Abstract
Somatotype characteristics are important for the selection of sporting activities, as well as and the prevalence of several chronic diseases. Nowadays the most common method of somatotyping is the Heath–Carter method, which calculates the somatotype base on 10 anthropometric parameters. Another possibility for evaluation of somatotype gives commonly used bioelectrical impedance analysis), but the accuracy of the proposed formulas is questioned. Therefore, we aimed to investigate the possibility of applying an artificial neural network to achieve the formulas, which allow us to determine the endomorphy and mesomorphy using data on body height and weight and raw bioelectrical impedance analysis data in young women. The endomorphy (Endo), ectomorphy (Ecto), and mesomorphy (Meso) ratings were determined using artificial neural networks and the Heath–Carter method. To identify critical parameters and their degree of impact on the artificial neural network outputs, a sensitivity analysis was performed. The multi-layer perceptron MLP 4-4-1 (input: body mass index (BMI), reactance, resistance, and resting metabolic rate) for the Endo somatotype was proposed (root mean squared error (RMSE) = 0.66, χ2 = 0.66). The MLP 4-4-1 (input: BMI, fat-free mass, resistance, and total body water) for the Meso somatotype was proposed (RMSE = 0.76, χ2 = 0.87). All somatotypes (Endo, Meso and Ecto) can be calculated using MLP 2-4-3 (input: BMI and resistance) with accuracy RMSE = 0.67 and χ2 = 0.51. The bioelectrical impedance analysis and Heath–Carter method compliance was evaluated with the statistical algorithm proposed by Bland and Altman. The artificial neural network-based formulas allow us to determine the endomorphy and mesomorphy in young women’s ratings with high accuracy and agreement with the Heath–Carter method. The results of our study indicate the successful application of artificial neural network-based model in predicting the somatotype of young women. The artificial neural network model can be practically used in bioelectrical impedance analysis devices in the future.
Highlights
Somatometry is a fundamental research method in anthropology, involving the measurement of individual body proportions and sizes
It should be noted that the ectomorphy is be calculated on the basis of height and weight, it does not require further simplifications and it was excluded in the calculation from MLP with one output layer
The present study showed the possibility of Ecto, Endo and Meso somatotypes calculation in young women using artificial neural networks (ANNs) based on the bioelectrical impedance analysis (BIA) results
Summary
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 morphoconstitutional point of view” [2]. The somatotype describes the actual morphological state 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—muscularity, 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]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
