Literature DB >> 17124602

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

Sungho Jo1, Steve G Massaquoi.   

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.

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Year:  2006        PMID: 17124602     DOI: 10.1007/s00422-006-0126-0

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  10 in total

1.  Hypothetical neural control of human bipedal walking with voluntary modulation.

Authors:  Sungho Jo
Journal:  Med Biol Eng Comput       Date:  2007-11-03       Impact factor: 2.602

2.  Decoding intra-limb and inter-limb kinematics during treadmill walking from scalp electroencephalographic (EEG) signals.

Authors:  Alessandro Presacco; Larry W Forrester; Jose L Contreras-Vidal
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2012-03       Impact factor: 3.802

3.  Performance Limitations in Sensorimotor Control: Trade-Offs Between Neural Computation and Accuracy in Tracking Fast Movements.

Authors:  Shreya Saxena; Sridevi V Sarma; Munther Dahleh
Journal:  Neural Comput       Date:  2020-03-18       Impact factor: 2.026

4.  Adaptive hindlimb split-belt treadmill walking in rats by controlling basic muscle activation patterns via phase resetting.

Authors:  Soichiro Fujiki; Shinya Aoi; Tetsuro Funato; Yota Sato; Kazuo Tsuchiya; Dai Yanagihara
Journal:  Sci Rep       Date:  2018-11-26       Impact factor: 4.379

5.  Neuromusculoskeletal model that walks and runs across a speed range with a few motor control parameter changes based on the muscle synergy hypothesis.

Authors:  Shinya Aoi; Tomohiro Ohashi; Ryoko Bamba; Soichiro Fujiki; Daiki Tamura; Tetsuro Funato; Kei Senda; Yury Ivanenko; Kazuo Tsuchiya
Journal:  Sci Rep       Date:  2019-01-23       Impact factor: 4.379

6.  A Pathological Condition Affects Motor Modules in a Bipedal Locomotion Model.

Authors:  Daisuke Ichimura; Tadashi Yamazaki
Journal:  Front Neurorobot       Date:  2019-09-20       Impact factor: 2.650

7.  Variant and Invariant Spatiotemporal Structures in Kinematic Coordination to Regulate Speed During Walking and Running.

Authors:  Hiroko Oshima; Shinya Aoi; Tetsuro Funato; Nobutaka Tsujiuchi; Kazuo Tsuchiya
Journal:  Front Comput Neurosci       Date:  2019-09-20       Impact factor: 2.380

8.  Forward dynamic simulation of Japanese macaque bipedal locomotion demonstrates better energetic economy in a virtualised plantigrade posture.

Authors:  Hideki Oku; Naohiko Ide; Naomichi Ogihara
Journal:  Commun Biol       Date:  2021-03-08

Review 9.  Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation.

Authors:  Seungmoon Song; Łukasz Kidziński; Xue Bin Peng; Carmichael Ong; Jennifer Hicks; Sergey Levine; Christopher G Atkeson; Scott L Delp
Journal:  J Neuroeng Rehabil       Date:  2021-08-16       Impact factor: 4.262

10.  Evaluation of a Neuromechanical Walking Control Model Using Disturbance Experiments.

Authors:  Seungmoon Song; Hartmut Geyer
Journal:  Front Comput Neurosci       Date:  2017-03-14       Impact factor: 2.380

  10 in total

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