Mismeasurement of the virtual human body: analysing error of landmark acquisition

Authors

DOI:

https://doi.org/10.18778/1898-6773.87.3.06

Keywords:

digitization, landmarks, 3D model, digitization error, morphology, morphometry

Abstract

Modern physical anthropology increasingly employs non-invasive methods that use 3D models representing the human body. Frequently, these are 3D models of a person’s physical appearance, i.e., face or body. A traditional approach to analyse these records is to process discrete points (landmarks, feature points) collected manually on the model surface. The digitization of landmarks and associated errors have been sufficiently studied in the context of the human face, due to its functional and aesthetic importance. However, other parts of the human body have not received the same level of attention.

The aim of the present study was to quantify the error of body landmarks when collected in 3D fullbody models and to explore how it relates to other model properties, such as a demographic and somatic indicators.

The study tested two datasets of 10 body landmarks acquired in 60 models (32 males and 28 females). The data acquisition was carried out during the time span of 14 days. The magnitude of the digitization error for each point was acquired and tested between groups defined according to their anatomical location (shoulders, arms, legs; torso and limbs or body side), sex, age, height and body type.

The results of this study showed that the error of digitising landmarks in a 3D model was greater compared to the error reported in the literature when acquiring landmarks on the human body. The digitization error was independent of participants’ age, sex, height, and body type but was correlated with the anatomical location, where the upper chest, neck, and back on the knee yielded the highest digitization errors. In addition, this study showed that landmarks located on the shoulders and arms exhibited an error which was correlated negatively with the volume of the lower and upper half of the body and positively with the body depth.

Downloads

Download data is not yet available.

References

Adams DC, Collyer ML, Kaliontzopoulou A, Baken E. 2021. Geomorph: Software for geometric morphometric analyses. R package version 3.3.2. Available at: https://cran.r-project.org/package=geomorph
View in Google Scholar

Aldridge K, Boyadjiev SA, Capone GT, DeLeon VB, Richtsmeier JT. 2005, Precision and error of three-dimensional phenotypic measures acquired from 3dMD photogrammetric images. Am J Med Genet 138A:247–253. https://doi.org/10.1002/ajmg.a.30959
View in Google Scholar DOI: https://doi.org/10.1002/ajmg.a.30959

Arnqvist G, Mårtensson T. 1998. Measurement error in geometric morphometrics: empirical strategies to assess and reduce its impact on measures of shape. Acta Zool Acad Sci Hung 44:73–96.
View in Google Scholar

Atamtürk D, Pelin C, Duyar İ. 2019. Estimation of sex from scapular measurements: use of the bone area as a criterion. Euras J Anthropol 10(1):39–45.
View in Google Scholar

Ben Azouz Z, Rioux M, Shu C. 2006. Characterizing human shape variation using 3D anthropometric data. Visual Comput 22:302–314. https://doi.org/10.1007/s00371-006-0006-6
View in Google Scholar DOI: https://doi.org/10.1007/s00371-006-0006-6

Benfer RA. 1975. Morphometric analysis of Cartesian coordinates of the human skull. Am J Phys Anthropol 42:371–382. https://doi.org/10.1002/ajpa.1330420305
View in Google Scholar DOI: https://doi.org/10.1002/ajpa.1330420305

Blaak E. 2001. Gender differences in fat metabolism. Current Opinion in Clinical Nutrition & Metabolic Care 4(6):499– 502.
View in Google Scholar DOI: https://doi.org/10.1097/00075197-200111000-00006

Bromiley PA, Schunke AC, Ragheb H. 2014. Semi-automatic landmark point annotation for geometric morphometrics. Front Zool 11:61. https://doi.org/10.1186/s12983-014-0061-1
View in Google Scholar DOI: https://doi.org/10.1186/s12983-014-0061-1

Bookstein FL. 1991. Morphometric Tools for Landmark Data Geometric and Biology. Cambrige University, Cambrige.
View in Google Scholar DOI: https://doi.org/10.1017/CBO9780511573064

Bouchard TJ, Lykken DT, McGue M, Segal NL, Tellegen A. 1990. Sources of human psychological differences: The Minnesota study of twins reared apart. Science 250(4978): 223–228.
View in Google Scholar DOI: https://doi.org/10.1126/science.2218526

Caple J, Stephan CN. 2016. A standardized nomenclature for craniofacial and facial anthropometry. Int J Legal Med 130(3):863–879. https://doi.org/10.1007/s00414-015-1292-1
View in Google Scholar DOI: https://doi.org/10.1007/s00414-015-1292-1

