Yasuharu KOIKE, Mitsuo KAWATO
Estimation of Dynamic Joint Torques
and Trajectory Formation from Surface
EMG Signals Using a Neural Network Model
Abstract:The human arm has at least seven degrees of freedom: the shoulder has three, the elbow has one and the wrist has three. The number of related muscles is about thirty. Quantitative dynamical models of the arm have been playing critical roles in developing recent computational theories of motor control.
Unfortunately, construction of a reliable quantitative model based just on reductionistic approach turned out quite difficult if not entirely impossible. We have focused on constructing a forward dynamics model (FDM) of human arm motion in the form of an artificial neural network while using physiological recordings of EMG signals and simultaneous measurement of movement trajectories. In previous studies we have already succeeded in: (1) estimating joint torques under isometric conditions in the horizontal plane (2) estimating four degrees-of-freedom posture in 3-D space and (3) estimating joint angular acceleration and reconstructing trajectories in the horizontal plane from surface EMG signals. In this paper, as the final step of our previous efforts, dynamic joint torques at the elbow and shoulder during movements in the horizontal plane are estimated from the surface EMG signals of 10 flexor and extensor muscles using a neural network model with a modular architecture.
Moreover different trajectories are reliably reconstructed only from the arm initial condition and the EMG time course using this network and Lagrangean equations of the arm dynamics. This is the first demonstration that multi-joint movements and posture maintenance can be quantitatively and accurately predicted from multiple surface EMG signals while including complicated via-point movements as well as co-contraction of muscles.
Keyword: EMG, Forward Dynamics Model, Muscle Model, Neural Network, Modular Learning