp. 169 - 176, 01/2019. Google Scholar

Fitzsimons, K., A. Maria Acosta, J. Dewald, and T. D. Murphey, "Ergodicity reveals assistance and learning from physical human-robot interaction", Science: Robotics, vol. 4, issue 29, 04/2019. Google Scholar

Kalinowska, A., K. Fitzsimons, J. Dewald, and T. D. Murphey, "Online User Assessment for Minimal Intervention During Task-Based Robotic Assistance", Robotics: Science & Systems, 06/2018. Google Scholar

Fitzsimons, K., E. Tzorakoleftherakis, and T. D. Murphey, "Optimal Human-In-The-Loop Interfaces Based on Maxwell's Demon", American Control Conference (ACC), Boston, MA, pp. 4397-4402, 07/2016. Google Scholar

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