Charlier P, Froesch P, Huynh-Charlier I. 2014. Use of 3D surface scanning to match facial shapes against altered exhumed remains in a context of forensic individual identification. Forensic Sci Med Pathol 10:654–661. https://doi.org/10.1007/s12024-014-9618-8
View in Google Scholar DOI: https://doi.org/10.1007/s12024-014-9618-8

Chen SYY, Lestrel PE, Kerr JWS, McColl JH. 2002. Describing shape changes in the human mandible using elliptical Fourier functions. Eur J Orthodont 22(3):205– 216. https://doi.org/10.1093/ejo/22.3.205
View in Google Scholar DOI: https://doi.org/10.1093/ejo/22.3.205

Craik K, Collings AJ. 2022. A preliminary study into the impact of using three-dimensional models in forensic anthropology learning and teaching. Science & Justice. https://doi.org/10.1016/j.scijus.2022.04.006
View in Google Scholar DOI: https://doi.org/10.1016/j.scijus.2022.04.006

von Cramon-Taubadel N, Frazier BC, Lahr MM. 2007. The problem of assessing landmark error in geometric morphometrics: Theory, methods, and modifications. Am J Phys Anthropol 134:24–35. https://doi.org/10.1002/ajpa.20616
View in Google Scholar DOI: https://doi.org/10.1002/ajpa.20616

Čuta M, Jurda M, Kováčová V, Jandová M, Bezděková V, Černý D, Urbanová P. 2024. Virtual fit and design improvements of a filtering half-mask for sub-adult wearers. Ergonomics. https://doi.org/10.1080/00140139.2023.2298984
View in Google Scholar DOI: https://doi.org/10.1080/00140139.2023.2298984

Daanen HAM, Ter Haar FB. 2013. 3D whole body scanners revisited. Displays 34(4):270–275.
View in Google Scholar DOI: https://doi.org/10.1016/j.displa.2013.08.011

Daboul A, Ivanovska T, Bülow R, Biffar R, Cardini A. 2018. Procrustes-based geometric morphometrics on MRI images: An example of inter-operator bias in 3D landmarks and its impact on big datasets. PLOS ONE 13(5):e0197675. https://doi.org/10.1371/journal.pone.019767
View in Google Scholar DOI: https://doi.org/10.1371/journal.pone.0197675

della Croce U, Cappozzo A, Kerrigan DC. 1999. Pelvis and lower limb anatomical landmark calibration precision and its propagation to bone geometry and joint angles. Med Biol Eng Comput 7(2):155– 61. https://doi.org/10.1007/BF02513282 PMID: 10396818.
View in Google Scholar DOI: https://doi.org/10.1007/BF02513282

Devarajan P, Istook CL 2004. Validation of female figure identification technique (FFIT) for apparel software. J Text Appar Technol Manag 4(1):1–23.
View in Google Scholar

Ferrario VF, Sforza C, Ciusa V, Dellavia C, Tartaglia GM. 2001. The effect of sex and age on facial asymmetry in healthy subjects: A cross-sectional study from adolescence to mid-adulthood. J Oral Maxillofac Surg 59(4):382–8. https://doi.org/10.1053/joms.2001.21872
View in Google Scholar DOI: https://doi.org/10.1053/joms.2001.21872

Fetter V, Prokopec M, Suchý J, Titlebachová S. 1967. Antropologie. Praha: Academia.
View in Google Scholar

Fourie Z, Damstra J, Gerrits PO, Ren Y. 2011. Evaluation of anthropometric accuracy and reliability using different three-dimensional scanning systems. Forensic Sci Int 207(1–3):127–134. https://doi.org/10.1016/j.forsciint.2010.09.018
View in Google Scholar DOI: https://doi.org/10.1016/j.forsciint.2010.09.018

Gibelli D, Palamenghi A, Poppa P, Sforza C, Cattaneo C, De Angelis, D. 2022. 3D-3D facial registration method applied to personal identification: Does it work with limited portions of faces? An experiment in ideal conditions. J Forensic Sci 00:1–7. https://doi.org/10.1111/1556-4029.15021
View in Google Scholar DOI: https://doi.org/10.1111/1556-4029.15021

Hara R, McGinley J, Briggs C. 2016. Predicting the location of the hip joint centres, impact of age group and sex. Sci Rep 6:37707. https://doi.org/10.1038/srep37707
View in Google Scholar DOI: https://doi.org/10.1038/srep37707

