# A Review of Commercially Available 3D Surface Imaging Systems for Body Composition Estimation
## 1 Introduction  
Body composition describes and quantifies components of body mass [1]. It is regarded as a central element of health and sports practice and research due to the impact of body composition components on all physiological functions [2, 3]. Consequently, body composition is regarded as essential in countless fields including growth, development, aging, sports performance and training, nutrition, obesity, malnutrition, pathology, and physical activity. Its descriptions can be broken down into five levels: atomic, molecular, cellular, tissue-organ, and whole body [4]. For each level, there are various models that consist of several components, whereby body mass is considered as the sum of all components of the model. There are several methods through which the various levels and associated compartments of body composition can be estimated. These methods include:  
* Direct: chemical carcass analysis and neutron carcass activation analysis [5],
* Indirect: using the relationship between cadaver data and other measurements such as densitometry, total body counting (40 K) measurements, dilution techniques, magnetic resonance imaging (MRI), and dual-energy X-ray absorptiometry (DEXA) [5],* Doubly indirect: using the relationship between direct or indirect measurements and body parameters including anthropometry (such as skinkolds, weight, height, girths), bioelectrical impedance analysis (BIA), and ultrasound [5]. Reviews of these methods can be found within [PERSON] et al. [6] or [PERSON] et al. [7]. In summary, there is an unmet need for an accurate, noninvasive, ionizing radiation-free body composition measurement suitable for regular use [8]--particularly when physical touch is culturally, practically, or socially unacceptable. Recent literature has suggested three-dimensional (3D) surface imaging to be a possible method of estimating body composition [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. 3D surface imaging (also known as 3D body, optical or photonic scanning) creates digital 3D images of the external geometry of the human body, which when stitched together creates a digital--to scale--representation, herein referred to as a humanoid (also known as a digital model, avatar, or twin) [18]. Using this technology, body composition is estimated through anthropometry using anthropometrics extracted from the humanoid. As such estimation of body composition through 3D surface imaging is a doubly indirect method; the prediction of body composition using regression algorithms of the relationship between anthropometrics and direct or indirect methods [5]. Thus, 3D surface imaging body composition estimations are dependent upon the accuracy of the humanoid mesh--and similar to body composition estimations through manual anthropometry--the accuracy of landmarking, anthropometry definitions, and the predictive regression algorithm [10; 18]. This method provides the possibility of technician free, quick, contactless, ionizing radiation-free body composition measurement suitable for regular use [10; 18]. Thereby making the technology suitable for when physical touch is culturally, practically, or socially unacceptable. Previously, 3D surface imaging technology has been expensive and consequently inaccessible to many. However, the increasing usefulness of 3D surface imaging in entertainment, fashion, ergonomics, and health has bolstered the market [20], driving down prices and increasing accessibility. Each 3D surface imaging system uses different hardware, software, calibration techniques, anthropometric definitions, and data collection procedures--providing differing degrees of validity in body composition estimation [10; 18]. Thus, to ensure the selection of the most suitable system, the appropriate use and correct interpretation of its outputs, clarity of user and system details are necessary.