Article

Falls are the number one cause of injury in older adults. Lack of objective
evidence on the cause and circumstances of falls is often a barrier to effective
prevention strategies. Previous studies have established the ability of wearable
miniature inertial sensors (accelerometers and gyroscopes) to automatically
detect falls, for the purpose of delivering medical assistance. In the current
study, we extend the applications of this technology, by developing and
evaluating the accuracy of wearable sensor systems for determining the cause of
falls. Twelve young adults participated in experimental trials involving falls
due to seven causes: slips, trips, fainting, and incorrect shifting/transfer of
body weight while sitting down, standing up from sitting, reaching and turning.
Features (means and variances) of acceleration data acquired from four tri-axial
accelerometers during the falling trials were input to a linear discriminant
analysis technique. Data from an array of three sensors (left ankle+right
ankle+sternum) provided at least 83% sensitivity and 89% specificity in
classifying falls due to slips, trips, and incorrect shift of body weight during
sitting, reaching and turning. Classification of falls due to fainting and
incorrect shift during rising was less successful across all sensor combinations.
Furthermore, similar classification accuracy was observed with data from wearable
sensors and a video-based motion analysis system. These results establish a basis
for the development of sensor-based fall monitoring systems that provide
information on the cause and circumstances of falls, to direct fall prevention
strategies at a patient or population level.
CI - Copyright (c) 2013 Elsevier B.V. All rights reserved.

Langue : ANGLAIS