2011 Poster Sessions : Reconstruction and Analysis of Task-Driven Dynamic Motions in Human Musculoskeletal Systems

Student Name : Emel Demircan
Advisor : Oussama Khatib
Research Areas: Artificial Intelligence
Understanding human motor control involves studying the principles used to optimize dynamic movement. Robotics-based approach of exploring natural human skills provides generalized motion analysis techniques and physiologically accurate performance predictions applicable in the domains of sport medicine, rehabilitation and physical therapy. This process requires accurate modeling and simulation of musculoskeletal kinematics, reconstruction of motion dynamics and analysis of elite performer motor skills. Recently, we demonstrated that the task-based control approach can efficiently reconstruct a subject’s motion dynamics in real world task-space when given a scaled model and marker based motion capture data. Our marker space reconstruction methodology provides the full motion dynamics by operating in marker space and automatically resolving the kinematic constraints of the markers. This control framework is used to analyze high performance human motion such as that of elite football players and golfers. Task-driven human performance criteria, including the muscular effort expenditure, the feasible set of operational space accelerations and the torque sensitivity as a function of joint range of motion are implemented to characterize these athletic skills based on the musculoskeletal physiology. Our findings suggest that task-based control approach is an attractive tool to reconstruct and analyze the dynamic motion of humans and other complex articulated body systems in a computationally efficient manner.

Emel Demircan is a Mechanical Engineering PhD student in Professor Oussama Khatib's robotics group within Artificial Intelligence Laboratory. Her research focuses on the application of dynamics and control theory for the simulation and analysis of biomechanical and robotic systems. She is broadly interested in human motion analysis to investigate high performance skills for athletic training and performance improvement and rehabilitation of patients with impaired sensory-motor function. Emel received her MS('07) in Mechanical Engineering from Stanford University and BS('06) in Mechanical Engineering and Industrial Engineering from Bogazici University.