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Abstract
Falls among the elderly population are a major cause of morbidity and mortality. Approximately one in three people, over the age of 65, fall each year. Falls result in a reduction in one's overall quality of life, not only as a result of injuries, but from a restriction in activity due to a fear of falling and loss of independence. Falls are a leading cause of hospitalisation among the elderly and place a significant burden on healthcare systems. Validated clinical tests and associated models, built upon assessment of functional ability, have been devised to estimate an individual's risk of falling in the near future. Those identified as at-risk of falling may be targeted for interventative treatment. The migration of these clinical models estimating falls risk to a surrogate technique, for use in the unsupervised environment, might broaden the reach of falls risk screening beyond the clinical arena. This study details an approach which characterises the movements of 68 elderly subjects performing a directed routine of unsupervised physical tasks. The movement characterisation is achieved through the use of a single tri-axial accelerometer-based ambulatory monitor attached to the waist. A number of falls related features, extracted from the accelerometry signals, combined with a linear least squares model, maps to a clinically validated measure of falls risk with a correlation of ρ = 0.80 (p < 0.001). The extracted features were also mapped to the scores obtained from assessment of knee-extension strength, body sway, edge contrast sensitivity and proprioception, with correlations of ρ = 0.65 (p < 0.001), ρ = 0.58 (p < 0.001), ρ = 0.46 (p < 0.001) and ρ = 0.30 (p < 0.05), respectively. The results show the potential of body-worn sensors to evaluate falls risk and falls risk factors in an objective and deterministic manner. The unsupervised assessment enables falls risk to be tracked longitudinally, opening up opportunities for the improvement management of falls in the elderly.