Fusion of Clinical, Self-Reported, and Multisensor Data for Predicting Falls

Abstract

Falls are among the frequent causes of the loss of mobility and independence in the elderly population. Given the global population aging, new strategies for predicting falls are required to reduce the number of their occurrences. In this study, a multifactorial screening protocol was applied to 281 community-dwelling adults aged over 65, and their 12-month prospective falls were annotated. Clinical and self-reported data, along with data from instrumented functional tests, involving inertial sensors and a pressure platform, were fused using early, late, and slow fusion approaches. For the early and late fusion, a classification pipeline was designed employing stratified sampling for the generation of the training and test sets. Grid search with cross-validation was used to optimize a set of feature selectors and classifiers. According to the slow fusion approach, each data source was mixed in the middle layers of a multilayer perceptron. The three studied fusion approaches yielded similar results for the majority of the metrics. However, if recall is considered to be more important than specificity, then the result of the late fusion approach providing a recall of 78.6 is better compared with the results achieved by the other two approaches.