Children's Motor Intelligence Evaluation System Based on Multi-data Fusion

Area of Science:

• Child Development
• Sports Science
• Educational Technology

Background:

• Traditional childhood motor evaluations often use a one-size-fits-all approach, neglecting individual differences in physical intelligence.
• Overemphasis on teacher-led instruction limits children's active engagement and personalized feedback in physical education.

Purpose of the Study:

• To develop and evaluate a novel children's motor intelligence evaluation system utilizing multidata fusion technology.
• To address the limitations of traditional assessment methods by incorporating diverse data sources for a comprehensive evaluation.

Main Methods:

• The study focused on children's autonomous sports games, defining key evaluation indicators: classroom performance, physical fitness, motor skills, and extracurricular fitness.
• Employed a fuzzy neural network algorithm for evaluating exercise physiology data, followed by adaptive weighted data fusion and Dempster-Shafer (D-S) evidence theory for motor intelligence assessment.

Main Results:

• The multidata fusion evaluation system demonstrated intelligent and comprehensive assessment of children's motor abilities.
• The new system showed a 5% improvement in evaluation results compared to traditional methods, leading to more equitable outcomes.
• The enhanced evaluation approach positively impacted children's sports confidence and encouraged effective physical activity.

Conclusions:

• Multidata fusion offers a superior approach to evaluating children's motor intelligence, accommodating individual differences.
• This system enhances the accuracy and fairness of assessments, promoting better physical development and engagement in early childhood education.