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The Scientific World Journal
Volume 2014, Article ID 541408, 6 pages
Research Article

A Study of Correlations within the Dimensions of Lower Limb Parts for Personal Identification in a Sudanese Population

1Department of Basic Medical Sciences, College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Mail Code 3127, P.O. Box 3660, Riyadh 11481, Saudi Arabia
2Anatomy Department, Faculty of Medicine, University of Khartoum, P.O. Box 102, Khartoum 11111, Sudan

Received 26 March 2014; Accepted 5 August 2014; Published 19 October 2014

Academic Editor: Silvano Bertelloni

Copyright © 2014 Altayeb Abdalla Ahmed. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The presence of an isolated limb or limb parts from different individuals presents a major challenge for medicolegal investigators in establishing identification in cases of wars, mass disasters, and criminal assaults because different populations have different sizes and proportions. The measurement of lower limb dimensions showed a high success rate in establishing individual identity in terms of sex and stature in various populations. However, there is a paucity of data concerning the correlation within the lower limb parts. This study aims to assess the existence of relationships within lower limb parts and to develop regression formulae to reconstruct limb parts from one another. The tibial length, bimalleolar breadth, foot length, and foot breadth of 376 right-handed Sudanese adults were measured. The results showed that all variables were significantly larger in males than in females. A significant positive correlation was found within the lower limb parts. Sex-specific linear equations and multiple regression equations were developed to reconstruct the lower limb parts in the presence of single dimension or multiple dimensions from the same limb. The use of multiple regression equations provided a better reconstruction than simple regression equations. These results are significant in forensics and orthopedic reconstructive surgery.