Byoung-keon Daniel Park, PhD
ASSOCIATE RESEARCH Scientist
University of Michigan Transportation Research Institute (UMTRI)
CONTACT
E-mail: keonpark@umich.edu
Address: 2901 Baxter Rd. Ann Arbor, Michigan, U.S.A. 48105
ABOUT ME
“I am an Associate Research Scientist in the Biosciences Group at the University of Michigan Transportation Research Institute (UMTRI) and the Chief Engineer at HumanShape LLC. I joined UMTRI as research faculty in 2015 and have primarily focused on improving human safety and comfort through researching improved design tools. My current research focuses on innovation in statistical analysis and modeling complex human anatomy data motivated by applications across a range of domains, including vehicle occupant protection, personal protective equipment, vehicle design, and healthcare. Ultimately, my research has the potential to apply to the design of any product or system that includes physical interaction with human users.
Research Areas
Parametric digital human model development - Statistical body shape model developments, parametric skeleton modeling (e.g. lower extremities and skull), Posable body shape models, etc.
Model-based automatic anthropometric measurements - Automatic standard body dimension prediction, Body shape estimation under clothing/equipment, data-driven body landmark and joint location estimation, etc.
Model-based naturalistic behavior analysis - Marker-less motion capture, naturalistic human behavior capture in fields, Multi people behavior tracking, data-driven motion prediction, etc.
Others - Computer-aided orthopedic surgery, function-based geometric modeling, virtual/augmented reality.
What's New
[3D Scap Prediction From Face and Neck Shape] Head shape data obtained with optical scanners include hair artifacts so that the scalp is not accurately measured unless the subject is bald. I have developed a new model-based methodology to predict three-dimensional scalp shapes from face and neck shapes. A statistical head shape model (http://humanshape.org/head/) was used, which was developed using a principal component analysis in a previous study based on face and scalp data from 180 ethnically diverse men and women. Detailed information can be found in this paper (download paper).
