Author(s)

G. Shan & X. Zhang

Abstract

As the population ages, falls are becoming a major health problem, not only for those with some degree of balance/mobility impairment, but also among healthy active seniors. Previous studies suggest that the degradation of human sensorimotor function related to age contributes to falls. The aim of this research was to develop a practical solution for quantifying the age-related sensori-motor degradation. Therefore, a new testing apparatus, the Dynamic Balance Testing Platform (DBTP), was developed for quantifying sensori-motor decline using Artificial Neural Network (ANN) modeling. The DBTP consists of two layers – an immobile base and a mobile perturbation layer. The perturbation layer is balanced on a single center pivot; each of the four corners is supported by an air cylinder, with vertically downwards by a maximum of 2.5 cm. The platform was designed to reflect the natural oscillations in a subject’s center of gravity (COG). Shifts in COG positions altered the load on each of the four supporting air cylinders, leading to measurable vertical displacements. The DBTP captures the subject’s ability to balance dynamically on a single point of support. An ANN model, trained by 60 subjects’ data (age: 18–65+), was developed for the DBTP. Seniors with and without Tai-Chi training were used to contrast any influence from Tai-Chi on the sensori-motor aging. The result demonstrated that Tai-Chi slowed down the effects of sensori-motor aging. This study validated that DBTP is a feasible tool in aging exercise studies. Keywords: sensori-motor, degradation, ANN modeling, COG, dynamic balance, training effects.

Keywords

Keywords: sensori-motor, degradation, ANN modeling, COG, dynamic balance, training effects.