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Abstract

Stature estimation is a crucial step in the identification of dismembered and skeletonized human remains. It indicates the individual experiences of nutritional intake, genetic makeup, and disease history. The adult Yadav population of Firozabad, Uttar Pradesh, was considered in this study for stature estimation on the basis of hand and foot lengths, and the r value is determined using a simple linear regression analysis. The sample size includes 380 participants, consisting of 190 male participants and 190 female participants aged between 18 and 64 years. The assessed parameters included age, stature, right hand length (RHL), left hand length (LHL), right foot length (RFL), and left foot length (LFL). As compared to males, females exhibited a higher correlation (r = 0.665) between stature and LHL. The derived regression formulas were, stature = 70.632 + 4.799(HL) ± 5.489 cm and stature = 74.860 + 3.291(FL) ± 5.344 cm for females and stature = 108.757 + 3.091(HL) ±10.654 cm and stature = 81.028 + 3.291(FL) ± 11.284 cm for males. The mean age, stature, RHL, LHL, RFL and LFL were 44 ± 11 years, 166.11 ± 6.71 cm, 18.51 ± 1.06 cm, 18.59 ± 1.04 cm, 25.84 ± 1.28 cm and 25.86 ± 1.23 cm in case of males and 36 ± 11 years, 151.93 ± 5.78 cm, 17.03 ± 0.78 cm, 17.03 ± 0.81 cm, 23.41 ± 1.13 cm and 23.41 ± 1.15 cm in case of the females, respectively. In the study population, the stature and LFL showed the highest correlation value (r = 0.814). Based on these findings, it is concluded that foot length of individuals is a more dependable variable for precise estimation of stature in the Yadav population.

Keywords

Forensic science forensic anthropology stature estimation regression model hand and foot dimensions

Introduction

Personal identification is a primary objective in the field of forensic research. The estimation of stature is widely recognized as a critical component of forensic anthropology, contributing significantly to the establishment of an individual’s biological profile. Stature refers to a person’s natural height when they are standing upright.^{1, 2} It is considered one of the key criteria, often referred to as the “big four,” in forensic anthropology for the purpose of identifying individuals, along with sex, age, and ancestry.¹ Leonardo da Vinci’s promotion of Vitruvius’ work and his idea that the human body has specific proportions set the stage for the development of stature measurement. His classic painting, “The Vitruvian Man,” used illustrations of these proportions.³

Researchers have been working to develop the latest techniques for estimating stature over time to assist in personal identification.^1–4 These techniques can be either mathematical or anatomical. The anatomical methods, first proposed by Dwight in 1894, estimate the overall height. Later, in 1956, fully modified the anatomical method for stature estimation.⁴ In this method, the height of the vertebral column, skull, tibia, calcaneus, talus, and femur is all added together. The main drawback of this method is that it often cannot provide an accurate estimate of stature because not all of these bones are always available.⁴

The mathematical approach utilizes one or more bones to create regression equations that estimate a person’s stature.⁵ Due to the extensive use of regression models by forensic scientists, measurements of certain body parts can now be easily employed to predict the anthropometry of another part, such as using measurements of the hand and foot to estimate stature.^6–12

Interactions between genetic and environmental factors can lead to variations within and between populations. Numerous studies have shown the importance of tailoring regression formulas to specific sex and population characteristics to ensure accuracy and relevance.^13–23

Most studies conducted on stature estimation in India tend to overlook the significance of sub-ethnic groups, which typically consist of homogeneous populations.^{6–11, 18, 19, 21} There is still a lack of information regarding the most accurate model for estimating adult stature using hand and foot measurements within the Yadav population. This study, the first of its kind, focuses on a homogenous population with a shared genetic background and exposure to a single environment, particularly a rural setting. By controlling for environmental and genetic heterogeneity, this study aims to offer insights into the most effective method for estimating stature using hand and foot lengths among the adult Yadav population. This research employed a basic linear regression methodology and correlation coefficients (R values) to determine the most effective method for estimating stature based on hand and foot measurements among the adult Yadav population.

