D. B. Stefan and D. A. Gilbert, "New Tools for Body Image Analysis: A Modern Framework Using 3D Body Scanning", in Proc. of 7th Int. Conf. on 3D Body Scanning Technologies, Lugano, Switzerland, 2016, pp. 29-44, https://doi.org/10.15221/16.029.
Title:
New Tools for Body Image Analysis: A Modern Framework Using 3D Body Scanning
Authors:
David B. STEFAN 1, David A. GILBERT 2
1 Novaptus Systems Inc., Chesapeake VA, USA;
2 The Hague Plastic and Cosmetic Surgery Center, Norfolk VA, USA
Abstract:
Background. Body Image Analysis (BIA) is an important area of research. It attempts to assess how the subject perceives their physical appearance, which can often differ substantially with the actual appearance of their body. One practical application is its use in qualifying bariatric patients prior to undertaking a weight loss surgery procedure. The psychologist plays as important a part in approving the weight loss surgery as the surgeon. The psychologist must undercover disorders such as binge eating and other behavioral distortions that may jeopardize successful surgical results or complicate surgical recovery. The tools used for BIA were developed in the 1970s and early 1980s and have remained relatively stagnant. This paper discusses replacing the traditional 2D paper diagrams in use today with realistic 3D body scanning images. It also provides architectural framework options to incorporate and expand the use of a 3D body scanner within a weight loss surgery clinic or conduct BIA within a cloud based environment.
Existing Methods. Currently, the individual is given a drawing with a series of 9 gender specific figures. These figures range from very thin to progressively overweight. Each figure has a number associated with it and the outlines of the figures are very generic. The individual is asked to select which one of the nine figures they believe most closely resembles the present image of themselves. Further, they are asked which of the images they would most like to resemble 1 year after weight loss surgery. Finally, they are then asked which one of the images they would be satisfied with as a maintainable long term goal. These questions attempt to objectify cognition of present body appearance. They also attempt to uncover expectations of the weight loss surgery result. This process can be immensely improved by using 3D body scanning technology, personalized images and computerized scoring techniques.
Updated Methods. 3D body scanning is poised to replace the traditional paper body image figures. The existing figures outline a single female of gynecoid shape or male of android shape from slender to grossly overweight. The individual is asked to associate the perception of their particular shape and size within a set of images that may in no way resemble their own physical shape. This is replaced by a normalized set of 3D images based on statistical shape categorizations. The individual identifies with this "top-level" shape, selects the shape and a series of numbered volumetric scaled images then appears. This selection is registered and the individual's pre-operative 3D body scan is then displayed next to it. This allows cognitive recognition between the actual and perceived body image. The desired post-operative body image goal and the post-operative satisfaction image are stored. All assessment questions are collected via computerized program, and successive post-operative scans are used for iterative satisfaction updates.
Discussion. There are two architectures to be discussed. One architecture presumes that a 3D body scanner is on premise. In this case, individuals undergoing BIA testing can use personalized scan information in near real time. The other architecture to be outlined bypasses the need for an in-clinic body scanner. Instead, this architecture is cloud based and relies on statistical models generated from thousands of 3D body scans. In this case height, weight and certain circumferential measurements are used to look up or generate the 3D body image. Regardless of which architecture is used, there is a vast improvement in body model realism, data collection and data analysis capability.
Details:
Full paper: 16.029.pdf
Proceedings: 3DBST 2016, 30 Nov.-1 Dec. 2016, Lugano, Switzerland
Pages: 29-44
DOI: 10.15221/16.029
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