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

Sensors

Mau-Tsuen Yang¹ 

关键词: Kinect;    toddler;    childcare;    fall risk;    early-warning;   

DOI  :  10.3390/s131216985

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.