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Biol Cybern. 2007 Mar;96(3):279-307. Epub 2006 Nov 24.

A model of cerebrocerebello-spinomuscular interaction in the sagittal control of human walking.

Author information

1
Department of Electrical Engineering and Computer Science, Computer Science and Artificial Intelligence Laboratory, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. shjo@csail.mit.edu

Abstract

A computationally developed model of human upright balance control (Jo and Massaquoi on Biol cybern 91:188-202, 2004) has been enhanced to describe biped walking in the sagittal plane. The model incorporates (a) non-linear muscle mechanics having activation level -dependent impedance, (b) scheduled cerebrocerebellar interaction for control of center of mass position and trunk pitch angle, (c) rectangular pulse-like feedforward commands from a brainstem/ spinal pattern generator, and (d) segmental reflex modulation of muscular synergies to refine inter-joint coordination. The model can stand when muscles around the ankle are coactivated. When trigger signals activate, the model transitions from standing still to walking at 1.5 m/s. Simulated natural walking displays none of seven pathological gait features. The model can simulate different walking speeds by tuning the amplitude and frequency in spinal pattern generator. The walking is stable against forward and backward pushes of up to 70 and 75 N, respectively, and with sudden changes in trunk mass of up to 18%. The sensitivity of the model to changes in neural parameters and the predicted behavioral results of simulated neural system lesions are examined. The deficit gait simulations may be useful to support the functional and anatomical correspondences of the model. The model demonstrates that basic human-like walking can be achieved by a hierarchical structure of stabilized-long loop feedback and synergy-mediated feedforward controls. In particular, internal models of body dynamics are not required.

PMID:
17124602
DOI:
10.1007/s00422-006-0126-0
[Indexed for MEDLINE]

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