Download e-book for iPad: Iterative Learning Control for Electrical Stimulation and by Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie

By Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, Katie L. Meadmore

ISBN-10: 1447167252

ISBN-13: 9781447167259

ISBN-10: 1447167260

ISBN-13: 9781447167266

Iterative studying keep an eye on (ILC) has its origins within the keep watch over of procedures that practice a job repetitively with the intention to enhancing accuracy from trial to trial through the use of details from prior executions of the duty. This short indicates how a vintage software of this system – trajectory following in robots – could be prolonged to neurological rehabilitation after stroke.
Regaining top limb stream is a crucial step in a go back to independence after stroke, however the analysis for such restoration has remained negative. Rehabilitation robotics offers the chance for repetitive task-oriented move perform reflecting the significance of such extreme perform tested by way of traditional healing examine and motor studying concept. beforehand this system has no longer allowed suggestions from one perform repetition to steer the following, additionally implicated as a major think about treatment. The authors exhibit how ILC can be utilized to regulate exterior practical electric stimulation of sufferers’ muscle groups whereas they're many times appearing a job in keeping with the identified results of stimulation in past repetitions. because the motor nerves and muscle tissues of the arm reaquire the power to transform an purpose to maneuver right into a movement of exact trajectory, strength and rapidity, before everything extreme exterior stimulation can now be scaled again steadily till the fullest attainable independence of move is achieved.

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Additional info for Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation

Example text

The same goal could, of course, be achieved if it were possible to obtain more rapid activation dynamics from the patients using the system. 32). To verify this, simulations were conducted where the output of this system was compared with that of the linearizing control law applied to the full model shown in Fig. 6. The applied input, w, was chosen to result in approximate tracking of the more rapid of the demands used. 8 shows results using the identified model parameters of one of the patients tested in the clinical trial described later in this chapter.

It is desirable to select a ratio that makes the task feel natural to the patient, but does not lead to too narrow a system bandwidth which would necessitate Fig. 9 Block diagram of feedback control system Fig. 234 Hz b −300 −1 10 Fig. 1603 Hz 0 5 10 15 Time (s) an excessive controller effort and correspondingly large levels of muscle torque in order to accomplish the task. Step responses corresponding to the systems examined are shown in Fig. 11. The next subsections examine the effect of the end-effector dynamics on the ILC law performance.

2. With the use of robotic assistance, it is shown in Freeman et al. (2009c) that the behavior of the electrically stimulated tricep can be approximated by the system represented schematically in Fig. 6. The approximations invoked are based on experimentally confirmed properties of the human arm model and the ability of the robotic control system to provide accurate tracking of ϑˆ u by ϑu . Given Tβ ≥ 0, 1) above requires that ϑ ∗f (t) is monotonically decreasing. Moreover, given the geometry of the task shown in Fig.

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Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation by Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, Katie L. Meadmore


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