Optimizing Furniture Design for Junior Secondary School Students Using Predictive Analytics of Growth Patterns

EasyChair Preprint 14343

Abstract

The growth patterns of junior secondary school students present a unique challenge in designing school furniture that remains comfortable and supportive over time. Predictive analytics offers a promising approach to address this challenge by forecasting future growth trends and body dimensions of students. This study explores the application of predictive analytics in anticipating growth patterns to inform the design of adjustable and adaptable furniture. By analyzing historical data on student growth trajectories, demographic trends, and physiological changes, predictive models can generate insights into future dimensions and ergonomic needs. The integration of these insights into furniture design ensures that the furniture can be easily modified to accommodate students' evolving physical requirements, thereby enhancing comfort, safety, and usability. This approach not only extends the functional lifespan of school furniture but also supports the well-being and academic performance of students by providing a more personalized and adaptable learning environment.

Keyphrases: 1. Predictive Analytics, 10. Design Simulation, 11. Anthropometric Data, 12. Longitudinal Growth Data, 13. Smart Furniture, 14. Ergonomic Adjustment, 15. Educational Furniture Design, 2. Growth Patterns, 3. Adjustable Furniture, 4. Ergonomic Design, 5. Time-Series Forecasting, 6. Machine Learning Models, 7. Regression Analysis, 8. Furniture Adaptability, 9. Student Comfort

@booklet{EasyChair:14343,
  author    = {John Owen},
  title     = {Optimizing Furniture Design for Junior Secondary School Students Using Predictive Analytics of Growth Patterns},
  howpublished = {EasyChair Preprint 14343},
  year      = {EasyChair, 2024}}