Research Hain

*Research*
PREDICTIVE MOTOR CONTROL OF THE HEAD AND NECK
	Timothy C. Hain, MD Professor of Neurology, Otolaryngology & PT MD, University of Illinois-Chicago Board certified in Clinical Neurology BS, University of Illinois-Urbana	Contact Information Phone#: (312) 503-5167 Fax#: (773) 373-0294 Email: t-hain@northwestern.edu
In other words, in redundant control situations, you don't find "the solution", but you just find one acceptable solution where behavioral goals are met.
This page describes the approach of a lab studying predictive motor control of the head and trunk. It reflects work done by Dr. Wynne Lee (presently retired), and Dr. Timothy Hain, at Northwestern University in Chicago Illinois. We also collaborate with Dr. Emily Keshner and Dr. Barry Peterson in the NU dept. Physiology, using similar methodology to study reflex control.
Introduction Stabilization of the head and neck is a "mission critical" function. The head is a platform for the eyes and ears, and controlling it's motion is critical to interpreting information from these senses. When the head is uncontrolled (such as in a motor vehicle accident), serious injury may result. The head and neck in the upright position are intrinsically unstable and would fall over without stabilization. Critical biological systems are generally controlled by redundant processes. Consider for example, control of a nuclear power plant. For the head, it is easy to point out several control interactive systems:
biomechanical - inertia, stiffness, viscosity reflex feedback - vestibular, somatosensory and ocular reflexes		voluntary control predictive and feedforward control
These four types of control differ critically in their timing with respect to a perturbation:
pre-perturbation - prediction or feedforward biomechanical - at onset		reflex feedback - 30msec earliest voluntary - 100msec earliest
Thus for situations where biomechanical controls are inadequate, predictive strategies are obviously optimal. The long delay involved in voluntary control means that it will be ineffective for many situations. What might prediction accomplish ? There are quite a few possibilities:
predictive torque predictive modulation of biomechanics (stiffness or biomechanics)		predicitive modulation of reflexes (i.e enhance or diminish VCR, CCR, OCR) predicitive modulation of voluntary responses (speed up or change the size or duration of voluntary responses)
Let us consider the situation where the head is being pulled backward by a pulley attached to a weight, and at some time, the weight is dropped (under control of the subject). Clearly one might use any one or a combination of the above mechanisms to stabilize the head. One might even try several out until the most effective one were find. This might result in considerable variability in performance until a methodology for stabilization of the head is found by the subject. This example points out two important things related to redundant control:
Multiple solutions, or families of solutions are expected		Inter and intrasubject variability can be expected
In other words, in redundant control situations, you don't find "the solution", but you just find one acceptable solution where behavioral goals are met.
How do you study redundant interactive systems ? Our general approach is to use control system engineering techniques (mathematical modeling) to simulate our data. In general, we set up a redundant control system incorporating what is known about biomechanics, and sensory feedback systems. Generally, we use Matlab/Simulink to implement the system. For an experimental dataset, we find an optimal solution by varying parameters. We then explore the error surface to see if there are families or relationships between parameters that provide equally good solutions. Our current experimental paradigm is to use a high-performance linear sled to move seated persons on a track. The input in this situation is linear sled motion. As an output, we measure head position with rate sensors and linear acceleration sensors. This provides us the linear and angular position of the head, trunk and neck. By comparing output for predictable and unpredictable motion, we can infer differences in control.		FUNDING National Institutes of Health, NINDS, NIDCD Linear sled
References from the Hain/Lee lab: Bedford, D.B., Steege, J.W., Lee, W.A. (2000) Effects of vision on head stability and torques during voluntary trunk movements. Neuroscience Letters, 282, 9-12. Chang, A. H., Lee, W.A., Patton, J. (2000) Practice-related changes in lumbar torque on a standing pull task. Clinical Biomechanics, 15, 726-735. Keshner EA, Hain TC, Chen KJ. Predicting control mechanisms for human head stabilization by altering the passive mechanics. J. Vest. Research. Patton, J.L., Pai, Y.-C., Lee, W.A. (1999) Evaluation of a model for assessing dynamic stability during balanced movements. Posture and Gait, 9, 38-49. Patton, J.L., Pai, Y-C., Lee, W.A. (2000). Effects of practice on dynamic balance stability margins during multijoint pulling. Experimental Brain Research, 135:117-126. Peng GCY, Hain TC, Peterson BW: A dynamical model for reflex activated head movements in the horizontal plane. Biological Cybernetics, 75, 309-319, 1996. Model of Head Motor Control (yaw), 1996. Peng CGY, Hain TC, Peterson BW. Predictions of vestibulo-collic (VCR) and cervico-collic (CCR) reflex contributions to head stability during trunk perturbations in the horizontal plane. IEEE Transactions in BME, 1999

Timothy C. Hain, MD

Contact Information

FUNDING