Regression Models for Predicting Peak and Continuous Three-Dimensional Spinal Loads during Symmetric and Asymmetric Litting Tasks

摘要

Most biomechanical assessments of spinal loading during industrial work have focused on estimating peak spinal compressive forces under static and saglttally symmetric conditions. The main objective of this study was to explore the poten- tial of feasibly predicting three-dimensional (3D) spinal loadjng in jndustry from various combinations of trunk kinematics, kinetics, and subject-load characteris- tics. The study used spinal loading, predicted by a validated electromyography- assisted model, from l1 male participants who performed a series of symmetric and asymmetric lifts. Three classes of models were developed: (a)models using workplace, subject, and trunk motion parameters as independent variables (kine- matic models); (b) models using workplace, subject, and measured moments variables (kinetic models); and (c) models incorporating workplace, subject, trunk motion, and measured moments variables (combined models). The results showed that peak 3D spinal loading during symmetric and asymmetric lifting were predicted equally well using all three types of regression models. Continuous 3D loading was predicted best using the combined modeIs. When the use of such models is infeasible, the kinematic models can provide adequate pre- dictions. Finally, lateral shear forces (peak and continuous) were consistently underestimated using all three types of models. The study demonstrated the fea- sibility of predicting 3D loads on the spine under specific symmetric and asym- metric lifting tasks without the need for collecting IMG iinformation . However, further validation and development of the models should be conducted to assess and extend their applicability to lifting conditions other than those presented in this study. Actual or potential applications of this research include exposure assessment in epidemioligical studies, ergonomic intervention, and laboratory task assessment.