New Application of Bioelectrical Impedance Analysis by the Back Propagation Artificial Neural Network Mathematically Predictive Model of Tissue Composition in the Lower Limbs of Elderly People

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.

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Predicting composition of leg sections with anthropometry and bioelectrical impedance analysis, using magnetic resonance imaging as reference

Clinical Science ◽

10.1042/cs0960647 ◽

1999 ◽

Vol 96 (6) ◽

pp. 647-657 ◽

Cited By ~ 38

Author(s):

N. J. FULLER ◽

C. R. HARDINGHAM ◽

M. GRAVES ◽

N. SCREATON ◽

A. K. DIXON ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Adipose Tissue ◽

Magnetic Resonance ◽

Coefficient Of Variation ◽

Lower Limb ◽

Bioelectrical Impedance Analysis ◽

Bioelectrical Impedance ◽

Impedance Analysis ◽

Tissue Composition ◽

Resonance Imaging

Magnetic resonance imaging (MRI) was used to evaluate and compare with anthropometry a fundamental bioelectrical impedance analysis (BIA) method for predicting muscle and adipose tissue composition in the lower limb. Healthy volunteers (eight men and eight women), aged 41 to 62 years, with mean (S.D.) body mass indices of 28.6 (5.4) kg/m2 and 25.1 (5.4) kg/m2 respectively, were subjected to MRI leg scans, from which 20-cm sections of thigh and 10-cm sections of lower leg (calf) were analysed for muscle and adipose tissue content, using specifically developed software. Muscle and adipose tissue were also predicted from anthropometric measurements of circumferences and skinfold thicknesses, and by use of fundamental BIA equations involving section impedance at 50 kHz and tissue-specific resistivities. Anthropometric assessments of circumferences, cross-sectional areas and volumes for total constituent tissues matched closely MRI estimates. Muscle volume was substantially overestimated (bias: thigh, -40%; calf, -18%) and adipose tissue underestimated (bias: thigh, 43%; calf, 8%) by anthropometry, in contrast to generally better predictions by the fundamental BIA approach for muscle (bias: thigh, -12%; calf, 5%) and adipose tissue (bias: thigh, 17%; calf, -28%). However, both methods demonstrated considerable individual variability (95% limits of agreement 20–77%). In general, there was similar reproducibility for anthropometric and fundamental BIA methods in the thigh (inter-observer residual coefficient of variation for muscle 3.5% versus 3.8%), but the latter was better in the calf (inter-observer residual coefficient of variation for muscle 8.2% versus 4.5%). This study suggests that the fundamental BIA method has advantages over anthropometry for measuring lower limb tissue composition in healthy individuals.

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Application of Artificial Neural Network for Predicting Shaft and Tip Resistances of Concrete Piles

Earth Sciences Research Journal ◽

10.15446/esrj.v19n1.38712 ◽

2015 ◽

Vol 19 (1) ◽

pp. 85-93 ◽

Cited By ~ 56

Author(s):

Ehsan Momeni ◽

Ramli Nazir ◽

Danial Jahed Armaghani ◽

Harnedi Maizir

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Back Propagation ◽

Load Test ◽

Static Load Test ◽

Concrete Piles ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

Artificial Neural Network Ann

Axial bearing capacity (ABC) of piles is usually determined by static load test (SLT). However, conducting SLT is costly and time-consuming. High strain dynamic pile testing (HSDPT) which is provided by pile driving analyzer (PDA) is a more recent approach for predicting the ABC of piles. In comparison to SLT, PDA test is quick and economical. Implementing feed forward back-propagation artificial neural network (ANN) for solving geotechnical problems has recently gained attention mainly due to its ability in finding complex nonlinear relationships among different parameters. In this study, an ANN-based predictive model for estimating ABC of piles and its distribution is proposed. For network construction purpose, 36 PDA tests were performed on various concrete piles in different project sites. The PDA results, pile geometrical characteristics as well as soil investigation data were used for training the ANN models. Findings indicate the feasibility of ANN in predicting ultimate, shaft and tip bearing resistances of piles. The coefficients of determination, R², equal to 0.941, 0.936, and 0.951 for testing data reveal that the shaft, tip and ultimate bearing capacities of piles predicted by ANN-based model are in close agreement with those of HSDPT. By using sensitivity analysis, it was found that the length and area of the piles are dominant factors in the proposed predictive model. ResumenLa Capacidad Axial de Soporte (ABC, en inglés) de un pilote de construcción se determina usualmente a través de una Prueba de Carga Estática (SLT, inglés). Sin embargo, estas pruebas son costosas y demandan tiempo. La evaluación de las Dinámicas de Alto Esfuerzo de Pilotes (HSDPT, inglés), que la provee el programa de Análisis de Excavación (PDA, inglés), es una forma de aproximación más reciente para preveer la Capacidad Axial de Soporte. En comparación con la Prueba de Cargas Estática, la evaluación PDA es rápida y económica. La implementación de Redes Neuronales Arficiales (ANN, en inglés) que permita resolver problemas geotécnicos ha ganado atención recientemente debido a su posibilidad de hallar relaciones no lineales entre los diferentes parámetros. En este estudio se propone un modelo predictivo ANN para estimar la Capacidad Axial de Soporte de pilotes y su distribución. Para fines de una red de construcción se realizaron 36 pruebas PDA en pilotes de diferentes proyectos. Los resultados de los Análisis de Excavación, las características geométricas de los pilotes, al igual que los datos de investigación del suelo se utilizaron para probar los modelos ANN. Los resultados indican la viabilidad del modelo ANN en predecir la resistencia de los pilotes. Los coeficientes de correlación, R², que alcanzaron 0.941, 09.36 y 0.951 para la evaluación de los datos, revelan que la capacidad del pilotaje en el último rodamiento, en el cojinete del eje y en la punta que se predijeron con el modelo ANN concuerda con las establecidas a través del HSDPT. A través del análisis de respuesta se determinó que la longitud y el área de los pilotes son factores dominantes en el modelo predictivo propuesto.

