Abstract
Many biomechanical and medical analyses rely on the availability of reliable body segment parameter estimates. Current techniques typically take many manual measurements of the human body, in conjunction with geometric models or regression equations. However, such techniques are often criticised. 3D scanning offers many advantages, but current systems are prohibitively complex and costly. The recent interest in natural user interaction (NUI) has led to the development of low cost (~£200) sensors capable of 3D body scanning, however, there has been little consideration of their validity. A scanning system comprising four Microsoft Kinect sensors (a typical NUI sensor) was used to scan twelve living male participants three times. Volume estimates from the system were compared to those from a geometric modelling technique. Results demonstrated high reliability (ICC>0.7, TEM<1 %) and presence of a systematic measurement offset (0.001m\(^{3}\)), suggesting the system would be well received by healthcare and sports communities.
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Lerch, T., MacGillivray, M., Domina, T.: 3D Laser Scanning: A Model of Multidisciplinary Research. Journal of Textile and Apparel Technology and Management 5(3), 1–22 (2006)
Robinson, A., Mccarthy, M., Brown, S., Evenden, A., Zou, L.: Improving the quality of measurements through the implementation of customised reference artefacts. In: 3D Body Scanning Technologies, pp. 235–246 Lugano (2012)
Piovesan, D., Pierobon, A., Dizio, P., Lackner, J.R.: Comparative analysis of methods for estimating arm segment parameters and joint torques from inverse dynamics. Journal of Biomechanical Engineering 133(3), 31003(1)-31003(15) (March 2011)
Durkin, J.L., Dowling, J.J., Andrews, D.M.: The measurement of body segment inertial parameters using dual energy X-ray absorptiometry. Journal of Biomechanics 35(12), 1575–1580 (2002)
Damavandi, M., Farahpour, N., Allard, P.: Determination of body segment masses and centers of mass using a force plate method in individuals of different morphology. Medical Engineering & Physics 31(9), 1187–1194 (2009)
Pearsall, D.J., Costigan, P.A.: The effect of segment parameter error on gait analysis results. Gait & Posture 9(3), 173–183 (1999)
Rao, G., Amarantini, D., Berton, E., Favier, D.: Influence of body segments’ parameters estimation models on inverse dynamics solutions during gait. Journal of Biomechanics 39(8), 1531–1536 (2006)
Bauer, J.J., Pavol, M.J., Snow, C.M., Hayes, W.C.: MRI-derived body segment parameters of children differ from age-based estimates derived using photogrammetry. Journal of Biomechanics 40(13), 2904–2910 (2007)
Pearsall, D.J., Reid, J.G., Livingston, L.A.: Segmental inertial parameters of the human trunk as determined from computed tomography. Annuals of Biomedical Engineering 24(2), 198–210 (1996)
Cheng, C.K., Chen, H.H., Chen, C.S., Chen, C.L., Chen, C.Y.: Segment inertial properties of Chinese adults determined from magnetic resonance imaging. Clinical Biomechanics 15(8), 559–566 (2000)
Martin, P.E., Mungiole, M., Longhill, J.M.: The use of magnetic resonance imaging for measuring segment inertial properties. Journal of Biomechanics 22(4), 367–376 (1989)
Dempster, W.: Space Requirements of the Seated Operator. Technical report, Michigan (1955)
Zatsiorsky, V.: The Mass and Inertia Characteristics of the Main Segments of the Human Body. Biomechanics VIII(B), 1152–1159 (1983)
Leva, P.D.: Adjustments to Zatsiorsky-Seluyanov’s Segment Inertia Parameters. Journal of Biomechanics 29(9), 1223–1230 (1996)
Gittoes, M.J.R., Kerwin, D.G.: Component inertia modelling of segmental wobbling and rigid masses. Journal of Applied Biomechanics 22(2), 148–154 (2006)
Pearsall, D., Reid, J.: The Study of Human Body Segment Parameters in Biomechanics: A Historical Review and Current Status Report. Sports Medicine 18(5), 126–140 (1994)
Hanavan, E.P.: A Mathematical Model of the Human Body. PhD thesis, USAF Institute of Technology, Ohio (1964)
Yeadon, M.R.: The simulation of aerial movement-II. A mathematical inertia model of the human body. Journal of Biomechanics 23(1), 67–74 (1990)
Challis, J.H.: Precision of the Estimation of Human Limb Inertial Parameters. Journal of Applied Biomechanics 15, 418–428 (1999)
Wicke, J., Dumas, G.A.: Influence of the Volume and Density Functions Within Geometric Models for Estimating Trunk Inertial Parameters. Journal of Applied Biomechanics 26, 26–31 (2010)
Schranz, N., Tomkinson, G., Olds, T., Daniell, N.: Three-dimensional anthropometric analysis: differences between elite Australian rowers and the general population. Journal of sports sciences 28(5), 459–469 (2010)
