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.
Highlights
Toddlers are likely to fall because their heads are heavier in proportion to the rest of their bodies and they are still learning how to find their balance at this stage
The performance of the proposed fall risk assessment system was evaluated based on the aforementioned testing set of 100 video clips captured by a Kinect at home, composed of more than 9000 color and depth frames
We proposed an early-warning fall assessment system for toddler healthcare at home
Summary
Toddlers are likely to fall because their heads are heavier in proportion to the rest of their bodies and they are still learning how to find their balance at this stage. Missed and late detections can lead to dangerous situations, and false alarms can cause users to lose trust in a system and ignore system alerts. Various wearable sensors, such as accelerometers and gyroscopes, have been proposed to detect elderly falls [2,3] for making automatic emergency calls. Combining the individual characteristics of the accelerometers and gyroscopes in an inertial measurement unit (IMU), the latest hybrid approaches [9,10,11] can detect falls or pre-impacts more reliably
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