Materials and Methods

Study Design and Sample Size

In this cross-sectional study, a population-based assessment was conducted among the Yadav community residing in the rural areas of Firozabad district, Uttar Pradesh, India. The Yadavs, a non-elite, endogamous peasant group, have historically received limited attention in research, with a predominant focus on urban populations. By conducting this study in a rural context, we aim to fill this knowledge gap and provide insights into stature estimates that are particularly relevant to the Yadav population. We included 380 individuals (190 males and 190 females) from the Yadav community in Firozabad, Uttar Pradesh, for this study. The research was carried out following approval from the institutional research and ethics committee, and written informed consent was obtained from all subjects after a comprehensive explanation of the methodology.

Number and Sampling of the Subject

Using G*power 3.1 by Faul et al. (2009), we calculated the required minimum sample size to be 59.24 Drawing from a similar study conducted previously by Asadujjaman et al. (2019), we set the effect size (f2) at a value of 0.35, the significance level (α) at 0.05, the statistical power (1–β) at 0.80, and the number of predictors at four. Initially, a total of 488 people were selected through simple random sampling to achieve the target of 244 responses. However, due to an expected dropout rate of 50% and the fact that sample collection primarily occurred during morning hours when many individuals were engaged in occupational commitments, rendering them unavailable for sample provision, the sample size was subsequently reduced to 380. The study included 190 male and 190 female Yadav individuals between the ages of 18 and 64 who provided samples for the research.

Selection of Subject

Inclusion Criteria

This study consisted of healthy male and female participants of the age range 18–64 years, belonging to the Yadav community residing in the rural areas of Firozabad district, Uttar Pradesh.

Exclusion Criteria

The study excluded individuals with specific medical conditions and characteristics that could affect stature measurements. This included those with physical deformities, injuries, diseases, fractures, amputations, or a history of surgical procedures that impact stature. Furthermore, participants with neurological conditions that could influence limb measurements, such as cerebrovascular disease, were not included. Additionally, the study excluded individuals with limb abnormalities such as meromelia, polydactyly, or syndactyly. Those of tribal or mixed ancestry who had a previous medical history of genetic disorders such as Marfan’s syndrome or achondroplasia, as well as individuals with endocrine disorders such as dwarfism, acromegaly, and diabetes mellitus, were also not part of the study. Final, participants with a history of acquired trauma that could potentially affect stature were excluded from the research.

Procedure for Collection of Anthropometric Measurements

Before conducting any measurements, participants were provided with detailed information regarding the objectives of the study. Each participant willingly participated in the measurement protocol after granting written informed consent, indicating their comprehension of the aims of the study. All measurements were taken during the morning hours to minimize the influence of diurnal variation and obtain reliable data. The standard measuring methods described by Marfell-Jones (2012) were followed, and a standard measuring instrument with a centimeter scale was used to determine stature, hand length, and foot length.²³

Stature measurements were obtained using an “anthropometer.” Stature, in this context, refers to the height achieved when a person stands upright. To measure stature, participants were instructed to stand in an erect position without wearing any footwear or headgear. The measurement involved assessing the distance from the ground to the highest anatomical point on the head.¹⁰

Hand measurements were taken using an “anthropometric rod compass.” To do this, we first identified and marked the styloid processes of both the radius and ulna using a marker pen. Subsequently, we drew an interstyloid line and marked its midpoint. The hand length was measured from the midpoint of the inter-styloid line to the fingertip of the middle finger, and measurements were recorded for both the right and left hands.⁷

Foot measurements were taken using an “anthropometric rod compass.” The foot length was determined by measuring the farthest distance between the heel and the tip of the longest toe. Both the left and right feet were assessed for their lengths.¹⁰

Each participant underwent dual testing, and the mean value was used to reduce potential measurement errors when all readings fell within a 0.4-cm range. In cases where the 0.4-cm criteria were not met, two initial assessments were conducted, and the mean result was utilized.

Following the guidelines of Pederson and Gore (2004),25 the lead researcher calculated the mean value through dual measurements and conducted intra-observer reliability and validity assessments to evaluate measurement dependability and accuracy. To establish the association between stature and each factor, a regression equation of the form “stature = constant + regression coefficient × variable” was employed.

