Assessment System for Child Head Injury from Falls Based on Neural Network Learning

Area of Science:

• Pediatric Health
• Artificial Intelligence
• Biomechanical Engineering

Background:

• Toddlers are susceptible to severe head injuries from falls at home.
• Existing solutions for fall assessment are limited in effectiveness and precision.

Purpose of the Study:

• To develop a real-time assessment system for head injury in toddlers following falls.
• To utilize AI and video analysis for precise injury evaluation.

Main Methods:

• Phase I: Joint data extraction using Open Pose from surveillance video, followed by Long Short-Term Memory (LSTM) network and 3D transform model integration.
• Phase II: Derivation of head acceleration, calculation of Head Injury Criterion (HIC) values, and development of an injury classification model.
• Data Collection: 200 RGB videos of toddlers (13-30 months) falling, with 500 clips for training/validation and 300 for testing.

Main Results:

• The AI system demonstrated a high classification accuracy of 96.67% in assessing head injuries from falls.
• The framework successfully integrated spatial and temporal information from key body joints.

Conclusions:

• The study validates the feasibility of a real-time AI technique for monitoring and assessing head injuries in toddlers resulting from falls.
• This technology offers a potential solution for early detection and intervention of fall-related head trauma in young children.