1Assistant Professor,
2Assistant Professor,
3Associate Professor,
4Associate Professor,
5Associate Professor,
*Corresponding author: E-mail address: mukul.med@gmail.com
Identification plays a crucial role in forensic and medico-legal investigations. In situations where only body fragments such as hands or feet are available, height estimation becomes challenging. While previous studies have explored stature estimation using long bones, limited research has focused on hand length, particularly in non-Caucasian populations. This study aims to establish the correlation between hand length and height and derive regression equations for stature estimation in the Raebareli population.
A cross-sectional study was conducted at a tertiary health care centre, involving 200 adult participants (100 males, 100 females) aged 20 to 23 years. Hand length and height were measured following standardized anthropometric protocols. Statistical analyses, including Pearson’s correlation and linear regression, were performed using Jamovi version 2.6.44.
The findings revealed a strong positive correlation between hand length and height for both sexes, with all correlations being statistically significant (p < 0.001). For males, the correlation coefficients for left- and right-hand length were r = 0.619 and r = 0.612, respectively. For females, these values were slightly higher at r = 0.627 and r = 0.640, respectively. Linear regression equations were derived separately for males and females, showing that hand length serves as a reliable predictor of height.
This study confirms that hand length exhibits a strong and significant correlation with height in both males and females. The regression models developed provide an effective method for stature estimation, particularly in forensic cases where only hand fragments are available. These findings are especially relevant for the population of Raebareli and similar ethnic groups, offering valuable anthropometric and forensic applications.
Hand length, Height estimation, Anthropometry, Forensic identification, Regression analysis, Raebareli population