Statistical Analysis

The SPSS software (version 20.0) and Microsoft Excel 2020 were used for data analysis and to assess the statistical significance. Reliability assessments are vital to ensure that the data and measurements are consistent, dependable, and not significantly affected by random variability or measurement error. The study involved repeated measurements made by the same observer. The intraclass correlation coefficient is well-suited for such scenarios and is designed to assess the consistency among multiple measurements made by the same observer. The reliability of the measurements was evaluated through the intraclass correlation coefficient. The precision, accuracy, and validity of the instruments were determined by calculating the relative and absolute technical errors of measurement, as well as the coefficient of reliability. Simple linear regression was employed to analyze the association between the independent variables, including right- and left hand length and foot length, and the dependent variable, measured stature. The beta coefficients and constants for all variables were computed. An unpaired t-test was utilized to calculate the mean differences between the measured and estimated stature. Various models were compared using the standard errors of the estimates and R values. Furthermore, a multiple regression analysis was conducted to predict stature based on combined hand and foot somatometric measurements. A two-tailed p value of .001 was regarded as statistically significant.

Results

Descriptive statistics were calculated for all 380 studied participants, and the results are presented in Tables 1 –4. Table 1 provides an overview of the mean age, stature, right and left hand lengths, as well as right and left foot lengths for all participants, regardless of their gender. The average age across the cohort was 40 years, with mean values for stature, right and left hand length, right and left foot length recorded as 155.47 cm, 17.40 cm, 17.42 cm, 24.02 cm, and 24.02 cm, respectively.

Table 1.

Descriptive Statistics for Age and Anthropometric Measurements of Study Participants.

Parameter	Mean	SEM	SD	Min	Max	Var
Age (years)	40	1	12	18	64	135
Stature (cm)	155.47	0.44	8.60	136.40	186.30	74.11
RHL (cm)	17.40	0.05	1.07	14.40	21.80	1.16
LHL (cm)	17.42	0.05	1.10	14.30	21.60	1.22
RFL (cm)	24.02	0.08	1.57	20.30	29.30	2.48
LFL (cm)	24.02	0.08	1.58	20.30	29.20	2.50

Note: SEM = Standard error of mean, SD = Standard deviation, Var = Variance, Min = Minimum, Max = Maximum, RHL = Right hand length, LHL = Left hand length, RFL = Right foot length, LFL = Left foot length.

Table 2.

Descriptive Statistics for Age and Anthropometric Measurements of Male Participants.

Parameter	Mean	SEM	SD	Min	Max	Var
Age (years)	44	1	11	18	64	119
Stature (cm)	166.11	0.68	6.71	150.80	186.30	45.05
RHL (cm)	18.51	0.10	1.06	14.40	21.80	1.14
LHL (cm)	18.59	0.10	1.04	14.30	21.60	1.10
RFL (cm)	23.41	0.13	1.28	22.40	29.30	1.63
LFL (cm)	23.41	0.12	1.23	22.40	29.20	1.52

The results of Tables 2 and 3 provide quantitative descriptions of the measured variables, focusing on females and males as separate groups. The study observed that males generally exhibited higher mean values for all measured parameters, including stature, foot length, and foot width, compared to females. The results of Table 4 present correlation coefficients, regression equations, and correlation tests to illustrate the relationships between variables. Overall, the left foot showed a stronger correlation with stature (r = 0.814). However, when examined by gender, stature demonstrated a higher correlation with the left hand in females (r = 0.665) and the left foot in males (r = 0.624).

Table 3.

Descriptive Statistics for Age and Anthropometric Measurements of Female Participants.

Parameter	Mean	SEM	SD	Min	Max	Var
Age (years)	38	1	11	18	62	132
Stature (cm)	151.93	0.34	5.78	136.40	174.30	33.51
RHL (cm)	17.03	0.04	0.78	15.30	19.80	0.62
LHL (cm)	17.03	0.04	0.81	15.20	20.10	0.66
RFL (cm)	23.41	0.06	1.13	20.30	27.40	1.28
LFL (cm)	23.41	0.06	1.15	20.30	27.50	1.33

Table 4.

Pearson’s Correlation, Significance, and Regression Analysis of Stature Predicted from Hand and Foot Length.

