title: Fall risk assessment and early-warning for toddler behaviors at home.
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.
authors: Department of Computer Science & Information Engineering, National Dong-Hwa University, No. 1, Sec. 2, Da-Hsueh Rd., Shoufeng, Hualien 974, Taiwan. mtyang@mail.ndhu.edu.tw.
authors: Mau-Tsuen
authors: MT
authors: Yang
authors: Min-Wen
authors: MW
authors: Chuang
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D002654:Child Care / Q000379:methods*; D002675:Child, Preschool; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods*; D004856:Postural Balance / Q000502:physiology; D011187:Posture / Q000502:physiology; D018570:Risk Assessment; D060388:Support Vector Machine