Hostname: page-component-74656f9884-2dmjx Total loading time: 0 Render date: 2026-11-10T09:47:10.243Z Has data issue: false hasContentIssue false
  • English
  • Français

Pneumatically powered robotic exercise device to induce a specific force profile in target lower extremity muscles

Published online by Cambridge University Press:  27 June 2014

Gregory C. Henderson  and
Jun Ueda
Gregory C. Henderson
Affiliation:
The G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 771 Ferst Drive, Atlanta, GA 30332-0405, USA
Jun Ueda*
Affiliation:
The G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 771 Ferst Drive, Atlanta, GA 30332-0405, USA
*
*Corresponding author. E-mail: jun.ueda@me.gatech.edu
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.

The goal of this research is to establish a methodology to actively control a pneumatically driven robotic device that can induce specific muscle force patterns in target muscles during a subject's voluntary movement. In this paper, the generation of constant forces in the rectus femoris muscle throughout the knee extension, i.e., isotonic contractions, was studied. Due to a highly nonlinear nature of mapping the joint torque to muscle force, a simple application of constant torques to the knee joint would not realize isotonic contractions. The proposed robotic exercise accounted for nonlinear moment arms of muscles as functions of joint angles and nonlinear coordination of multiple muscles in the neuromuscular system to accomplish individual muscle control. A pneumatically powered one degree-of-freedom device that can impose active force feedback control has been designed and built. An exercise-planning algorithm has been developed that involved a musculoskeletal model of the lower body, and the dynamics of a pneumatic actuator. Five constant force profiles were tested for 20 healthy volunteers and electromyographic signals were collected while the device was applying calculated force profiles.

Keywords

Isolation exerciseMuscle force controlIsotonic contractionMuscle optimizationMusculoskeletal modelLower extremitiesPneumatically powered exercise deviceElectromyogramRehabilitation

Information

Type
Articles
Information
Robotica , Volume 32 , Issue 8: Rehabilitation Robotics and Human-Robot Interaction , December 2014 , pp. 1281 - 1299
Copyright
Copyright © Cambridge University Press 2014 

