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Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home
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; in revised form: 22 November 2013 / Accepted: 26 November 2013 / Published: 10 December 2013
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.
Yang M-T, Chuang M-W. Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home. Sensors. 2013; 13(12):16985-17005.
Yang, Mau-Tsuen; Chuang, Min-Wen. 2013. "Fall Risk Assessment and Early-Warning for Toddler Behaviors at Home." Sensors 13, no. 12: 16985-17005.