Xiaolei Lv
Abstract
Inverse dynamics is an important and challenging problem in human motion modeling, synthesis and simulation, as well as in robotics and biomechanics. Previous solutions to inverse dynamics are often noisy and ambiguous particularly when double stances occur. In this paper, we present a novel inverse dynamics method that accurately reconstructs biomechanically valid contact information, including center of pressure, contact forces, torsional torques and internal joint torques from input kinematic human motion data. Our key idea is to apply statistical modeling techniques to a set of preprocessed human kinematic and dynamic motion data captured by a combination of an optical motion capture system, pressure insoles and force plates. We formulate the data-driven inverse dynamics problem in a maximum a posteriori (MAP) framework by estimating the most likely contact information and internal joint torques that are consistent with input kinematic motion data. We construct a low-dimensional data-driven prior model for contact information and internal joint torques to reduce ambiguity of inverse dynamics for human motion. We demonstrate the accuracy of our method on a wide variety of human movements including walking, jumping, running, turning and hopping and achieve state-of-the-art accuracy in our comparison against alternative methods. In addition, we discuss how to extend the data-driven inverse dynamics framework to motion editing, filtering and motion control.
Supplemental Material
Available for Download
Supplemental file.
- Adelsberger, R., and Troster, G. 2013. Pimu: A wireless pressure-sensing imu. In Intelligent Sensors, Sensor Networks and Information Processing, 2013 IEEE Eighth International Conference on, IEEE, 271--276.Google Scholar
- Aladdin, R., and Kry, P. 2012. Static pose reconstruction with an instrumented bouldering wall. In Proceedings of the 18th ACM symposium on Virtual reality software and technology, ACM, 177--184. Google ScholarDigital Library
- Anybodytech, 2016. http://www.anybodytech.com/.Google Scholar
- Artec3D, 2016. https://www.artec3d.com/.Google Scholar
- Bazaraa, M. S., Sherali, H., and Shetty, C. 1993. Nonlinear programming: theory and algorithms. John Wiley&Sons, New York.Google Scholar
- Bergmann, G., Deuretzbacher, G., Heller, M., Graichen, F., Rohlmann, A., Strauss, J., and Duda, G. 2001. Hip contact forces and gait patterns from routine activities. Journal of Biomechanics 34, 7, 859 -- 871.Google ScholarCross Ref
- Bishop, C. M., et al. 1995. Neural networks for pattern recognition. Google ScholarDigital Library
- Brubaker, M. A., Sigal, L., and Fleet, D. J. 2009. Estimating contact dynamics. In Computer Vision, 2009 IEEE 12th International Conference on, IEEE, 2389--2396.Google Scholar
- Cohen, M. F. 1992. Interactive spacetime control for animation. In ACM SIGGRAPH Computer Graphics, vol. 26, ACM, 293--302. Google ScholarDigital Library
- Corazza, S., Gambaretto, E., Mundermann, L., and Andriacchi, T. P. 2010. Automatic generation of a subject-specific model for accurate markerless motion capture and biomechanical applications. Biomedical Engineering, IEEE Transactions on 57, 4, 806--812.Google ScholarCross Ref
- De Leva, P. 1996. Adjustments to zatsiorsky-seluyanov's segment inertia parameters. Journal of biomechanics 29, 9, 1223--1230.Google ScholarCross Ref
- Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., John, C. T., Guendelman, E., and Thelen, D. G. 2007. Opensim: open-source software to create and analyze dynamic simulations of movement. IEEE transactions on biomedical engineering 54, 11, 1940--1950.Google Scholar
- Forner-Cordero, A., Koopman, H., and Van der Helm, F. 2006. Inverse dynamics calculations during gait with restricted ground reaction force information from pressure insoles. Gait & posture 23, 2, 189--199.Google Scholar
- Ha, S., Bai, Y., and Liu, C. K. 2011. Human motion reconstruction from force sensors. In Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, ACM, 129--138. Google ScholarDigital Library
- Ho, W.-H., Shiang, T.-Y., Lee, C.-C., and Cheng, S.-Y. 2013. Body segment parameters of young chinese men determined with magnetic resonance imaging. Medicine and science in sports and exercise 45, 9, 1759--1766.Google Scholar
- Hreljac, A., and Marshall, R. N. 2000. Algorithms to determine event timing during normal walking using kinematic data. Journal of biomechanics 33, 6, 783--786.Google ScholarCross Ref
- Jain, S., and Liu, C. K. 2011. Controlling physics-based characters using soft contacts. ACM Trans. Graph. (SIGGRAPH Asia) 30 (Dec.), 163:1--163:10. Google ScholarDigital Library
- Kistler, 2016. http://www.kistler.com.Google Scholar
- Ko, H., and Badler, N. I. 1996. Animating human locomotion with inverse dynamics. Computer Graphics and Applications, IEEE 16, 2, 50--59. Google ScholarDigital Library
- Kry, P. G., and Pai, D. K. 2006. Interaction capture and synthesis. In ACM Transactions on Graphics (TOG), vol. 25, ACM, 872--880. Google ScholarDigital Library
- Liu, L., Yin, K., van de Panne, M., Shao, T., and Xu, W. 2010. Sampling-based contact-rich motion control. ACM Transctions on Graphics 29, 4, Article 128. Google ScholarDigital Library