References

1.Ueda, J., Ming, D., Krishnamoorthy, V., Shinohara, M. and Ogasawara, T.., “Individual muscle control using an exoskeleton robot for muscle function testing,” IEEE Trans. Neural Syst. Rehabil. Eng. 18, 339–350 (Aug. 2010).Google Scholar
2.Riener, R. and Fuhr, T., “Patient-driven control of FES supported standing-up: A simulation study,” IEEE Trans. Rehabil. Eng. 6, 113–124 (Jun. 1998).Google Scholar
3.Piazza, S. J. and Delp, S. L.. “The influence of muscles on knee flexion during the swing phase of gait,” J. Biomech. 29, 723–733 (1996).Google Scholar
4.Shen, X. and Goldfarb, M.. “Simultaneous force and stiffness control of a pneumatic actuator,” J. Dyn. Syst. Meas. Control 129, 425–434 (Jul. 2007).Google Scholar
5.Fujita, T., Kawashima, K. and Kagawa, T. “Effect of servo valve dynamic on precise position control of a pneumatic servo table,” Int. J. Autom. Technol. 2 (1), 43–48 (2008).CrossRefGoogle Scholar
6.Andersson, S., Soderberg, A. and Bjorklund, S., “Friction models for sliding dry, boundary and mixed lubricated contacts,” Tribol. Int. 40 (4), 580–587 (2007).CrossRefGoogle Scholar
7.Shu, N. and Bone, G. M., “Development of a Nonlinear Dynamic Model for a Servo Pneumatic Positioning System,” Proceedings of the 2005 IEEE International Conference on Mechatronics and Automation, vol. 1. (2005) pp. 43–48.Google Scholar
8.Zhu, Y. and Barth, E. J., “Impedance Control of a Pneumatic Actuator for Contact Tasks,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005) (April 18–22, 2005) pp. 987–992.Google Scholar
9.Crowninshield, R. D.et al., “A physiologically based criterion of muscle force prediction in locomotion,” J. Biomech. 14, 793–801 (1981).Google Scholar
10.Yamaguchi, G., Dynamic Modeling of Musculoskeletal Motion (Kluwer, Netherlands, 2001).CrossRefGoogle Scholar
11.van Bolhuis, B. and Gielen, C., “A comparison of models explaining muscle activation patterns for isometric contractions,” Biol. Cybern. 81 (3), 249–261 (1999).CrossRefGoogle ScholarPubMed
12.Erdemir, A., McLean, S., Herzog, W. and van den Bogert, A. J., “Model-based estimation of muscle forces exerted during movements,” Clin. Biomech. 22, 131–154 (2007).Google Scholar
13.Biggs, J. and Horch, K., “A three-dimensional kinematic model of the human long finger and the muscles that actuate it,” Med. Eng. Phys. 21 (9), 625–639 (1999).Google Scholar
14.Delp, S., Loan, J., Hoy, M., Zajac, F., Topp, E., Rosen, J., Center, V. and Alto, P., “An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures,” IEEE Trans. Biomed. Eng. 37 (8), 757–767 (1990).Google Scholar
15.Maurel, W. and Thalmann, D., “A case study on human upper limb modeling for dynamic simulation,” Comput. Methods Biomech. Biomed. Eng. 2 (1), 65–82 (1999).Google Scholar
16.Veeger, H., Van der Helm, F., Van der Woude, L., Pronk, G. and Rozendal, R., “Inertia and muscle contraction parameters for musculoskeletal modeling of the shoulder mechanism,” J. Biomech. 24 (7), 615–629 (1991).Google Scholar
17.Patriarco, A. B., Mann, R. W., Simon, S. R. and Mansour, J. M., “An evaluation of the approaches of optimization methods in the prediction of muscle forces during human gait,” J. Biomech. 14, 513–525 (1981).Google Scholar
18.Raikova, R. T. and Prilutsky, B. I., “Sensitivity of predicted muscle force to parameters of the optimization-based human leg model revealed by analytical and numerical analyses,” J. Biomech. 34, 1243–1255 (2001).Google Scholar
19.Stokes, I. A. F. and Gardner-Morse, M., “Lumbar spinal muscle activation synergies predicted by multi-criteria cost function,” J. Biomech. 34, 733–740 (2001).Google Scholar
20.Infantolino, B. W. and Challis, J. H., “Architectural properties of the first dorsal interosseous muscle,” J. Anat. 216 (4), 463–469 (2010).Google Scholar
21.Kellis, E. and Baltzopoulos, V., “Muscle activation differences between eccentric and concentric isokinetic exercise,” Med. Sci. Sports Exerc. 30, 1616–1623 (1998).Google Scholar
22.Westing, S. H., Cresswell, A. G. and Thorstensson, A.., “Muscle activation during maximal voluntary eccentric and concentric knee extension,” Eur. J. Appl. Physiol. 62, 104–108 (1991).Google Scholar
23.Kazerooni, H., Racine, J.-L., Huang, L. and Steger, R.., “On the Control of the Berkeley Lower Extremity Exoskeleton (BLEEX),” Proceedings of the IEEE International Conference on Robotics and Automation (Apr. 2005) pp. 4353–4360.Google Scholar
24.Kawamoto, H., Lee, S., Kanbe, S. and Sankai, Y.., “Power Assist Method for HAL-3 Using EMG-Based Feedback Controller,” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 2 (2003) pp. 1648–1653.Google Scholar
