Parametric Modeling of Human Anatomy

Parametric Modeling of Human Anatomy Human anatomy has complex morphological structure, hard to be modeled, so that only simplified or averaged anatomy models that has 50th percentile configurations of the population has been generally used in many engineering and bioscience areas. To make these human anatomy models parametric and realistic, I have developed and applied a statistical modeling approach. This approach includes (1) structural-homologizing method for subject data to find the correspondences between the different subjects, (2) statistical analyzing method using principal component analysis to find statistically significant factors among the variances in the dataset, and (3) parameterization method to intuitively change the model with a few given parameters such as stature, body mass index and age.

Statistical body shape models

Statistical body shape models are allow for an intuitive generation of a subject-specific body shape with a few given individual's body characteristics such as stature and body weight. This parametric behavior of the model also allows systematic analyses of effects of each parameter on the body shape.

Two statistical methods are generally used for developing the models: principal component analysis (PCA) and multivariate regression analysis. PCA reduces the high dimensionality of the original datasets so that a body information (3D coordinates of vertices, anatomical landmarks, joint locations, and anthropometric data, etc) can be represented with a few value, a.k.a., "PC scores". These PC scores, which efficiently accounts for the original data distribution, then are associated meaningful parameters like stature and BMI using the multivariate regression analysis.

Body shape variation generated using a body shape model

Figure. Manikin samples generated using the child statistical body shape model

Dr. Matt Reed and I have come up with various methods to make the models more realistic and applicable. The featured ideas are as follows:

  • Non-rigid registration of scans while preserving anatomical homologies across the scans using RBF and Implicit-surface fitting methods
  • Bootstrapping standardization process to improve the model quality using a rapid PC-based fitting method
  • Advanced regression analysis to describe the expected nonlinearity in body shape change with different postures

These models will find much more applications including anthropometrics, ergonomics, vehicle design, product development, medical diagnosis, apparel design, and etc

More detailed methodologies can be found in these papers:

  • LI, Z., Park, B-K., Liu, W., Zhang, J., Reed, M. P., Rupp, J. D., Hoff, C. N., Hu, J. (2015). A Statistical Skull Geometry Model for Children 0-3 Years Old. PLOS ONE, In press
  • Park, B-K., Reed, M. P. (2015). Parametric body shape model of standing children aged 3–11 years. Ergonomics. 1:1-12. Epub ahead of print
  • Park, B. K. D., Reed, M. P., Kaciroti, N., Love, M., Miller, A. L., Appugliese, D. P., & Lumeng, J. C. (2016). shapecoder: a new method for visual quantification of body mass index in young children. Pediatric Obesity.
  • Park, B. K. D., Ebert, S., & Reed, M. P. (2017). A parametric model of child body shape in seated postures. Traffic injury prevention, 1-4.

Rapid Anthropometric and Motion Prediction

Numerous 3d studies have been conducted, increasing the availability of data on body shape, particularly in standing postures. The utility of raw body scan data is limited, however. Often, traditional anthropometric dimensions are estimated based on the scan data, while the scans themselves are not widely used. I have developed a new method to rapidly predict standard anthropometric dimensions from the scan using a statistical body shape model. This statistical model was built based on laser scan data with a set of manually-measured anthropometric variables from subjects, so that it allow for predicting anthropometric variables if the model has a similar body shape to the target. I have developed a rapid fitting method to fit the model to the scan by finding the least-square fit between the model and the scan in a body shape space, so that the anthropometric variables can be predicted from the fit model.

· Park, B-K, Lumeng, J.C., Lumeng, C.N., Ebert, S.M., and Reed, M.P. (2014). Child body shape measurement using depth cameras and a statistical body shape model. Ergonomics, 58(2):301-309. 10.1080/00140139.2014.965754

· Park, B. and Reed, M., Characterizing Vehicle Occupant Body Dimensions and Postures Using a Statistical Body Shape Model, SAE Technical Paper 2017-01-0497, 2017, doi:10.4271/2017-01-0497.

· Park, B-K.D., Corner, B.D., Kearney, M., and Reed, M.P. (2016). Estimating human body characteristics under clothing using a statistical body shape model. Proc. 4th International Digital Human Modeling Conference. Montreal, Canada.

· Reed, M.P., Park, B-K. D., and Corner, B.D. (2016). Predicting seated body shape from standing body shape. Proc. 4th International Digital Human Modeling Conference. Montreal, Canada.

· Reed, M.P., Park, B-K., Kim, K.H., and Jones, M.L.H. (2015). Statistical prediction of body landmark locations on surface scans. Proceedings of the 19th Triennial Congress of the International Ergonomics Association, Melbourne, Australia.

· Park, B-K.D., and Reed, M.P. (2016). Rapid Generation of Custom Avatars using Depth Cameras. Proc. 3rd International Digital Human Modeling Conference. Tokyo, Japan.

Computational Patient-Specific Modeling

From the collaboration with orthopedic surgeons, I have developed a computational patient-specific models to assist the surgeons to make better decisions before orthopedic surgeries like high-tibial osteotomy. In this work, a 3-dimensional generic analytic model, that is applicable to any bone morphologies, was developed to estimate the current status of a patient and to find the best surgical parameters for the patient. Various types of software were developed for this purpose, including a program to reconstruct a patient-specific model from CT images, a surgery planning program to find the best way to meet the surgical requirements, and a surgery-assistant program to inform the surgeon about the current adjustment status of the bone during operation.

· Park, B-K., Bae, J. H., Koo, B. Y., & Kim, J. J. (2014). Function-based morphing methodology for parameterizing patient-specific models of human proximal femurs. Computer-Aided Design, 51, 31-38.

· Park, B. K., & Kim, J. J. (2012). A sharable format for multidisciplinary finite element analysis data. Computer-Aided Design, 44(7), 626-636.