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Validation of bioelectrical impedance analysis for estimating limb lean mass in free-living Caucasian elderly people

Clinical Nutrition ◽

10.1016/j.clnu.2016.04.011 ◽

2017 ◽

Vol 36 (2) ◽

pp. 577-584 ◽

Cited By ~ 7

Author(s):

Marina De Rui ◽

Nicola Veronese ◽

Francesco Bolzetta ◽

Linda Berton ◽

Sara Carraro ◽

...

Keyword(s):

Elderly People ◽

Lean Mass ◽

Bioelectrical Impedance Analysis ◽

Bioelectrical Impedance ◽

Impedance Analysis ◽

Free Living

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Body Composition and Bioelectrical Impedance Analysis-Derived Raw Variables in Pole Dancers

10.21203/rs.3.rs-53472/v1 ◽

2020 ◽

Author(s):

Giada Ballarin ◽

Luca Scalfi ◽

Fabiana Monfrecola ◽

Paola Alicante ◽

Alessandro Bianco ◽

...

Keyword(s):

Body Composition ◽

Bioelectrical Impedance Analysis ◽

Bioelectrical Impedance ◽

Impedance Analysis ◽

Lower Limbs ◽

Muscle Quality ◽

Whole Body ◽

Training Level ◽

Upper Limbs ◽

High Frequencies

Abstract Background: Pole dance is a type of functional training whose effects on body composition have been only poorly explored. Bioelectrical impedance analysis (BIA)is a field method to estimate fat-free mass (FFM), fat mass (FM), etc. In addition, of particular interest for athletes, raw BIA variables such as impedance ratio =IR (between impedance-Z at high frequencies and Z at low frequencies) and phase angle=PhA may be considered as promising markers of muscle quality since they arerelated to body cell mass (BCM) and the ratio between extracellular water and intracellular water.The aim of the study was to evaluate the effect of pole dancing training on body composition and especially on IR and PhA of the whole body, upper limbs and lower limbs. Methods: Forty female pole dancers (age 27.4±5.1 years, body weight 57.0±6.9 kg, body mass index-BMI 22.2±2.3 kg/m²) and fifty-nine control young women (26.8±4.7 years, 58.6±6.4 kg, BMI 22.3±1.8 kg/m²) participated in the study.BIA was performed on the whole body, upper limbs and lower limbs at 5-50-100-250 kHz. FFM, FFM index, FMand percentage of FM were then predicted. Raw BIA variables were also considered: IR and PhA, and also bioelectrical impedance indexes (stature²/Z, related to body water compartments). Arm muscle area (AMA) and arm fat area (AFA) were calculated from triceps skinfold and arm circumference. Results: Compared to controls pole dancers exhibited higher FFM index and BI indexes at low and high frequencies as well as lower percentage of FM. Whole-body BI indexes correlated with AMA but not with AFA. PhA was greater and IRs were smaller in pole dancers than controls for the whole body and more markedly for upper limbs, whereas there were no differences for lower limbs. When considering training level, professional and amateur pole dancers did not differ with respect to body weight and BMI. After adjusting for weight, FFM and FFMI were greater in the more trained than in the less trained group, while FM and %FM were smaller. Whole-body PhA and IRs as well as BI indexes tended to be higher in the professionalsthan amateurs, with much more significant differences in upper limb PhA and IRs. Conclusions: Pole dance training has a significant effect, possibly depending also on training level, not only on FFM and FM, but also on those raw BIA variables that may be considered as markers of muscle quality.

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