Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-04T21:05:51.682Z Has data issue: false hasContentIssue false
  • English
  • Français

Design and validation of the RiceWrist-S exoskeleton for robotic rehabilitation after incomplete spinal cord injury

Published online by Cambridge University Press:  20 June 2014

Ali Utku Pehlivan ,
Fabrizio Sergi ,
Andrew Erwin ,
Nuray Yozbatiran ,
Gerard E. Francisco  and
Marcia K. O'Malley
Ali Utku Pehlivan*
Affiliation:
Mechatronics and Haptic Interfaces Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX 77005
Fabrizio Sergi
Affiliation:
Mechatronics and Haptic Interfaces Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX 77005 Department of PM & R, Baylor College of Medicine, Houston, TX 77030
Andrew Erwin
Affiliation:
Mechatronics and Haptic Interfaces Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX 77005
Nuray Yozbatiran
Affiliation:
Department of PM & R and UTHealth Motor Recovery Lab, University of Texas Health Science Center at Houston, TX 77030
Gerard E. Francisco
Affiliation:
Department of PM & R and UTHealth Motor Recovery Lab, University of Texas Health Science Center at Houston, TX 77030
Marcia K. O'Malley
Affiliation:
Mechatronics and Haptic Interfaces Laboratory, Department of Mechanical Engineering, Rice University, Houston, TX 77005
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Robotic devices are well-suited to provide high intensity upper limb therapy in order to induce plasticity and facilitate recovery from brain and spinal cord injury. In order to realise gains in functional independence, devices that target the distal joints of the arm are necessary. Further, the robotic device must exhibit key dynamic properties that enable both high dynamic transparency for assessment, and implementation of novel interaction control modes that significantly engage the participant. In this paper, we present the kinematic design, dynamical characterization, and clinical validation of the RiceWrist-S, a serial robotic mechanism that facilitates rehabilitation of the forearm in pronation-supination, and of the wrist in flexion-extension and radial-ulnar deviation. The RiceWrist-Grip, a grip force sensing handle, is shown to provide grip force measurements that correlate well with those acquired from a hand dynamometer. Clinical validation via a single case study of incomplete spinal cord injury rehabilitation for an individual with injury at the C3-5 level showed moderate gains in clinical outcome measures. Robotic measures of movement smoothness also captured gains, supporting our hypothesis that intensive upper limb rehabilitation with the RiceWrist-S would show beneficial outcomes.

Keywords

Robot dynamicsExoskeletonsRobotic rehabilitationHuman-robot interaction
Type
Articles
Information
Robotica , Volume 32 , Issue 8: Rehabilitation Robotics and Human-Robot Interaction , December 2014 , pp. 1415 - 1431
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.Schaechter, J. D., “Motor rehabilitation and brain plasticity after hemiparetic stroke,” Prog. Neurobiology 73, 61–72 (Jun. 2004).Google Scholar
2.Hogan, N., Krebs, H. I., Rohrer, B., Palazzolo, J. J., Dipietro, L., Fasoli, S. E., Stein, J., Hughs, R., Frontera, W. R., Lynch, D. and Volpe, B. T., “Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery,” Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery 43 (5), 605 (2006).Google Scholar
3.Edgerton, V. R., Tillakaratne, N. J. K., Bigbee, A. J., de Leon, R. D. and Roy, R. R., “Plasticity of the spinal neural circuitry after injury,” Annu. Rev. Neurosci. 27 (1), 145–167 (Jul. 2004).Google Scholar
