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The purpose of this study was to develop a predictive model to generate representative 3D paediatric femur models for age 0–3 years based on child characteristics. Computed tomography (CT) images of 79 decedent (age 0–3 years) femurs were used to develop a statistical shape model characterising femur shape. A regression model predicted shape parameters from child characteristics (age, height, and weight). The predictive model demonstrated good fit (R2 = 0.976) in predicting femur shape (surface coordinates) in the test set (n = 17) and thus, can be used to generate representative 3D paediatric femur models in the absence of CT imaging. [ABSTRACT FROM AUTHOR]

The objective of this study was to characterize head biomechanics of video-recorded falls involving young children in a licensed childcare setting. Children 12 to < 36 months of age were observed using video monitoring during daily activities in a childcare setting (in classrooms and outdoor playground) to capture fall events. Sensors (SIM G) incorporated into headbands worn by the children were used to obtain head accelerations and velocities during falls. The SIM G device was activated when linear acceleration was ≥ 12 g. 174 video-recorded falls activated the SIM G device; these falls involved 31 children (mean age = 21.6 months ± 5.6 SD). Fall heights ranged from 0.1 to 1.2 m. Across falls, max linear head acceleration was 50.2 g, max rotational head acceleration was 5388 rad/s2, max linear head velocity was 3.8 m/s and max rotational head velocity was 21.6 rad/s. Falls with head impact had significantly higher biomechanical measures. There was no correlation between head acceleration and fall height. No serious injuries resulted from falls—only 1 child had a minor injury. In conclusion, wearable sensors enabled characterization of head biomechanics during video-recorded falls involving young children in a childcare setting. Falls in this setting did not result in serious injury. [ABSTRACT FROM AUTHOR]