Background. Haptic robots allow the exploitation of known motor learning mechanisms, representing a valuable option for motor treatment after stroke. The aim of this feasibility multicentre study was to test the clinical efficacy of a haptic prototype, for the recovery of hand function after stroke. Methods. A prospective pilot clinical trial was planned on 15 consecutive patients enrolled in 3 rehabilitation centre in Italy. All the framework features of the haptic robot (e.g., control loop, external communication, and graphic rendering for virtual reality) were implemented into a real-time MATLAB/Simulink environment, controlling a five-bar linkage able to provide forces up to 20？[N] at the end effector, used for finger and hand rehabilitation therapies. Clinical (i.e., Fugl-Meyer upper extremity scale; nine hold pegboard test) and kinematics (i.e., time; velocity; jerk metric; normalized jerk of standard movements) outcomes were assessed before and after treatment to detect changes in patients' motor performance. Reorganization of cortical activation was detected in one patient by fMRI. Results and Conclusions. All patients showed significant improvements in both clinical and kinematic outcomes. Additionally, fMRI results suggest that the proposed approach may promote a better cortical activation in the brain. 1. Background Hand and finger dexterities are fundamental for many activities carried out by a person in order to be independent. Stroke can reduce motor function due to the resulting death of associated brain cells. Stroke leads to permanent neurological impairment in at least 12.6 million people worldwide [1, 2], and in up to 75% of the subjects, motor deficits involve the upper limb [3]. Nowadays, almost all the activities that deal with physical therapy and training tools for rehabilitation have focused on relearning movements of the abilities that the patients had stroke before. Currently, traditional rehabilitative interventions are mainly focused on the passive facilitation of isolated movements or on the promotion of alternative movements to those used before motor diseases [4, 5]. These need to emerge as a consequence of the increasing incidence of stroke patients and the related costs associated to rehabilitation care. Recent findings from movement neuroscience demonstrated that the human neuromuscular system presents use-dependent plasticity, intended as changes in the pattern of neurons’ connectivity [6], not only in healthy but also in neurologically diseased patients, so poststroke patients can experience significant benefits if
References
[1]
WHO, World Report on Disability. in Book World Report on Disability, World Health Organization, 2011.
[2]
R. W. V. Flynn, R. S. M. MacWalter, and A. S. F. Doney, “The cost of cerebral ischaemia,” Neuropharmacology, vol. 55, no. 3, pp. 250–256, 2008.
[3]
S. Prabhakaran, E. Zarahn, C. Riley et al., “Inter-individual variability in the capacity for motor recovery after ischemic stroke,” Neurorehabilitation and Neural Repair, vol. 22, no. 1, pp. 64–71, 2008.
[4]
R. P. S. Van Peppen, G. Kwakkel, S. Wood-Dauphinee, H. J. M. Hendriks, P. J. Van der Wees, and J. Dekker, “The impact of physical therapy on functional outcomes after stroke: What's the evidence?” Clinical Rehabilitation, vol. 18, no. 8, pp. 833–862, 2004.
[5]
J. W. Krakauer, “Motor learning: its relevance to stroke recovery and neurorehabilitation,” Current Opinion in Neurology, vol. 19, no. 1, pp. 84–90, 2006.
[6]
L. Sawaki, “Use-dependent plasticity of the human motor cortex in health and disease,” IEEE Engineering in Medicine and Biology Magazine, vol. 24, no. 1, pp. 36–39, 2005.
[7]
S. J. Page, D. R. Gater, and P. Bach-Y-Rita, “Reconsidering the motor recovery plateau in stroke rehabilitation,” Archives of Physical Medicine and Rehabilitation, vol. 85, no. 8, pp. 1377–1381, 2004.
[8]
J. Broeren, M. Dixon, K. Stibrant Sunnerhagen, and M. Rydmark, “Rehabilitation after stroke using virtual reality, haptics (force feedback) and telemedicine,” Studies in health technology and informatics, vol. 124, pp. 51–56, 2006.
