Hostname: page-component-7b7567c8c-2n9c9 Total loading time: 0 Render date: 2026-11-14T17:24:54.810Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimization-based dynamic 3D human running prediction: effects of foot location and orientation

Published online by Cambridge University Press:  04 March 2014

Hyun-Joon Chung,
Yujiang Xiang,
Jasbir S. Arora and
Karim Abdel-Malek
Hyun-Joon Chung
Affiliation:
Center for Computer-Aided Design (CCAD), The University of Iowa, Iowa City, IA 52242, USA
Yujiang Xiang*
Affiliation:
Department of Mechanical Engineering, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Jasbir S. Arora
Affiliation:
Center for Computer-Aided Design (CCAD), The University of Iowa, Iowa City, IA 52242, USA
Karim Abdel-Malek
Affiliation:
Center for Computer-Aided Design (CCAD), The University of Iowa, Iowa City, IA 52242, USA
*
*Corresponding author. E-mail: yujxiang@gmail.com
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

This paper presents optimization-based dynamic three-dimensional (3D) human running prediction. A predictive dynamics method is used to formulate the running problem, and normal running is formulated as a symmetric and cyclic motion. In addition, a slow jog along curved paths has been formulated. It is a non-symmetric running motion, so a stride formulation has been used. The dynamic effort and impulse are used as the performance measure, and the upper body yawing moment is also included in the performance measure. The joint angle profiles and joint torque profiles are calculated for the full-body human model, and the ground reaction force is determined. The effects of foot location and orientation on the running motion prediction are simulated and studied. Simulation results from this methodology show good correlation with experimental data obtained from human subjects.

Keywords

RunningSlow jogImpulseArm swingOptimization

Information

Type
Articles
Information
Robotica , Volume 33 , Issue 2 , February 2015, pp. 413 - 435
Copyright
Copyright © Cambridge University Press 2014 

