@article{ref1,
title="Inertial sensing-based pre-impact detection of falls involving near-fall scenarios",
journal="IEEE transactions on neural systems and rehabilitation engineering",
year="2014",
author="Lee, Jung Keun and Robinovitch, Stephen and Park, Edward",
volume="23",
number="2",
pages="258-266",
abstract="Although near-falls (or recoverable imbalances) are common episodes for many older adults, they have received a little attention and were not considered in the previous laboratory-based fall assessments. Hence, this paper addresses near-fall scenarios in addition to the typical falls and activities of daily living (ADLs). First, a novel vertical velocity-based pre-impact fall detection method using a wearable inertial sensor is proposed. Second, to investigate the effect of near-fall conditions on the detection performance and feasibility of the vertical velocity as a fall detection parameter, the detection performance of the proposed method (Method 1) is evaluated by comparing it to that of an acceleration-based method (Method 2) for the following two different discrimination cases: falls vs. ADLs (i.e. excluding near-falls) and falls vs. non-falls (i.e. including near-falls). Our experiment results show that both methods produce similar accuracies for the fall vs. ADL detection case; however Method 1 exhibits a much higher accuracy than Method 2 for the fall vs. non-fall detection case. This result demonstrates the superiority of the vertical velocity over the peak acceleration as a fall detection parameter when the near-fall conditions are included in the non-fall category, in addition to its capability of detecting pre-impact falls.<p /> <p>Language: en</p>",
language="en",
issn="1534-4320",
doi="10.1109/TNSRE.2014.2357806",
url="http://dx.doi.org/10.1109/TNSRE.2014.2357806"
}