Nikon Corp: Metris D100 Laser Scanner (2011)
Henderson, R., Schulmeister, K.: Laser Safety. Taylor and Francis (2003)
Vitronic: Vitus 3D Body Scanner (2011)
TC2: TC2 NX-16 3D Body Scanner. Technical report, Cary, USA (2010)
Weiss, A., Hirshberg, D., Black, M.J.: Home 3D body scans from noisy image and range data. In: 13th International Conference on Computer Vision, Barcelona (2011)
Amazon: Kinect & Kinect Adventures for XBox 360 (2012)
Primesense Ltd: Three Dimensional Scanning using Speckle patterns (2009)
Shotton, J., Sharp, T., Fitzgibbon, A., Cook, M., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-Time Human Pose Recognition in Parts from Single Depth Images. IEEE CVPR 3, 1297–1304 (2011)
Boehm, J.: Natural user interface sensors for human body measurement. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX(B3), 531–536 (2012)
Khoshelham, K.: Accuracy analysis of Kinect depth data. In: Lichti, D., Habib, A. (eds.) ISPRS Workshop Laser Scanning, Calgary, 1–6 (2010)
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using kinect-style depth cameras for dense 3D modeling of indoor environments. The International Journal of Robotics Research 31(5), 647–663 (2012)
Labelle, K.: Evaluation of Kinect joint tracking for clinical and in home stroke rehabilitation tools. PhD thesis, Notre Dame, Indiana (2011)
Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., Fitzgibbon, A.: KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera. In: Symposium, U.I.S.T. (ed.) Santa, pp. 559–568. CA, Barbara (2011)
Stampfli, P., Rissiek, A., Trieb, R., Seidi, A.: SizeITALY - The Actual Italian Measurement Survey. In: 3D Body Scanning Technologies, Lugano, Switzerland (2012) 261–268
Boehm, J.: Accuracy Investigation for Natural User Interface Sensors. In: Low Cost 3D Sensors, Algorithms and Applications, Berlin (2011)
Menna, F., Remondino, F., Battisti, R., Nocerino, E.: Geometric investigation of a gaming active device. In: Proceedings of the SPIE 8085(XI) (2011) 80850G(1)-80850G(15)
Clarkson, S., Choppin, S., Hart, J., Heller, B., Wheat, J.: Calculating Body Segment Inertia Parameters from a Single Rapid Scan Using the Microsoft Kinect. In: Consulting, Hometrica (ed.) 3D Body Scanning Technologies, pp. 153–163, Lugano. Hometrica Consulting (2012)
Clarkson, S., Wheat, J., Heller, B., Webster, J., Choppin, S.: Distortion Correction of Depth Data from Consumer Depth Cameras. In: Consulting, Hometrica (ed.) 3D Body Scanning Technologies, pp. 426–437, Long Beach. Hometrica Consulting (2013)
Khoshelham, K., Elberink, S.O.: Accuracy and resolution of Kinect depth data for indoor mapping applications. Sensors 12(2), 1437–1454 (2012)
Soderkvist, I., Wedin, P.A.: Determining the movements of the skeleton using well configured markers. Journal of Biomechanics 26(12), 1473–1477 (1993)
Kirby, R., Price, N., MacLeod, D.: The Influence of Foot Position on Standing Balance. Journal of Biomechanics 20(4), 423–427 (1987)
International Standards Office: ISO 20685, 3-D Scanning Methodologies for Internationally Compatible Anthropometric Databases (2010)
Kouzaki, M., Masani, K.: Reduced postural sway during quiet standing by light touch is due to finger tactile feedback but not mechanical support. Experimental brain research 188(1), 153–158 (2008)
Lackner, J., Rabin, E., DiZio, P.: Stabilization of posture by precision touch of the index finger with rigid and flexible filaments. Experimental Brain Research 139(4), 454–464 (2001)
Schranz, N., Tomkinson, G., Olds, T., Petkov, J., Hahn, A.G.: Is three-dimensional anthropometric analysis as good as traditional anthropometric analysis in predicting junior rowing performance? Journal of sports sciences 30(12), 1241–1248 (2012)
Stewart, A., Sutton, L.: Body Composition in Sport, Exercise and Health, 1st edn. Routledge, Oxon (2012)
Outram, T., Domone, S., Wheat, J.: The reliability of trunk segment inertial parameter estimates made from geometric models. In: 30th Annual Conference of Biomechanics in Sports. Number 113, pp. 47–50 (2012)
Munro, B.: Statistical Methods for Health Care Research, 4th edn. Lippincott Williams and Wilkins, Philadelphia (2000)
de Vet, H.C.W., Terwee, C.B., Knol, D.L., Bouter, L.M.: When to use agreement versus reliability measures. Journal of clinical epidemiology 59(10), 1033–1039 (2006)
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Clarkson, S., Wheat, J., Heller, B., Choppin, S. (2015). Assessing the Suitability of the Microsoft Kinect for Calculating Person Specific Body Segment Parameters. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8925. Springer, Cham. https://doi.org/10.1007/978-3-319-16178-5_26
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