Daisy Tong

Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke

BackgroundNovel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort.MethodsFive hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression.ResultsFrom baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced.ConclusionsThe concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.

Phase-lead iterative learning control algorithms for functional electrical stimulation-based stroke rehabilitation

A control system for stroke rehabilitation is developed which combines electrical stimulation with a robotic support system to provide assistance to stroke patients performing three-dimensional upper-limb reaching tasks in a virtual reality environment. The electrical stimulation is applied to two muscles in the subject’s arm using an iterative learning control scheme which learns from data collected over previous trials of the task in order to achieve accurate movement. The principal components of the system are described and experimental results confirm its feasibility for application to upper-limb stroke rehabilitation.

FES based rehabilitation of the upper limb using input/output linearization and ILC

To provide effective stroke rehabilitation, a control scheme is developed for upper arm tracking in 3D space using electrical stimulation. In accordance with clinical need, the case where stimulation is applied to two muscles in the arm and shoulder is considered, with the arm supported against gravity by an exoskeletal mechanism. An upper limb model with five degrees of freedom is first developed to represent the unconstrained upper arm, and an input/output linearization controller is applied to decouple the actuated joint angles, and combined with a state-feedback optimal tracking controller. Linear iterative learning controllers are then designed to enforce precise tracking over repeated attempts at the task, and stability conditions for the unactuated joint angles are given. Experimental results confirm practical performance.

Design & control of a 3D stroke rehabilitation platform

An upper limb stroke rehabilitation system is developed which combines electrical stimulation with mechanical arm support, to assist patients performing 3D reaching tasks in a virtual reality environment. The Stimulation Assistance through Iterative Learning (SAIL) platform applies electrical stimulation to two muscles in the arm using model-based control schemes which learn from previous trials of the task. This results in accurate movement which maximises the therapeutic effect of treatment. The principal components of the system are described and experimental results confirm its efficacy for clinical use in upper limb stroke rehabilitation.

Application of Newton-method Based ILC to 3D Stroke Rehabilitation

A nonlinear model-based iterative learning control (ILC) algorithm is applied to the problem of upper limb stroke rehabilitation. A 3D system is developed to assist stroke patients performing 3D upper limb reaching tasks in a virtual reality environment, combining a mechanical support with electrical stimulation applied to two muscles in the patients arm. ILC is shown to provide accurate trajectory tracking, which maximises the systems potential to provide effective treatment during future clinical trials. Principal components of the system are described and experimental results confirm its efficacy for use in upper limb stroke rehabilitation.