Three-Dimensional (3D) Anthropometry

established Updated: 2026-05-31
anthropometry 3d-scanning product-design digital-human-modeling body-measurement ergonomics physical-accommodation

Three-Dimensional (3D) Anthropometry

Status: established
Last updated: 2026-05-31
Sources: 9781119636113.Ch11.Pdf
Tags: [anthropometry, 3d-scanning, product-design, digital-human-modeling, body-measurement, ergonomics, physical-accommodation]

Summary

Anthropometry is the study of measurement of the human body, dealing with the measurement of size, mass, shape, and inertial properties (Chaffin et al., 2006). Three-dimensional (3D) anthropometry uses advanced scanning technology to capture the complex geometry and morphology of human body parts, especially curved surface profiles that cannot be adequately represented by traditional one-dimensional (1D) or two-dimensional (2D) measurements. 3D anthropometry provides versatile volumetric information and detailed curved surfaces essential for human-centered product design (Ma & Niu, 2021).

Body

Context

Ma and Niu (2021), in their handbook chapter on three-dimensional (3D) anthropometry and its applications in product design, examine how advanced scanning captures the geometry and morphology of the human body — especially curved surfaces that one- and two-dimensional measurements cannot represent — and how that data feeds human-centered product design. Following Chaffin et al. (2006), they treat anthropometry as the study of body size, mass, shape, and inertial properties, and trace the field from traditional contact measurement to non-contact 3D scanning, covering data acquisition, processing, and application. Within this knowledge base the article anchors the physical-ergonomics and body-measurement strand of human-centered design: it supplies the measurement foundation that Digital Human Modeling consumes, connects to the population-fit concerns of User Requirements Methods and Design For All, and sits beside the discipline overview in Human Factors Ergonomics Discipline.

Key Points

Body measurement is ancient, traceable to Egypt (3500–2200 BC) and to the Chinese Inner Canon of Huangdi, with the word "anthropometry" coined in 1839 from Greek ánthropos and métron. Traditional 1D/2D anthropometry takes direct contact measurements between skeletal landmarks using stadiometers, anthropometers, and tape measures: tools are cheap and standardised, but the method is slow, labour-intensive, dependent on examiner skill, prone to recording error, and omits curved surfaces. Ma and Niu describe a development from 1D/2D to 3D, contact to non-contact, and manual to computerised automatic measurement (Jones & Rioux, 1997), with 3D systems offering speed, precision, automation, and rich geometric data at higher cost and space requirements (PDF p. 4, orig. p. 285). Major 3D surveys include CAESAR, the NIOSH Head and Face survey, SizeUK, SizeUSA, SizeGermany, SizeChina, and Taiwanese and Chinese minors' databases (PDF p. 2, orig. p. 283).

Data acquisition follows three stages: preparation and calibration, capture (yielding point clouds), and processing to reconstruct surfaces and extract body parts. Three optoelectronic scanner types are common — laser, structured-light, and multi-image. Errors arise from machine factors (hardware limits, software, vibration, calibration), human factors (subject movement and breathing, operator landmark errors), and environmental factors (lighting), while noise comes from reflection errors, environmental disturbance, and scanning-method error (Chang et al., 2007) (PDF pp. 5–6, orig. pp. 286–287). Missing data appear as holes from occlusion (armpit, crotch), blind spots (top of head, sole of foot), and light absorption. Countermeasures include glass supports, standard tight clothing, asking subjects to remain still, and a Hair Thickness Offset of 2–3 mm for head scans (PDF pp. 6–7, orig. pp. 287–288).

Data quality rests on accuracy, reliability, and integrity, with ISAK setting millimetre criteria for some dimensions (PDF p. 7, orig. p. 288). Point clouds take four forms — scattered, linear, arrayed, and polygonal. Key processing steps are noise reduction (Wiener, Kalman, least-squares, KNN filtering), missing-data handling (distinguishing real holes from gaps by intuitive knowledge and edge regularity), and point-cloud registration to fuse perspectives via iterative transformation (PDF pp. 7–9, orig. pp. 288–290). Landmarks are identified automatically (ALR), digitally (DLR), or physically (PLR); automatic approaches use prior knowledge, geometric features, or template matching (Olds & Honey, 2005). Liu et al. (2017) reached up to 95.9% facial-landmark recognition using Spin Image descriptors with Hidden Markov Models, far above artificial neural networks (PDF pp. 9–11, orig. pp. 290–292).

