Characterization of spinal curvature in upright and reclined posture for human body model positioning

OBJECTIVE: Future autonomous vehicles increase the use of reclined seating configurations, driving the need for adapted occupant restraint systems that are increasingly developed using human body models (HBMs). This study provides biofidelic target data for positioning HBMs in reclined postures, addressing the current limitation of default upright posture. METHODS: An existing MRI dataset of 11 volunteers (5 male, 6 female; age 39.2 ± 12.7 years; height 174 ± 9.3 cm; body mass index (BMI) 24.8 ± 2.7) was analyzed to quantify spinal curvature and pelvic alignment. Participants were scanned while seated in a replica of a vehicle seat in upright (U) and reclined (R) seating configurations (20° and 50° backrest angle). For each posture, scan markers were placed on the endplates of sacrum and vertebral bodies from L5 to T6 in midsagittal plane, yielding 50 two-dimensional markers per volunteer and posture. These markers were utilized for calculation of intervertebral angles (IVA) and global angles including sacral slope (SS), lumbar lordosis (LL: L5-L1), and thoracolumbar angle (TLK: L2-T10). Additionally, pelvis angle (PA) was measured as the angle between the line connecting the pubic tubercle with the anterior superior iliac spine and the vertical axis. Combining PA with SS resulted in the composite angle PASS. To characterize global spinal curvature, third-order polynomial curve-fitting and principal component analysis (PCA) with marker coordinates normalized by arc length were analyzed. Polynomial curve-fitting was also applied in a preliminary manner to explore potential transformations into reclined dependent on upright posture. RESULTS: ). Lumbar IVAs exceeded thoracic IVAs in both postures, with the largest posture-related changes in lumbar segments L5/L4 and L4/L3 as well as SS. PCA of normalized spinal curvature identified three principal components accounting for more than 97% of the total variance across seating configurations, while the first component (≥ 85%) demonstrates the greatest changes in SS and lumbar IVAs at ±2 SD. Fitting of a polynomial curve resulted in a good fit with a third order polynomial for both seating postures. Prediction of reclined posture using polynomial curves could be achieved with a mean RMSE of 13.82 mm. CONCLUSIONS: Created data revealed significant posture-dependent changes in sacral slope and lumbar IVAs. A preliminary methodology for predicting reclined spinal curvature from upright posture is presented. Findings require further validation with larger datasets and consideration of covariates to ensure robustness.