The importance of human body composition research has increased due to the prevalence of obesity, a health disorder characterized by excess body fat. Obesity is a public health concern because of its associations with chronic diseases, including type 2 diabetes, hypertension, and coronary heart disease.1,2
The World Health Organization (WHO) classifies obesity in terms of body mass index (BMI), calculated by dividing body weight (in kilograms) by squared height (in square meters).1
However, BMI is only a crude conjecture of body fat, as it can be influenced by muscularity, age, gender, and ethnicity.2
More precise, noninvasive, inexpensive methods are needed to estimate the impact of the size and shape of the body on the distribution and degree of adiposity and the associated health risks.
A multiplicity of techniques have been developed for direct measurements of the amount and distribution of body fat. Previous technology, such as hydrodensitometry3
(underwater weighing) and air displacement plethysmography,4
focused on densitometry methods for determinations of body volume and estimations of overall density. These methods give a percent of body fat based on empirical predicting models.5,6
The significant time and subject burden of hydrodensitometry with subjects repeatedly being submerged in water limited its applicability to certain populations. Currently, volumetric measurements have been improved by air displacement technology such as the BodPod®
. But its bulkiness and high expense have reserved its use to research and special facilities. A more rapid and inexpensive method to estimate body fat is bioelectrical impedance analysis,7
although this method is not recommended for research in persons with altered hydration states.8
Dual-energy x-ray absorptiometry9
(DXA), computed tomography10
(CT), and magnetic resonance imaging11
are more advanced and accurate techniques, but their significant expense and nonportability restricts their use to medical and research settings. Furthermore, the ionizing radiation of DXA would limit its continual use; the stronger radiation of CT is much more worrisome.
The escalation of worldwide obesity has intensified the demand for a convenient, safe, and relatively inexpensive device for the estimation of body size, shape and composition. Recent studies have shown that three-dimensional (3-D) body surface imaging is a potential alternative to assess body fat and to predict the risk of metabolic syndrome.12,13
This type of instrument, commonly called a body scanner, captures the surface geometry of the human body by utilizing digital techniques. Body scanning is a densitometry method for body fat assessment that provides noncontact, fast, and accurate body measurements. Anthropometric parameters computed by this system include waist and hip circumferences, sagittal abdominal diameter, segmental volumes, and body surface area. Therefore, body scanning provides more comprehensive measurements than traditional anthropometric tools.
Although body-scanning technologies have been evolving for the past several decades, the application of 3-D body measurement for body composition assessment is still at an early stage. The development of body scanning initially focused on custom clothing, character animation, and other applications.14,15
Currently, several body scanners are commercially available, but their high price and large size limit their practicality for field studies. In addition, because software systems capable of performing body composition assessment are rarely available, there is a need to promote 3-D body measurement for body composition research. Thus, the purpose of this study was to develop a portable, low-cost, 3-D body surface imaging system that would be readily accessible to body composition researchers and public health practitioners.
The majority of current body scanners are based on laser scanning, structured light, or stereo vision. In laser scanning, consecutive profiles are captured by sweeping a laser stripe across the surface.16
This system is more intricate and expensive than the other two types as it involves moving parts. Structured light utilizes a sequence of regular patterns to encode spatial information.17
It requires dedicated lighting and a dark environment, which makes the hardware relatively more complex than stereo vision. Stereo vision18
works similarly in concept to human binocular vision, and in principle, it is a passive method that it is not dependent on a light source. The major challenge in stereo vision is in disparity computation when the surface is without texture. Unfortunately, human skin is not rich in texture. Therefore, a projector to generate artificial texture on the scanned surface is usually required in a practical system. Data acquisition by stereo vision is very fast because one image from each camera can be captured simultaneously. In contrast, images must be captured sequentially in synchronization with pattern projections in structured light. In the case of whole body scanning, image acquisition by structured light can be further slowed down, because multiple sensing units in the system cannot work simultaneously; otherwise pattern projections from different units may interfere with each other. Rapid data acquisition is critical to the curtailment of artifacts caused by body movements because a slight body position movement may induce unacceptable inaccuracy in quantification of body volume. Therefore, the new system presented here is based on stereo vision.