References

1.Fujimoto, Y., “Trajectory Generation of Biped Running Robot with Minimum Energy Consumption,” Proceedings of the IEEE international Conference on Robotics and Automation, New Orleans, USA (Apr. 26–May 1, 2004) pp. 3803–3808.Google Scholar
2.Hodgins, J. K., “Three-Dimensional Human Running,” Proceedings of the International Conference on Robotics and Automation, Minneapolis, USA (Apr. 22–28, 1996) pp. 3271–3276.Google Scholar
3.Hirai, K., Hirose, M., Haikawa, Y. and Takenaka, T., “The Development of Honda Humanoid Robot,” Proceedings of the IEEE international Conference on Robotics and Automation, Leuven, Belgium (May 16–20, 1998) pp. 1321–1326.Google Scholar
4.Kim, Y., Lee, B., Yoo, J., Choi, S. and Kim, J., “Humanoid Robot HanSaRam: Yawing Moment Cancellation and ZMP Compensation,” Proceedings of AUS International Symposium on Mechatronics, Sharjah, U.A.E (Apr. 19–21, 2005).Google Scholar
5.Mu, X. and Wu, Q., “Synthesis of a complete sagittal gait cycle for a five-link biped robot,” Robotica 21, 581–587 (2003).Google Scholar
6.Nagasaki, T., Kajita, S., Yokoi, K., Kaneko, K. and Tanie, K., “Running Pattern Generation and its Evaluation using a Realistic Humanoid Model,” Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan (Sep. 14–19, 2003) pp. 1336–1342.Google Scholar
7.Novacheck, T., “Review paper: The biomechanics of running,” Gait Posture 7, 77–95 (1998).Google Scholar
8.Õunpuu, S., “The biomechanics of walking and running,” Clin. Sports Med. 13 (4)843–863 (1994).Google Scholar
9.Park, J. and Kwon, O., “Impedance Control for Running of Biped Robots,” Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Jul. 20–24, 2003) pp. 944–949.Google Scholar
10.Xiang, Y., Arora, J. S. and Abdel-Malek, K., “Physics-based modeling and simulation of human walking: A review of optimization-based and other approaches,” Struct. Multidiscip. Optim. 42, 1–23 (2010).Google Scholar
11.Milliron, M. J. and Cavanagh, P. R., “Sagittal Plane Kinematics of the Lower Extremity during Distance Running,” In: Biomechanics of Distance Running (Cavanagh, P. R., ed.) (Human Kinetics, Champaign, IL, 1990) pp. 65–105.Google Scholar
12.Simpson, K. and Bates, B., “The effects of running speed on lower extremity joint moments generated during the support phase,” Int. J. Sport Biomech. 6, 309–324 (1990).Google Scholar
13.Arora, J. S., Introduction to Optimum Design, 3rd ed. (Academic Press, San Diego, USA, 2012).Google Scholar
14.Hardt, M., von Stryk, O., Wollherr, D. and Buss, M., “Development and Control of Autonomous, Biped Locomotion using Efficient Modeling, Simulation, and Optimization Techniques,” Proceeding of IEEE International Conference on Robotics and Automation, Taipei, Taiwan (Sep. 14–19, 2003) pp. 1356–1361.Google Scholar
15.von Stryk, O. and Bulirsch, R., “Direct and indirect methods for trajectory optimization,” Ann. Oper. Res. 37, 357–373 (1992).Google Scholar
16.Xiang, Y., Arora, J. S., Rahmatalla, S. and Abdel-Malek, K., “Optimization-based dynamic human walking prediction: One step formulation,” Int. J. Numer. Methods Eng. 79 (6), 667–695 (2009).Google Scholar
17.Xiang, Y., Arora, J. S. and Abdel-Malek, K., “Optimization-based prediction of asymmetric human gait,” J. Biomech. 44 (4), 683–693 (2011).Google Scholar
18.Xiang, Y., Arora, J. S., Rahmatalla, S., Marler, T., Bhatt, R. and Abdel-Malek, K., “Human lifting simulation using a multi-objective optimization approach,” Multibody Syst. Dyn. 23 (4), 431–451 (2010).Google Scholar
19.Xiang, Y., Arora, J. S. and Abdel-Malek, K., “3D human lifting motion prediction with different performance measures,” Int. J. Humanoid Robot. 9 (2), 1250012 (2012).CrossRefGoogle Scholar
20.Xiang, Y., Chung, H. J., Kim, J. H., Bhatt, R., Rahmatalla, S., Yang, J., Marler, T., Arora, J. S. and Abdel-Malek, K., “Predictive dynamics: An optimization-based novel approach for human motion simulation,” Struct. Multidiscip. Optim. 41 (3), 465–479 (2010).Google Scholar
21.Xiang, Y., Arora, J. S. and Abdel-Malek, K., “Hybrid predictive dynamics: A new approach to simulate human motion,” Multibody Syst. Dyn. 28 (3), 199–224 (2012).Google Scholar
22.Kim, J. H., Xiang, Y., Yang, J., Arora, J. S. and Abdel-Malek, K., “Dynamic motion planning of overarm throw for a biped human multibody system,” Multibody Syst. Dyn. 24 (1), 1–24 (2010).Google Scholar
23.Collins, S., Adamczyk, P. and Kuo, A. D., “Dynamic Arm Swinging in Human Walking,” Proc. R. Soc. Biol. Sci. 276, 3679–3688 (2009).CrossRefGoogle ScholarPubMed
24.Denavit, J. and Hartenberg, R. S., “A kinematic notation for lower-pair mechanisms based on matrices,” ASME J. Appl. Mech. 22, 215–221 (1955).Google Scholar
25.Hollerbach, J. M., “A recursive Lagrangian formulation of manipulator dynamics and a comparative study of dynamics formulation complexity,” IEEE Trans. Syst. Man Cybern. 11 (10), 730–736 (1980).CrossRefGoogle Scholar
26.Xiang, Y., Arora, J. S. and Abdel-Malek, K., “Optimization-based motion prediction of mechanical systems: Sensitivity analysis,” Struct. Multidiscip. Optim. 37 (6), 595–608 (2009).Google Scholar
27.Chung, H. J., Optimization-Based Dynamic Prediction of 3D Human Running Ph.D. Thesis (Iowa City, IA: The University of Iowa, 2009).Google Scholar
28.Vukobratović, M. and Borovac, B., “Zero-moment point - thirty five years of its life,” Int. J. Humanoid Robot. 1 (1), 157–173 (2004).Google Scholar
29.Roussel, L., Canuda-de-Wit, C. and Goswami, A., “Generation of Energy Optimal Complete Gait Cycles for Biped Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium (May 16–20, 1998) pp. 2036–2041.Google Scholar
30.Bedford, A. and Fowler, W., Engineering Mechanics: Dynamics, 3rd ed. (Prentice Hall, New Jersey, 2002).Google Scholar
31.Williams, K. R. and Cavanagh, P. R., “Relationship between distance running mechanics, running economy, and performance,” J. Appl. Phys. 63 (3), 1236–1245 (1987).Google Scholar
32.Norkin, C. C. and White, D. J., Measurement of Joint Motion: A Guide to Goniometry, 3rd ed. (F. A. Davis Co, Philadelphia, 2003).Google Scholar
33.Xiang, Y., Arora, J. S., Chung, H. J., Kwon, H. J., Rahmatalla, S., Bhatt, R. and Abdel-Malek, K., “Predictive simulation of human walking transitions using an optimization formulation,” Struct. Multidiscip. Optim. 45 (5), 759–772 (2012).Google Scholar
34.Gill, P. E., Murray, W. and Saunders, M. A., “SNOPT: An SQP algorithm for large-scale constrained optimization,” SIAM J. Optim. 12, 979–1006 (2002).CrossRefGoogle Scholar
35.Rahmatalla, S., Xiang, Y., Smith, R., Meusch, J. and Bhatt, R., “A validation framework for predictive human models,” Int. J. Hum. Factors Modelling Simul. 2 (1/2), 67–84 (2011).Google Scholar
36.Xiang, Y., Rahmatalla, S., Arora, J. S. and Abdel-Malek, K., “Enhanced optimisation-based inverse kinematics methodology considering joint discomfort,” Int. J. Hum. Factors Modelling Simul. 2 (1/2), 111–126 (2011).Google Scholar
37.Bruderlin, A. and Calvert, T., “Knowledge-Driven, Interactive Animation of Human Running,” Proceedings of the Conference on Graphics Interface, Toronto, Canada (May 22–24, 1996) pp. 213–221.Google Scholar
38.Kim, J. H., Xiang, Y., Bhatt, R., Yang, J., Chung, H. J., Arora, J. S. and Abdel-Malek, K., “Generating effective whole-body motions of a human-like mechanism with efficient ZMP formulation,” Int. J. Robot. Autom. 24 (2), 125–136 (2009).Google Scholar