human subject performing movements, interacting with a computer-controlled haptic interface producing force fields during motion

Impedance Adaptive Controller of the Human Arm

This project is done together with Rieko Osu and Mitsuo Kawato, Erato and ATR, Japan, Ted Milner and Dave Franklin, Simon Fraser University, Canada, and Yasuharu Koike, Tokyo Institute of Technology. We infer the neuro-mechanical control of the human arm by performing experiments using EMG, computer-controlled haptic and visual interfaces, and by modelling this control using biomechanics, control theory and artificial neural networks.

Shadmehr&Mussa-Ivaldi [1] have analyzed motor adaptation when arm movements are repeated in a velocity dependent force field. We have measured stiffness [2] in these conditions and shown that in this case stiffness depends mainly on the torque produced by muscles [3], similar to the postural case [4]. This would mean that endpoint stiffness is just is a byproduct of muscle mechanics. However, we were recently able to show a highly significant increase of stiffness in a divergent elastic force field [5]. This means that humans are able to control the endpoint impedance to perform stable movements in unstable environment dynamics. This result was predicted by Hogan in 1985 [6], but previous experiments to confirm it all failed.

In summary, the human arm controller seems to be composed of a learning controller with Internal Dynamic Model (IDM) and adaptive impedance. The learning mechanims seem to be completely different in both cases: The IDM would be formed automatically, while adaptation of impedance still requires attention even after learning. We are currently analyzing the differences between these two kind of learning using EMG. We will design psychophysiological experiments to find out the features of these two distinct learning processes, and the role both have for interacting with arbitrary dynamic environments. While learning of an IDM is probably processed by supervised learning in the Cerebellum, adaptation of the endpoint stiffness may be performed by reinforcement learning in the Basal Ganglia and Cerebral Cortex.

We also develop two kinds of computational models:


[1]    R. Shadmehr and F.A. Mussa-Ivaldi (1994) Adaptive Representation of Dynamics During Learning of a Motor Task, Journal of Neuroscience 14 (5)
[2]    E. Burdet, R. Osu, D. Franklin, T.E. Milner and M. Kawato (1999) A Method for Measuring Hand Stiffness during Multi-joint Arm Movements. Subm. to Journal of Biomechanics
[3]    E. Burdet, R. Osu, T.E. Milner and M. Kawato (2000) Measuring stiffness during arm movements in various dynamic environments, in preparation. Also: Proc. of the 1999 ASME Annual Symposium on Haptic Interfaces and Virtual Environments for Teleoperator Systems, Nashville, USA
[4]    H. Gomi and R.Osu (1998) Task-Dependent viscoelasticity of human multijoint arm and Its Spatial Characteristics for Interaction with Environments, The Journal of Neuroscience 18(21): 8965-8978.
[5]    E. Burdet, R. Osu, T.E. Milner and M. Kawato (2000) Voluntary Adaptation of Muscle Impedance to Unstable Dynamics (in preparation)
[6]    N. Hogan (1985) The Mechanics of Multi-joint Posture and Movement Control, Biological Cybernetics 52: 315-331
[7]    E. Burdet, KP Tee, CM Chew, J Peters and V Loo (2001) Hybrid IDM/Impedance in Human Movements, Proc. of The First International Symposium on Measurement, Analysis and Modeling of Human Functions.
[8]    Y. Koike and M. Kawato (1995) Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biological Cybernetics 73: 291-300

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March 2001, by Etienne Burdet