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Sensors 2013, 13(12), 16985-17005; doi:10.3390/s131216985
Article

Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home

*  and
Department of Computer Science & Information Engineering, National Dong-Hwa University, No. 1, Sec. 2, Da-Hsueh Rd., Shoufeng, Hualien 974, Taiwan
* Author to whom correspondence should be addressed.
Received: 19 September 2013 / Revised: 22 November 2013 / Accepted: 26 November 2013 / Published: 10 December 2013
(This article belongs to the collection Sensors for Globalized Healthy Living and Wellbeing)
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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.
Keywords: Kinect; toddler; childcare; fall risk; early-warning Kinect; toddler; childcare; fall risk; early-warning
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Yang, M.-T.; Chuang, M.-W. Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home. Sensors 2013, 13, 16985-17005.

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