Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-24T03:02:05.907Z Has data issue: false hasContentIssue false
  • English
  • Français

Stiffness modulation of a cable-driven leg exoskeleton for effective human–robot interaction

Published online by Cambridge University Press:  28 April 2021

N. S. S. Sanjeevi  and
Vineet Vashista Open the ORCID record for Vineet Vashista [Opens in a new window]
N. S. S. Sanjeevi
Affiliation:
Human Centered Robotics Lab, IIT Gandhinagar, Ahmedabad, India E-mail: [email protected]
Vineet Vashista*
Affiliation:
Human Centered Robotics Lab, IIT Gandhinagar, Ahmedabad, India E-mail: [email protected]
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

With the widespread development of leg exoskeletons to provide external force-based repetitive training for gait rehabilitation, the prospect of undesired movement adaptation due to applied forces and imposed constraints require adequate investigation. A cable-driven leg exoskeleton, CDLE, presents a lightweight, flexible, and redundantly actuated architecture that enables the possibility of system parameters modulation to alter human–robot interaction while applying the desired forces. In this work, multi-joint stiffness performance of CDLE is formulated to systematically analyze human–CDLE interaction. Further, potential alterations in CDLE architecture are presented to tune human–CDLE interaction that favors the desired human leg movement during a gait rehabilitation paradigm.

Keywords

Human–Robot interactionGait rehabilitationExoskeletonCable-driven robotsStiffness modulation
Type
Article
Information
Robotica , Volume 39 , Issue 12 , December 2021 , pp. 2172 - 2192
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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

