@article{ref1,
title="An unobtrusive fall detection and alerting system based on Kalman filter and Bayes network classifier",
journal="Sensors (Basel)",
year="2017",
author="He, Jian and Bai, Shuang and Wang, Xiaoyi",
volume="17",
number="6",
pages="s17061393-s17061393",
abstract="Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s17061393",
url="http://dx.doi.org/10.3390/s17061393"
}