Estimation of Stature and Weight using Selected Dominant Hand and Handprints Measurements among a Selected Young Adult Population in Sri Lanka

Abstract

Background: Estimation of stature, weight, and gender are the fundamentals of personal identification in forensic science of crime victims and missing person’s identification. Objective: To formulate sex specific simple and multiple regression formula to estimate stature and weight of an individual using dimensions of dominant hand and handprints among a group of selected young adults Sri Lanka Methods: The sample comprises 122 males and 234 females in the age range of 19-28 years at the KIU campus. Selected dominant hand dimensions, handprint measurements, and stature were measured. Independent sample t-test and linear regression were performed using the SPSS version 28.0. Results: Results showed that all the hand dimensions measured were higher in males and the differences were statistically significant (p<0.05). The selected hand and handprint measurements showed significantly positive correlation (p<0.05) with the stature and weight in both sexes. Derived sex-specific equations were employed to calculate the estimated stature and weight of individuals. Stature prediction accuracy for simple linear regression equation standard estimating error (SEE) was ranged from ±4.33 to 5.92 cm and ±4.04 to 5.22 cm for males and females. In case of right and left handprint measurements, the range was ±4.62 to 6.02 cm and ±4.62 to 5.88 cm for multiple regression the range was ±4.41 to 5.09 cm for dominant hand and handprints dimensions. Body weight prediction using hand dimensions SEE was ranged ±7.72 to 12.86 kg and ±5.69 to 9.57 kg for male and female while for multiple regression was ±7.42 kg and 5.44 kg. The multiple regression formulas were more reliable in predicting the individual’s weight and stature than the simple linear regression models as a lower SSE and higher R and R-square values. Conclusion: In the studied sample of young adults, dimensions of hand and handprints are reliable standards in estimating stature and weight. The multiple regression formulas were more reliable than the simple linear regression models as a lower SEE and higher R-square values. Keywords: Bodyweight, Hand and handprint, Prediction, Regression analysis, Stature