Das, S. K., “Who steps stroke surveillance system: Feasibility in India,” Indian J. Med. Res. 130(4), 359–361 (2009).Google ScholarPubMed
Olney, S. J. and Richards, C., “Hemiparetic gait following stroke. Part I: Characteristics,”Gait Posture 4(2), 136–148 (1996).10.1016/0966-6362(96)01063-6CrossRefGoogle Scholar
Banala, S. K., Kim, S. H., Agrawal, S. K. and Scholz, J. P., “Robot Assisted Gait Training with Active Leg Exoskeleton (Alex),” 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (2008) pp. 653–658.Google Scholar
George Hornby, T., Zemon, D. H. and Campbell, D., “Robotic-assisted, bodyweight-supported treadmill training in individuals following motor incomplete spinal cord injury,” Phys. Therapy 85(1), 52–66 (2005).CrossRefGoogle Scholar
Vashista, V., Martelli, D. and Agrawal, S. K., “Locomotor adaptation to an asymmetric force on the human pelvis directed along the right leg,” IEEE Trans. Neural Syst. Rehabil. Eng. 24(8), 872–881 (2015).CrossRefGoogle Scholar
Dohring, M. E. and Daly, J. J., “Automatic synchronization of functional electrical stimulation and robotic assisted treadmill training,” IEEE Trans. Neural Syst. Rehabil. Eng. 16(3), 310–313 (2008).CrossRefGoogle ScholarPubMed
Thieme, H., “Enhanced gait-related improvements after therapist-versus roboticassisted locomotor training in subjects with chronic stroke: A randomized controlled study,” Physioscience 4(04), 195–196 (2008).10.1055/s-2008-1027907CrossRefGoogle Scholar
Zanotto, D., Stegall, P. and Agrawal, S. K., “Alex III: A Novel Robotic Platform with 12 DOFs for Human Gait Training,” 2013 IEEE International Conference on Robotics and Automation (IEEE, 2013) pp. 3914–3919.CrossRefGoogle Scholar
Riener, R., Lünenburger, L., Maier, I. C., Colombo, G. and Dietz, V., “Locomotor training in subjects with sensori-motor deficits: an overview of the robotic gait orthosis lokomat,” J. Healthcare Eng. 1(2), 197–216 (2010).CrossRefGoogle Scholar
Veneman, J. F., Kruidhof, R., Hekman, E. E. G., Ekkelenkamp, R., Van Asseldonk, E. H. F. and Van Der Kooij, H., “Design and evaluation of the lopes exoskeleton robot for interactive gait rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 15(3), 379–386 (2007).10.1109/TNSRE.2007.903919CrossRefGoogle ScholarPubMed
Stegall, P., Winfree, K., Zanotto, D. and Agrawal, S. K., “Rehabilitation exoskeleton design: Exploring the effect of the anterior lunge degree of freedom,” IEEE Trans. Rob. 29(4), 838–846 (2013).CrossRefGoogle Scholar
Srivastava, S., Kao, P.-C., Kim, S. H., Stegall, P., Zanotto, D., Higginson, J. S., Agrawal, S. K. and Scholz, J. P., “Assist-as-needed robotaided gait training improves walking function in individuals following stroke.” IEEE Trans. Neural Syst. Rehabil. Eng. 23(6), 956–963 (2014).CrossRefGoogle ScholarPubMed
Alamdari, A. and Krovi, V., “Design and analysis of a cable-driven articulated rehabilitation system for gait training,” J. Mech. Rob. 8(5), 051018 (2016).CrossRefGoogle Scholar
Banala, S. K., Agrawal, S. K., Kim, S. H. and Scholz, J. P., “Novel gait adaptation and neuromotor training results using an active leg exoskeleton,” IEEE/ASME Trans. Mech. 15(2), 216–225 (2010).10.1109/TMECH.2010.2041245CrossRefGoogle Scholar
Jezernik, S., Colombo, G. and Morari, M., “Automatic gait-pattern adaptation algorithms for rehabilitation with a 4-DOF robotic orthosis,” IEEE Trans. Rob. Autom. 20(3), 574–582 (2004).Google Scholar
Banala, S. K., Agrawal, S. K., Fattah, A., Krishnamoorthy, V., Hsu, W.-L., Scholz, J. and Rudolph, K., “Gravity-balancing leg orthosis and its performance evaluation.”, IEEE Trans. Rob. 22(6), 1228–1239 (2006).CrossRefGoogle Scholar
Veneman, J. F., Ekkelenkamp, R., Kruidhof, R., van der Helm, F. C. T. 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. Rob. Res. 25(3), 261–281 (2006).Google Scholar
Zanotto, D., Lenzi, T., Stegall, P. and Agrawal, S. K., “Improving transparency of powered exoskeletons using force/torque sensors on the supporting cuffs,” In: IEEE 13th International Conference on Rehabilitation Robotics (ICORR) (IEEE, 2013) pp. 1–6.CrossRefGoogle Scholar
Gui, K., Liu, H. and Zhang, D., “A practical and adaptive method to achieve EMG-based torque estimation for a robotic exoskeleton,” IEEE/ASME Trans. Mech. 24(2), 483–494 (2019).CrossRefGoogle Scholar
Li, Z., Yuan, Y., Luo, L., Su, W., Zhao, K., Xu, C., Huang, J. and Pi, M., “Hybrid brain/muscle signals powered wearable walking exoskeleton enhancing motor ability in climbing stairs activity,” IEEE Transactions on Medical Robotics and Bionics. 1(4), 218–227 (2019).CrossRefGoogle Scholar
