Artificial neural networks and center-of-pressure modeling: a practical method for sensorimotor-degradation assessment.
Numerous methods for studying the prevention of falls and age-related sensorimotor degradation have been proposed and tested. Some approaches are too impractical to use with seniors or too expensive for practitioners. Practitioners desire a simple, reliable technique. The goals of this research were to develop such an approach and to apply it in exploring the effect of Tai Chi on age-related sensorimotor degradation. The method employed artificial-neural-network (ANN) models trained by using individuals' center-of-pressure (COP) measurements and age. Ninety-six White and Chinese adults without Tai Chi training were tested. In contrast, a third group, Chinese seniors with Tai Chi training, was tested to ascertain any influence from Tai Chi on sensorimotor aging. This study supported ANN technology with COP data as a feasible tool in the exploration of sensorimotor degradation and demonstrated that Tai Chi slowed down the effects of sensorimotor aging.
Shan
Gongbing
G
Department of Kinesiology, University of Lethbridge, Alberta, Canada.
Daniels
Dayna
D
Gu
Rongri
R
D000058:Accidental Falls / Q000517:prevention & control; D000328:Adult; D000367:Age Factors; D000368:Aged; D000375:Aging / Q000502:physiology*; D000416:Alberta; D044466:Asian People / Q000706:statistics & numerical data; D003198:Computer Simulation; D006801:Humans; D008875:Middle Aged; D016571:Neural Networks, Computer; D004856:Postural Balance / Q000502:physiology*; D011434:Proprioception / Q000502:physiology*; D020886:Somatosensory Disorders / Q000208:ethnology / Q000503:physiopathology; D026302:Tai Ji; D044465:White People / Q000706:statistics & numerical data