@article{ref1,
title="Fall risk assessment and early-warning for toddler behaviors at home",
journal="Sensors (Basel)",
year="2013",
author="Yang, Mau-Tsuen and Chuang, Min-Wen",
volume="13",
number="12",
pages="16985-17005",
abstract="Accidental falls are the major cause of serious injuries in toddlers, with most of these falls happening at home. Instead of providing immediate fall detection based on short-term observations, this paper proposes an early-warning childcare system to monitor fall-prone behaviors of toddlers at home. Using 3D human skeleton tracking and floor plane detection based on depth images captured by a Kinect system, eight fall-prone behavioral modules of toddlers are developed and organized according to four essential criteria: posture, motion, balance, and altitude. The final fall risk assessment is generated by a multi-modal fusion using either a weighted mean thresholding or a support vector machine (SVM) classification. Optimizations are performed to determine local parameter in each module and global parameters of the multi-modal fusion. Experimental results show that the proposed system can assess fall risks and trigger alarms with an accuracy rate of 92% at a speed of 20 frames per second.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s131216985",
url="http://dx.doi.org/10.3390/s131216985"
}