@article{ref1,
title="Predicting a fall based on gait anomaly detection: a comparative study of wrist-worn three-axis and mobile phone-based accelerometer sensors",
journal="Sensors (Basel)",
year="2023",
author="Kocuvan, Primož and Hrastič, Aleksander and Kareska, Andrea and Gams, Matjaž",
volume="23",
number="19",
pages="-",
abstract="Falls by the elderly pose considerable health hazards, leading not only to physical harm but a number of other related problems. A timely alert about a deteriorating gait, as an indication of an impending fall, can assist in fall prevention. In this investigation, a comprehensive comparative analysis was conducted between a commercially available mobile phone system and two wristband systems: one commercially available and another representing a novel approach. Each system was equipped with a singular three-axis accelerometer. The walk suggestive of a potential fall was induced by special glasses worn by the participants. The same standard machine-learning techniques were employed for the classification with all three systems based on a single three-axis accelerometer, yielding a best average accuracy of 86%, a specificity of 88%, and a sensitivity of 86% via the support vector machine (SVM) method using a wristband. A smartphone, on the other hand, achieved a best average accuracy of 73% also with an SVM using only a three-axis accelerometer sensor. The significance analysis of the mean accuracy, sensitivity, and specificity between the innovative wristband and the smartphone yielded a p-value of 0.000. Furthermore, the study applied unsupervised and semi-supervised learning methods, incorporating principal component analysis and t-distributed stochastic neighbor embedding. To sum up, both wristbands demonstrated the usability of wearable sensors in the early detection and mitigation of falls in the elderly, outperforming the smartphone.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s23198294",
url="http://dx.doi.org/10.3390/s23198294"
}