In product design, Ma and Niu describe six accommodation approaches (Dianat et al., 2018): univariate percentile, population-based, user-centered, subjective-factor, prototype, and digital human modeling (PDF p. 11, orig. p. 292). The HFES 300 (2004) procedure runs from problem definition through target population, database, case selection, and application. Fitting maximises product-user match (objectively assessable in 3D, but subjective in perception), while sizing and grading use multivariate methods such as PCA and k-means or k-medoids clustering. 3D parametric modeling represents body geometry as triangular meshes driven by parameters, enabling personalised design without individual scanning, and static models can be made dynamic by segmenting at joints and applying kinematics (Tsao & Ma, 2016) — though joint location, complex joint movement, soft-tissue distortion, and obesity complicate this (PDF pp. 12–14, orig. pp. 293–295). Product-oriented applications include pilot helmets (three-layer designs needing full facial geometry), ear products (earphones conforming to the 95th-percentile auricle, elongated shapes for retention), glasses (nose-pad and temple fit from head shape), and footwear (heel height shifting forefoot load and peak pressure, addressed through insoles and shoe-last design with CAD/CAM) (PDF pp. 14–16, orig. pp. 295–297). Digital Human Modeling software such as Jack, DELMIA, and RAMSIS uses 3D data to simulate job and product design across aerospace, automotive, clothing, and wearables (PDF p. 3, orig. p. 284).

Conclusion

Ma and Niu (2021) conclude that 3D anthropometry's volumetric, curved-surface data is essential to human-centered product design and points toward individualised design and mass customization — meeting individual needs at near-mass-production efficiency through sizing systems and adjustable designs (Kotha & Pine, 1994) — alongside quantified "man-machine-environment" matching for interfaces and increasingly portable scanners (HandySCAN, Kinect, depth cameras, smartphone apps). The throughline is a shift from standardisation toward customisation, whose adoption depends on resolving cost, soft-tissue and joint-modeling challenges, and consumer concerns about complex design processes.

References

Chaffin, D.B., Andersson, G.B.J. & Martin, B.J. (2006) Occupational Biomechanics. Hoboken, NJ: Wiley-Interscience. To be validated.

Chang, C.C., Li, Z., Cai, X. & Dempsey, P. (2007) 'Error control and calibration in three-dimensional anthropometric measurement of the hand by laser scanning with glass support', Measurement: Journal of the International Measurement Confederation, 40(1), pp. 21–27. To be validated.

Dianat, I., Molenbroek, J. & Castellucci, H.I. (2018) 'A review of the methodology and applications of anthropometry in ergonomics and product design', Ergonomics, 61(12), pp. 1696–1720. To be validated.

Jones, P.R.M. & Rioux, M. (1997) 'Three-dimensional surface anthropometry: applications to the human body', Optics and Lasers in Engineering, 28, pp. 89–117. To be validated.

Kotha, S. & Pine, B.J. (1994) 'Mass customization: The new frontier in business competition', The Academy of Management Review, 19(3), p. 588. To be validated.

Liu, J.C., Zhang, L., Chen, X. & Niu, J.W. (2017) 'Facial landmark automatic identification from three dimensional (3D) data by using Hidden Markov Model (HMM)', International Journal of Industrial Ergonomics, 57, pp. 10–22. To be validated.

Ma, L. & Niu, J. (2021). Three-dimensional (3D) anthropometry and its applications in product design. In G. Salvendy & W. Karwowski (Eds.), Handbook of Human Factors and Ergonomics (5th ed., pp. 283-302). John Wiley & Sons. ma2021anthropometry

Olds, T. & Honey, F. (2005) 'The use of 3D whole-body scanners in anthropometry', in Marfell-Jones, M., Stewart, A. & Olds, T. (eds.) Kinanthropometry IX. London: Routledge, pp. 1–12. To be validated.

Tsao, L. & Ma, L. (2016) 'Using subject-specific three-dimensional (3D) anthropometry data in digital human modelling: Case study in hand motion simulation', Ergonomics, 59(11), pp. 1526–1539. To be validated.

Open Questions

  • How can joint locations be reliably determined from 3D surface scans for dynamic modeling?
  • What standards should govern the necessary number of scanned 3D models for creating parameterized models?
  • How can general fitting criteria be established across product categories given subjective preference variations?
  • How can portable 3D scanning be made economically viable for widespread consumer product customization?
  • What methods best address soft tissue deformation in dynamic 3D anthropometry applications?