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