Models to estimate the palm surface area of students in a tertiary institution in Abeokuta, Nigeria

The need to use palm surface area (PSA) in ergonomics, burn therapy, thermal physiology, and medicine necessitated the studies on its determination. The purpose of this study was to determine the PSA of students in a tertiary institution in Nigeria, propose a model to obtain the PSA, and use the neural network to reflect the effects of age, height, and weight on PSA. A total of 400 students comprising of 185 male and 215 female students aged between 16- and 25-years old were used for the study. Nonlinear regression analysis on SPSS 16.0 statistical package was used to extract the best fit correlation for Nigerian students with PSA as the dependent variable; and height, weight, and age as independent variables. The neural network on SPSS 16.0 was used to design the multilayer perceptron to predict PSA using age, height, and weight of the students. The estimated values using the models were not statistically different from the obtained values from the study (p = 0.000). The prediction of PSA showed that the neural network can be trained with almost no significant error of the output and that there was a nonlinear relationship between the input and output variables.