An EMG-assisted modeling approach to assess passive lumbar tissue loading in vivo during trunk bending

Abstract

Lower back pain (LBP) is a condition with high prevalence and high cost both in the United States and around the world. The magnitude of mechanical loading on spine is strongly associated with the occurrence of LBP. Previously, to assess spinal loading, biologically assisted biomechanical models were developed to estimate trunk muscle contraction forces. Loadings on lumbar passive tissues are estimated using anatomical models. However, despite the substantial individual variability in lumbar ligament geometry and viscoelastic properties, the existing anatomical models do not account for these differences. As such, the accuracy of model prediction is compromised especially when mid to full range of trunk motions are involved. This paper describes a new modeling approach to assess lumbar passive tissue loading with the consideration of individual differences in lumbar passive tissue properties. A data set that has trunk bending data from 13 human participants was analyzed; on average, lumbar passive tissue contributes to ∼89% of the total spinal compression force at fully flexed trunk postures; the estimated spinal tissue loadings were in feasible ranges as reported from previous cadaver studies; the estimated spinal loadings were also mostly in agreement with results from previous in vivo studies.