@article{ref1,
title="Accelerometer-based fall detection using machine learning: training and testing on real-world falls",
journal="Sensors (Basel)",
year="2020",
author="Palmerini, Luca and Klenk, Jochen and Becker, Clemens and Chiari, Lorenzo",
volume="20",
number="22",
pages="e6479-e6479",
abstract="Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an inertial sensor on the lower back during 143 real-world falls (the most extensive collection to date) from the FARSEEING repository. Such data were obtained from continuous real-world monitoring of subjects with a moderate-to-high risk of falling. We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. The obtained results suggest that algorithms can learn effectively from features extracted from a multiphase fall model, consistently overperforming more conventional features. The most promising method (support vector machines and features from the multiphase fall model) obtained a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. The reported results and methodologies represent an advancement of knowledge on real-world fall detection and suggest useful metrics for characterizing fall detection systems for real-world use.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s20226479",
url="http://dx.doi.org/10.3390/s20226479"
}