Medical & Biological Engineering & Computing

, Volume 53, Issue 9, pp 815–828 | Cite as

Upper-limb kinematic reconstruction during stroke robot-aided therapy

  • E. Papaleo
  • L. Zollo
  • N. Garcia-Aracil
  • F. J. Badesa
  • R. Morales
  • S. Mazzoleni
  • S. Sterzi
  • E. Guglielmelli
Original Article

Abstract

The paper proposes a novel method for an accurate and unobtrusive reconstruction of the upper-limb kinematics of stroke patients during robot-aided rehabilitation tasks with end-effector machines. The method is based on a robust analytic procedure for inverse kinematics that simply uses, in addition to hand pose data provided by the robot, upper arm acceleration measurements for computing a constraint on elbow position; it is exploited for task space augmentation. The proposed method can enable in-depth comprehension of planning strategy of stroke patients in the joint space and, consequently, allow developing therapies tailored for their residual motor capabilities. The experimental validation has a twofold purpose: (1) a comparative analysis with an optoelectronic motion capturing system is used to assess the method capability to reconstruct joint motion; (2) the application of the method to healthy and stroke subjects during circle-drawing tasks with InMotion2 robot is used to evaluate its efficacy in discriminating stroke from healthy behavior. The experimental results have shown that arm angles are reconstructed with a RMSE of 8.3 × 10−3 rad. Moreover, the comparison between healthy and stroke subjects has revealed different features in the joint space in terms of mean values and standard deviations, which also allow assessing inter- and intra-subject variability. The findings of this study contribute to the investigation of motor performance in the joint space and Cartesian space of stroke patients undergoing robot-aided therapy, thus allowing: (1) evaluating the outcomes of the therapeutic approach, (2) re-planning the robotic treatment based on patient needs, and (3) understanding pathology-related motor strategies.