25.Blaya, J. A. and Herr, H., “Adaptive control of a variable-impedance ankle–foot orthosis to assist drop-foot gait,” IEEE Trans. Neural Syst. Rehabil. Eng. 12 (1), 24–31 (Mar. 2004).Google Scholar
26.Volpe, B., Huerta, P., Zipse, J., Rykman, A., Edwards, D., Dipietro, L., Hogan, N. and Krebs, H., “Robotic devices as therapeutic and diagnostic tools for stroke recovery,” Arch. Neurol. 66 (9), 1086–1086 (2009).CrossRefGoogle ScholarPubMed
27.Furusho, J., Sakaguchi, M., Takesue, N. and Koyonagi, K., “Development of ER brake and its application to force display,” J. Intell. Mater. Syst. Struct. 13, 425–429 (2002).Google Scholar
28.Book, W. and Ruis, D., “Control of a Robotic Exercise Machine” Proceedings of the Joint Automatic Control Conference, paper WA.2A (1981) 5 pp.Google Scholar
29.Richer, E. and Hurmuzlu, Y., “A high performance pneumatic force actuator system: Part I – nonlinear mathematical model.” ASME J. Dyn. Syst. Meas. Control. 122, 416–425 (2000).Google Scholar
30.Kamper, D. G., Fischer, H. C. and Cruz, E. G., “Impact of finger posture on mapping from muscle activation to joint torque,” Clin. Biomech. 21, 361–369 (2006).Google Scholar
31.Deshpande, A. D., Balasubramanian, R., Lin, R., Dellon, B. and Matsuoka, Y., “Understanding Variable Moment Arms for the Index Finger MCP Joints Through the ACT Hand,” Proceedings of the IEEE Conference on Biomedical Robotics and Biomechatronics (2008) pp. 776–782.Google Scholar
32.Buchanan, T. S., Lloyd, D. G., Manal, K. and Besier, T. F., “Neuromusculoskeletal modeling: Estimation of muscle forces and joint moments and movements from measurements of neural command,” J. Appl. Biomech. 20, 367–395 (2004).Google Scholar
33.Cheng, A. J. and Rice, C. L., “Fatigue and recovery of power and isometric torque following isotonic knee extensions,” J. Appl. Physiol. 99, 1446–1452 (2005).Google Scholar
34.Knapik, J. J., Wright, J. E., Mawdsley, R. H.et al. “Isometric, isotonic, and isokinetic torque variations in four muscle groups through a range of joint motion.” Phys. Ther. 63, 938–947 (1983).Google Scholar
35.Ueda, J. and Ding, M., “Individual Control of Redundant Skeletal Muscles Using an Exoskeleton Robot,” In: Redundancy in Robot Manipulators and Multi-Robot Systems, Lecture Notes in Electrical Engineering, vol. 57 (Milutinovic, D. and Rosen, J., eds.) (Springer, New York, NY, 2013) pp. 183–199, ISBN: 978-3-642-33970-7.Google Scholar
36.Henderson, G., “Pneumatically-Powered Robotic Exoskeleton to Exercise Specific Lower Extremity Muscle Groups in Humans,” Master's Thesis (Georgia Institute of Technology, Atlanta, GA, 2012).Google Scholar
37.Hidler, J., Nichols, D., Pelliccio, M., Brady, K., Campbell, D. D., Kahn, J. H. and Hornby, T. G., “Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke,” Neurorehabil. Neural Repair 23 (1), 5–13 (2009).Google Scholar
38.Delp, S., Anderson, F., Arnold, A., Loan, P., Habib, A., John, C., Guendelman, E. and Thelen, D., “Opensim: Open-source software to create and analyze dynamic simulations of movement,” IEEE Trans. Biomed. Eng. 54 (11), 1940–1950 (2007).Google Scholar
39.Damsgaard, M., Rasmussen, J., Christensen, S., Surma, E. and de Zee, M., “Analysis of musculoskeletal systems in the ANYBODY modeling system,” Simul. Modelling Pract. Theory 14 (8), 1100–1111 (2006).Google Scholar
40.Steele, K. M., Seth, A., Hicks, J. L., Schwartz, M. and Delp, S. L., “Muscle contributions to support during single-limb stance in crouch gait,” J. Biomech. 43, 2099–2105 (2010).Google Scholar
41.Weir, J. P., Wagner, L. L. and Housh, T. J., “Linearity and reliability of the IEMG vs. torque relationship for the forearm flexors and leg extensors,” Am. J. Phys. Med. Rehabil. 71, 283–287 (1992).Google Scholar
42.Anderson, F. C. and Pandy, M. G., “Dynamic optimization of human walking,” J. Biomech. Eng. 123, 381–390 (2001).Google Scholar
43.MotCo Project, available at: http://www.clbme.bas.bg/projects/motco/ (accessed June 11, 2014).Google Scholar
44.Brown, E., Aomoto, K., Ikeda, A., Ogasawara, T., Yoshitake, Y., Shinohara, M. and Ueda, J., “Comparison of Ultrasound Muscle Stiffness Measurement and Electromyography Towards Validation of an Algorithm for Individual Muscle Control,” Proceedings of the 2013 ASME Dynamic Systems and Control Conference (DSCC '13) (2013) Paper No. DSCC2013-4093, pp. V002T28A005; 10 pages, doi:10.1115/DSCC2013-4093. available as CD-ROM.Google Scholar
45.Noritsugu, T. and Takaiwa, M., “Robust Positioning Control of Pneumatic Servo System with Pressure Control Loop,” Proceedings of 1995 IEEE International Conference on Robotics and Automation, vol. 3 (1995) pp. 2613–2618.Google Scholar