research-article
Authors: Yang Shen, Jianwei Sun, Ji Ma, Jacob Rosen
2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)
Pages 611 - 617
Published: 24 June 2019 Publication History
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Abstract
In physical rehabilitation, exoskeleton assistive devices aim to restore lost motor functions of a patient suffering from neuromuscular or musculoskeletal disorders. These assistive devices are classified as operating in one of two modes: (1) passive mode, in which the exoskeleton passively moves its joints through the full range (or a subset) of the patient’s motion during engagement, or (2) assist-as-needed (AAN) mode, in which the exoskeleton provides assistance to the joints of the patient, either by initiating the movements or assisting the patient’s movements to complete the task at hand. Achieving high physical human-robot interaction (pHRI) transparency is an open problem for multiple degrees-of-freedom (DOFs) redundant exoskeletons. Using the EXO-UL8 exoskeleton, this study compares two multi-joint admittance control schemes (hyper parameter-based, and Kalman Filter-based) with comfort optimization to improve human-exoskeleton transparency. The control schemes were tested by three healthy subjects who completed reaching tasks while assisted by the exoskeleton. Kinematic information in both joint and task space, as well as force-and torque-based power exchange between the human arm and exoskeleton, are collected and analyzed. The results show that the preliminary Kalman Filter-based control scheme matches the performance of the existing hyper parameter-based scheme, highlighting the potential of the Kalman Filter-based approach for additional performance.
References
[1]
E. J. Benjamin, M. J. Blaha, S. E. Chiuve, M. Cushman, S. R. Das, R. Deo, S. D. de Ferranti, J. Floyd, M. Fornage, C. Gillespie, C. R. Isasi, M. C. Jiménez, L. C. Jordan, S. E. Judd, D. Lackland, J. H. Lichtman, L. Lisabeth, S. Liu, C. T. Longenecker, R. H. Mackey, K. Matsushita, D. Mozaffarian, M. E. Mussolino, K. Nasir, R. W. Neumar, L. Palaniappan, D. K. Pandey, R. R. Thiagarajan, M. J. Reeves, M. Ritchey, C. J. Rodriguez, G. A. Roth, W. D. Rosamond, C. Sasson, A. Towfighi, C. W. Tsao, M. B. Turner, S. S. Virani, J. H. Voeks, J. Z. Willey, J. T. Wilkins, J. H. Wu, H. M. Alger, S. S. Wong, and P. Muntner, “Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association,” Circulation, vol. 135, pp. e146–e603, 3 2017.
[2]
P. Langhorne, J. Bernhardt, and G. Kwakkel, “Stroke rehabilitation,” Lancet, vol. 377, pp. 1693–1702, 2011.
[3]
H. S. Lo and S. Q. Xie, “Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects,” Medical Engineering & Physics, vol. 34, no. 3, pp. 261–268, 2012.
[4]
N. Jarrassé, T. Proietti, V. Crocher, J. Robertson, A. Sahbani, G. Morel, and A. Roby-Brami, “Robotic exoskeletons: a perspective for the rehabilitation of arm coordination in stroke patients,” Frontiers in Human Neuroscience, vol. 8, p. 947, 12 2014.
[5]
D. J. Reinkensmeyer, E. Burdet, M. Casadio, J. W. Krakauer, G. Kwakkel, C. E. Lang, S. P. Swinnen, N. S. Ward, and N. Schweighofer, “Computational neurorehabilitation: modeling plasticity and learning to predict recovery.,” Journal of neuroengineering and rehabilitation, vol. 13, no. 1, p. 42, 2016.
[6]
A. A. Blank, J. A. French, A. U. Pehlivan, and M. K. O’Malley, “Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy,” Current physical medicine and rehabilitation reports, vol. 2, no. 3, pp. 184–195, 2014.
[7]
J. Lobo-Prat, P. N. Kooren, A. H. Stienen, J. L. Herder, B. F. Koopman, and P. H. Veltink, “Non-invasive control interfaces for intention detection in active movement-assistive devices,” Journal of NeuroEngineering and Rehabilitation, vol. 11, no. 1, 2014.