[9]
J. Broeren, K. S. Sunnerhagen, and M. Rydmark, “A kinematic analysis of a haptic handheld stylus in a virtual environment: a study in healthy subjects,” Journal of NeuroEngineering and Rehabilitation, vol. 4, article 13, 2007.
[10]
J. Broeren, A. Bjorkdahl, L. Claesson et al., “Virtual rehabilitation after stroke,” Stud Health Technol Inform, vol. 136, pp. 77–82, 2008.
[11]
J. Broeren, M. Rydmark, A. Bj？rkdahl, and K. S. Sunnerhagen, “Assessment and training in a 3-dimensional virtual environment with haptics: a report on 5 cases of motor rehabilitation in the chronic stage after stroke,” Neurorehabilitation and Neural Repair, vol. 21, no. 2, pp. 180–189, 2007.
[12]
M. S. Cameirao, S. B. Badia, E. Duarte, A. Frisoli, and P. F. Verschure, “The combined impact of virtual reality neurorehabilitation and its interfaces on upper extremity functional recovery in patients with chronic stroke,” Stroke, vol. 43, pp. 2720–2728, 2012.
[13]
M. L. Aisen, H. I. Krebs, N. Hogan, F. McDowell, and B. T. Volpe, “The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke,” Archives of Neurology, vol. 54, no. 4, pp. 443–446, 1997.
[14]
S. E. Fasoli, H. I. Krebs, J. Stein, W. R. Frontera, and N. Hogan, “Effects of robotic therapy on motor impairment and recovery in chronic stroke,” Archives of Physical Medicine and Rehabilitation, vol. 84, no. 4, pp. 477–482, 2003.
[15]
P. S. Lum, C. G. Burgar, P. C. Shor, M. Majmundar, and M. Van der Loos, “Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke,” Archives of Physical Medicine and Rehabilitation, vol. 83, no. 7, pp. 952–959, 2002.
[16]
L. E. Kahn, M. L. Zygman, W. Z. Rymer, and D. J. Reinkensmeyer, “Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study,” Journal of NeuroEngineering and Rehabilitation, vol. 3, article 12, 2006.
[17]
S. Hesse, C. Werner, M. Pohl, S. Rueckriem, J. Mehrholz, and M. L. Lingnau, “Computerized arm training improves the motor control of the severely affected arm after stroke: a single-blinded randomized trial in two centers,” Stroke, vol. 36, no. 9, pp. 1960–1966, 2005.
[18]
M. W. Spong, Robot Dynamics and Control, John Wiley & Sons, 1989.
S. C. Venema and B. Hannaford, “A probabilistic representation of human workspace for use in the design of human interface mechanisms,” IEEE/ASME Transactions on Mechatronics, vol. 6, no. 3, pp. 286–294, 2001.
[21]
R. Shadmehr and F. A. Mussa-Ivaldi, “Adaptive representation of dynamics during learning of a motor task,” Journal of Neuroscience, vol. 14, no. 5, pp. 3208–3224, 1994.
[22]
A. Deakin, H. Hill, and V. M. Pomeroy, “Rough guide to the Fugl-Meyer assessment upper limb section,” Physiotherapy, vol. 89, no. 12, pp. 751–758, 2003.
[23]
E. Croarkin, J. Danoff, and C. Barnes, “Evidence-based rating of upper-extremity motor function tests used for people following a stroke,” Physical Therapy, vol. 84, no. 1, pp. 62–74, 2004.
[24]
A. Heller, D. T. Wade, and V. A. Wood, “Arm function after stroke: measurement and recovery over the first three months,” Journal of Neurology Neurosurgery and Psychiatry, vol. 50, no. 6, pp. 714–719, 1987.
[25]
B. Rohrer, S. Fasoli, H. I. Krebs et al., “Movement smoothness changes during stroke recovery,” Journal of Neuroscience, vol. 22, no. 18, pp. 8297–8304, 2002.
