  Mechanical loads on the human spine play a crucial role in the etiology of
disorders of the lower back. In vivo evaluations are complex and highly
invasive, therefore musculoskeletal biomechanical models are used to estimate
muscle forces and spinal loads during static and dynamic activities.
Subject-specific models instead of generic models have the potential to improve
diagnosis and optimize clinical treatment by predicting accurate outcomes on an
individual basis. In this study, we developed a method to model a personalized
sagittal spinal alignment based on the external back profile obtained from
motion capture (MoCap) data to improve the estimation of spinal kinematics and
intervertebral joint loading. The spinal alignment adjustments proposed in this
study were applied to a full-body generic musculoskeletal model, combined from
a well-validated model for the thoracolumbar spine and a model of the lower
extremities, to allow simulating predominant activities of daily living. This
novel method predicts intervertebral joint centers with an average mean error
of 0.99 +/- 0.68 cm in the frontal axis and 1.21 +/- 0.97 cm in the transverse
axis relative to radiographic reference data. The maximum mean joint prediction
errors are 1.34 +/- 0.73 cm in the frontal axis (T1/T2 joint) and 1.65 +/- 1.11
cm in the transverse axis (T3/T4 joint). The generated subject-specific models
in this study were able to reproduce spine kinematics and dynamics for each
subject and derive qualitative joint load predictions with high task
repeatability. The results of this study demonstrate the suitability of the
proposed method and associated OpenSim workflow for inter- and intra-subject
investigations of muscle activity and joint reaction forces in the spine.