Harris EF, Smith RN 2009. Accounting for measurement error: a critical but often overlooked process. Arch Oral Biol 54 (Suppl 1:S)107–117. https://doi.org/10.1016/j.archoralbio.2008.04.010
View in Google Scholar DOI: https://doi.org/10.1016/j.archoralbio.2008.04.010

Jones PRM, Rioux M. 1997. Three-dimensional surface anthropometry: Applications to the human body. Optics and Lasers in Engineering 28(2):89–117.
View in Google Scholar DOI: https://doi.org/10.1016/S0143-8166(97)00006-7

Jurda M, Urbanová P, Chmelík J. 2019. Digital restoration of fragmentary human skeletal remains: Testing the feasibility of virtual reality. J Forensic Leg Med 66:50–57. https://doi.org/10.1016/j.jflm.2019.06.005
View in Google Scholar DOI: https://doi.org/10.1016/j.jflm.2019.06.005

Katina S, McNeil K, Ayoub A, Guilfoyle B, Khambay B, Siebert P, et al. 2016. The definitions of three-dimensional landmarks on the human face: an interdisciplinary view. J Anat 228:355–365. https://doi.org/10.1111/joa.12407
View in Google Scholar DOI: https://doi.org/10.1111/joa.12407

Kaashki NN, Hu P. Munteanu, A. 2023. Anet: A Deep Neural Network for Automatic 3D Anthropometric Measurement Extraction. IEEE Transactions on Multimedia 25:831–844. https://doi.org/10.1109/TMM.2021.3132487
View in Google Scholar DOI: https://doi.org/10.1109/TMM.2021.3132487

Kouchi M, Mochimaru M. 2011. Errors in landmarking and the evaluation of the accuracy of traditional and 3D anthropometry. Appl Ergon 42(3):518–527. https://doi.org/10.1016/j.apergo.2010.09.011
View in Google Scholar DOI: https://doi.org/10.1016/j.apergo.2010.09.011

Kullmer O. 2008. Benefits and risks in virtual anthropology. J Anthropol Sci 86:205–207.
View in Google Scholar

Madadi M, Bertiche H, Escalera S. 2021. Deep Unsupervised 3D Human Body Reconstruction from a Sparse set of Landmarks. International Journal of Computer Vision. https://doi.org/10.1007/S11263-021-01488-2
View in Google Scholar DOI: https://doi.org/10.1007/s11263-021-01488-2

Muehlenbein MP. 2010. Commentary: a Primer on Human Subjects Applications and Informed Consents. Human Evolutionary Biology 150.
View in Google Scholar DOI: https://doi.org/10.1017/CBO9780511781193.012

Murrie DC, Gardner BO, Kelley S, Dror IE. 2019. Perceptions and estimates of error rates in forensic science: A survey of forensic analysts. Forensic Sci Int 302:109887. https://doi.org/10.1016/j.forsciint.2019.109887
View in Google Scholar DOI: https://doi.org/10.1016/j.forsciint.2019.109887

Navarro P, Ramallo V, Cintas C, Ruderman A, de Azevedo S, Paschetta C, et al. 2020. Body shape: Implications in the study of obesity and related traits. Am J Hum Biol 32(2):e23323. https://doi.org/10.1002/ajhb.23323
View in Google Scholar DOI: https://doi.org/10.1002/ajhb.23323

Ross A, Williams S. 2008. Testing Repeatability and Error of Coordinate Landmark Data Acquired from Crania. J Forensic Sci 53:782–785. https://doi.org/10.1111/j.1556-4029.2008.00751.x
View in Google Scholar DOI: https://doi.org/10.1111/j.1556-4029.2008.00751.x

Ruescas-Nicolau AV, De Rosario H, Bernabé EP, Juan MC. 2024. Positioning errors of anatomical landmarks identified by fixed vertices in homologous meshes. Gait & Posture 108:215–221.
View in Google Scholar DOI: https://doi.org/10.1016/j.gaitpost.2023.11.024

Ruff C. 2002. Variation in human body size and shape. Annual Review of Anthropology 31:211–232. https://doi.org/10.1146/annurev.anthro.31.040402.085407
View in Google Scholar DOI: https://doi.org/10.1146/annurev.anthro.31.040402.085407

Ryan-Stewart H, Faulkner J, Jobson S. 2022. The Impact of Technical Error of Measurement on Somatotype Categorization. Applied Sciences 12(6):3056. https://doi.org/10.3390/app12063056
View in Google Scholar DOI: https://doi.org/10.3390/app12063056

