# Application of Artificial Neural Network to Somatotype Determination
## 1 Introduction  
Somatometry is a fundamental research method in anthropology, involving the measurement of individual body proportions and sizes. A somatotype assessment gives a categorization of physique by using anthropometric measurement relating to body shape and composition, such as adiposity (fatness), musculo-skeletal build, and linearity or slenderness [1]. Somatotype \"expresses genetic determinism, observed from the morpho-constitutional point of view\" [2]. The somatotype describes the actual morphologicalstate of the individual expressed by three numbers, each of which represents one of three primary body components: endomorphy--mesomorphy--ectomorphy [3]. Simplifying, endomorphy--fatness, mesomorphy--muscular, while ectomorphy defines thinness. The somatotype changes with age, and e.g., ectomorphy in females aged over 60 years has decreased and females tend to higher endomorphy values [4]. The evaluation of a somatotype and body composition is important for the selection of sporting activities [5]. Somatotyping in elite athletes is important for studying the dynamics of development of specific body shape during training processes and competitive periods [6; 7; 8]. It also helps for developing body building and performance [1; 6; 9]. Somatotype characteristics are a subject of research for a relation between somatotype, obesity, and particular features of nutrition [10], as well as the prevalence of several chronic diseases [11; 12; 13]. The most common method of somatotyping used today is the Heath-Carter method, which calculates the somatotype base on 10 anthropometric parameters using standard methods and licensed anthropometric instruments, without restrictions on ethnicity, gender or age (above 2 years) [14]. Values of anthropometric measures are entered into equations. The Heath-Carter method involves traditional methods for body fat estimation using skinfold calipers. The accurate assessment is strong depending on the technical experience and training, and requires specially trained and experienced personnel to perform it. It was shown that inter- and intra-individual variability in the selection of skinfold sites, depth and time delay in reading the calipers reduce the accuracy of this procedure and remain a major source of error associated with this technique [15; 16]. Moreover, the disadvantages of anthropometric measurements limit the availability of large-scale research [17]. Measurements should be made in several repetitions by 2 researchers, can be tiring and depressing for the participant and can be negatively perceived as interference with the physical space. In addition, anthropometric measurements involving the naked body of the patient may be difficult to accept and should, therefore, be limited using other methods. Next to skinfolds measurement to establish body composition (fat content, muscle mass and water) relatively simple and more accurate method is bioelectrical impedance analysis (BIA). This is the most common, non-invasive, low-cost used method in sports, dietetics, medicine, healthcare. BIA bases on the electrical properties of biological tissues, and is defined as the ability of biological tissue to impede electric current [18]. Specific equations programmed in the BIA devices are used to calculate the body components, taking into account gender, age, height, weight and ethnicity. BIA, in comparison with the skinfold method mentioned above, gives more precise results determining lean or fat mass in humans [19; 20]. The BIA method omits the procedures of repeatedly touching the patient, especially in places considered sensitive. Physical contact is generally limited, which has become more important recently. However, BIA is a non-invasive, easy to use and low-cost method [21], and gives an opportunity to the evaluation of somatotype based on created formulas [17]. In literature, a few formulas based on BIA data and linear regression analysis were proposed for different population groups to assess the endomorphy and mesomorphy [17; 22; 23].