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Devices and sensors for identification of fallers can be used to implementactions to prevent falls and to allow the elderly to live an independent lifewhile reducing the long-term care costs. In this study we aimed to investigatethe accuracy of Timed Up and Go test, for fallers' identification, using fusionof features extracted from accelerometer data. Single and dual tasks TUG(manual and cognitive) were performed by a final sample (94% power) of 36community dwelling healthy older persons (18 fallers paired with 18non-fallers) while they wear a single triaxial accelerometer at waist withsampling rate of 200Hz. The segmentation of the TUG different trials and itscomparative analysis allows to better discriminate fallers from non-fallers,while conventional functional tests fail to do so. In addition, we show thatthe fusion of features improve the discrimination power, achieving AUC of 0.84(Sensitivity=Specificity=0.83, 95% CI 0.62-0.91), and demonstrating theclinical relevance of the study. We concluded that features extracted fromsegmented TUG trials acquired with dual tasks has potential to improveperformance when identifying fallers via accelerometer sensors, which canimprove TUG accuracy for clinical and epidemiological applications.

Better than Counting Seconds: Identifying Fallers among Healthy Elderly using Fusion of Accelerometer Features and Dual-Task Timed Up and Go