4.Lynskey, J. V., Belanger, A. and Jung, R., “Activity-dependent plasticity in spinal cord injury,” J. Rehabil. Res. Dev. 45 (2), 229–240 (Dec. 2008).Google Scholar
5.Go, A., Mozaffarian, D., Roger, V., Benjamin, E., Berry, J., Borden, W., Bravata, D., Dai, S., Ford, E., et al., “Heart disease and stroke statistics–2013 update: A report from the American Heart Association,” Heart disease and stroke statistics–2013 update: A report from the American Heart Association 127, e8 (2013).Google Scholar
6.Anon, , “Spinal Cord Injury Facts and Figures at a Glance,” National Spinal Cord Injury Statistical Center (Feb. 2012).Google Scholar
7.Berkowitz, M., Spinal Cord Injury: An Analysis of Medical and Social Costs (Demos Medical Pub, 1998).Google Scholar
8.Lo, A., Guarino, P., Richards, L., Haselkorn, J., Wittenberg, G., Federman, D., Ringer, R., Wagner, T., Krebs, H., Volpe, B.et al., “Robot-assisted therapy for long-term upper-limb impairment after stroke,” New England J. Med. 362 (19), 1772–1783 (2010).Google Scholar
9.Bütefisch, C., Hummelsheim, H., Denzler, P., and Mauritz, K., “Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand,” J. Neurological Sci. 130 (1), 59–68 (1995).CrossRefGoogle ScholarPubMed
10.Yozbatiran, N., Berliner, J., O'Malley, M. K., Pehlivan, A. U., Kadivar, Z., Boake, C. and Francisco, G. E., “Robotic training and clinical assessment of upper extremity movements after spinal cord injury: a single case report,” J. Rehabil. Med. 44 (2), 186–188 (2012).CrossRefGoogle ScholarPubMed
11.Krebs, H. I., Palazzolo, J. J., Dipietro, L., Ferraro, M., Krol, J., Rannekleiv, K., Volpe, B. T. and Hogan, N., “Rehabilitation robotics: Performance-based progressive robot-assisted therapy,” Auton. Robots 15, 7–20 (Jun. 2003).Google Scholar
12.Wolbrecht, E., Chan, V., Reinkensmeyer, D. and Bobrow, J., “Optimizing compliant, model-based robotic assistance to promote neurorehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 16 (3), 286–297 (2008).CrossRefGoogle ScholarPubMed
13.Fasoli, S. E., Krebs, H. I., Stein, J., Frontera, W. R. and Hogan, N., “Effects of robotic therapy on motor impairment and recovery in chronic stroke,” Arch. Phys. Med. Rehabil. 84 (4), 477–482 (2003).Google Scholar
14.Colombo, R., Pisano, F., Micera, S., Mazzone, A., Delconte, C., Carrozza, M. C., Dario, P. and Minuco, G., “Robotic techniques for upper limb evaluation and rehabilitation of stroke patients,” IEEE Trans. Neural Syst. Rehabil. Eng. 13 (3), 311–324 (2005).Google Scholar
15.Hesse, S., Schulte-Tigges, G., Konrad, M., Bardeleben, A. and Werner, C., “Robot-assisted arm trainer for the passive and active practice of bilateral forearm and wrist movements in hemiparetic subjects.” Arch. Phys. Med. Rehabil. 84 (6), 915–20 (2003).Google Scholar
16.Oblak, J., Cikajlo, I. and Matjacic, Z., “Universal haptic drive: A robot for arm and wrist rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 18 (3), 293–302 (2010).Google Scholar
17.Krebs, H. I., Volpe, B. T., Williams, D., Celestino, J., Charles, S. K., Lynch, D. and Hogan, N., “Robot-aided neurorehabilitation: A robot for wrist rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 15 (3), 327–335 (2007).Google Scholar
18.Squeri, V., Masia, L., Giannoni, P., Sandini, G. and Morasso, P., “Wrist rehabilitation in chronic stroke patients by means of adaptive, progressive robot aided therapy,” IEEE Trans. Neural Syst. Rehab. Eng., 22 (2), 1–14, (2013).Google Scholar
19.Perry, J. C., Rosen, J. and Burns, S., “Upper-limb powered exoskeleton design,” IEEE/ASME Trans. Mechatronics 12 (4), 408–417 (2007).Google Scholar
