@article{ref1,
title="Fall detection from a manual wheelchair: preliminary findings based on accelerometers using machine learning techniques",
journal="Assistive technology",
year="2023",
author="Abou, Libak and Fliflet, Alexander and Presti, Peter and Sosnoff, Jacob J. and Mahajan, Harshal P. and Frechette, Mikaela L. and Rice, Laura A.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Automated fall detection device for individuals who use wheelchairs to minimize consequences of falls is lacking. This study aimed to develop and train a fall detection algorithm to differentiate falls from wheelchair mobility activities using machine learning techniques. Thirty, healthy, ambulatory, young adults simulated falls from a wheelchair and performed other wheelchair-related mobility activities in a laboratory. Neural Network classifiers were used to train the algorithm developed based on data retrieved from accelerometers mounted at the participant's wrist, chest, and head. <br><br>RESULTS indicate excellent accuracy to differentiate between falls and wheelchair mobility activities. The sensors mounted at the wrist, chest, and head presented with an accuracy of 100%, 96.9%, and 94.8%, respectively using data from 258 falls and 220 wheelchair mobility activities. This pilot study indicates that a fall detection algorithm developed in a laboratory setting based on fall accelerometer patterns can accurately differentiate wheelchair-related falls and wheelchair mobility activities. This algorithm should be integrated into a wrist-worn devices and tested among individuals who use a wheelchair in the community.<p /> <p>Language: en</p>",
language="en",
issn="1040-0435",
doi="10.1080/10400435.2023.2177775",
url="http://dx.doi.org/10.1080/10400435.2023.2177775"
}