Titlebook: Control System Design for Electrical Stimulation in Upper Limb Rehabilitation; Modelling, Identific Chris Freeman Book 2016 Springer Intern

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Feedback Control Design,mpaired participants the applied control schemes remain mostly open-loop, triggered (Davoodi et al., First International Conference on Neural Interface and Control, 2005, [.]), or based on electromyographic (EMG) (Li et al., Proceedings of the 2nd Biennial IEEE/RAS-EMBS International Conference on B

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Iterative Learning Control Design,ehabilitation process, which involves neurologically impaired participants repeatedly performing tracking movements with their affected arm, with a rest period in between attempts during which their arm is returned to the starting position. We will use the data collected over previous task attempts

misshapen

Intercept

Constrained ILC for Human Motor Control, everyday activities such as eating, washing or manipulating objects. In this chapter we extend the problem definition to encompass fully functional motion, and develop ILC control algorithms which enforce tracking of these extended task representations. The framework is then illustrated by comparin

拋物線

,Clinical Application: Goal-Orientated Stroke?Rehabilitation,To confirm its utility for rehabilitation, these are now used to assist tasks involving real objects, with ES applied to the wrist and finger extensors, as well as to muscles in the arm and shoulder. As highlighted in Chap.?., there is strong evidence that functional improvement following training i

critique

Electrode Array Control Design, using locally linear models which embed a restricted stimulation subspace. Clinically feasible model identification procedures are proposed for this form to replace the identification method of Chap.?. which is unsuitable for arrays due to the impracticality of manipulating each joint of the hand a

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stratum-corneum

Conclusions and Future Research Directions,ate to patients’ homes, where it can be used without direct supervision from a therapist over longer training periods. These include: (1) mechanisms to reduce or eliminate lengthy and often fatiguing model identification tests, (2) approaches for controllers to automatically compensate for physiolog

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