Parameter (Stature vs.)	Correlation Value (R)	p Value	Inference (Correlation)	Regression Formula
All Subjects
Right hand length	0.757	.00	High and very significant	Stature = 50.407 + 6.029 × RHL ± 4.393
Left hand length	0.777	.00	High and very significant	Stature = 50.407 + 4.772 × LHL ± 5.383
Right foot length	0.809	.00	High and very significant	Stature = 49.316 + 4.419× RFL ± 3.976
Left foot length	0.814	.00	High and very significant	Stature = 49.178 + 4.425 × LFL ± 3.913
Female
Right hand length	0.658	.00	High and very significant	Stature = 69.773 + 4.826 × RHL ± 5.595
Left hand length	0.665	.00	High and very significant	Stature = 71.492 + 4.772 × LHL ± 5.383
Right foot length	0.649	.00	High and very significant	Stature = 74.486+ 3.307 × RFL± 5.408
Left foot length	0.654	.00	High and very significant	Stature = 75.235 + 3.276 × LFL ± 5.281
Male
Right hand length	0.461	.00	Significant	Stature = 112.558 + 2.893 × RHL ± 10.720
Left hand length	0.514	.00	High and significant	Stature = 104.956 + 3.289 × LHL ± 10.589
Right foot length	0.609	.00	High and very significant	Stature = 83.599 + 3.192 × RFL ± 11.164
Left foot length	0.624	.00	High and very significant	Stature = 78.457 + 3.390 × LFL ± 11.405

Note: **The correlation exhibits statistical significance at the 0.01 significance level (two-tailed).

In essence, the results indicated gender differences in certain parameters, such as stature, foot, and hand measurements. Moreover, the correlation between stature and different body parts was found to vary across genders. These findings may have implications for understanding the relationship between body dimensions and overall physical development, as well as for the development of appropriate measurement tools for use in both sexes.

Discussion

For a significant duration, a close association has been established between an individual’s stature and the measurements of different body segments; these findings are commonly used in medico-legal inquiries.⁴ Rollet’s (1889) pioneering work sheds light on the fascinating interplay between height and long bone length, serving as a testament to his remarkable investigative prowess. With meticulous care, he scrutinized the femur, fibula, tibia, radius, ulna, and humerus of French cadavers, taking precise measurements and compiling a comprehensive report.⁵⁷ Human hands and feet have been identified as reliable indicators of stature for forensic identification, particularly in cases of accidents, mass disasters, or situations where only mutilated bodies are encountered.^{6–23, 27–56} Anthropologists have employed predictive regression models to accurately estimate stature by utilizing somatometric measurements of various body parts, such as hand and foot lengths.^{9, 12, 24}

This (anthropological) population-based study, conducted in India, aimed to predict an individual’s stature based on the lengths of their hands and feet and the interrelationships between these measurements. It was determined that this approach can provide valuable and precise data for use in estimation and identification procedures within the field of forensics and other investigative sciences. Several authors^27–56 have documented significant variations in stature among different populations. In the Indian subcontinent, multiple researchers have conducted studies on stature estimation. India, being a vast country with diverse population groups exhibiting numerous interracial and interethnic differences, experiences variations in stature across different regions. Consequently, the formulas used for estimating stature also exhibit variations among these diverse population groups. The population under study demonstrated a significant positive correlation between stature and both hand and foot length, which aligns with findings in related research.^{7, 8, 10, 11, 27} Furthermore, males exhibited greater somatometric dimensions compared to females (for males: Stature = 166.11 ± 6.71 cm, right hand length (RHL) = 18.51 ± 1.06 cm, left hand length (LHL) = 18.59 ± 1.04 cm, right foot length (RFL) = 23.41 ± 1.28 cm, left foot length (LFL) = 23.41 ± 1.23 cm; for females: Stature = 151.93 ± 5.78 cm, RHL = 17.03 ± 0.78 cm, LHL = 17.03 ± 0.81 cm, RFL = 23.41 ± 1.13 cm, LFL = 23.41 ± 1.15 cm).

