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

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

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Received: 19 September 2013; in revised form: 22 November 2013 / Accepted: 26 November 2013 / Published: 10 December 2013
(This article belongs to the collection Sensors for Globalized Healthy Living and Wellbeing)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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
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MDPI and ACS Style

Yang, M.-T.; Chuang, M.-W. Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home. Sensors 2013, 13, 16985-17005.

AMA Style

Yang M-T, Chuang M-W. Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home. Sensors. 2013; 13(12):16985-17005.

Chicago/Turabian Style

Yang, Mau-Tsuen; Chuang, Min-Wen. 2013. "Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home." Sensors 13, no. 12: 16985-17005.

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