Advisor:  Lena Ting - Biomedical Engineering, GA Tech

Committee Members:  Tom Burkholder – Applied Physiology, GA Tech, Daniel Goldman – Physics, GA Tech, Karen Liu – Computer Science, GA Tech and Randy Trumbower – Rehabilitation Medicine, Emory

In order to move and balance in every-day life requires stable interaction between the neural, muscular and skeletal systems of our bodies. However, little is known about how these systems interact to stabilize the body in the presence of the inherent constraints of muscle properties, skeletal geometry and neural conduction delays.  These interactions offer a vast number of redundant strategies for stable behavior and likely impose trade-offs in selecting a particular neural strategy or postural configuration to achieve stability. My goal is to develop simple predictive models of human movement to investigate the interactions between neural and musculoskeletal systems necessary for motor control.  

I will use the behavior of frontal plane motion of human standing balance as a paradigm to investigate the changes required in the nervous and musculoskeletal systems to compensate for changes in stance width and neural feedback. The aims of this study are to predict and validate changes in stability and to quantify the trade-offs in energy and stability for different stance-widths and feedback delays during postural perturbations. A model will be developed for mathematical analysis of stability and simulation of frontal plane motion. Experiments will be performed on old and young subjects to validate the model and test model predictions.

To better understand both healthy and neurologically-impaired subjects, this research proposes to develop models validated by experimental data to quantify the interactions and contributions of nervous and musculoskeletal systems to the stability of human standing balance. The results may be useful in developing quantitative measures of standing stability and help to explain the consequences of various neuromusculoskeletal deficits. Ultimately, this framework could be used to diagnose motor control deficits, develop improved prosthetics and aid in the engineering of legged robots.