[8]
P. Haggard, K. Hutchinson, and J. Stein, “Patterns of coordinated multi-joint movement,” Experimental Brain Research, vol. 107, pp. 254–266, 12 1995.
[9]
L. Peternel, T. Noda, T. Petrič, A. Ude, J. Morimoto, and J. Babič, “Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation,” PLOS ONE, vol. 11, p. e0148942, 2 2016.
[10]
E. Todorov, T. Erez, and Y. Tassa, “MuJoCo: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5026–5033, IEEE, 10 2012.
[11]
Y. Shen, P. W. Ferguson, J. Ma, and J. Rosen, “Chapter 4 - Upper Limb Wearable Exoskeleton Systems for Rehabilitation: State of the Art Review and a Case Study of the EXO-UL8 Dual-Arm Exoskeleton System,” in Wearable Technology in Medicine and Health Care (R. K.-Y. Tong, ed.), pp. 71–90, Academic Press, 2018.
[12]
N. Jarrassé and G. Morel, “Connecting a human limb to an exoskeleton,” IEEE Transactions on Robotics, vol. 28, no. 3, pp. 697–709, 2012.
Digital Library
[13]
Z. Li, B. Wang, F. Sun, C. Yang, Q. Xie, and W. Zhang, “SEMG-based joint force control for an upper-limb power-assist exoskeleton robot,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 3, pp. 1043–1050, 2014.
[14]
J. Huang, W. Huo, W. Xu, S. Mohammed, and Y. Amirat, “Control of Upper-Limb Power-Assist Exoskeleton Using a Human-Robot Interface Based on Motion Intention Recognition,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 4, pp. 1257–1270, 2015.
[15]
A. Kilicarslan, S. Prasad, R. G. Grossman, and J. L. Contreras-Vidal, “High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5606–5609, IEEE, 7 2013.
[16]
Y. Shen, J. Ma, B. Dobkin, and J. Rosen, “Asymmetric Dual Arm Approach For Post Stroke Recovery Of Motor Functions Utilizing The EXO-UL8 Exoskeleton System: A Pilot Study,” in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), vol. 2018, pp. 1701–1707, IEEE, 7 2018.
[17]
Y. Shen, B. P.-Y. Hsiao, J. Ma, and J. Rosen, “Upper limb redundancy resolution under gravitational loading conditions: Arm postural stability index based on dynamic manipulability analysis,” in 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pp. 332–338, IEEE, 11 2017.
[18]
F. Augugliaro and R. D’Andrea, “Admittance control for physical human-quadrocopter interaction,” in 2013 European Control Conference (ECC), pp. 1805–1810, IEEE, 7 2013.
[19]
P. Lee, S. Wei, J. Zhao, and N. I. Badler, “Strength guided motion,” ACM SIGGRAPH Computer Graphics, vol. 24, no. 4, pp. 253–262, 1990.
Digital Library
[20]
B. Huang, Z. Ye, Z. Li, W. Yuan, and C. Yang, “Admittance control of a robotic exoskeleton for physical human robot interaction,” in 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM), (Hefei), pp. 245–250, 2017.
[21]
P. W. Ferguson, B. Dimapasoc, Y. Shen, and J. Rosen, “Design of a Hand Exoskeleton for Use with Upper Limb Exoskeletons,” in Wearable Robotics: Challenges and Trends (M. C. Carrozza, S. Micera, and J. L. Pons, eds.), Biosystems & Biorobotics, pp. 276–280, Cham: Springer International Publishing, 2019.
Index Terms
Admittance Control Scheme Comparison of EXO-UL8: A Dual-Arm Exoskeleton Robotic System
Applied computing
Life and medical sciences
Computer systems organization
Embedded and cyber-physical systems
Robotics
Computing methodologies
Artificial intelligence
Control methods
Robotic planning
Planning and scheduling
Robotic planning
Index terms have been assigned to the content through auto-classification.
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2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)
Jun 2019
1282 pages
Copyright © 2019.
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IEEE Press
Publication History
Published: 24 June 2019
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