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A SYSTEM FOR UPPER LIMB REHABILITATION AND MOTOR FUNCTION EVALUATION

    In the conventional upper-limb rehabilitation process, patients have to be relying on therapists to do the exercise and assessments. Using robotic rehabilitation devices, patients can practice independently and intensively with their upper paretic limb. In this study, we hypothesized that a multi-DOF passive mechanism coupled with multi-DOF 3D sensory feedback could provide: (1) safe and nature active exercise; (2) various combinations of degrees of freedom (DOF) for the training of different specific joints; (3) the possibility to realize ideal trajectory. In order to test the hypothesis, we designed a seven-DOF passive exoskeleton-based system for the upper extremity, integrated with virtual reality (VR) technology based 3D feedback. An experiment was done on six healthy subjects and three subjects with upper-limb impairment. All subjects did not experience any problems when handling the device during the intervention. Moreover, Fugl–Meyer Score of the upper extremity Assessment (FMA) scale showed that the three patients have increased the score by 19, 23 and 14, respectively. Wolf Motor Function Test (WMFT) scale showed that the three patients have increased their scores by 22, 22 and 14, respectively.

    References

    • P.   Muntner et al. , Stroke   33 , 1209 ( 2002 ) . Crossref, ISIGoogle Scholar
    • C.   Mathers , D. M.   Fat and J.   Boerma , The Global Burden of Disease: 2004 Update ( World Health Organization , 2008 ) . CrossrefGoogle Scholar
    • T.   Platz , Der. Nervenarzt.   74 , 841 ( 2003 ) . Crossref, ISIGoogle Scholar
    • L. M.   Carey et al. , Stroke   36 , 625 ( 2005 ) . Crossref, ISIGoogle Scholar
    • C.   Bütefisch et al. , J. Neurol. Sci.   130 , 59 ( 1995 ) . Crossref, ISIGoogle Scholar
    • G.   Kwakkel , B. J.   Kollen and H. I.   Krebs , Neurorehab. Neural. Re.   22 , 111 ( 2008 ) . Crossref, ISIGoogle Scholar
    • P. S.   Lum et al. , Arch. Phys. Med. Rehab.   83 , 952 ( 2002 ) . Crossref, ISIGoogle Scholar
    • R.   Loureiro et al. , Auton. Robot.   15 , 35 ( 2003 ) . Crossref, ISIGoogle Scholar
    • Y. Ren, H.-S. Park and L.-Q. Zhang, Developing a whole-arm exoskeleton robot with hand opening and closing mechanism for upper limb stroke rehabilitation, 2009 IEEE 11th Int. Conf. on Rehabilitation Robotics (IEEE, Kyoto International Conference Center, Japan, 2009) pp. 761–765. Google Scholar
    • C. Carignan, J. Tang and S. Roderick, Development of an exoskeleton haptic interface for virtual task training, Intelligent Robots and Systems, 2009. IEEE/RSJ Int. Conf. Intelligent Robot and Systems (2009) pp. 3697–3702. Google Scholar
    • H. S.   Lo and S. Q.   Xie , Med. Eng. Phys.   34 , 261 ( 2012 ) . Crossref, ISIGoogle Scholar
    • K.   Kiguchi et al. , Robot. Auton. Syst.   56 , 678 ( 2008 ) . Crossref, ISIGoogle Scholar
    • H. I. Krebs et al. , J. NeuroEng. Rehabil.   1 , 5 ( 2004 ) . CrossrefGoogle Scholar
    • D. J.   Reinkensmeyer et al. , J. Rehabil. Res. Dev.   37 , 653 ( 2000 ) . ISIGoogle Scholar
    • G.   Rosati , P.   Gallina and S.   Masiero , IEEE. T. Neur. Syst. Rehabil.   15 , 560 ( 2007 ) . Crossref, ISIGoogle Scholar
    • M. Schoone, P. Van Os and A. Campagne, Robot-Mediated Active Rehabilitation (ACRE) A user trial, Proc. of the 2007 IEEE 10th Int. Conf. on Rehabilitation Robotics (ICORR) (IEEE, Noordwijk, The Netherlands, 2007) pp. 477–481. Google Scholar
    • J.   Klein et al. , IEEE. T. Robot.   26 , 710 ( 2010 ) . Crossref, ISIGoogle Scholar
    • J.   Eschweiler et al. , J. Neuroeng. Rehabil.   11 , 3 ( 2014 ) . Crossref, ISIGoogle Scholar
    • S. H. You et al. , Stroke   36 , 1166 ( 2005 ) . Crossref, ISIGoogle Scholar
    • J. Z.   Davis , Top. Stroke. Rehabil.   13 , 1 ( 2006 ) . Crossref, ISIGoogle Scholar
    • H. I.   Krebs et al. , Neuro. Rehabil.   23 , 81 ( 2008 ) . Google Scholar
    • T.   Platz et al. , Neurorehab. Neural. Res.   23 , 706 ( 2009 ) . Crossref, ISIGoogle Scholar
    • A. S.   Rizzo and G. J.   Kim , Presence-Teleop Virt.   14 , 119 ( 2005 ) . Crossref, ISIGoogle Scholar
    • D. Jack et al. , IEEE. T. Neur. Sys. Reh.   9 , 308 ( 2001 ) . Crossref, ISIGoogle Scholar
    • R. J. Sanchez et al. , IEEE. T. Neur. Sys. Rehabil.   14 , 378 ( 2006 ) . Crossref, ISIGoogle Scholar
    • S.   Parasuraman , A. W.   Oyong and V. L.   Jauw , J. Mech. Med. Biol.   11 , 691 ( 2011 ) . Link, ISIGoogle Scholar
    • W.   Yu , H.   Soma and J.   Gonzalez , J. Mech. Med. Biol.   12 , ( 2012 ) . Google Scholar
    • S. P.   Arjunan and D. K.   Kumar , J. Mech. Med. Biol.   11 , 581 ( 2011 ) . Link, ISIGoogle Scholar
    • F.   Widjaja et al. , J. Mech. Med. Biol.   11 , 1347 ( 2011 ) . Link, ISIGoogle Scholar
    • K.   Baheux et al. , Technol. Health. Care.   13 , 245 ( 2005 ) . CrossrefGoogle Scholar
    • R. Colombo et al. , IEEE. T. Neur. Syst. Rehabil.   13 , 311 ( 2005 ) . Crossref, ISIGoogle Scholar
    • S. J.   Housman , K. M.   Scott and D. J.   Reinkensmeyer , Neurorehab. Neural. Re.   23 , 505 ( 2009 ) . Crossref, ISIGoogle Scholar
    Published: 21 August 2014