Development of a postural control model for balance control

• Main Author: Teo, Wen Qi.
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Final Year Project
• Language: English
• Published: 2010
• Subjects: DRNTU::Engineering::Mathematics and analysis::Simulations
• Online Access: http://hdl.handle.net/10356/40496
• Institution: Nanyang Technological University

The research focuses on the understanding and investigation of balance control which may be useful in preventing unnecessary fall-related injuries or incidents. The objective of this study was to predict balance control behaviors characterized by center-of-pressure (COP)-based measures. A mathematical model for balance control was developed and analyzed in comparison with previous studies. The study of balance control may serve as prevention towards unnecessary fall-related injuries and understanding of the postural control system. Factors like old age and external loads have contributed favorably to the instability of balance control. As COP-based measures are associated to the behaviors during upright stance, the proposed model was able to deal with the changes in these COP-based measures. Simultaneously by analyzing the changes, the chances of recognizing the postural control mechanisms were made possible. The study has verified that the proposed model was capable of providing probable mechanisms as to how the postural control system maintains its upright balance. Furthermore, human sway behaviors were accurately simulated with these models using the Matlab Simulink program to create a balance control model based on the human postural system. The main components of the control model are the proportional integral derivative (PID) controller, neural controller, noise source and feedback time delay. The PID controller was employed because it is capable of compensating for the output signal by the proportional, integral, and derivation function of the error signal. An optimization procedure had been carried out to minimize the cost function or errors that may exist between the sway measures and the model parameters. The results of the procedure and simulations were analyzed and compared to previous findings. The results from the simulations indicate that the 9 sway measures investigated had a relatively high correlation with each other. Also, it is concluded that stiffness, damping and the amount of noise level play an important role in determining the sway measures and hence the minute sway behaviors observed in the human subjects.