The observed somatometric differences between males and females could be attributed to the earlier onset of pubertal development in females compared to males, which leads to limb growth ceasing more quickly in females. In this study, the coefficient of correlation for stature and left hand length was notably stronger in females with an r value of 0.65 (p < .01) than in males with an r value of 0.51 (p < .01). However, it’s important to note that the correlation coefficients observed in this research were comparatively lower for both genders when compared to similar studies. These differences may be attributed to racial and population variations, which could have influenced the outcomes.^{7, 27}

Female foot length showed a statistically significant correlation with stature (r = 0.65, p < .01) as compared to the male counterparts. These results align with the similar studies of Krishan (2008) and Khanapurkar et al., 2012.^{8, 39} The stature predictions obtained from the regression equation utilized in this study are highly appropriate and applicable equations available for the Yadav population of Uttar Pradesh, as they have been tested randomly on individuals of the Yadav population of Uttar Pradesh. The formula for females is S = 4.826(RHL) + 69.773 cm; S = 4.772(LHL) + 71.492 cm; S = 3.307(RFL) + 74.486 cm; and S = 3.276(LFL) + 75.235 cm and while for male it was S = 2.893(RHL) + 112.558 cm; S = 3.289(LHL) + 104.956 cm; S = 3.192(RFL) + 83.599 cm; and S = 3.390(LFL) + 78.457 cm. These regression equations, utilized to estimate the stature from hand and foot measurements, will contrast across different populations due to the inherent differences in human body dimensions from one region to another.

Based on the results obtained from the study, it becomes evident that the regression equation used for stature estimation exhibits distinct variations between male and female cohorts. Furthermore, upon examining findings from various authors, it is clear that no two regression equations are congruent. Tables 5 and 6 present previously reported studies that explore the relationship between hand length and foot length with stature in various geographical regions.

Table 5.

Showing the Relationship of Hand Length with Stature.

S. No.	Author	Region	Population	Gender	r value	Linear Regression Equation
1.	Krishan and Sharma, 2007⁷	Himachal Pradesh	Students	Male	0.599	S = 88.243 + 4.39 HL
				Female	0.686	S = 81.314 + 4.42 HL
2.	Rastogi et al., 2008¹³	North India	Students	Male	0.660	H = 74.691 + 4.866 HL
				Female	0.737	H = 73.374 + 4.754 HL
3.	Rastogi et al., 2008¹³	South India	Students	Male	0.734	H = 65.979 + 5.323 HL
				Female	0.701	H = 79.953 + 4.398 HL
4.	Agnihotri et al., 2008¹⁴	Mauritius	Students	Male	0.594	H = 94.835 + 4.187 HL
				Female	0.739	H = 74.404 + 4.945 HL
5.	Ilayperuma et al., 2009¹⁵	Sri Lankan	Students	Male	0.58	H=103.732 + 3.493 HL
				Female	0.59	H = 93.689 + 3.625 HL
6.	Habib et al., 2010¹⁶	Egyptian	Students	Male	0.697	S = 57.70 + 6.06 RHL
				Female	0.495	S = 101.13 + 3.39 RHL
7.	Chikhalkar et al., 2010¹⁷	Mumbai	Students	Male and female	0.590	H = 116.69 + 2.665 HL
8.	Ahemad and Purkait, 2011¹⁸	Madhya Pradesh	General	Male	0.558	H = 940.29 + 4.169 HL
9.	Ishak et al., 2012¹⁹	Western Australia	General	Male	0.73	H = 69.723 + 5.567 RHL
				Female	0.69	H = 57.135 + 6.057 RHL
10.	Vijeta and Kapoor, 2012²⁰	Himachal Pradesh	General	Male	0.554	H = 79.23 + 4.91 RHL
				Female	0.570	H = 86.03 + 4.11 RHL
11.	Ozaslan et al., 2012²¹	Turkey	General	Male	0.578	S = 922.01 + 4.15 HL
				Female	0.309	S = 1116.56 + 2.80 HL
12.	Tang et al., 2012²²	Southern China	General	Male	0.650	S = 105.361 + 3.547 RHL
				Female	0.646	S = 97.280 + 3.661 RHL
13.	Kaur et al., 2013²⁶	North India	General	Male	0.589	S = 130.90 + 2.398 HL
				Female	0.550	S = 160.41 + 0.027 HL
14.	Uhrová et al., 2015²⁷	Slovakia	Students	Male	0.63	H = 93.11 + 4.63 RHL
				Female	0.58	H = 84.69 + 4.75 RHL
15.	Pal et al., 2016²⁸	West Bengal	General	Female	0.688	H = 88.1 + 3.88 HL
16.	Iddalgave et al., 2017²⁹	Karnataka	Students	Male	0.513	H = 96.238 + 4.024 RHL
				Female	0.64	H = 87.6 + 4.12 RHL
17.	Kim et al., 2018³⁰	South Korea	General	Male	0.620	H = 79.610 + 4.945 HL
				Female	0.502	H = 87.362 + 4.106 HL
18.	Zulkifly et al., 2018³¹	Sarawak	General	Male	0.76	H = 48.653 + 6.030 RHL
				Female	0.68	H = 65.455 + 4.955 RHL
19.	Asadujjaman et al., 2019³²	Bangladesh	General	Male	0.545	H = 87.498 + 4.431 RHL
				Female	0.644	H = 81.15 + 4.14 RHL
20.	Nanayakkara et al., 2021³³	Sri Lanka	Students	Male	0.676	H = 75.57 + 5.207 HL
				Female	0.661	H = 79.5 + 4.597 HL
21.	Mohamed et al., 2022³⁴	Maldives	General	Male	0.65	H = 3.599 HL + 99.819
				Female	0.69	H = 4.869 HL + 73.301
22.	Madadin and Menezes, 2022³⁵	Saudi Arabian	General	Male	0.643	H = 4.622 HL + 851.702
23.	Present study	North Indian	General	Male	0.461	H = 112.558 + 2.893 RHL
				Female	0.658	H = 69.773 + 4.826 RHL

