Date of Degree
MS (Master of Science)
The accuracy of modern digital human models has led to the development of human simulation engines capable of performing a complex analysis of the biometrics and kinematics / dynamics of a digital model. While the capabilities of these simulations have seen much progress in recent years, they are hindered by a fundamental limitation regarding the diversity of the models compatible with the simulation engine, which in turn results in a reduction in the scope of the applications available to the simulation. This is typically due to the necessary implementation of a musculoskeletal structure within the model, as well as the inherent mass and inertial data that accompany it. As a result a significant amount of time and expertise is required to make a digital human model compatible with the simulation. In this research I present a solution to this limitation by outlining a process to develop a set of mutually compatible human models that spans the range of feasible body shapes and allows for a “free” exploration of body shape within the shape manifold. Additionally, a method is presented to represent the human body shapes with a reduction of dimensionality, via a spectral shape descriptor, that enables a statistical analysis that is both more computationally efficient and anthropometrically accurate than traditional methods. This statistical analysis is then used to develop a set of representative models that succinctly represent the full scope of human body shapes across the population, with applications reaching beyond the research-oriented simulations into commercial human-centered product design and digital modeling.
The capabilities of digital human models has increased dramatically over the past few years, and as a result have played an increasing role in the design process. By utilizing a digital human model in the design process one can validate the use of the design without the need for prototyping, and as a result generate a significant amount of savings in the design stage. Beyond the commercial sector, many research groups have developed advanced human simulation software that are capable of performing an in-depth analysis of a human in a variety of situations, based off of the response of their digital human model. However, due to the complexity of the analysis it can be difficult to implement new human models into these software as there is a lack of compatibility between models. In both of these applications, the accuracy of the model used is imperative to obtain reasonable results.
This research presents a method capable of generating a set of digital human models that are all mutually compatible, thus allowing a more seamless implementation into a digital simulation software. Additionally, a process is outlined that can generate a representative set of human models from a statistical population analysis that is both more computationally efficient and anthropometrically accurate than traditional methods. These representative models then encompass the entire scope of human body shapes and can be used to validate designs across all demographics without the need for prototyping.
Additionally, the statistical clusters used to generate the representative models can be used to improve the anthropometric modeling of the human population. This, in turn, provides benefits to commercial sectors such as garment design as well as improving the capabilities of human factors analysis and human centered design.
publicabstract, Anthropometric, human, manifold, Modeling, spectral, statistic
Copyright 2016 Samuel Spicer Mate
Additional FilesFemale Sammon Map with cluster.mp4 (3585 kB)
Mate Supplement File pt.1
Male Sammon Map with cluster.mp4 (4171 kB)
Mate Supplement File pt. 2