Safe Fall: Use of Predictive Modeling and Machine Vision Techniques for Fall Analysis and Fall Quality.

Area of Science:

• Biomechanics
• Computer Vision
• Pediatric Injury Prevention

Background:

• Falls are a major cause of childhood injuries.
• Current school-based fall prevention lacks objective biomechanical assessment, relying on subjective observation.

Purpose of the Study:

• To introduce the Safe Fall framework, combining judo-inspired education with computer vision for objective fall analysis.
• To quantify safe falling strategies in schoolchildren and evaluate the intervention's effectiveness.

Main Methods:

• Utilized a multi-stage computer vision pipeline (YOLOv8, SAM 2, MMPose) to analyze video recordings of 285 schoolchildren.
• Implemented a judo-inspired educational program focused on safe falling techniques.
• Employed unsupervised clustering and Random Forest classification for motor profile analysis and fall quality assessment.

Main Results:

• Significant improvements (p<0.05) observed in 60% of kinematic metrics, including increased descent rate (+61.4%) and expanded rolling ranges.
• Demonstrated a shift from hazardous "freezing" to controlled energy dissipation behaviors.
• Achieved 98.3% accuracy and 0.998 AUC in distinguishing fall quality using a Random Forest classifier.

Conclusions:

• The Safe Fall framework offers a scalable, evidence-based method for injury risk reduction in schools.
• Integrating pedagogical training with automated vision modeling provides objective biomechanical assessment for fall prevention.
• This approach facilitates a transition towards safer motor profiles in children's falling techniques.