Table 6.

Showing the Relationship of Foot Length with Stature.

S. No.	Author	Region	Population	Gender	r value	Linear Regression Equation
1.	Agnihotri et al., 2007⁶	Mauritius	Students	Male	0.720	H = 68.586 + 4.036 RFL
1.	Agnihotri et al., 2007⁶	Mauritius	Students	Female	0.608	H = 77.059 + 3.536 RFL
2.	Krishan and Sharma, 2007⁷	Himachal Pradesh	Students	Male	0.732	H = 68.085 + 4.054 FL
2.	Krishan and Sharma, 2007⁷	Himachal Pradesh	Students	Female	0.739	H = 71.941 + 3.703 FL
3.	Patel et al., 2007³⁶	Gujarat	Students	Female	0.80	H = 75.41 + 3.43 FL
4.	Ilayperuma et al., 2008³⁷	Sri Lankan	Students	Male	0.724	H = 79.042 + 3.590 FL
4.	Ilayperuma et al., 2008³⁷	Sri Lankan	Students	Female	0.719	H = 65.549 + 3.944 FL
5.	Sen and Ghosh, 2008¹¹	North Bengal	General	Male	0.626	H = 83.518 + 3.282 FL
5.	Sen and Ghosh, 2008¹¹	North Bengal	General	Female	0.692	H = 67.009 + 3.707 FL
6.	Kanchan et al., 2008⁹	Punjab	General	Male	0.759	H = 93.269 + 2.819 RFL
6.	Kanchan et al., 2008⁹	Punjab	General	Female	0.512	H = 103.27 + 2.365 RFL
7.	Chikhalkar et al., 2010¹⁷	Mumbai	Students	Male and female	0.610	H = 79.723 + 3.650 FL
8.	Rani et al., 2011³⁸	Delhi	Students	Male	0.808	H = 98.320 + 3.050 RFL
8.	Rani et al., 2011³⁸	Delhi	Students	Female	0.808	H = 90.207 + 3.374 RFL
9.	Khanapurkar and Radke, 2012³⁹	Maharashtra	Students	Male	0.645	H = 90.0 + 3.2 FL
9.	Khanapurkar and Radke, 2012³⁹	Maharashtra	Students	Female	0.702	H = 72.8 + 3.7 FL
10.	Mansur et al., 2012⁴⁰	Nepal	Students	Male	0.688	H = 100.1 + 2.74 RFL
10.	Mansur et al., 2012⁴⁰	Nepal	Students	Female	0.587	H = 96.31 + 2.66 RFL
11.	Ozaslan et al., 2012²¹	Turkey	General	Female	0.496	H = 941.95 + 2.96 FL
11.	Ozaslan et al., 2012²¹	Turkey	General	Male	0.696	H = 840.88 + 3.52 FL
12.	Babu et al., 2013⁴¹	Secunderabad	General	Male	0.583	H = 82.830 + 3.468 RFL
12.	Babu et al., 2013⁴¹	Secunderabad	General	Female	0.66	H = 73.523 + 3.615 RFL
13.	Kautilya et al., 2013⁴²	Chennai	General	Female	0.657	H = 83.24 + 0.337 RFL
14.	Khairulmazidah et al., 2013⁴³	Malaysia	Students	Male	0.697	H = 84.663 + 3.321 RFL
15.	Ozoko, 2013⁴⁴	Nigeria	Students	Male	0.779	H = 84.45 + 3.559 FL
16.	Dayananda et al., 2014⁴⁵	Kolar Tamil Nadu	Students	Male	0.636	H = 69.3663 × 3.663 FL
17.	Parekh et al., 2014⁴⁶	Ahmadabad Gujarat	Students	Male	0.989	H = 74.75 + 3.42 FL
17.	Parekh et al., 2014⁴⁶	Ahmadabad Gujarat	Students	Female	0.988	H = 63.62 + 3.61 FL
18.	Sutay et al., 2014⁴⁷	Madhya Pradesh	Students	Male	0.688	H = 140.59 + 1.35 FL
