Abstract
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.
Highlights
Over one third of adults over the age of 65 will fall each year [1]
When 5 hidden nodes were used, much greater time was needed for the solution to converge to an mean squared error (MSE) error of less than 0.01 or 0.001, with much of the samples reaching the maximum limit of 500 epochs before failing to reach the goal (Figure 2)
The input type by error goal by hidden nodes interaction was not detected for the center of mass (CoM)-base of support (BoS) (P = .849), CoMv-BoS (P = .877) or BoS Area (P = .477) correlations
Summary
Over one third of adults over the age of 65 will fall each year [1]. Falls are associated with injury and morbidity, and reductions in physical, psychological, and social capacities [2,3]. Falls in the elderly are a complicated phenomenon comprising multifactoral risk factors, including both intrinsic and extrinsic issues [4]. Prior studies have shown that decreased lowerextremity muscle strength and cognitive function are significant predictors of falls among older adults [4,5,6]. Extrinsic factors, or those pertaining to environmental hazards, contribute significantly to fall incidents and can include objects to trip over, poor lighting, slippery surfaces, or inappropriate furniture [3]. The ability to understand which of the multitude of neuromuscular, cognitive, and sensory factors most contribute to balance control ability during gait can provide further ability to diagnose and treat elderly at risk of falling
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
