@article{ref1,
title="A novel detection model and its optimal features to classify falls from low- and high-acceleration activities of daily life using an insole sensor system",
journal="Sensors (Basel)",
year="2018",
author="Cates, Benjamin and Sim, Taeyong and Heo, Hyun Mu and Kim, Bori and Kim, Hyunggun and Mun, Joung Hwan",
volume="18",
number="4",
pages="s18041227-s18041227",
abstract="In order to overcome the current limitations in current threshold-based and machine learning-based fall detectors, an insole system and novel fall classification model were created. Because high-acceleration activities have a high risk for falls, and because of the potential damage that is associated with falls during high-acceleration activities, four low-acceleration activities, four high-acceleration activities, and eight types of high-acceleration falls were performed by twenty young male subjects. Encompassing a total of 800 falls and 320 min of activities of daily life (ADLs), the created Support Vector Machine model’s Leave-One-Out cross-validation provides a fall detection sensitivity (0.996), specificity (1.000), and accuracy (0.999). These classification results are similar or superior to other fall detection models in the literature, while also including high-acceleration ADLs to challenge the classification model, and simultaneously reducing the burden that is associated with wearable sensors and increasing user comfort by inserting the insole system into the shoe.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s18041227",
url="http://dx.doi.org/10.3390/s18041227"
}