Paper Info
Title
A model of the neuro-musculo-skeletal system for anticipatory adjustment of human locomotion during obstacle avoidance.
Abstract
Theoretical studies on human locomotion have shown that a stable and flexible gait emerges from the dynamic interaction between the rhythmic activity of a neural system composed of a neural rhythm generator (RG) and the rhythmic movement of the musculo-skeletal system. This study further explores the mechanism of the anticipatory control of locomotion based on the emergent properties of a neural system that generates the basic pattern of gait. A model of the neuro-musculo-skeletal system to execute the task of stepping over a visible obstacle with both limbs during walking is described. The RG in the neural system was combined with a system referred to as a discrete movement generator (DM), which receives both the output of the RG and visual information regarding the obstacle and generates discrete signals for modification of the basic gait pattern. A series of computer simulations demonstrated that an obstacle placed at an arbitrary position can be cleared by sequential modifications of gait: (1) modulating the step length when approaching the obstacle and (2) modifying the trajectory of the swing limbs while stepping over it. This result suggests that anticipatory adjustments are produced not by the unidirectional flow of the information from visual signals to motor commands but by the bi-directional circulation of information between the DM and the RG. The validity of this model is discussed in relation to motor cortical activity during anticipatory modifications in cats and the ecological psychology of visuo-motor control in humans.
Year
DOI
Venue
1998
10.1007/s004220050408
Biological Cybernetics
Keywords
Field
DocType
Neural System,Obstacle Avoidance,Motor Command,Gait Pattern,Rhythmic Movement
Obstacle avoidance,Human locomotion,Obstacle,Gait,Control theory,Psychology,Neural system,Rhythm,Trajectory,Swing
Journal
Volume
Issue
ISSN
78
1
0340-1200
Citations 
PageRank 
References 
68
6.36
2
Authors
1
Name
Order
Citations
PageRank
Gentaro Taga134241.30