Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-27T12:04:24.788Z Has data issue: false hasContentIssue false
  • English
  • Français

Joint role exploration in sagittal balance by optimizing feedback gains

Published online by Cambridge University Press:  31 January 2014

Dengpeng Xing  and
Jianbo Su
Dengpeng Xing*
Affiliation:
Institute of Automation, Chinese Academy of Sciences, Beijing, China
Jianbo Su
Affiliation:
Department of Automation, Shanghai Jiao Tong University, Shanghai, China
*
*Corresponding author. E-mail: xingdengpeng@hotmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

This paper investigates the contributions of each joint in perturbed balance by employing multiple balance strategies and exploring gain scheduling. Hybrid controllers are developed for sagittal standing in response to constant pushes, and a hypothesis is then investigated that postural feedback gains in standing balance should change with perturbation size via an optimization approach. Related research indicates the roles of each joint: the ankles apply torque to the ground, the hips and/or arms generate horizontal ground forces, and the knees and hips squat. To investigate it from an optimization point of view, this paper uses a horizontal push of a given size, direction, and location as a perturbation, and optimizes controllers for different push sizes, directions, and locations. It applies to the ankle, hip, squat, and arm swinging strategies in standing balance. By comparing the capability of handling disturbances and investigating the feedback gains of each strategy, this paper quantitatively analyzes the contributions of each joint to perturbed balance. We believe this work is also instructive to study the progressive behavioral changes as the model gets more and more complex.

Keywords

Joint rolesPerturbed balanceOptimizationBalance strategy
Type
Articles
Information
Robotica , Volume 33 , Issue 1 , January 2015 , pp. 127 - 139
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Runge, C. F., Shupert, C. L., Horak, F. B. and Zajac, F. E., “Ankle and hip postural strategies defined by joint torques,” Gait Posture 10 (2), 280–289 (2001).Google Scholar
2.Hemami, H., Barin, K. and Pai, Y. C., “Quantitative analysis of the ankle strategy under translational platform disturbance,” IEEE Trans. Neural Syst. Rehabil. Eng. 14 (4), 470–480 (2006).CrossRefGoogle ScholarPubMed
3.Nashner, L. and McCollum, G., “The organization of postural movements: A formal basis and experimental synthesis,” Behav. Brain Sci. 8 (1), 135–150 (1985).CrossRefGoogle Scholar
4.Horak, F. and Nashner, L., “Central programming of postural movements: Adaptation to altered support-surface configurations,” J. Neurophysiol. 55 (6), 1369–1381 (1986).CrossRefGoogle ScholarPubMed
5.Park, S., Horak, F. B. and Kuo, A. D., “Postural feedback responses scale with biomechanical constraints in human standing,” Exp. Brain Res. 154 (4), 417–427 (2004).CrossRefGoogle ScholarPubMed
6.Kuo, A. D., “An optimal control model for analyzing human postural balance,” IEEE Trans. Biomed. Eng. 42 (1), 87–101 (1995).CrossRefGoogle ScholarPubMed
7.Alexandrov, A. V., Frolov, A. A., Horak, F. B., Carlson-kuhta, P. and Park, S., “Feedback equilibrium control during human standing,” Biol. Cybern. 93 (5), 309–322 (2005).CrossRefGoogle ScholarPubMed
8.Kim, S., Horak, F. B., Carlson-Kuhta, P. and Park, S., “Postural feedback scaling deficits in Parkinson's disease,” J. Neurophysiol. 102, 2910–2920 (2009).Google Scholar
9.Kim, S., Atkeson, C. G. and Park, S., “Perturbation-dependent selection of postural feedback gain and its scaling,” J. Biomech. 45 (8), 1379–1386 (2012).CrossRefGoogle ScholarPubMed
10.Alexandrov, A. V. and Frolov, A. A., “Closed-loop and open-loop control of posture and movement during human upper trunk bending,” Biol. Cybern. 104 (6), 425–438 (2011).CrossRefGoogle Scholar
11.Atkeson, C. G. and Stephens, B. J., “Multiple Balance Strategies from One Optimization Criterion,” Proceedings of the International Conference on Humanoid Robots (2007) pp. 57–64.Google Scholar
12.Liu, C. and Atkeson, C. G., “Standing Balance Control Using a Trajectory Library,” Proceedings of the International Conference on Intelligent Robots and Systems (2009) pp. 3031–3036.Google Scholar
13.Stephens, B. and Atkeson, C. G., “Modeling and Control of Periodic Humanoid Balance Using the Linear Biped Model,” Proceedings of the International Conference on Humanoid Robots (2009) pp. 379–384.Google Scholar
14.Yin, K., Loken, K. and van de Panne, M., “SIMBICON: Simple biped locomotion control,” ACM Trans. Graph. 26 (3), 150:1–10 (2007).CrossRefGoogle Scholar
15.Yin, K., Pai, D. K. and Van de Panne, M., “Data-driven interactive balancing behaviors,” Proceedings of the Pacific Graphics (2005) pp. 118–121.Google Scholar
16.Xing, D., Atkeson, C. G., Su, J. and Stephens, B. J., “Gain Scheduled Control of Perturbed Standing Balance,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems (2010) pp. 4063–4068.Google Scholar
17.Xing, D. and Liu, X., “Multiple balance strategies for humanoid standing control,” Acta Autom. Sin. 37 (2), 234–239 (2011).CrossRefGoogle Scholar
18.Gill, P. E., Murray, W. and Saunders, M. A., “SNOPT: An SQP algorithm for large-scale constrained optimization,” SIAM J. Optim. 12 (4), 979–1006 (2002).CrossRefGoogle Scholar
19.Alexandrov, A. V., Frolov, A. A. and Massion, J., “Biomechanical analysis of movement strategies in human forward trunk bending. I. Modeling,” Biol. Cybern. 84, 425–434 (2001).CrossRefGoogle ScholarPubMed
20.Alexandrov, A. V., Frolov, A. A. and Massion, J., “Biomechanical analysis of movement strategies in human forward trunk bending. II. Experimental study,” Biol. Cybern. 84, 435–443 (2001).CrossRefGoogle ScholarPubMed