Hostname: page-component-7b7567c8c-2n9c9 Total loading time: 0 Render date: 2026-11-15T04:10:21.400Z Has data issue: false hasContentIssue false
  • English
  • Français

A novel approach for representing and generalising periodic gaits

Published online by Cambridge University Press:  13 August 2014

Hsiu-Chin Lin,
Matthew Howard and
Sethu Vijayakumar
Hsiu-Chin Lin*
Affiliation:
Institute of Perception Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, UK
Matthew Howard
Affiliation:
Department of Informatics, King's College London, London, UK
Sethu Vijayakumar
Affiliation:
Institute of Perception Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, UK
*
*Corresponding author. E-mail: H.Lin-21@sms.ed.ac.uk
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.

Our goal is to introduce a more appropriate method of representing, generalising and comparing gaits; particularly, walking gait. Human walking gaits are a result of complex, interdependent factors that include variations resulting from embodiments, environment and tasks, making techniques that use average template frameworks suboptimal for systematic analysis or corrective interventions. The proposed work aims to devise methodologies for being able to represent gaits and gait transitions such that optimal policies that eliminate the inter-personal variations from tasks and embodiments may be recovered. Our approach is built upon (i) work in the domain of nullspace policy recovery and (ii) previous work in generalisation for point-to-point movements. The problem is formalised using a walking-phase model, and the nullspace learning method is used to generalise a consistent policy from multiple observations with rich variations. Once recovered, the underlying policies (mapped to different gait phases) can serve as reference guideline to quantify and identify pathological gaits while being robust against interpersonal and task variations. To validate our methods, we have demonstrated robustness of our method with simulated sagittal two-link gait data with multiple ground truth constraints and policies. Pathological gait identification was then tested on real-world human gait data with induced gait abnormality, with the proposed method showing significant robustness to variations in speed and embodiment compared to template-based methods. Future work will extend this to kinetic features and higher dimensional features.

Keywords

RehabilitationHuman biomechanicsRobots for clinical assessmentImitationBipeds

Information

Type
Articles
Information
Robotica , Volume 32 , Issue 8: Rehabilitation Robotics and Human-Robot Interaction , December 2014, pp. 1225 - 1244
Copyright
Copyright © Cambridge University Press 2014 

