Lumbar Moment Estimation of Engine Drivers in a Static Sitting Working Position by using Multiple Linear Regression

Abstract

The paper presents a simpler and more precise model of lumbar moment prediction based on single linear, or multiple linear regression with two predictors. The body mass index (BMI) as the predictor contains two of the most important static anthropometric measures, height and mass, whose separated role in lumbar moment prediction, as well as their mutual relations, have not been sufficiently investigated. This study analysed mass, height, age and BMI as lumbar moment predictors, on a sample of 50 Croatian male engine drivers. Two prediction models were compared: (1) multiple linear regression prediction with mass and height as predictors; (2) single linear regression with mass as the only predictor. Results confirmed the multiple regression model as the best one (R2= 0.9015 with standard error of prediction 1.26), having the mass of the best predictor. Surprisingly, the single regression model with mass as predictor explained only 3.6% of lumbar moment variance less than multiple regression model, with related standard error of prediction 1.46 (mean percentage value of the relative error was only 0.8% higher than at multiple regression model). The obtained findings suggest high prediction potential of mass and height that should be verified on various subject samples.

Keywords

• lumbar moment
• engine driver
• static siting position
• multiple linear regression
• predictors