A Model for Detecting Balance Impairment and Estimating Falls Risk in the Elderly
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Traumatic falls are a prevalent and costly threat to elderly adults. Accurate risk assessment is necessary for reducing incidence of falls. The objective of this study was to test the feasibility of a balance impairment detection model using tasks of sample categorization and falls risk estimation. Model design included an artificial neural network and a statistical discrimination method. The first system produced an individual categorization value, which was then assessed in the second system for relative risk of falls, compared to a normative distribution of healthy elderly peers. Input data included leg muscle electromyographic amplitudes, temporal-distance measures of gait, and medio-lateral measures of whole body center of mass motion. These input data were compiled from a sample of healthy elderly adults (n = 19) and a sample with impaired balance (n = 10) to develop and test the model. Accuracy of sample categorization was assessed using a relative operating characteristic (ROC) value. For relative risk estimation, categorical delineation of risk level was adopted. Sample categorization results reached ROC values of 0.890. Relative risk was frequently assessed at high or very high risk for experiencing falls. Temporal-distance measures were most influential in categorization accuracy, producing the most consistent risk estimates. Combined inputs further improved model performance. This model shows potential for detecting balance impairment and estimating falls risk; thereby indicating need for referral for falls prevention intervention.
KeywordsArtificial neural network Sample categorization Relative Risk Prevention
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