References

1.Multon, F., France, L., Cani-Gascuel, M.-P. and Debunne, G., “Computer animation of human walking: A survey,” J. Vis. Comput. Animat. 10 (1), 39–54 (1999).3.0.CO;2-2>CrossRefGoogle Scholar
2.Boulic, R., Thalmann, N. and Thalmann, D., “A global human walking model with real-time kinematic personification,” Vis. Comput. 6, 344–358 (1990).Google Scholar
3.Pongas, D., Mistry, M. and Schaal, S., “A Robust Quadruped Walking Gait for Traversing Rough Terrain,” Proceedings of the International Conference on Robotics and Automation, Rome, Italy (Apr. 10–14, 2007) pp. 1474–1479.Google Scholar
4.Fukuoka, Y., Kimura, H. and Cohen, A. H., “Adaptive dynamic walking of a quadruped robot on irregular terrain based on biological concepts,” Int. J. Robot. Res. 22 (3–4), 187–202 (2003).CrossRefGoogle Scholar
5.Duschau-Wicke, A., von Zitzewitz, J., Caprez, A., Lunenburger, L. and Riener, R., “Path control: A method for patient-cooperative robot-aided gait rehabilitation sky,” IEEE Trans. Neural Syst. Rehabil. Eng. 18 (1), 38–48 (2010).Google Scholar
6.Artemiadis, P. and Krebs, H., “On the Potential Field-Based Control of the Mit-Skywalker,” Proceedings of the International Conference on Robotics and Automation, Shanghai, China (May 9–13, 2011) pp. 1427–1432.CrossRefGoogle Scholar
7.Veneman, J., Kruidhof, R., Hekman, E., Ekkelenkamp, R., van Asseldonk, E. and van der Kooij, H., “Design and evaluation of the lopes exoskeleton robot for interactive gait rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 15, 379–386 (2007).CrossRefGoogle ScholarPubMed
8.Hidler, J., Nichols, D., Pelliccio, M. and Brady, K., “Advances in the understanding and treatment of stroke impairment using robotic devices,” Top. Stroke Rehabil. 12, 22–35 (2005).Google Scholar
9.Jezernik, S., Colombo, G. and Morari, M., “Automatic gait-pattern adaptation algorithms for rehabilitation with a 4-DOF robotic orthosis,” IEEE Trans. Robot. Autom. 20 (3), 574–582 (2004).Google Scholar
10.Veneman, J. F., Ekkelenkamp, R., Kruidhof, R., Van Der Helm, F. C. and Van Der Kooij, H., “A series elastic- and bowden-cable-based actuation system for use as torque actuator in exoskeleton-type robots,” Int. J. Robot. Res. 25, 261–281 (2006).Google Scholar
11.Riener, R., Lunenburger, L., Jezernik, S., Anderschitz, M., Colombo, G. and Dietz, V., “Patient-cooperative strategies for robot-aided treadmill training: first experimental results,” IEEE Trans. Neural Syst. Rehabil. Eng. 13 (3), 380–394 (2005).Google Scholar
12.Stokes, V., Andersson, C. and Forssberg, H., “Rotational and translational movement features of the pelvis and thorax during adult human locomotion,” J. Biomech. 22 (1), 43–50 (1989).Google Scholar
13.Ivanenko, Y. P., Poppele, R. E. and Lacquaniti, F., “Five basic muscle activation patterns account for muscle activity during human locomotion,” J. Physiol. 556, 267–282 (2004).Google Scholar
14.Jolliffe, I. T., Principal Components Analysis (Springer-Verlag, 1986).Google Scholar
15.Howard, M., Klanke, S., Gienger, M., Goerick, C. and Vijayakumar, S., “A novel method for learning policies from variable constraint data,” Auton. Robots 27, 105–121 (2009).CrossRefGoogle Scholar
16.Towell, C., Howard, M. and Vijayakumar, S., “Learning Nullspace Policies,” Proceedings of the International Conference on Intelligent Robots and Systems, Taipei, Taiwan (Oct. 18–22, 2010) pp. 241–248.CrossRefGoogle Scholar
17.Lee, S. and Sankai, Y., “Power Assist Control for Walking Aid with HAL-3 based on Emg and Impedance Adjustment Around Knee Joint,” Proceedings of the International Conference on Intelligent Robots and Systems, Lausanne, Switzerland (Sep. 30–Oct. 5, 2002) pp. 1499–1504.Google Scholar
18.Whittle, M. W., Gait Analysis: An Introduction, 3rd ed. (Butterworth-Heinemann, Oxford, UK, 2003).Google Scholar
19.Kajita, S., Kanehiro, F., Kaneko, K., Fujiwara, K., Harada, K., Yokoi, K. and Hirukawa, H., “Biped Walking Pattern Generation by Using Preview Control of Zero-Moment Point,” Proceedings of the International Conference on Robotics and Automation, Taipei, Taiwan (Sep. 14–19, 2003) pp. 1620–1626.Google Scholar
20.Ogura, Y., Aikawa, H., Shimomura, K., Kondo, H., Morishima, A., Lim, H. and Takanishi, A., “Development of a New Humanoid Robot Wabian-2,” Proceedings of the International Conference on Robotics and Automation, Orlando, USA (May 15–19, 2006) pp. 76–81.Google Scholar
21.Guan, Y., Neo, E. S., Yokoi, K. and Tanie, K., “Stepping over obstacles with humanoid robots,” IEEE Trans. Robot. 22, 958–973 (2006).CrossRefGoogle Scholar
22.Ijspeert, A. J., Nakanishi, J. and Schaal, S., “Learning Attractor Landscapes for Learning Motor Primitives,” In: Advances in Neural Information Processing Systems (Becker, S., Thrun, S. and Obermayer, K., eds.) (MIT Press, Cambridge, 2003) pp. 1547–1554.Google Scholar
23.Morimoto, J. and Atkeson, C., “Nonparametric representation of an approximated poincaré map for learning biped locomotion,” Auton. Robots 27, 131–144 (2009).Google Scholar
24.Ijspeert, A. and Crespi, A., “Online Trajectory Generation in an Amphibious Snake Robot Using a Lamprey-Like Central Pattern Generator Model,” Proceedings of the International Conference on Robotics and Automation, Roma, Italy (Apr. 10–14, 2007) pp. 262–268.Google Scholar
25.Nakamura, Y., Mori, T., aki Sato, M. and Ishii, S., “Reinforcement learning for a biped robot based on a cpg-actor-critic method,” Neural Netw. 20 (6), 723–735 (2007).CrossRefGoogle ScholarPubMed
26.Winter, D. A., “Kinematic and kinetic patterns in human gait: Variability and compensating effects,” Hum. Mov. Sci. 3 (1–2), 51–76 (1984).Google Scholar
27.Broomhead, D. and Lowe, D., “Radial basis functions, multi-variable functional interpolation and adaptive networks,” Complex Syst. 2, 321–355 (1988).Google Scholar
28.Howard, M. and Vijayakumar, S., “Reconstructing Null-Space Policies Subject to Dynamic Task Constraints in Redundant Manipulators,” Proceedings of the Workshop on Robotics and Mathematics, Coimbra, Portugal (Sep. 17–19, 2007) pp. 109–115.Google Scholar
29.Roetenberg, D., Luinge, H. and Slycke, P., “Xsens mvn: Full 6-DOF Human Motion Tracking Using Miniature Inertial Sensors,” Xsens Technologies BV, Enschede, The Netherlands, 2009.Google Scholar
30.Öberg, T., Karsznia, A. and Öberg, K., “Basic gait parameters: Reference data for normal subjects, 10–79 years of age,” J. Rehabil. Res. Dev. 30, 210–210 (1993).Google ScholarPubMed