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Image-based center of mass estimation of the human body via 3D shape and kinematic structure

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Abstract

This paper presents a method to estimate a time-sequential trajectory of the center of mass (CoM) of an athlete from a multi-view set of cameras. Collecting the CoM typically requires large-scale measuring systems or attaching sensors to the athletes. To mitigate such hardware limitations, the present study takes a multi-view video-based approach. The proposed method reconstructs subjects’ voxels from a set of multi-view frames and weights each voxel with body part-dependent weights to calculate a CoM. Our results, using real data measured in a studio, showed that the proposed method can estimate CoM within 20 mm concerning center of pressure measures.

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  1. https://www.gimp.org/.

  2. https://www.xsens.com/products/xsens-mvn-analyze/.

References

  1. Baumgartner RN, Chumlea WC, Roche AF (1989) Estimation of body composition from bioelectric impedance of body segments. Am J Clin Nutr 50(2):221–226

    Article  Google Scholar 

  2. Brennan SM, Kollár L, Springer G (2003) Modelling the mechanical characteristics and on-snow performance of snowboards. Sports Eng 6(4):193–206

    Article  Google Scholar 

  3. Bullas AM, Choppin S, Heller B, Wheat J (2016) Validity and repeatability of a depth camera-based surface imaging system for thigh volume measurement. J Sports Sci 34(20):1998–2004

    Article  Google Scholar 

  4. Cao S, Chen K, Nevatia R (2016) Activity recognition and prediction with pose based discriminative patch model. In: 2016 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 1–9

  5. Cao Z, Simon T, Wei SE, Sheikh Y (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: Computer vision and pattern recognition (CVPR), Hawaii, US

  6. Carpentier J, Benallegue M, Mansard N, Laumond JP (2016) Center-of-mass estimation for a polyarticulated system in contact—a spectral approach. IEEE Trans Robot 32(4):801–822

    Article  Google Scholar 

  7. Chew DK, Ngoh KJH, Gouwanda D, Gopalai AA (2018) Estimating running spatial and temporal parameters using an inertial sensor. Sports Eng 21(2):115–122

    Article  Google Scholar 

  8. Clarkson S, Wheat J, Heller B, Choppin S (2016) Assessment of a microsoft kinect-based 3d scanning system for taking body segment girth measurements: a comparison to ISAK and ISO standards. J Sports Sci 34(11):1006–1014

    Article  Google Scholar 

  9. Corazza S, Muendermann L, Chaudhari A, Demattio T, Cobelli C, Andriacchi TP (2006) A markerless motion capture system to study musculoskeletal biomechanics: visual hull and simulated annealing approach. Ann. Biomed. Eng. 34(6):1019–1029

    Article  Google Scholar 

  10. Corazza S, Mündermann L, Gambaretto E, Ferrigno G, Andriacchi TP (2009) Markerless motion capture through visual hull, articulated ICP and subject specific model generation. Int J Comput Vis 87(1):156. https://doi.org/10.1007/s11263-009-0284-3

    Article  Google Scholar 

  11. Dawes R, Mann M, Weir B, Pike C, Golds P, Nicholson M (2012) Enhancing viewer engagement using biomechanical analysis of sport. In: Proc. of the NEM Summit, pp 121—126

  12. Drücker S, Schneider K, Ghothra NK, Bargmann S (2018) Finite element simulation of pole vaulting. Sports Eng 21(2):85–93

    Article  Google Scholar 

  13. González A, Hayashibe M, Fraisse P (2012) Estimation of the center of mass with Kinect and Wii balance board. In: 2012 IEEE/RSJ International Conference on Intell. Robots and Systems (IROS), IEEE, pp 1023–1028

  14. González A, Hayashibe M, Bonnet V, Fraisse P (2014) Whole body center of mass estimation with portable sensors: using the statically equivalent serial chain and a Kinect. Sensors 14(9):16955–16971

    Article  Google Scholar 

  15. Göpfert C, Pohjola MV, Linnamo V, Ohtonen O, Rapp W, Lindinger SJ (2017) Forward acceleration of the centre of mass during ski skating calculated from force and motion capture data. Sports Eng 20(2):141–153

    Article  Google Scholar 

  16. Hale SA, Hertel J, Olmsted-Kramer LC (2007) The effect of a 4-week comprehensive rehabilitation program on postural control and lower extremity function in individuals with chronic ankle instability. J Orthop Sports Phys Ther 37:303–311

    Article  Google Scholar 

  17. Hrysomallis C (2011) Balance ability and athletic performance. Sports Med 41(3):221–232

    Article  Google Scholar 

  18. Imamura RT, Hreljac A, Escamilla RF, Edwards WB (2006) A three-dimensional analysis of the center of mass for three different judo throwing techniques. J Sports Sci Med 5:122–131

