Abstract:
:In this study, human arm movement was reconstructed from electromyography (EMG) signals using a forward dynamics model acquired by an artificial neural network within a modular architecture. Dynamic joint torques at the elbow and shoulder were estimated for movements in the horizontal plane from the surface EMG signals of 10 flexor and extensor muscles. Using only the initial conditions of the arm and the EMG time course as input, the network reliably reconstructed a variety of movement trajectories. The results demonstrate that posture maintenance and multijoint movements, entailing complex via-point specification and co-contraction of muscles, can be accurately computed from multiple surface EMG signals. In addition to the model's empirical uses, such as calculation of arm stiffness during motion, it allows evaluation of hypothesized computational mechanisms of the central nervous system such as virtual trajectory control and optimal trajectory planning.
journal_name
Biol Cybernjournal_title
Biological cyberneticsauthors
Koike Y,Kawato Mdoi
10.1007/BF00199465subject
Has Abstractpub_date
1995-09-01 00:00:00pages
291-300issue
4eissn
0340-1200issn
1432-0770journal_volume
73pub_type
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journal_title:Biological cybernetics
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