An artificial neural network estimation of gait balance control in the elderly using clinical evaluations.
The use of motion analysis to assess balance is essential for determining the underlying mechanisms of falls during dynamic activities. Clinicians evaluate patients using clinical examinations of static balance control, gait performance, cognition, and neuromuscular ability. Mapping these data to measures of dynamic balance control, and the subsequent categorization and identification of community dwelling elderly fallers at risk of falls in a quick and inexpensive manner is needed. The purpose of this study was to demonstrate that given clinical measures, an artificial neural network (ANN) could determine dynamic balance control, as defined by the interaction of the center of mass (CoM) with the base of support (BoS), during gait. Fifty-six elderly adults were included in this study. Using a feed-forward neural network with back propagation, combinations of five functional domains, the number of hidden layers and error goals were evaluated to determine the best parameters to assess dynamic balance control. Functional domain input parameters included subject characteristics, clinical examinations, cognitive performance, muscle strength, and clinical balance performance. The use of these functional domains demonstrated the ability to quickly converge to a solution, with the network learning the mapping within 5 epochs, when using up to 30 hidden nodes and an error goal of 0.001. The ability to correctly identify the interaction of the CoM with BoS demonstrated correlation values up to 0.89 (P<.001). On average, using all clinical measures, the ANN was able to estimate the dynamic CoM to BoS distance to within 1 cm and BoS area to within 75 cm2. Our results demonstrated that an ANN could be trained to map clinical variables to biomechanical measures of gait balance control. A neural network could provide physicians and patients with a cost effective means to identify dynamic balance issues and possible risk of falls from routinely collected clinical examinations.
Department of Human Physiology, University of Oregon, Eugene, Oregon, United States of America.
Vipul
V
Lugade
Rehabilitation Medicine Associates of Eugene-Springfield, P.C., Eugene, Oregon, United States of America.
Victor
V
Lin
Department of Computer and Information Sciences, University of Oregon, Eugene, Oregon, United States of America.
Arthur
A
Farley
Department of Human Physiology, University of Oregon, Eugene, Oregon, United States of America.
Li-Shan
LS
Chou
D000058:Accidental Falls / Q000517:prevention & control; D000368:Aged; D000369:Aged, 80 and over; D003071:Cognition; D005240:Feasibility Studies; D005260:Female; D005684:Gait; D015577:Geriatric Assessment; D034381:Hearing Loss; D006801:Humans; D008297:Male; D053580:Muscle Strength; D016571:Neural Networks, Computer; D004856:Postural Balance