Predicting Human Motor Response to Expected and Unexpected Loads

The human motor system achieves its goals working through an actuator (muscle) that can hardly be considered ideal in traditional engineering terms: it functions neither as a good motion source nor a good force source. But somehow humans cope with constantly bending under load and apparently even embrace the challenges presented by a body dynamically coupled to the environment. Reverse-engineering the human motor system has many aims and applications, including design and control of more compliant, energy-efficient, and safe robots. To date, conceptions of human motor control have been developed to describe responses to expected loads or unexpected loads, but not both. In this talk I will describe the outcome of collaborative work with Jim Freudenberg, Jeff Cook, and our recently minted co-advised PhD Bo Yu in which we developed a model to describe how humans grasp and lift objects of known and unknown weights. The motor command is embodied in the trajectory of a motion source while the biomechanics of muscle and tissue are captured in an impedance that intervenes between the motion source and driving point. We first set parameters of the backdrive impedance using a system identification experiment, then formulate an input estimation problem to produce trajectories of the feedforward motion source. The elaborated model is able to predict driving point trajectories under both expected and unexpected loads.
R. Brent Gillespie received the B.S. degree in mechanical engineering from the University of California, Davis, in 1986, the M.M. degree in music (piano performance) from the San Francisco Conservatory of Music, San Francisco, CA, in 1989, and the M.S. and Ph.D. degrees in mechanical engineering from Stanford University, Stanford, CA, in 1992 and 1996. He is currently with the Department of Mechanical Engineering, University of Michigan, Ann Arbor. His current research interests include haptic interface and teleoperator control, human motor control, and robot-assisted rehabilitation after neurological injury.