Keywords

Upper-limb kinematics Rehabilitation robotics Stroke rehabilitation 

References

  1. 1.
    Badler N, Tolani D (1996) Real-time inverse kinematics of the human arm. Presence 5:393–401PubMedGoogle Scholar
  2. 2.
    Balasubramanian S, Colombo R, Sterpi I, Sanguineti V, Burdet E (2012) Robotic assessment of upper limb motor function after stroke. Am J Phys Med Rehabil 91:S255–S269CrossRefPubMedGoogle Scholar
  3. 3.
    Bosecker C, Dipietro L, Volpe BT, Krebs HI (2010) Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke. Neurorehabil Neural Repair 24:62–69CrossRefPubMedGoogle Scholar
  4. 4.
    Cirstea MC, Levin MF (2000) Compensatory strategies for reaching in stroke. Brain 123:940–953CrossRefPubMedGoogle Scholar
  5. 5.
    Colombo R, Pisano F, Micera S et al (2008) Assessing mechanisms of recovery during robot-aided neurorehabilitation of the upper limb. Neurorehabil Neural Repair 22:50–63CrossRefPubMedGoogle Scholar
  6. 6.
    Colombo R, Sterpi I, Mazzone A, Pisano F, Delconte C (2011) Modeling upper limb clinical scales by robot-measured performance parameters. In: IEEE international conference on rehabilitation robotics (ICORR) (pp 1–5)Google Scholar
  7. 7.
    Denavit J, Hartenberg SH (1955) A kinematic notation for lower-pair mechanisms based on matrices. ASME J Appl Mech 22:215–221Google Scholar
  8. 8.
    Dipietro L, Krebs HI, Fasoli SE, Volpe BT, Stein J, Bever C, Hogan N (2007) Changing motor synergies in chronic stroke. J Neurophys 98:757–768CrossRefGoogle Scholar
  9. 9.
    Dipietro L, Krebs HI, Fasoli SE, Volpe BT, Hogan N (2009) Submovement changes characterize generalization of motor recovery after stroke. Cortex 45:318–324CrossRefPubMedGoogle Scholar
  10. 10.
    Flash T, Meirovitch Y, Barliya A (2012) Models of human movement: trajectory planning and inverse kinematics studies. Rob Auton Syst 61:330–339CrossRefGoogle Scholar
  11. 11.
    Fugl-Meyer AR, Jaasko L, Leyman I, Olsson S, Steglind S (1975) The post stroke hemiplegic patient. A method for evaluation of physical performance. Scand J Rehabil Med 7:1331Google Scholar
  12. 12.
    Go AS, Mozaffarian D, Roger VL (2013) Heart disease and stroke statistics—2013 update : a report from the American heart association. Circulation 127:e6–e245CrossRefPubMedGoogle Scholar
  13. 13.
    Guglielmelli E, Johnson MJ, Shibata T (2009) Guest editorial special issue on rehabilitation robotics. IEEE TRO 25:477–480Google Scholar
  14. 14.
    Kim H, Miller LM, Byl N, Abrams G, Rosen J (2012) Redundancy resolution of the human arm and an upper limb exoskeleton. IEEE Trans Biomed Eng 59:1770–1779CrossRefPubMedGoogle Scholar
  15. 15.
    Kreutz-Delgado K, Long M, Seraji H (1990) Kinematic analysis of 7 DoF anthropomorphic limb. Proc IEEE Int Conf Robot Autom 2:824–830CrossRefGoogle Scholar
  16. 16.
    Langhorne P, Bernhardt J, Kwakkel G (2011) Stroke rehabilitation. Lancet 377:1693–1702 (review. Neurorehabil Neural Repair. 22:111121)CrossRefPubMedGoogle Scholar
  17. 17.
    Li Z, Kim H, Milutinovi D, Rosen J (2013) Synthesizing redundancy resolution criteria of the human arm posture in reaching movements. In: Milutinovi D, Rosen J (eds) Redundancy in robot manipulators and multi-robot systems. Springer, Berlin, pp 201–240Google Scholar
  18. 18.
    Mayagoitia Ruth E, Nene Anand V, Veltink Peter H (2002) Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems. J Biomech 35(4):537–542CrossRefPubMedGoogle Scholar
  19. 19.
    Medendorp WP, Crawford JD, Henriques DYP, Van Gisbergen JAM, Gielen CCAM (2000) Kinematic strategies for upper arm-forearm coordination in three dimensions. J Neurophys 84:2302–2316Google Scholar
  20. 20.
    Mehrholz J, Hdrich A, Platz T, Kugler J, Pohl M (2012) Electromechanical and robot-assisted arm training after stroke updated review. Stroke 43(12):e172–e173CrossRefGoogle Scholar
  21. 21.
    Mihelj M (2006) Hum Arm Kinemat Robot Based Rehabil. Robotica 24:377–383CrossRefGoogle Scholar
  22. 22.
    Norouzi-Gheidari N, Archambault PS, Fung J (2012) Effects of robot-assisted therapy on stroke rehabilitation in upper limbs: systematic review and meta-analysis of the literature. J Rehabil Res Dev 49:479–496CrossRefPubMedGoogle Scholar
  23. 23.
    OBrien MD (1986) Aids to the examination of the peripheral nervous system (3rd edn). London. Bailliere TindallGoogle Scholar
  24. 24.
    Papaleo E, Zollo L, Sterzi S, Guglielmelli E (2012) An inverse kinematics algorithm for upper-limb joint reconstruction during robot-aided motor therapy. In: BIOROB-IEEE/RAS-EMBS international conference on biomedical robotics and biomechatronics (pp 1983–1988)Google Scholar
  25. 25.
    Patel S, Park H, Bonato P, Chan L, Rodgers M (2012) A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil 9:1–17CrossRefGoogle Scholar
  26. 26.
    Rab G, Petuskey K, Bagley A (2002) A method for determination of upper extremity kinematics. Gait posture 15(2):113–119CrossRefPubMedGoogle Scholar
  27. 27.
    Richards L, Pohl P (1999) Therapeutic interventions to improve upper extremity recovery and function. Clin Geriatr Med 15:819–832PubMedGoogle Scholar
  28. 28.
    Rohrer B, Fasoli S, Krebs HI et al (2002) Movement smoothness changes during stroke recovery. J Neurosci 22:8297–8304PubMedGoogle Scholar
  29. 29.
    Sciavicco L, Villani L (2009) Robotics: modelling, planning and control. Springer, BerlinGoogle Scholar
  30. 30.
    Siciliano B (1990) Kinematic control of redundant robot manipulators: a tutorial. J Intell Robot Syst 3(3):201–212CrossRefGoogle Scholar
  31. 31.
    Soechting JF, Buneo CA, Herrmann U, Flanders M (1995) Moving effortlessly in three dimensions: Does donders law apply to arm movement? J Neurosci 15:6271–6280PubMedGoogle Scholar
  32. 32.
    Tolani D, Goswami A, Badler NI (2000) Realtime inverse kinematics techniques for anthropomorphic limbs. Graph Models 62:353–388CrossRefPubMedGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2015

Authors and Affiliations

  • E. Papaleo
    • 1
  • L. Zollo
    • 1
  • N. Garcia-Aracil
    • 2
  • F. J. Badesa
    • 2
  • R. Morales
    • 2
  • S. Mazzoleni
    • 3
  • S. Sterzi
    • 4
  • E. Guglielmelli
    • 1
  1. 1.Laboratory of Biomedical Robotics and BiomicrosystemsUniversità Campus Bio-Medico di RomaRomeItaly
  2. 2.Virtual Reality and Robotics LabUniversidad Miguel Hernandez de ElcheElcheSpain
  3. 3.BioRobotics InstituteScuola Superiore Sant’Anna PisaPisaItaly
  4. 4.Operative Unit of Physical Medicine and RehabilitationUniversità Campus Bio-Medico di RomaRomeItaly

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