[26]
H.-L. Teulings, J. L. Contreras-Vidal, G. E. Stelmach, and C. H. Adler, “Parkinsonism reduces coordination of fingers, wrist, and arm in fine motor control,” Experimental Neurology, vol. 146, no. 1, pp. 159–170, 1997.
[27]
K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, “Comparing hemodynamic models with DCM,” NeuroImage, vol. 38, no. 3, pp. 387–401, 2007.
[28]
K. N. Arya, R. Verma, and R. K. Garg, “Estimating the minimal clinically important difference of an upper extremity recovery measure in subacute stroke patients,” Topics in Stroke Rehabilitation, vol. 18, no. 1, pp. 599–610, 2011.
[29]
I. Carpinella, D. Cattaneo, S. Abuarqub, and M. Ferrarin, “Robot-based rehabilitation of the upper limbs in multiple sclerosis: feasibility and preliminary results,” Journal of Rehabilitation Medicine, vol. 41, no. 12, pp. 966–970, 2009.
[30]
E. Vergaro, V. Squeri, G. Brichetto et al., “Adaptive robot training for the treatment of incoordination in Multiple Sclerosis,” Journal of NeuroEngineering and Rehabilitation, vol. 7, no. 1, article 37, 2010.
[31]
I. C. Brunner, J. S. Skouen, and L. I. Strand, “Is modified constraint-induced movement therapy more effective than bimanual training in improving arm motor function in the subacute phase post stroke? A randomized controlled trial,” Clinical Rehabilitation, vol. 26, no. 12, pp. 1078–1086, 2012.
[32]
L. Shaw, H. Rodgers, C. Price et al., “BoTULS: a multicentre randomized controlled trial to evaluate the clinical effectiveness and cost-effectiveness of treating upper limb spasticity due to stroke with botulinum toxin type A,” Health Technology Assessment, vol. 14, no. 26, 2010.
[33]
P. Lindberg, C. Schmitz, H. Forssberg, M. Engardt, and J. Borg, “Effects of passive-active movement training on upper limb motor function and cortical activation in chronic patients with stroke: a pilot study,” Journal of Rehabilitation Medicine, vol. 36, no. 3, pp. 117–123, 2004.
[34]
J. Higgins, N. M. Salbach, S. Wood-Dauphinee, C. L. Richards, R. C？té, and N. E. Mayo, “The effect of a task-oriented intervention on arm function in people with stroke: a randomized controlled trial,” Clinical Rehabilitation, vol. 20, no. 4, pp. 296–310, 2006.
[35]
M. Bowler, F. Amirabdollahian, and K. Dautenhahn, “Using an embedded reality approach to improve test reliability for NHPT tasks,” in Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR '11), July 2011.
[36]
M. C. Cirstea and M. F. Levin, “Compensatory strategies for reaching in stroke,” Brain, vol. 123, no. 5, pp. 940–953, 2000.
[37]
A. D'Avella, A. Portone, L. Fernandez, and F. Lacquaniti, “Control of fast-reaching movements by muscle synergy combinations,” Journal of Neuroscience, vol. 26, no. 30, pp. 7791–7810, 2006.
[38]
A. Viau, A. G. Feldman, B. J. McFadyen, and M. F. Levin, “Reaching in reality and virtual reality: a comparison of movement kinematics in healthy subjects and in adults with hemiparesis,” Journal of NeuroEngineering and Rehabilitation, vol. 1, p. 11, 2004.
[39]
R. J. Nudo, B. M. Wise, F. SiFuentes, and G. W. Milliken, “Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct,” Science, vol. 272, no. 5269, pp. 1791–1794, 1996.
[40]
C. D. Takahashi, L. Der-Yeghiaian, V. Le, R. R. Motiwala, and S. C. Cramer, “Robot-based hand motor therapy after stroke,” Brain, vol. 131, no. 2, pp. 425–437, 2008.