18.	Sutay et al., 2014⁴⁷	Madhya Pradesh	Students	Female	0.587	H = 121.50 + 1.604 FL
19.	Tobias et al., 2014⁴⁸	Western Nigeria	Students	Male	0.7	H = 71.19 + 3.858 FL
19.	Tobias et al., 2014⁴⁸	Western Nigeria	Students	Female	0.8	H = 73.15 + 3.578 FL
20.	Malik et al., 2015⁴⁹	Lahore	Students	Female	0.63	H = 88.210 + 2.93 FL
21.	Rajesh et al., 2015⁵⁰	Puducherry	Students	Male	0.821	H = 98.159 + 3.746 RFL
22.	Saharan & Arun, 2015⁵¹	Mysore	Students	Male	0.720	H = 101.95 + 2.6 LFL
23.	Agarwal et al., 2015⁵²	TMU residents	Students	Male	0.702	H = 69.99 + 3.93 RFL
23.	Agarwal et al., 2015⁵²	TMU residents	Students	Female	0.484	H = 89.82 + 2.95 RFL
24.	Taneja et al., 2016⁵³	Udaipur	General	Male	0.184	H = 145.71 + 0.692 × FL
25.	Kanwar et al., 2016⁵⁴	Mahakaushal	General	Male	0.958	H = 6.52 × FL − 1.30
25.	Kanwar et al., 2016⁵⁴	Mahakaushal	General	Female	0.936	H = 6.82 × FL − 6.45
26.	Iddalgave et al., 2017⁵⁵	Karnataka	Students	Male	0.616	H = 89.9 + 3.23 RFL
26.	Iddalgave et al., 2017⁵⁵	Karnataka	Students	Female	0.424	H = 130.97 + 1.20 RFL
27.	Saleem et al., 2018⁵⁶	GMC Jammu	Students	Male	0.761	H = 58.4 + 4.41 FL
27.	Saleem et al., 2018⁵⁶	GMC Jammu	Students	Female	0.690	H = 69.7 + 3.83 FL
28.	Present study	North India	General	Male	0.609	S = 83.599 + 3.192 RFL
28.	Present study	North India	General	Female	0.649	S = 74.486 + 3.307 RFL

Consequently, it is evident that the use of a regression equation depends on the specific population and gender under study, making it inappropriate to use them interchangeably. This highlights the importance of developing separate regression equations for estimating height within each population group, which contributes to the establishment of a robust foundational database. A key limitation of this study is its modest sample size, which may affect the reliability and interpretability of the analyses. A limited number of participants may reduce statistical power, increase the risk of type II errors, and restrict the ability to generalize findings to broader populations. Additionally, findings from a smaller group may not adequately reflect the variation present in broader populations, as important differences in demographic, genetic, or environmental profiles could be missed. Consequently, these results should be viewed with caution. Future research should prioritize recruiting larger and more diverse cohorts to strengthen the credibility and wider relevance of such findings, enable more precise estimations, and facilitate deeper exploration of subgroup patterns.