Schlager S. 2017. Morpho and Rvcg – Shape Analysis in R. In: Zheng G, Li S, Szekely G (eds.). Statistical Shape and Deformation Analysis 217–256. Academic Press. ISBN 9780128104934.
View in Google Scholar DOI: https://doi.org/10.1016/B978-0-12-810493-4.00011-0

Sforza C, Ferrario VF. 2006. Soft-tissue facial anthropometry in three dimensions: from anatomical landmarks to digital morphology in research, clinics and forensic anthropology. J Anthropol Sci 84:97–124.
View in Google Scholar

Simmons K, Istook C. 2003. Body measurement techniques: Comparing 3D body-scanning and anthropometric methods for apparel applications. J Fash Mark Manag 7:306–332. https://doi.org/10.1108/13612020310484852
View in Google Scholar DOI: https://doi.org/10.1108/13612020310484852

Slice DE. 1996. Three-dimensional, generalized resistant fitting and the comparison of least-squares and resistant-fit residuals. Advances in Morphometrics. pp. 179–199. New York: Plenum Press.
View in Google Scholar DOI: https://doi.org/10.1007/978-1-4757-9083-2_15

Sukno FM, Waddington JL, Whelan Paul F. 2015. 203d facial landmark localization with asymmetry patterns and shape regression from incomplete local features. IEEE Trans Cybernet 45:1717–1730. https://doi.org/10.1109/tcyb.2014.2359056
View in Google Scholar DOI: https://doi.org/10.1109/TCYB.2014.2359056

Ulijaszek SJ, Kerr DA. 1999. Anthropometric measurement error and the assessment of nutritional status. Br J Nutr 82(3):165–177. https://doi.org/10.1017/s0007114599001348
View in Google Scholar DOI: https://doi.org/10.1017/S0007114599001348

Utermohle CJ, Zegura SL. 1982. Intra- and interobserver error in craniometry: A cautionary tale. Am J Phys Anthropol 57:303–310.
View in Google Scholar DOI: https://doi.org/10.1002/ajpa.1330570307

Urbanová P. 2009. Variation of the Orbital Rim Using Elliptic Fourier Analysis. https://doi.org/10.1142/9789814355247_0013
View in Google Scholar

Urbanová P. 2011. Variation of the Orbital Rim by using the Elliptic Fourier Analysis. In Pete E. Lestrel. BIOLOGICAL SHAPE ANALYSIS Proceedings of the 1st International Symposium Tsukuba. Singapore: World Scientific. pp. 221–241. ISBN 978-981-4355-23-0. https://dx.doi.org/10.1142/9789814355247_0013
View in Google Scholar DOI: https://doi.org/10.1142/9789814355247_0013

Urbanová P. 2016. Performance of distance-based matching algorithms in 3D facial identification. Egyptian Journal of Forensic Sciences 6(2):135–151. https://doi.org/10.1016/j.ejfs.2016.04.004
View in Google Scholar DOI: https://doi.org/10.1016/j.ejfs.2016.04.004

Urbanová P, Ferková, Z, Jandová M, Jurda M, Černý D, Sochor J. 2018. Introducing the FIDENTIS 3DFace Database. Anthropol Rev 81(2):202–223.
View in Google Scholar DOI: https://doi.org/10.2478/anre-2018-0016

Xi P, Lee WS, Shu C. 2007. Analysis of segmented human body scans. Proceedings of Graphics Interface 2007. pp. 19–26.
View in Google Scholar DOI: https://doi.org/10.1145/1268517.1268523

Zelditch M, Swiderski D, Sheets HD. 2012. Geometric morphometrics for biologists: a primer. Academic Press.
View in Google Scholar

ISO 20685-1:2018: 3-D scanning methodologies for internationally compatible anthropometric databases — Part 1: Evaluation protocol for body dimensions extracted from 3-D body scans.
View in Google Scholar

ISO 20685-2:2015 Ergonomics — 3-D scanning methodologies for internationally compatible anthropometric databases — Part 2: Evaluation protocol of surface shape and repeatability of relative landmark positions.
View in Google Scholar

ISO 7250-1:2017 Basic human body measurements for technological design Part 1: Body measurement definitions and landmarks.
View in Google Scholar

[TC]² Labs, 2016 [online] https://www.tc2.com/tc2-19b-3d-body-scanner.html (20.08.2021).
View in Google Scholar

Downloads

Published

2024-10-07

How to Cite

Černý, D., & Urbanová, P. (2024). Mismeasurement of the virtual human body: analysing error of landmark acquisition. Anthropological Review, 87(3), 77–95. https://doi.org/10.18778/1898-6773.87.3.06

Issue

Section

Articles

Funding data

Most read articles by the same author(s)

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.