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MLA
Anne M. Agur, Azam Khan, Jacobo Bibliowicz, Jeremy P.M. Mogk, John E. Lloyd, Sidney Fels. The Parametric Human Project: building a probabilistic atlas of human anatomy. Proceedings of the International Society of Biomechanics Congress. 2013.
BibTeX
@inproceedings{agur2013parametrichuman,
	author = {Anne M. Agur and Azam Khan and Jacobo Bibliowicz and Jeremy P.M. Mogk and John E. Lloyd and Sidney Fels},
	title = {The Parametric Human Project: building a probabilistic atlas of human anatomy.},
	booktitle = {Proceedings of the International Society of Biomechanics Congress},
	year = {2013},
},

The Parametric Human Project: Building a Probabilistic Atlas of Human Anatomy

Congress of the International Society of Biomechanics
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Jeremy Mogk, John Lloyd, Jacky Bibliowicz, Anne Agur, Sidney Fels, Azam Khan

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Abstract

The Parametric Human Project (PHP) is an academic and industrial research consortium in Digital Human Modelling. Our vision, and mission, is to create a comprehensive data-driven, statistically-based parametric model of the human anatomy, to facilitate biomechanical and ergonomic simulation of human performance and behavior. We emphasize the “parametric” aspect of humans to be able to generate a model of a specific human, but also to advance our knowledge of human variation and the co-variation of groups of morphological features. The immediate focus is to create a multi-scale parametric model of the human musculoskeletal anatomy. Here we highlight the work-to-date in data collection, volumetric mesh generation and registration, anatomical feature identification, and parameterization, as well as our plans moving forward to analyze and aggregate data into a statistically based atlas and parametric model. Currently, the data repository consists of high-resolution scans of over 1000 bones, as well as muscle fibre bundle and tendon surface data that were digitized during cadaveric dissection. Point cloud data from the bone scans are being used to generate volumetric meshes of varying geometry and resolution, so that we can assess skeletal feature identification algorithms, as well as begin to develop statistical shape models of the various bones. Muscle fibre bundle data have allowed us to reconstruct and quantify the architectural arrangement of all 20 forearm muscles, in situ, from a single specimen. The online repository encourages participation from the multitude of disciplines that are critical to expand our knowledge of human variation.

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