Digital anthropometry: a critical review

Anthropometry, Greek for human measurement, is a tool widely used across many scientific disciplines. Clinical nutrition applications include phenotyping subjects across the lifespan for assessing growth, body composition, response to treatments, and predicting health risks. The simple anthropometric tools such as flexible measuring tapes and calipers are now being supplanted by rapidly developing digital technology devices. These systems take many forms, but excitement today surrounds the introduction of relatively low cost three-dimensional optical imaging methods that can be used in research, clinical, and even home settings. This review examines this transformative technology, providing an overview of device operational details, early validation studies, and potential applications. Digital anthropometry is rapidly transforming dormant and static areas of clinical nutrition science with many new applications and research opportunities.

[1]  D H Harris,et al.  The Loughborough anthropometric shadow scanner (LASS). , 1989, Endeavour.

[2]  Steven Paquette,et al.  3D scanning in apparel design and human engineering , 1996, IEEE Computer Graphics and Applications.

[3]  Miguel Carvalho,et al.  An overview of the current three-dimensional body scanners for anthropometric data collection , 2015 .

[4]  Craig A. Knoblock,et al.  A Survey of Digital Map Processing Techniques , 2014, ACM Comput. Surv..

[5]  Xin Li,et al.  Symmetry and template guided completion of damaged skulls , 2011, Comput. Graph..

[6]  Chengcui Zhang,et al.  Feature Extraction from 2D Images for Body Composition Analysis , 2015, 2015 IEEE International Symposium on Multimedia (ISM).

[7]  V. Somers,et al.  Reliability of a 3D Body Scanner for Anthropometric Measurements of Central Obesity. , 2016, Obesity, open access.

[8]  Joaquim Salvi,et al.  A state of the art in structured light patterns for surface profilometry , 2010, Pattern Recognit..

[9]  Richard Szeliski,et al.  High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Andreas Kolb,et al.  Kinect range sensing: Structured-light versus Time-of-Flight Kinect , 2015, Comput. Vis. Image Underst..

[11]  Sebastian Thrun,et al.  3D shape scanning with a time-of-flight camera , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  S B Heymsfield,et al.  Clinically applicable optical imaging technology for body size and shape analysis: comparison of systems differing in design , 2017, European Journal of Clinical Nutrition.

[13]  Masaaki Mochimaru,et al.  Errors in landmarking and the evaluation of the accuracy of traditional and 3D anthropometry. , 2011, Applied ergonomics.

[14]  Zoran Popovic,et al.  The space of human body shapes: reconstruction and parameterization from range scans , 2003, ACM Trans. Graph..

[15]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Hans-Peter Meinzer,et al.  Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..

[17]  HoraudRadu,et al.  An overview of depth cameras and range scanners based on time-of-flight technologies , 2016 .

[18]  B K Ng,et al.  Clinical anthropometrics and body composition from 3D whole-body surface scans , 2016, European Journal of Clinical Nutrition.

[19]  S. S. Iyengar,et al.  On Computing Mapping of 3D Objects , 2014, ACM Comput. Surv..

[20]  Peter R. M. Jones,et al.  Three-dimensional surface anthropometry: Applications to the human body , 1997 .

[21]  S B Heymsfield,et al.  Automated anthropometric phenotyping with novel Kinect-based three-dimensional imaging method: comparison with a reference laser imaging system , 2016, European Journal of Clinical Nutrition.

[22]  Leonidas J. Guibas,et al.  Example-Based 3D Scan Completion , 2005 .

[23]  Swades De,et al.  RF energy harvester-based wake-up receiver , 2015, 2015 IEEE SENSORS.

[24]  Toralf Kirsten,et al.  Body typing of children and adolescents using 3D-body scanning , 2017, PloS one.

[25]  S. Heymsfield,et al.  Anthropometry: continued refinements and new developments of an ancient method. , 2017, The American journal of clinical nutrition.

[26]  Peter Ahnert,et al.  Novel Anthropometry Based on 3D-Bodyscans Applied to a Large Population Based Cohort , 2016, PloS one.

[27]  Paolo Cignoni,et al.  A low cost 3D scanner based on structured light , 2001 .

[28]  Antonino De Lorenzo,et al.  A Smartphone Application for Personal Assessments of Body Composition and Phenotyping , 2016, Sensors.

[29]  Zhengyou Zhang,et al.  Microsoft Kinect Sensor and Its Effect , 2012, IEEE Multim..

[30]  Cynthia L. Istook,et al.  Body measurement techniques , 2003 .

[31]  Andrea Giachetti,et al.  Digital three-dimensional anthropometry detection of exercise-induced fat mass reduction in obese women , 2015, Sport Sciences for Health.

[32]  Peter Liepa,et al.  Filling Holes in Meshes , 2003, Symposium on Geometry Processing.

[33]  Roy P. Pargas,et al.  Automatic measurement extraction for apparel from a three-dimensional body scan , 1997 .