Predictive Analytics for Customizing School Backpacks Based on Student Anthropometric Data

EasyChair Preprint 14342

Abstract

This study investigates the use of predictive analytics to design ergonomically optimized school backpacks tailored to the anthropometric profiles of students. By leveraging advanced machine learning techniques and comprehensive anthropometric data, the research aims to develop predictive models that recommend backpack designs minimizing the risk of musculoskeletal disorders. The study focuses on identifying key anthropometric measurements that influence backpack ergonomics and using this data to create customized backpack designs that enhance comfort and safety. The findings are expected to provide valuable insights for manufacturers and educators, contributing to the development of backpacks that promote better posture, reduce physical strain, and support the overall well-being of students.

Keyphrases: - Adjustable features, - Personalized solutions, Anthropometric data, Backpacks, Comfort Enhancement, Data-driven design, Design efficiency, Ergonomic tools, Predictive Analytics, customization, ergonomic assessment, ergonomic design, health outcomes, load management, long term impact, product design, product innovation, user experience, user feedback, weight distribution

@booklet{EasyChair:14342,
  author    = {John Owen},
  title     = {Predictive Analytics for Customizing School Backpacks Based on Student Anthropometric Data},
  howpublished = {EasyChair Preprint 14342},
  year      = {EasyChair, 2024}}