Robot-aided rehabilitation of neural function in the upper extremities

Rehabilitation Engineering Group, Automatic Control Laboratory, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland.
Acta neurochirurgica. Supplement 02/2007; 97(Pt 1):465-71. DOI: 10.1007/978-3-211-33079-1-61
Source: PubMed


Repetitive movements can improve muscle strength and movement coordination in patients with neurological disorders and impairments. Robot-aided approaches can serve to enhance the rehabilitation process. They can not only improve the therapeutic outcome but also support clinical evaluation and increase the patient motivation. This chapter provides an overview of existing systems that can support the movement therapy of the upper extremities in subjects with neurological pathologies. The devices are compared with respect to technical function, clinical applicability, and clinical outcomes.

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    • "Robots can be broadly classified as: (i) passive, where the system constrains the patient’s arm to a determined range of motion (without actuation); (ii) active, where the system moves the patient’s arm along a predefined path (through electromechanical actuation, pneumatics, etc.); and (iii) interactive, where the system reacts to the patient’s inputs to provide an optimal assistive strategy [14]. Moreover, these devices take the form of either an actuated robotic arm, that is, the end-effector type, or an actuated robotic suite that encloses the affected limb, that is, the exoskeletal type. "
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    ABSTRACT: In this paper, we demonstrate that healthy adults respond differentially to the administration of force feedback and the presentation of scientific content in a virtual environment, where they interact with a low-cost haptic device. Subjects are tasked with controlling the movement of a cursor on a predefined trajectory that is superimposed on a map of New York City's Bronx Zoo. The system is characterized in terms of a suite of objective indices quantifying the subjects' dexterity in planning and generating the multijoint visuomotor tasks. We find that force feedback regulates the smoothness, accuracy, and duration of the subject's movement, whereby converging or diverging force fields influence the range of variations of the hand speed. Finally, our findings provide preliminary evidence that using educational content increases subjects' satisfaction. Improving the level of interest through the inclusion of learning elements can increase the time spent performing rehabilitation tasks and promote learning in a new context.
    PLoS ONE 12/2013; 8(12):e83945. DOI:10.1371/journal.pone.0083945 · 3.23 Impact Factor
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    • "Mehrholz et al. [26] indicated in a systematic review that motor strength of the paretic arm and motor function are more likely to improve when patients after stroke train with electromechanical devices and allow stroke patients to practice intensively by themselves [12] [19] [31]. These electromechanical training systems of arm and hand performance after stroke may be roughly divided into passive (stabilising the arm and hand), active systems (actuators moving the arm and/or hand) and interactive systems, the latter of which react to patients' inputs to provide an optimal assistance strategy [29]. Most of these systems are equipped with high-end electronics, mechanical features and software. "

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    • "Robots provide both movement controllability and measurement reliability, which makes them ideal instruments to help neurologists and therapists address the challenges facing neurorehabilitation. In a recent review, Riener examined technical differences between several robotic devices[37]. These devices take the form of either an actuated robotic arm (i.e., a manipulandum) or joystick, or an actuated robotic suit that encloses the affected limb like an exoskeletal frame. "
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    ABSTRACT: Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols. We first describe current knowledge of the natural history of arm recovery after stroke and of outcome prediction in individual patients. Rehabilitation strategies and outcome measures for impairment versus function are compared. The topics of dosage, intensity, and time of rehabilitation are then discussed. Robots are particularly suitable for both rigorous testing and application of motor learning principles to neurorehabilitation. Computational motor control and learning principles derived from studies in healthy subjects are introduced in the context of robotic neurorehabilitation. Particular attention is paid to the idea of context, task generalization and training schedule. The assumptions that underlie the choice of both movement trajectory programmed into the robot and the degree of active participation required by subjects are examined. We consider rehabilitation as a general learning problem, and examine it from the perspective of theoretical learning frameworks such as supervised and unsupervised learning. We discuss the limitations of current robotic neurorehabilitation paradigms and suggest new research directions from the perspective of computational motor learning.
    Journal of NeuroEngineering and Rehabilitation 03/2009; 6(1):5. DOI:10.1186/1743-0003-6-5 · 2.74 Impact Factor
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