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Research Abstracts - 2006
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Hierarchical Neural Control of Human Sagittal Balance and Bipedal Walking

Sungho Jo & Steve Massaquoi


We propose and test a hierarchical neural model to explain human postural balance and walking.


Improved human control models could allow better understanding of the human sensorimotor control system and contribute to the design of devices to enhance human function or advanced robotic performance. There is interest in especially natural control of human standing balance and walking. Humans seem to use specific postural strategies for upright balance or efficient walking algorithm to cope with any environment. Moreover, the cerebellum is understood to play a critical role in upright stability. Analyzing human movements in terms of neurobiological systems by an improved human control model would help establish a connection between the structure and function of the human sensorimotor control system. A recurrent integrator proportional integral derivative model has been used to account for cerebrocerebellar stabilization and scaling of transcortical proprioceptive feedback in the control of horizontal planar arm movements [1]. It may be possible to extend the mode for reaching arm movements to explain postural balance.

Previous work

A recently developed model of human upright balance control [2][3] has been enhanced to describe sagittal planar walking.  The model incorporates non-linear muscle mechanics having activation level –dependent impedance, scheduled cerebro- and vestibulo-cerebellar interaction for control of center of mass position and trunk pitch angle, scaled square pulse-like feedforward commands from a brainstem/spinal pattern generator, and segmental reflex modulation of muscular synergies to refine inter-joint coordination.  The brainstem/spinomuscular control component incorporates two hierarchical levels. The lower level implements independent control of each leg.  The upper level implements the bipedal alternation of four state control of stance phase with a passive swing phase. The model can transition from standstill to walking at 1.21 m/s. Simulated natural walking displays none of seven sagittal plane pathological gait features. And simulated neural lesions result in several of pathological gait. The walking is stable to modest pushes in the forward and backward directions at most up to 70 and 75 N, respectively, and to sudden changes in trunk mass up to 18.5%.  The model shows that control of basic human-like walking can be achieved using stabilized-long loop feedback, and rudimentary, hierarchical feedforward, synergy-mediated control.  In particular, internal models of body dynamics are not required. The reproduction of basic clinical gait deficits supports the model’s proposed functional-anatomical correspondences.


To verify and extend the above preliminary findings, kinematic and electromyographic data is culled from the physiological and bioengineering literature. The parameters in the model are tuned empirically to fit the available data as closely as possible. Then the model is subjected novel disturbances (of greater or lesser magnitude) to determine whether the model is generically stable, and whether the behavior and electromyographic signals remain physiologically realistic. The sensitivity of performance to various specific parameter changes is explored. Further extension of the model including switching control and adaptive control principles are explored as well


The current line of investigation is valuable for practical development of bio-machines as well as scientific contribution to brain research. Verified control models will attain multidisiplinary contribution.

Research Support

This work has been supproted financially by NSF KDI IBN-9873478. We receive collegial support from other research groups of the MIT-Harvard HST NeuroEngineering Research Collaborative http://hst.harvard.edu/nerc/of which our research group is a member.


[1] Massaquoi S.G. Modelling the function of the cerebellum in scheduled linear servo control of simple horizontal planar arm movements Electrical Engineering and Computer Science, MIT, Feb. 1999.

[2] Jo S. A model of cerebellum-mediated long-loop control of upright balance. Society for Neuroscience 32nd annual meeting, Orlando, FL, 2002.

[3] Jo S, Massaquoi S.G., A model of cerebellum stabilized and scheduled hybrid long-loop control of upright balance, Biol Cybern, 91, 188-202, 2004.  

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