    Google Scholar 

  19. Kobayashi N, Sato S, Matsuzaki Y, Nakamura A (2017) Basic study on appearance-based proficiency evaluation of the football inside kick. In: 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN), IEEE, pp 1234–1239

  20. de Leva P (1996) Adjustments to zatsiorsky-seluyanov’s segment inertia parameters. J Biomech 29(9):1223–1230

    Article  Google Scholar 

  21. Mapelli A, Zago M, Fusini L, Galante ACD, Sforza C (2014) Validation of a protocol for the estimation of three-dimensional body center of mass kinematics in sport. Gait Posture 39(1):460–465

    Article  Google Scholar 

  22. Martin WN, Aggarwal JK (1983) Volumetric descriptions of objects from multiple views. IEEE Trans Pattern Anal Mach Intell 2:150–158

    Article  Google Scholar 

  23. Mundermann L, Corazza S, Chaudhari AM, Alexander EJ, Andriacchi TP (2005) Most favorable camera configuration for a shape-from-silhouette markerless motion capture system for biomechanical analysis. https://doi.org/10.1117/12.587970

  24. Mündermann L, Corazza S, Andriacchi TP (2006) The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications. J Neuroeng Rehabilit 3(1):6

    Article  Google Scholar 

  25. Najafi B, Lee-Eng J, Wrobel JS, Goebel R (2015) Estimation of center of mass trajectory using wearable sensors during golf swing. J Sports Sci Med 14(2):354–363

    Google Scholar 

  26. Pai YC, Patton J (1997) Center of mass velocity-position predictions for balance control. J Biomech 30(4):347–354

    Article  Google Scholar 

  27. Saini M, Kerrigan DC, Thirunarayan MA, Duff-Raffaele M (1998) The vertical displacement of the center of mass during walking: a comparison of four measurement methods. J Biomech Eng 120(1):133–139

    Article  Google Scholar 

  28. Sheets AL, Abrams GD, Corazza S, Safran MR, Andriacchi TP (2011) Kinematics differences between the flat, kick, and slice serves measured using a markerless motion capture method. Ann Biomed Eng 39(12):3011

    Article  Google Scholar 

  29. Takeda K, Mani H, Hasegawa N, Sato Y, Tanaka S, Maejima H, Asaka T (2017) Adaptation effects in static postural control by providing simultaneous visual feedback of center of pressure and center of gravity. J Physiol Anthropol 36(1):31

    Article  Google Scholar 

  30. Tao W, Liu T, Zheng R, Feng H (2012) Gait analysis using wearable sensors. Sensors 12(2):2255–2283

    Article  Google Scholar 

  31. Tsitsoulis A, Bourbakis NG (2015) A methodology for extracting standing human bodies from single images. IEEE Trans Hum Mach Syst 45:1–12

    Article  Google Scholar 

  32. Welch CM, Banks SA, Cook FF, Draovitch P (2006) Hitting a baseball: a biomechanical description. J Orthop Sports Phys Ther 22(5):193–201

    Article  Google Scholar 

  33. Wieber PB (2006) Holonomy and nonholonomy in the dynamics of articulated motion. In: Fast motions in biomech. and robotics. Springer, Berlin, pp 411–425

  34. Winter DA, Patla AE, Frank JS (1990) Assessment of balance control in humans. Med Prog Technol 16(1):31–51

    Google Scholar 

  35. Zatsiorsky VM, King DL (1997) An algorithm for determining gravity line location from posturographic recordings. J Biomech 31(2):161–164

    Article  Google Scholar 

  36. Zecha D, Einfalt M, Eggert C, Lienhart R (2018) Kinematic pose rectification for performance analysis and retrieval in sports. In: The IEEE conference on computer vision and pattern recognition (CVPR) workshops

  37. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334

    Article  Google Scholar 

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Acknowledgements

This work was supported by Grant-in-Aid for JSPS Fellows (19J22153).

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Authors and Affiliations

  1. Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan

    Tomoya Kaichi, Shohei Mori, Hideo Saito & Dan Mikami

  2. Graz University of Technology, Graz, Austria

    Shohei Mori

  3. NTT Media Intelligence Laboratories, Yokosuka, Japan

    Kosuke Takahashi, Dan Mikami, Mariko Isogawa & Yoshinori Kusachi

Authors
  1. Tomoya Kaichi
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  2. Shohei Mori
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  3. Hideo Saito
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  4. Kosuke Takahashi
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  5. Dan Mikami
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  6. Mariko Isogawa
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  7. Yoshinori Kusachi
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Correspondence to Tomoya Kaichi.

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Kaichi, T., Mori, S., Saito, H. et al. Image-based center of mass estimation of the human body via 3D shape and kinematic structure. Sports Eng 22, 17 (2019). https://doi.org/10.1007/s12283-019-0309-2

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