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A systems-theoretic analysis of low-level human motor control: application to a single-joint arm model

  • Stefanie Brändle
  • Syn Schmitt
  • Matthias A. MüllerEmail author
Article
  • 36 Downloads

Abstract

Continuous control using internal models appears to be quite straightforward explaining human motor control. However, it demands both, a high computational effort and a high model preciseness as the whole trajectory needs to be converted. Intermittent control shows great promise for avoiding these drawbacks of continuous control, at least to a certain extent. In this contribution, we study intermittency at the motoneuron level. We ask: how many different, but constant muscle stimulation sets are necessary to generate a stable movement for a specific motor task? Intermittent control, in our perspective, can be assumed only if the number of transitions is relatively small. As application case, a single-joint arm movement is considered. The muscle contraction dynamics is described by a Hill-type muscle model, for the muscle activation dynamics both Hatze’s and Zajac’s approach are considered. To actuate the lower arm, up to four muscle groups are implemented. A systems-theoretic approach is used to find the smallest number of transitions between constant stimulation sets. A method for a stability analysis of human motion is presented. A Lyapunov function candidate is specified. Thanks to sum-of-squares methods, the presented procedure is generally applicable and computationally feasible. The region-of-attraction of a transition point, and the number of transitions necessary to perform stable arm movements are estimated. The results support the intermittent control theory on this level of motor control, because only very few transitions are necessary.

Keywords

Human motor control Intermittent control Stability analysis Sum-of-squares methods Region-of-attraction estimation Nonlinear and nonpolynomial system dynamics 

Mathematics Subject Classiication

92B99 

Notes

References

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Digital Powertrain Development, Mercedes-Benz AGStuttgartGermany
  2. 2.Institute for Modelling and Simulation of Biomechanical SystemsUniversity of StuttgartStuttgartGermany
  3. 3.Stuttgart Center for Simulation ScienceUniversity of StuttgartStuttgartGermany
  4. 4.Institute of Automatic ControlLeibnitz University HannoverHannoverGermany

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