20.Pehlivan, A. U., Rose, C. and O'Malley, M. K., “System characterization of ricewrist-s: A forearm-wrist exoskeleton for upper extremity rehabilitation,” in Proceedings of the 2013 IEEE International Conference on Rehabilitation Robotics (ICORR). IEEE (2013) pp. 1–6.Google Scholar
21.Asada, H., “A geometrical representation of manipulator dynamics and its application to arm design,” J. Dyn. Syst. Meas. Control 105 (3), 131–142 (1983).Google Scholar
22.Sciavicco, L. and Villani, L., Robotics: Modelling, Planning and Control (Springer, 2009).Google Scholar
23.Brewer, R., Leeper, A., and Salisbury, J. K., “A friction differential and cable transmission design for a 3-dof haptic device with spherical kinematics,” Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE (2011) pp. 2570–2577.Google Scholar
24.Ferre, M., Galiana, I. and Aracil, R., “Design of a lightweight, cost effective thimble-like sensor for haptic applications based on contact force sensors,” Design of a lightweight, cost effective thimble-like sensor for haptic applications based on contact force sensors 11, 11 495–509 (Jan. 2011).Google Scholar
25.Celik, O., O'Malley, M. K., Boake, C., Levin, H. S., Yozbatiran, N. and Reistetter, T. A., “Normalized movement quality measures for therapeutic robots strongly correlate with clinical motor impairment measures,” IEEE Trans. Neural Syst. Rehabil. Eng. 18 (4), 433–444 (2010).Google Scholar
26.Maynard, F. Jr. and Bracken, M., “Creasey,” G, Ditunno. JF, Donovan. WH, Ducker. TB, Garber. SL, Marino RJ, Stover SL, Tator CH, Waters RL, Wilberger, JE, Young WSO “International Standards for Neurological and Functional Classification of Spinal Cord Injury, Americam Spinal Injury Association.” Spinal Cord, 35 (5), 266–74 (1997).Google Scholar
27.Jebsen, R., Taylor, N., Trieschmann, R., Trotter, M. and Howard, L., “An objective and standardized test of hand function.” Arch. Phys. Med. Rehabil. 50 (6), 311–319 (1969).Google Scholar
28.Lyle, R. C., “A performance test for assessment of upper limb function in physical rehabilitation treatment and research,” Int. J. Rehabil. Res. 4 (4), 483–492 (1981).CrossRefGoogle ScholarPubMed
29.Martin, R., Johnston, K. and Sadowsky, C., “Neuromuscular electrical stimulation–assisted grasp training and restoration of function in the tetraplegic hand: A case series,” Am. J. Occup. Ther. 66 (4), 471–477 (2012).Google Scholar
30.Sears, E. Davis and Chung, K. C., “Validity and responsiveness of the jebsen–taylor hand function test,” J. Hand Surg. 35 (1), 30–37 (2010).Google Scholar
31.Hackel, M. E., Wolfe, G. A., Bang, S. M. and Canfield, J. S., “Changes in hand function in the aging adult as determined by the jebsen test of hand function,” Phys. Ther. 72 (5), 373–377 (1992).Google Scholar
32.Beebe, J. A. and Lang, C. E., “Relationships and responsiveness of six upper extremity function tests during the first 6 months of recovery after stroke,” Relationships and responsiveness of six upper extremity function tests during the first 6 months of recovery after stroke 33 (2), 96 (2009).Google Scholar
33.Francis, T. T. and Reddappa, P., “Comparative study on the wrist positions during raise maneuver and their effect on hand function in individuals with paraplegia,” Top. Spinal Cord Injury Rehabil. 19 (1), 42–46 (2013).Google Scholar
34.van der Lee, J. H., de Groot, V., Beckerman, H., Wagenaar, R. C., Lankhorst, G. J. and Bouter, L. M., “The intra- and interrater reliability of the Action Research Arm test: A practical test of upper extremity function in patients with stroke,” Arch. Phys. Med. Rehabil. 82 (1), 14–19 (2001).Google Scholar
35.Lang, C. E., Edwards, D. F., Birkenmeier, R. L. and Dromerick, A. W., “Estimating minimal clinically important differences of upper-extremity measures early after stroke,” Arch. Phys. Med. Rehabil. 89 (9), 1693–1700 (2008).CrossRefGoogle ScholarPubMed
