Stature estimation from partial measurements and maximum length of lower limb bones in Koreans

Lee, Je-Hun; Kim, Yi Suk; Lee, U-Young; Park, Dae-Kyoon; Jeong, Young‐Gil; Lee, Nam Seob; Han, Seung‐Yun; Han, Seung‐Ho

doi:10.1080/00450618.2013.877078

Cited by 16 publications

(7 citation statements)

References 24 publications

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“…The association between the femoral and tibial measurements, and stature had an equivalent correlation in males; however, the femur had a stronger association with stature in females. The association between the femur and stature in males in the current study was stronger than that reported for black and white Americans 49 , Spaniards 12 and Koreans 13 , but weaker than that noted for white South Africans 25 and black South Africans 24 (Table 4). The relationship between the femur and stature in females was stronger than that reported for White Americans 49 and Spaniards 12 , but weaker than associations reported for black South Africans 24 , white South Africans 25 and Koreans 13 (Table 4).…”

Section: Discussioncontrasting

confidence: 85%

“…Direct comparisons of stature are often limited as most stature estimation research relies on either cadaveric height or heights measured during autopsies. 8,10,13 The mean femoral and tibial length measurements from scanograms were 45.2 cm (±2.3 cm) and 38.2 cm (±2.1 cm) for males and 43.3 cm (±2.0 cm) and 36.5 cm (±2.1 cm) for females. The mean femoral measurement from the current study for males was smaller compared to those of black and white North Americans 49 , Spanish 12 and white South Africans 25 but slightly larger than that reported for Japanese 14 .…”

Section: Discussionmentioning

confidence: 99%

“…4 There has been a plethora of studies on stature reconstruction using measurements of long bones of upper and lower limbs in different parts of the world following the publication of arguably the largest study on stature reconstruction by Trotter and Gleser 4 in 1958. Regression equations have been formulated for populations including, but not limited to, the Portuguese 5 , Germans 6 , Bulgarian 7 , Polish 8 , Turks 9 , Croatians 10 , Mexicans 11 , Spaniards 12 , Koreans 13 and Japanese 14 . Regression equations have also been formulated from measurements of fragments of long bones for stature reconstruction 15 and other bony elements (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Updated lower limb stature estimation equations for a South African population group

Bidmos¹,

Brits²

2020

S. Afr. J. Sci.

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One of the main steps in the identification of an unknown person, from their skeletal remains, is the estimation of stature. Measurements of intact long bones of the upper and lower extremities are widely used for this purpose because of the high correlation that exists between these bones and stature. In 1987, Lundy and Feldesman presented regression equations for stature estimation for the black South African population group based on measurements of bones from the Raymond A. Dart Collection of Human Skeletons. Local anthropologists have questioned the validity of these equations. Living stature measurement and magnetic resonance imaging scanograms of 58 adult volunteers (28 males and 30 females) representing the modern black South African population group were obtained. Physiological length of the femur (FEPL) and physiological length of the tibia (TPL) were measured on each scanogram and substituted into appropriate equations of Lundy and Feldesman (S Afr J Sci. 1987;83:54–55) to obtain total skeletal height (TSHL&F). Measured total skeletal height (TSHMeas) for each subject from scanograms was compared with TSHL&F. Both FEPL and TPL presented with significantly high positive correlations with TSHMeas. A comparison between TSHL&F and TSHMeas using a paired t-test, showed a statistically significant difference – an indication of non-validity of Lundy and Feldesman’s equations. New regression equations for estimation of living stature were formulated separately for male and female subjects. The standard error of estimate was low, which compared well with those reported for other studies that used long limb bones. Significance: • Statistically significant differences were observed between measured and estimated skeletal height, thus confirming non-validity of Lundy and Fieldsman’s (1987) equations for lower limb bones. • New regression equations for living stature estimation were formulated for femur and tibia lengths, and the low standard error of estimates of equations compared well to results from other studies.

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Section: Discussioncontrasting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Updated lower limb stature estimation equations for a South African population group

Bidmos¹,

Brits²

2020

S. Afr. J. Sci.

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“…In studies conducted on Asian societies (Malaysia, and Korea), single and multiple regression equations were created for stature predictions, and the standard error of estimate in the single regression equations was between 2.91 and 8.53 cm, while that in the multiple regression equations was between 3.73 and 5.27 cm. Moreover, the lower leg and thigh lengths, and maximum femur and tibia lengths were the measurements used to obtain the best estimations (Nor et al, 2013;Lee et al, 2014). Single and multiple regression equations were developed for stature estimation from the bones of the lower and upper limbs in studies conducted on cadavers of individuals in European and Balkan countries (Portugal, Croatia, and Bulgaria).…”

Section: Discussionmentioning

confidence: 99%

Stature Estimation from Radiographic Measurements in Adult Anatolian Population

Açıkgöz¹,

Erkman

Binokay

et al. 2021

Int. J. Morphol.

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The purpose of the present study was to create a regression equation for measuring stature using measurements obtained from the long bone radiographs of adult individuals in Anatolian population. In this study, the maximum length measurements of the six long bones in the upper and lower limbs of 167 healthy individuals were determined from radiographic images. Single and multiple regression equations were created to predict the stature of the individuals from the maximum bone stature. From these equations, the standard error of estimate was determined in the range of 1.68-4.09 cm. As a result of this study, the obtained regression equations resulted in highly reliable and successful results in terms of predicting stature. These equations will provide convenient and predictive accuracy in the estimation of stature from skeletal remains obtained from societies that lived and living in Anatolia. Besides, we anticipate that it will guide researchers working in the fields of Forensic Anthropology, Forensic Medicine and Anatomy.

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“…One of the major limitations in attempting to estimate human information such as sex, stature, and age at crime and disaster scenes is that necessary anthropometric measurements can be missing [1][2][3]; previous researchers have shown that estimating the biological information of a human body using a variety of anthropometric measurements such as of the upper and lower limbs is effective [4][5][6]. However, many previous researchers have shown that estimates of biological information vary widely across different ancestry groups and sexes [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Missing Value Imputation in Stature Estimation by Learning Algorithms Using Anthropometric Data: A Comparative Study

Son

Kim

2020

Applied Sciences

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Estimating stature is essential in the process of personal identification. Because it is difficult to find human remains intact at crime scenes and disaster sites, for instance, methods are needed for estimating stature based on different body parts. For instance, the upper and lower limbs may vary depending on ancestry and sex, and it is of great importance to design adequate methodology for incorporating these in estimating stature. In addition, it is necessary to use machine learning rather than simple linear regression to improve the accuracy of stature estimation. In this study, the accuracy of statures estimated based on anthropometric data was compared using three imputation methods. In addition, by comparing the accuracy among linear and nonlinear classification methods, the best method was derived for estimating stature based on anthropometric data. For both sexes, multiple imputation was superior when the missing data ratio was low, and mean imputation performed well when the ratio was high. The support vector machine recorded the highest accuracy in all ratios of missing data. The findings of this study showed appropriate imputation methods for estimating stature with missing anthropometric data. In particular, the machine learning algorithms can be effectively used for estimating stature in humans.

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Stature estimation from partial measurements and maximum length of lower limb bones in Koreans

Cited by 16 publications

References 24 publications

Updated lower limb stature estimation equations for a South African population group

Updated lower limb stature estimation equations for a South African population group

Stature Estimation from Radiographic Measurements in Adult Anatolian Population

Missing Value Imputation in Stature Estimation by Learning Algorithms Using Anthropometric Data: A Comparative Study

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