An assessment of the realism of digital human manikins used for simulation in ergonomics

A principal component analysis of the relationship between the external body shape and internal skeleton for the upper body

Recent progress in 3D scanning technologies allows easy access to 3D human body envelope. To create personalized human models with an articulated linkage for realistic re-posturing and motion analyses, an accurate estimation of internal skeleton points, including joint centers, from the external envelope is required. For this research project, 3D reconstructions of both internal skeleton and external envelope from low dose biplanar X-rays of 40 male adults were obtained. Using principal component analysis technique (PCA), a low-dimensional dataset was used to predict internal points of the upper body from the trunk envelope. A least squares method was used to find PC scores that fit the PCA-based model to the envelope of a new subject. To validate the proposed approach, estimated internal points were evaluated using a leave-one-out (LOO) procedure, i.e. successively considering each individual from our dataset as an extra-subject. In addition, different methods were proposed to reduce the variability in data and improve the performance of the PCA-based prediction. The best method was considered as the one providing the smallest errors between estimated and reference internal points with an average error of 8.3mm anterior-posteriorly, 6.7mm laterally and 6.5mm vertically. As the proposed approach relies on few or no bony landmarks, it could be easily applicable and generalizable to surface scans from any devices. Combined with automatic body scanning techniques, this study could potentially constitute a new step towards automatic generation of external/internal subject-specific manikins.