@article{ref1,
title="HONEY: a multimodality fall detection and telecare system",
journal="Telemedicine journal and e-health",
year="2013",
author="Zhang, Quan and Ren, Lingmei and Shi, Weisong",
volume="19",
number="5",
pages="415-429",
abstract="Background: The increasing cost in terms of money and healthcare resources is driving healthcare providers to provide home-based telecare instead of institutionalized healthcare. Falling is one of the most common and dangerous accidents for elderly individuals and a significant factor affecting the living quality of the elderly. Many efforts have been put toward providing a robust method to detect falls accurately and in a timely manner. This study facilitated a reliable, safe, and real-time home-based healthcare environment, which we have termed the Home Healthcare Sentinel System (HONEY), to detect falls for elderly people in the home telecare environment. The basic idea of HONEY is a three-step detection scheme that consists of multimodality signal sources, including an accelerometer sensor, audio, images, and video clips via speech recognition and on-demand video techniques. Materials and Methods: The magnitude of acceleration, corresponding to a user's movements, triggers fall detection combining speech recognition and on-demand video. If a fall occurs, an alarm e-mail is delivered to medical staff or caregivers at once, containing the fall information, so that caregivers could make a primary diagnosis based on it. This article also describes the implementation of the prototype of HONEY. Results: A comprehensive evaluation with 10 volunteers shows that HONEY has high accuracy of 94% for fall detection, 18% higher than the Advanced Magnitude Algorithm (AMA), which is a wearable sensor-based method, and the false-positive and false-negative rates are 3% and 10%, respectively, 19% and 16% lower than AMA, respectively. The average response time for a detected fall is 46.2 s, which is also short enough for first aid. Conclusions: In summary, HONEY provides a highly reliable and convenient fall detection solution for the home-based environment.<p /> <p>Language: en</p>",
language="en",
issn="1530-5627",
doi="10.1089/tmj.2012.0109",
url="http://dx.doi.org/10.1089/tmj.2012.0109"
}