Developing Regression Models to Predict Anthropometric Variations for Designing Custom Ergonomic Office Chairs

EasyChair Preprint 14351

Abstract

This study aims to develop regression models to accurately predict anthropometric measurements for designing ergonomic workstations that enhance productivity and comfort for office workers. The research involves creating a comprehensive anthropometric database by collecting data from a diverse sample of office workers, encompassing various age groups, genders, and job roles. Regression models, including linear and multiple regression techniques, are employed to analyze the relationship between demographic factors and key anthropometric dimensions such as seated height, arm reach, and leg length. The predictive accuracy of these models is validated through cross-validation and statistical metrics like Mean Absolute Error (MAE) and R-squared.

Keyphrases: - **Adjustable Furniture**, - **Anthropometric Data**, - **Customization**, - **Data-Driven Design**, - **Ergonomic Assessment**, - **Ergonomic Workstations**, - **Health and Productivity**, - **Human Factors Engineering**, - **Machine Learning in Ergonomics**, - **Musculoskeletal Health**, - **Occupational Health**, - **Office Ergonomics**, - **Performance Metrics**, - **Posture Support**, - **Predictive Models**, - **Regression Analysis**, - **User Feedback**, - **User-Centric Design**, - **Workplace Comfort**, - **Workplace Productivity**

@booklet{EasyChair:14351,
  author    = {Wayzman Kolawole},
  title     = {Developing Regression Models to Predict Anthropometric Variations for Designing Custom Ergonomic Office Chairs},
  howpublished = {EasyChair Preprint 14351},
  year      = {EasyChair, 2024}}