36.Thorsen, R., Binda, L., Chiaramonte, S., Costa, D. Dalla, Redaelli, T., Occhi, E., Beghi, E. and Ferrarin, M., “Correlation among lesion level, muscle strength and hand function in cervical spinal cord injury,” Eur. J. Phys. Rehabil. Med. (2013).Google Scholar
37.Kuppuswamy, A., Balasubramaniam, A., Maksimovic, R., Mathias, C., Gall, A., Craggs, M. and Ellaway, P., “Action of 5hz repetitive transcranial magnetic stimulation on sensory, motor and autonomic function in human spinal cord injury,” Clin. Neurophysiol. 122 (12), 2452–2461 (2011).Google Scholar
38.Mathiowetz, V., Kashman, N., Volland, G., Weber, K., Dowe, M., Rogers, S., et al., “Grip and pinch strength: normative data for adults,” Arch. Phys. Med. Rehabil. 66 (2), 69–74 (1985).Google Scholar
39.Berghe, A. V., Van Laere, M., Hellings, S. and Vercauteren, M., “Reconstruction of the upper extremity in tetraplegia: Functional assessment, surgical procedures and rehabilitation,” Spinal Cord 29 (2), 103–112 (1991).Google Scholar
40.Vaisman, L., Dipietro, L. and Krebs, H. I., “A Comparative Analysis of Speed Profile Models for Wrist Pointing Movements,” Proceedings of the IEEE Transactions on Neural Systems and Rehabilitation Engineering (Dec. 2012) pp. 1–11.Google Scholar
41.Flash, T. and Hogan, N., “The coordination of arm movements: an experimentally confirmed mathematical model,” J. Neurosci. 5 (7), 1688–1703 (1985).CrossRefGoogle ScholarPubMed
42.Beppu, H., Suda, M. and Tanaka, R., “Analysis of cerebellar motor disorders by visually guided elbow tracking movement,” Brain 107 (3), 787–809 (1984).CrossRefGoogle ScholarPubMed
43.Rohrer, B., Fasoli, S., Krebs, H. I., Hughes, R., Volpe, B., Frontera, W. R., Stein, J., and Hogan, N., “Movement smoothness changes during stroke recovery,” J. Neurosci. 22 (18), 8297–8304 (2002).Google Scholar
44.Morasso, P., “Spatial control of arm movements,” Exp. Brain Res. 42 (2), 223–227 (1981).Google Scholar
45.Nelson, W. L., “Physical principles for economies of skilled movements,” Biol. Cybern. 46 (2), 135–147 (1983).CrossRefGoogle ScholarPubMed
46.Takada, K., Yashiro, K. and Takagi, M., “Reliability and sensitivity of jerk-cost measurement for evaluating irregularity of chewing jaw movements,” Physiol. Meas. 27 (7), 609–622 (Apr. 2006).Google Scholar
47.Hogan, N. and Sternad, D., “Sensitivity of smoothness measures to movement duration, amplitude and arrests,” J. Motor Behav. 41 (6), 529–534 (2009).Google Scholar
48.Wilcoxon, F., “Individual comparisons by ranking methods,” Biometrics Bull. 1 (6), 80–83 (1945).Google Scholar
49.van der Lee, J. H., Wagenaar, R. C., Lankhorst, G. J., Vogelaar, T. W., Devill, W. L.é and Bouter, L. M., “Forced use of the upper extremity in chronic stroke patients results from a single-blind randomized clinical trial,” Stroke 30 (11), 2369–2375 (1999).Google Scholar
50.Dong, R. G., Wu, J. Z., Welcome, D. E. and McDowell, T. W., “A new approach to characterize grip force applied to a cylindrical handle,” Med. Eng. Phys. 30 (1), 20–33 (Jan. 2008).Google Scholar
51.Tagliamonte, N. L., Scorcia, M., Formica, D., Campolo, D., and Guglielmelli, E., “Effects of impedance reduction of a robot for wrist rehabilitation on human motor strategies in healthy subjects during pointing tasks,” Adv. Robot. 25 (5), 537–562 (2011).Google Scholar
52.Kadivar, Z., Sullivan, J., Eng, D., Pehlivan, A., O'Malley, M., Yozbatiran, N. and Francisco, G., “Robotic Training and Kinematic Analysis of Arm and Hand After Incomplete Spinal Cord Injury: A Case Study,” 2011 IEEE International Conference on Rehabilitation Robotics (ICORR). IEEE (2011) pp. 1–6.Google Scholar