04871nam 22008055 450 991029981530332120200706003951.01-4471-6726-010.1007/978-1-4471-6726-6(CKB)3710000000436780(EBL)3567431(SSID)ssj0001525225(PQKBManifestationID)11820833(PQKBTitleCode)TC0001525225(PQKBWorkID)11497120(PQKB)10988204(DE-He213)978-1-4471-6726-6(MiAaPQ)EBC3567431(PPN)186397100(EXLCZ)99371000000043678020150625d2015 u| 0engur|n|---|||||txtccrIterative Learning Control for Electrical Stimulation and Stroke Rehabilitation /by Chris T. Freeman, Eric Rogers, Jane H. Burridge, Ann-Marie Hughes, Katie L. Meadmore1st ed. 2015.London :Springer London :Imprint: Springer,2015.1 online resource (130 p.)SpringerBriefs in Control, Automation and Robotics,2192-6786Description based upon print version of record.1-4471-6725-2 Includes bibliographical references at the end of each chapters and index.Iterative Learning Control: An Overview -- Technology Transfer to Stroke Rehabilitation -- ILC based Upper-Limb Rehabilitation— Planar Tasks -- Iterative Learning Control of the Unconstrained Upper Limb -- Goal-oriented Stroke Rehabilitation.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.SpringerBriefs in Control, Automation and Robotics,2192-6786Control engineeringRoboticsMechatronicsRehabilitation medicineBiomedical engineeringPhysiotherapyControl, Robotics, Mechatronicshttps://scigraph.springernature.com/ontologies/product-market-codes/T19000Rehabilitation Medicinehttps://scigraph.springernature.com/ontologies/product-market-codes/H55030Biomedical Engineering and Bioengineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/T2700XPhysiotherapyhttps://scigraph.springernature.com/ontologies/product-market-codes/H78000Control engineering.Robotics.Mechatronics.Rehabilitation medicine.Biomedical engineering.Physiotherapy.Control, Robotics, Mechatronics.Rehabilitation Medicine.Biomedical Engineering and Bioengineering.Physiotherapy.610.28Freeman Chris Tauthttp://id.loc.gov/vocabulary/relators/aut1057840Rogers Ericauthttp://id.loc.gov/vocabulary/relators/autBurridge Jane Hauthttp://id.loc.gov/vocabulary/relators/autHughes Ann-Marieauthttp://id.loc.gov/vocabulary/relators/autMeadmore Katie Lauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910299815303321Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation2494874UNINA