Anthropometry is a science which deals with the variables of human body such as weight, height, BMI etc. Theses variables show a unique relation in terms of their interdepended model. Thus, a genuine attempt is made by the research to identify the equation between anthropometric variables which may be used to predict one of the variables if other variables are known. Thus, variables for the study undertaken consist of weight, height, BMI, pulse rate and Blood pressure. Researcher has established a unique relation in terms of prediction equation, where researcher can predict blood pressure(y) in accordance with weight, height, waist, pulse rate, and sugar. The findings of the model established gave an accuracy of 68.30% with the error factor of 11%. This may be due to the other variables which were nor in the scope of the study.
Keywords: Anthropometry, Weight, Height, BMI, Pulse rate, Blood pressure and Sugar
1.1 Introduction
Anthropometry refers to the measurement of the human individual. An early tool of physical anthropology, it has been used for identification, for the purposes of understanding human physical variation, in palaeoanthropology and in various attempts to correlate physical with racial and psychological traits. Anthropometry involves the systematic measurement of the physical properties of the human body, primarily dimensional descriptors of body size and shape.[1] Alphonse Bertillon (1853–1914) is considered to be the father of anthropometry because of his many contributions to the field, including what we know today as the “mug shot.” [2]
1.2 Research Methodology 1.2.1 Aim of the research
The purpose of the research is to study the anthropometric variables and the impact of the same on blood pressure.
1.2.2 Method of data collection
The primary data collected from respondents include students of the College. Also, secondary data collect was from different biology and anatomy journals, internet etc.
1.2.3 Research design
The research resign deployed was descriptive as well as empirical in nature.
1.2.4 Sampling design
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The sampling design deployed was stratified simple random sampling method which includes 6 students, 7 Men and 5 women.
1.2.5 Research Instrument
The schedule prepared on the basis different anthropometric variables was formed to have a relevant data.
1.2.6 Mathematical Statistical Technique
The mathematical statistical technique deployed was Multiple regression.
1.2.7 Limitation of Study
1)The study was respected to a limited sample size.
2) Study do not cover an exhaustive list of anthropometric variables. 3) Only certain strata of people were selected for the sample.
1.3 Data Analysis and Findings
For the purpose of completing the research study, an attempt was made by the researcher to collect the relevant data and analyse the same using Multiple regression.
Blood pressure (High) Y Weight (Kg) X1 Height (Cms) X2 BMI (Cms) X3 Heart rate (bpm) X4 118 78.5 170 27.2 76 119 79.5 171 28.1 74 124 65 169 24.3 74 120 70 168 22.5 76 125 68 171 24.6 75 121 76 167 24.7 76 122 65 175 22.5 77 120 75 168 25.1 78 124 76 168 25,8 73 118 78.5 170 27.2 76 120 75 168 25.1 78 124 65 169 24.3 74 125 68 171 24.6 75 119 79.5 171 28.1 74 121 76 167 24.7 76 120 70 168 22.5 76
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Copyright © 2017, Scholarly Research Journal for Interdisciplinary Studies Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 219.538 44.477 4.936 .000 weight -.352 .160 -.783 -2.204 .050 height -.162 .244 -.138 -.665 .520 BMI .070 .481 .052 .146 .886 Heart_Beat -.621 .331 -.373 -1.876 .087
a. Dependent Variable: Blood_Pressure
Source : SPSS
ANOVAb
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 60.766 4 15.192 5.919 .009a
Residual 28.234 11 2.567
Total 89.000 15
a. Predictors: (Constant), Heart_Beat, weight, height, BMI b. Dependent Variable: Blood_Pressure
Source : SPSS Model Summary Mod el R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Chan ge 1 .826a .683 .567 1.60210 .683 5.919 4 11 .009
a. Predictors: (Constant), Heart_Beat, weight, height, BMI
Source : SPSS 1.4 Conclusion
From the above analysis and findings, it is concluded that the anthropometric variables have strong relation and has a strong impact on high blood pressure. The model derived from the data claim that there is a relation between high blood pressure, height, weight, BMI, heart rate, and pulse rate. Also, the authenticity of the model was calculated and was found to be accurate 68.30%. This plays an important role in industrial design, clothing design, ergonomics and architecture where statistical data about the distribution of body dimensions in the population are used to optimize products. Changes in
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lifestyles, nutrition, and ethnic composition of populations lead to changes in the distribution of body dimensions (e.g. the rise in obesity), and require regular updating of anthropometric data collections.
1.5 References
Ariful, Islam; Md. , Asadujjaman; Md. , Nuruzzaman; Md. Mosharraf, Hossain. "Ergonomics Consideration for Hospital Bed Design: A Case Study in Bangladesh". Journal of Modern Science and Technology 01 (01): 30-44.
"Anthropometry". biologydictionary.net. Retrieved 21 June2017.
Mogull, Robert G. (2004). Second-Semester Applied Statistics. Kendall/Hunt Publishing Company. p. 59. ISBN 0-7575-1181-3.
M. H. Kutner, C. J. Nachtsheim, and J. Neter (2004), "Applied Linear Regression Models", 4th ed., McGraw-Hill/Irwin, Boston (p. 25)
YangJing Long (2009). "Human age estimation by metric learning for regression problems" (PDF). Proc. International Conference on Computer Analysis of Images and Patterns: 74–82.
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