Li, X., Pan, Y., Chen, G. and Yu, H., “Adaptive human–robot interaction control for robots driven by series elastic actuators,” IEEE Transactions on Robotics. 33(1), 169–182 (2016).CrossRefGoogle Scholar
Kim, S. and Bae, J., “Force-mode control of rotary series elastic actuators in a lower extremity exoskeleton using model-inverse time delay control,” IEEE/ASME Trans. Mech. 22(3), 1392–1400 (2017).CrossRefGoogle Scholar
Boaventura, T., Hammer, L. and Buchli, J., “Interaction Force Estimation for Transparency Control on Wearable Robots Using a Kalman Filter,” In: Converging Clinical and Engineering Research on Neurorehabilitation II (Springer, Cham, 2017) pp. 489–493.CrossRefGoogle Scholar
Jin, X., Cui, X. and Agrawal, S. K., “Design of a Cable-Driven Active Leg Exoskeleton (C-Alex) and Gait Training Experiments with Human Subjects,”, 2015 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2015) pp. 5578–5583.CrossRefGoogle Scholar
Wu, M., George Hornby, T., Landry, J. M., Roth, H. and Schmit, B. D., “A cable-driven locomotor training system for restoration of gait in human SCI,” Gait Posture 33(2), 256–260 (2011).CrossRefGoogle ScholarPubMed
Badi, A., Saad, M., Gauthier, G. and Archambault, P., “Inverse kinematics for a novel rehabilitation robot for lower limbs,” In: Cable-Driven Parallel Robots (Springer, Cham, 2018) pp. 376–389.10.1007/978-3-319-61431-1_32CrossRefGoogle Scholar
Ming, A. and Higuchi, T., “Study of multiple degree-of-freedom positioning mechanism using wires (part1) - concept, design and control,” Int. J. Japan Soc. Precis. Eng. 28(2), 131–138 (1994).Google Scholar
Rezazadeh, S. and Behzadipour, S., “Workspace analysis of multibody cable-driven mechanisms,” J. Mech. Rob. 3(2), 021005 (2011).CrossRefGoogle Scholar
Cafolla, D., Russo, M. and Carbone, G., “CUBE, a cable-driven device for limb rehabilitation,” J. Bionic Eng. 16(3), 492–502 (2019).10.1007/s42235-019-0040-5CrossRefGoogle Scholar
Zhou, X., Jun, S.-k. and Krovi, V., “Tension distribution shaping via reconfigurable attachment in planar mobile cable robots,” Robotica 32(2), 245–256 (2014).CrossRefGoogle Scholar
Shadmehr, R. and Mussa-Ivaldi, F. A., “Adaptive representation of dynamics during learning of a motor task,” J. Neurosci. 14(5), 3208–3224 (1994).CrossRefGoogle ScholarPubMed
Lee, H. and Hogan, N., “Time-varying ankle mechanical impedance during human locomotion,” IEEE Trans. Neural Syst. Rehabil. Eng. 23(5), 755–764 (2014).CrossRefGoogle ScholarPubMed
Azocar, A. F. and Rouse, E. J., “Stiffness perception during active ankle and knee movement,” IEEE Trans. Biomed. Eng. 64(12), 2949–2956 (2017).Google ScholarPubMed
Winter, D. A., The Biomechanics and Motor Control of Human Gait: Normal, Elderly and Pathological (University of Waterloo Press, 1991).Google Scholar
Sanjeevi, N. S. S. and Vashista, V., “On the Stiffness Analysis of a Cable Driven Leg Exoskeleton,” 2017 International Conference on Rehabilitation Robotics (ICORR) (IEEE, 2017) pp. 455–460.CrossRefGoogle Scholar
Oh, S.-R. and Agrawal, S. K., “Cable suspended planar robots with redundant cables: Controllers with positive tensions.”, IEEE Trans. Rob. 21(3), 457–465 (2005).Google Scholar
Gouttefarde, M., Daney, D. and Merlet, J.-P., “Interval-analysis-based determination of the wrench-feasible workspace of parallel cable-driven robots,” IEEE Trans. Rob. 27(1), 1–13 (2011).10.1109/TRO.2010.2090064CrossRefGoogle Scholar
Borgstrom, P. H., Jordan, B. L., Sukhatme, G. S., Batalin, M. A. and Kaiser, W. J., “Rapid computation of optimally safe tension distributions for parallel cable-driven robots,” IEEE Trans. Rob. 25(6), 1271–1281 (2009).CrossRefGoogle Scholar
Mussa-Ivaldi, F. A., Hogan, N. and Bizzi, E., “Neural, mechanical, and geometric factors subserving arm posture in humans,” J. Neurosci. 5(10), 2732–2743 (1985).CrossRefGoogle ScholarPubMed
Verhoeven, R., Hiller, M. and Tadokoro, S., “Workspace, Stiffness, Singularities and Classification of Tendon-Driven Stewart Platforms,” 6th International Symposium on Advances in Robot Kinematics Salzburg, Austria (1998).CrossRefGoogle Scholar
Yeo, S. H., Yang, G. and Lim, W. B., “Design and analysis of cable-driven manipulators with variable stiffness,” Mech. Mach. Theory 69, 230–244 (2013).CrossRefGoogle Scholar
Sanjeevi, N. S. S. and Vashista, V., “Effect of Cable Co-Sharing on the Workspace of a Cable-Driven Serial Chain Manipulator,” Proceedings of the Advances in Robotics 2019 (2019) pp. 1–6.Google Scholar
Chen, S. F. and Kao, I., “Conservative congruence transformation for joint and Cartesian stiffness matrices of robotic hands and fingers,” Int. J. Rob. Res. 19(9), 835–847 (2000).CrossRefGoogle Scholar