Background: Motion assessment of the body's head-arms-trunk (HAT) using linked-segment models, along with an inverse dynamics approach, can enable in vivo estimations of inter-vertebral moments. However, this mathematical approach is prone to experimental errors because of inaccuracies in (i) kinematic measurements associated with soft tissue artifacts and (ii) estimating individual-specific body segment parameters (BSPs). The inaccuracy of the BSPs is particularly challenging for the multi-segment HAT due to high inter-participant variability in the HAT's BSPs and no study currently exists that can provide a less erroneous estimation of the joint moments along the spinal column.
Research Question: This study characterized three-dimensional (3D) inter-segmental moments in a multi-segment HAT model during multi-directional trunk-bending, after minimizing the experimental errors.
Method: Eleven healthy individuals participated in a multi-directional trunk-bending experiment in five directions with three speeds. A seven-segment HAT model was reconstructed for each participant, and its motion was recorded. After compensating for experimental errors due to soft tissue artifacts, and using optimized individual-specific BSPs, and center of pressure offsets, the inter-segmental moments were calculated via inverse dynamics.
Results: Our results show a significant effect of the inter-segmental level and trunk-bending directions on the obtained moments. Compensating for soft tissue artifacts contributed significantly to reducing errors. Our results indicate complex, task-specific patterns of the 3D moments, with high inter-participant variability at different inter-segmental levels, which cannot be studied using single-segment models or without error compensation.
Significance: Interpretation of inter-segmental moments after compensation of experimental errors is important for clinical evaluations and developing injury prevention and rehabilitation strategies.
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