Trunk kinematics (i.e. speed and acceleration), a risk factor associated with low back pain (LBP), is difficult to measure in the field and is not incorporated in popular lifting analysis tools. In this study, computationally efficient computer vision-based methods were used to estimate trunk kinematics from video recordings collected in a previous prospective study. We explored the relationships between trunk kinematics and health outcomes, and if incorporating trunk kinematics into the revised NIOSH Lifting Equation (RNLE) may improve its predictability of LBP risk. Significant correlations between the percentage of LBP and the average trunk speed and acceleration were found. A multivariate logistic regression model was constructed using the kinematics variables and the RNLE lifting index (LI) as independent variables to predict the probability that a task was associated with high LBP risk. When trunk kinematics was incorporated to the LI model, the predictability of the model improved significantly (p=0.003).
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