- Liu, L., Yin, K., and Guo, B. 2015. Improving sampling-based motion control. Computer Graphics Forum 34, 2. Google ScholarDigital Library
- Min, J., Chen, Y., and Chai, J. 2009. Interactive generation of human animation with deformable motion models. ACM Transactions on Graphics (TOG) 29, 1, 9. Google ScholarDigital Library
- Muico, U., Lee, Y., Popović, J., and Popović, Z. 2009. Contact-aware nonlinear control of dynamic characters. In ACM Transactions on Graphics (TOG), vol. 28, ACM, 81. Google ScholarDigital Library
- Myers, C. S., and Rabiner, L. R. 1981. A comparative study of several dynamic time-warping algorithms for connected-word recognition. Bell System Technical Journal 60, 7, 1389--1409.Google ScholarCross Ref
- Novel, 2016. http://www.novel.de.Google Scholar
- Oh, S. E., Choi, A., and Mun, J. H. 2013. Prediction of ground reaction forces during gait based on kinematics and a neural network model. Journal of biomechanics 46, 14, 2372--2380.Google ScholarCross Ref
- Rao, G., Amarantini, D., Berton, E., and Favier, D. 2006. Influence of body segments parameters estimation models on inverse dynamics solutions during gait. Journal of Biomechanics 39, 8, 1531--1536.Google ScholarCross Ref
- Ren, L., Jones, R. K., and Howard, D. 2008. Whole body inverse dynamics over a complete gait cycle based only on measured kinematics. Journal of Biomechanics 41, 12, 2750--2759.Google ScholarCross Ref
- Safonova, A., Hodgins, J. K., and Pollard, N. S. 2004. Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. In ACM Transactions on Graphics (TOG), vol. 23, ACM, 514--521. Google ScholarDigital Library
- Sok, K. W., Kim, M., and Lee, J. 2007. Simulating biped behaviors from human motion data. In ACM Transactions on Graphics (TOG), vol. 26, ACM, 107. Google ScholarDigital Library
- Sulejmanpašić, A., and Popović, J. 2005. Adaptation of performed ballistic motion. ACM Transactions on Graphics (TOG) 24, 1, 165--179. Google ScholarDigital Library
- Vicon, 2016. http://www.vicon.com.Google Scholar
- Vondrak, M., Sigal, L., and Jenkins, O. C. 2008. Physical simulation for probabilistic motion tracking. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, IEEE, 1--8.Google Scholar
- Vondrak, M., Sigal, L., Hodgins, J., and Jenkins, O. 2012. Video-based 3d motion capture through biped control. ACM Transactions On Graphics (TOG) 31, 4, 27. Google ScholarDigital Library
- Wei, X., Min, J., and Chai, J. 2011. Physically valid statistical models for human motion generation. ACM Transactions on Graphics (TOG) 30, 3, 19. Google ScholarDigital Library
- Witkin, A., and Kass, M. 1988. Spacetime constraints. In ACM Siggraph Computer Graphics, vol. 22, ACM, 159--168. Google ScholarDigital Library
- Xia, S., Wang, C., Chai, J., and Hodgins, J. 2015. Realtime style transfer for unlabeled heterogeneous human motion. ACM Transactions on Graphics (TOG) 34, 4, 119. Google ScholarDigital Library
- Xiang, Y., Arora, J. S., and Abdel-Malek, K. 2011. Optimization-based prediction of asymmetric human gait. Journal of Biomechanics 44, 4, 683--693.Google ScholarCross Ref
- Ye, Y., and Liu, C. K. 2008. Animating responsive characters with dynamic constraints in near-unactuated coordinates. In ACM Transactions on Graphics (TOG), vol. 27, ACM, 112. Google ScholarDigital Library
- Yeadon, M. R., and Morlock, M. 1989. The appropriate use of regression equations for the estimation of segmental inertia parameters. Journal of biomechanics 22, 6, 683--689.Google ScholarCross Ref
- Yin, K., and Pai, D. K. 2003. Footsee: an interactive animation system. In Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation, Eurographics Association, 329--338. Google ScholarDigital Library
- Zell, P., and Rosenhahn, B. 2015. A physics-based statistical model for human gait analysis. In Pattern Recognition. Springer, 169--180.Google Scholar
- Zhang, P., Siu, K., Zhang, J., Liu, C. K., and Chai, J. 2014. Leveraging depth cameras and wearable pressure sensors for full-body kinematics and dynamics capture. ACM Transactions on Graphics (TOG) 33, 6, 221. Google ScholarDigital Library
Index Terms
- Data-driven inverse dynamics for human motion
Recommendations
Inverse dynamics with rigid contact and friction
Inverse dynamics is used extensively in robotics and biomechanics applications. In manipulator and legged robots, it can form the basis of an effective nonlinear control strategy by providing a robot with both accurate positional tracking and active ...
Inverse Dynamics of Multilink Cable-Driven Manipulators With the Consideration of Joint Interaction Forces and Moments
Joint interaction forces and moments play a significant role within multilink cable-driven manipulators (MCDMs). In this paper, the consideration of joint interaction forces and moments in the objective function and constraints specific to the inverse ...
Inverse dynamics of the 6-dof out-parallel manipulator by means of the principle of virtual work
In this paper, the inverse dynamics of the 6-dof out-parallel manipulator is formulated by means of the principle of virtual work and the concept of link Jacobian matrices. The dynamical equations of motion include the rotation inertia of motor–coupler–...
Comments