Hand Anthropometric Data for Saudi Arabia Engineering Students of Aged 20-26 Years at King Khalid University
- 1 Zagazig University, Egypt
Copyright: © 2020 Mohamed A.A. Mansour. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Anthropometric data plays a significant role in the effective and accurate design of various devices and machines. The inclusion of anthropometric data helps ensure that devices or machines are safe, user-friendly and highly productive and efficient. In this study, 56 hand dimensions based on 266 Saudi Arabian inter-university adult males aged 20-26 years are described in terms of statistics, bivariate correlations and multivariate regression models for predicting hand anthropometric dimensions. All hand dimensions were measured using the correct instruments and techniques. The statistics reported are the minimum, maximum, mean, standard deviation, percentiles (1st, 5th, 50th, 95th and 99th), normality, skewness and kurtosis. Bivariate correlations and multiple regression models are tabulated. The 56 hand dimensions of Saudi adult males are presented for use by the designers of hand tools and equipment. Most hand dimensions are positively correlated at a 0.01/0.05 level of significance. Thirteen multiple regression models were developed for estimating hand length from other hand dimensions with coefficient determination factors ranging from 0.881 to 0.962. In addition, multiple regression equations for estimating hand dimensions from hand length and breadth/fist circumference were developed. The information in this paper will be useful for ergonomic design and the modifications of hand tools, personal protective equipment, workstations and interface systems imported into Saudi Arabia to reduce human error and improve public health.
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- Saudi Arabia
- Hand Anthropometry
- Bivariate Correlation
- Hand Tool Design
- Multivariate Regression Models