Computer Assisted Estimation of Anthropometric Parameters from Whole Body Scanner Data

Lovato, Christian; Castellani, Umberto; Fantoni, Simone; Milanese, Chiara; Zancanaro, Carlo; Giachetti, Andrea

doi:10.1007/978-3-642-10470-1_7

Cited by 3 publications

(9 citation statements)

References 11 publications

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“…Examples of interactive measurements are shown in Fig 4. The measurement set implemented tries to simulate the manual measurement currently performed in classical anthropometric research and validation is performed comparing the values of computer assisted and tape-based measurements. Preliminary encouraging results are reported in [14,16] and more accurate validation tests are currently being performed on a larger set of individuals. It must, however, noted that this kind of validation is not always reliable especially for the fact that tape-based measurements that should be compared with the electronic ones often involve complex procedures and body deformation.…”

Section: Application: Measuring Anthropometric Parametersmentioning

confidence: 98%

“…Automatic processing is also able to provide directly some measurements like volumes (of full body and subparts), lengths, maximal girths. These values have shown to be almost pose-independent [14] and clearly can encode relevant information on body shape.…”

Section: Application: Measuring Anthropometric Parametersmentioning

confidence: 99%

“…The aim of the research is to compute and validate morphological parameters from 3D models acquired with different scanners and to correlate the results with different metabolic indicators. User interfaces enabling the operator to perform and collect different computer assisted measurements on original 3D models exploiting also information derived from the previously described "low resolution" analysis have been realized and partially tested [14,16]. Examples of interactive measurements are shown in Fig 4. The measurement set implemented tries to simulate the manual measurement currently performed in classical anthropometric research and validation is performed comparing the values of computer assisted and tape-based measurements.…”

Section: Application: Measuring Anthropometric Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Geometrical Processing of 3D Body Scanner Data for Anthropometric Applications

Lovato¹,

Castellani²,

Zancanaro³

et al. 2010

Proceedings of the 1st International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 19-20 October 2010

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In this paper we describe a pipeline for the geometric processing of 3D meshes obtained by whole body scanners, aimed at providing support for anthropometric measurements. The output of whole body scanners is a cloud of points, usually transformed in a triangulated mesh through the use of specific algorithms in order to support the 3D visualization of the surface and the extraction of meaningful anthropometric landmarks and measurements. Anthropometric analysis is usually performed by using device specific and closed software solutions provided by scanner manufacturers, and requires often a careful acquisition, with strong constraints on subject pose. This may create problems in comparing data acquired in different places and performing large-scale multi-centric studies as well as in applying advanced shape analysis tools on the captured models. The aim of our work is to overcome these problems by selecting and customizing geometrical processing tools able to create an open and device-independent pipeline for the analysis of body scanner data. We also aim at developing and validating methods to extract automatically feature points, body segments and relevant measurements that can be used in anthropometric and metabolic research. The paper presents a general description of the proposed processing pipeline and the currently developed modules (hole filling, skeleton-based segmentation, heat kernel based landmark location), showing their usefulness in the automatic and computer assisted evaluation of anthropometric data.

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Section: Application: Measuring Anthropometric Parametersmentioning

confidence: 98%

Section: Application: Measuring Anthropometric Parametersmentioning

confidence: 99%

Section: Application: Measuring Anthropometric Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Geometrical Processing of 3D Body Scanner Data for Anthropometric Applications

Lovato¹,

Castellani²,

Zancanaro³

et al. 2010

Proceedings of the 1st International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 19-20 October 2010

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“…The extracted measures build the basis for further processing and thus automatic and reliable approaches are of importance. The literature proposes semi-automatic and automatic approaches like detecting landmarks [10] or using prior knowledge by regression learning [13], each with particular drawbacks. Our new approach follows the idea to extract anthropometric measurements according to landmarks and also incorporates prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…When relying on these landmarks, their precise localization becomes a crucial point. Besides manual positioning, the literature proposes to place them automatically according to a skeleton [10] or body segmentation [9]. However, in both cases landmarks are still too coarse when targeting at precise results in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

Precise and Automatic Anthropometric Measurement Extraction Using Template Registration

Wasenmüller¹,

Peters²,

Golyanik³

et al. 2015

Proceedings of the 6th International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 27-28 October 2015

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Anthropometric measures build the basis for many applications, such as custom clothing or biometric identity verification. Consequentially, the possibility to automatically extract them from human body scans is of high importance. In this paper we present a new approach based on landmarks and template registration. First, we propose a new method to define anthropometric measures once on a generic template using landmarks. After the initial definition the template can be registered against an individual body scan and the landmarks can be transferred to the scan using our second proposed algorithm. We apply our complete approach to real and synthetic human data and show that it outperforms the state-of-the-art for several measures.

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3D Digital Anthropometry Using the BodySCAN

Lovato¹,

Milanese²,

Giachetti³

et al. 2010

Proceedings of the 1st International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 19-20 October 2010

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Over the last several years a number of three-dimensional (3D) anthropometric studies have been performed using fast and contact free measurements by using 3D whole-body scanners. Several 3D scanners are available on the market with a potential for anthropometry; these devices are usually developed using laser or Moiré-fringing-based technologies. In the former a laser stripe is projected onto the body surface and images acquired by several cameras: in this way 3D points representing the body shape can be recovered by triangulation. Examples of this kind are the scanners developed by Cyberware, Hamamatsu, Vitronic or Human Solutions. In this work we evaluated a new 3D whole-body scanner using structured white light-the Breuckmann BodySCAN-for performance in anthropometric measurement. Twelve young adults (6 men and 6 women; age, 22,7±2,16 yrs; height, 168,2±7,38 cm; body mass 61,5±6,16 kg) underwent both manual and 3D anthropometry (25 measurements) wearing close-fitting underwear. Duplicate manual measurements taken by one experienced anthropometrist showed correlation coefficients r ranging 0.975-0.999; their means were significantly different in four out of 25 measurements by Student's t test. Duplicate digital measurements taken by one experienced anthropometrist and two naïve anthropometrists showed individual correlation coefficients r ranging 0.975-0.999 and means were significantly different in one out of 25 measurements. Most measurements taken by the experienced anthropometrist in the manual and digital mode showed significant correlation (intraclass correlation coefficient ranging 0.855-0.995, p<0.0001). It is concluded that the Breuckmann BodyScan is a reliable and effective tool for digital anthropometry.

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Computer Assisted Estimation of Anthropometric Parameters from Whole Body Scanner Data

Cited by 3 publications

References 11 publications

Geometrical Processing of 3D Body Scanner Data for Anthropometric Applications

Geometrical Processing of 3D Body Scanner Data for Anthropometric Applications

Precise and Automatic Anthropometric Measurement Extraction Using Template Registration

3D Digital Anthropometry Using the BodySCAN

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