The findings of this study have diverse applications, including forensic identification for solving criminal cases and disaster victim identification, aiding in clinical assessments to detect medical conditions, and contributing to anthropological research by understanding variations in hand and foot dimensions among different ethnic or regional groups, thereby enhancing the field of anthropometry.

Limitations

A significant limitation of the present study is the relatively small sample size, which restricts the generalizability of the results. To enhance reliability and reduce the wide standard deviations, future anthropometric investigations are recommended to be conducted with larger sample sizes. Additionally, the age range and region of the participants in this study present limitations. Therefore, it is advisable to conduct similar studies in different age groups and across various geographical regions. It’s essential to emphasize that the equations derived in this study are specific to the Yadav population and should not be applied to other populations. The models developed in this study are based on an adult sample and are not applicable to juveniles.

A key limitation of this study is the lack of external validation for the regression equations. Future research will focus on validating the model using independent datasets to assess its generalizability and predictive accuracy in broader contexts.

Conclusions

This study explores the correlation between measured stature, hand length, and foot length in the Yadav population of Uttar Pradesh, aiming to establish the feasibility of using these anthropometric parameters to estimate one another. Notably, hand length showed a stronger association with stature in females, while foot length exhibited a more pronounced association with stature in male participants. Consequently, it can be concluded that hand length in females and foot length in males can be more accurately utilized to estimate the stature of the Yadav population.

The regression model employed in this study carries practical implications for estimating the stature of the Yadav population and provides valuable insights for researchers and practitioners in the field of anthropometry. These findings underscore the importance of conducting population-specific studies with larger sample sizes in various geographical regions.

Moreover, it would be beneficial to consider conducting longitudinal studies to assess the stability and applicability of these regression models over time. A longitudinal approach can provide insights into how well these models perform and whether their effectiveness remains consistent or evolves as populations and demographics change.

Footnotes

Acknowledgements

We sincerely appreciate the support of all the participants who participated in the study and provided the data for the study.

Authors’ Contribution

Neha Kumari, Rajeev Ahirwar, Prakash Ranjan Mondal–Design study.

Neha Kumari–Practical performance.

Neha Kumari, Rajeev Ahirwar, Mamta, and Jyoti Verma–Data analysis.

Neha Kumari, Mamta, and Jyoti Verma–Preparation manuscript.

Prakash Ranjan Mondal, Rajeev Ahirwar, Mamta, and Jyoti Verma–Critical review manuscript.

Availability of Data and Materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics Approval

The study was approved by the Ethical Committee of the Department of Anthropology, University of Delhi (Ref. No. Anth/2022-23/639).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by UGC-NFSC Fellowship (2020-2021).

Informed Consent

Each participant gave their written informed consent for the various physical measurements to be taken.

ORCID iD

Prakash Ranjan Mondal

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Estimating Stature from Foot and Hand Dimensions: Insights from a Cross-sectional Study of the Yadav Population

Abstract

Keywords

Introduction

Materials and Methods

Study Design and Sample Size

Number and Sampling of the Subject

Selection of Subject

Inclusion Criteria

Exclusion Criteria

Procedure for Collection of Anthropometric Measurements

Statistical Analysis

Results

Descriptive Statistics for Age and Anthropometric Measurements of Study Participants.

Descriptive Statistics for Age and Anthropometric Measurements of Male Participants.

Descriptive Statistics for Age and Anthropometric Measurements of Female Participants.

Pearson’s Correlation, Significance, and Regression Analysis of Stature Predicted from Hand and Foot Length.

Discussion

Showing the Relationship of Hand Length with Stature.

Showing the Relationship of Foot Length with Stature.

Limitations

Conclusions

Footnotes

Acknowledgements

Authors’ Contribution

Availability of Data and Materials

Declaration of Conflicting Interests

Ethics Approval

Funding

Informed Consent

ORCID iD

References