Torso Segment Parameter Prediction in Working Adults

Abstract

Body segment parameters (BSPs) such as segment mass, center of mass, and radius of gyration are used as inputs in static and dynamic ergonomic and biomechanical models used to predict joint and muscle forces, and related risks of musculoskeletal injury. Because these models are sensitive to BSP values, accurate and representative parameters are necessary for injury risk prediction. While previous studies have determined segment parameters in the general population, as well as the impact of age and obesity levels on these parameters, estimated errors in the prediction of BSPs can be as large as 40% (Durkin, 2003). Thus, more precise values are required for attempting to predict injury risk in individuals. This study aims to provide statistical models for predicting torso segment parameters in working adults using whole body dual energy x-ray absorptiometry (DXA) scan data along with a set of anthropometric measurements. The statistical models were developed on a training subset of the study population, and validated on a testing subset. When comparing the model predictions to the actual BSPs of the testing subset, the predictions were, on average, within 5% of the calculated parameters, while previously developed predictions (de Leva, 1996) had average errors of up to 30%, indicating that the new statistical models greatly increase the accuracy in predicting BSPs.