Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation
Springer, 25 cze 2015 - 124
Iterative learning control (ILC) has its origins in the control of processes that perform a task repetitively with a view to improving accuracy from trial to trial by using information from previous executions of the task. This brief shows how a classic application of this technique – trajectory following in robots – can be extended to neurological rehabilitation after stroke.
Regaining upper limb movement is an important step in a return to independence after stroke, but the prognosis for such recovery has remained poor. Rehabilitation robotics provides the opportunity for repetitive task-oriented movement practice reflecting the importance of such intense practice demonstrated by conventional therapeutic research and motor learning theory. Until now this technique has not allowed feedback from one practice repetition to influence the next, also implicated as an important factor in therapy. The authors demonstrate how ILC can be used to adjust external functional electrical stimulation of patients’ muscles while they are repeatedly performing a task in response to the known effects of stimulation in previous repetitions. As the motor nerves and muscles of the arm reaquire the ability to convert an intention to move into a motion of accurate trajectory, force and rapidity, initially intense external stimulation can now be scaled back progressively until the fullest possible independence of movement is achieved.
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adjoint ILC algorithms ARAT arm model assist tracking bandwidth baseline Burridge JH chapter clinical trials components control law control system data glove deltoid deltoid muscles effect elbow Extensor feedback control FES applied forearm Freeman CT frequency functional electrical stimulation gantry robot hemiparesis hence Hughes A-M human arm IEEE IEEE Trans ILC design ILC law impairment improvement input intervention isometric force iterative learning control joint angles Kinect Kpek matrix maximum Meadmore KL motor movement muscle fatigue Newton ILC NOILC nonlinear outcome measures output parameters participants patient’s patient’s arm phase-lead ILC planar reaching reference trajectory Rehabil RMS error robustness Rogers shoulder shown shows signal slow trajectory stimulation applied stroke patients stroke rehabilitation therapy torque tracking accuracy tracking error tracking performance tracking tasks trajectory tracking triceps uk(p uk+1 unassisted unimpaired upper extremity upper limb vector virtual reality wrist