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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
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
Brennan SM, Kollár L, Springer G (2003) Modelling the mechanical characteristics and on-snow performance of snowboards. Sports Eng 6(4):193–206
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
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
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
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
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
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
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
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
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
Drücker S, Schneider K, Ghothra NK, Bargmann S (2018) Finite element simulation of pole vaulting. Sports Eng 21(2):85–93
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
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
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
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
Hrysomallis C (2011) Balance ability and athletic performance. Sports Med 41(3):221–232
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
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
de Leva P (1996) Adjustments to zatsiorsky-seluyanov’s segment inertia parameters. J Biomech 29(9):1223–1230
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
Martin WN, Aggarwal JK (1983) Volumetric descriptions of objects from multiple views. IEEE Trans Pattern Anal Mach Intell 2:150–158
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
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
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
Pai YC, Patton J (1997) Center of mass velocity-position predictions for balance control. J Biomech 30(4):347–354
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
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
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
Tao W, Liu T, Zheng R, Feng H (2012) Gait analysis using wearable sensors. Sensors 12(2):2255–2283
Tsitsoulis A, Bourbakis NG (2015) A methodology for extracting standing human bodies from single images. IEEE Trans Hum Mach Syst 45:1–12
Welch CM, Banks SA, Cook FF, Draovitch P (2006) Hitting a baseball: a biomechanical description. J Orthop Sports Phys Ther 22(5):193–201
Wieber PB (2006) Holonomy and nonholonomy in the dynamics of articulated motion. In: Fast motions in biomech. and robotics. Springer, Berlin, pp 411–425
Winter DA, Patla AE, Frank JS (1990) Assessment of balance control in humans. Med Prog Technol 16(1):31–51
Zatsiorsky VM, King DL (1997) An algorithm for determining gravity line location from posturographic recordings. J Biomech 31(2):161–164
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
Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334
This work was supported by Grant-in-Aid for JSPS Fellows (19J22153).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
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
- Center of mass
- Multi-view videos
- Visual hull
- Multi-view human pose estimation