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


METHODS
Five 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.


RESULTS
From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced.


CONCLUSIONS
The 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.
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK. klm@ecs.soton.ac.uk
Katie L
KL
Meadmore
Ann-Marie
AM
Hughes
Chris T
CT
Freeman
Zhonglun
Z
Cai
Daisy
D
Tong
Jane H
JH
Burridge
Eric
E
Rogers
D000328:Adult; D000368:Aged; D000465:Algorithms; D001132:Arm / Q000502:physiology; D001185:Artificial Intelligence; D004558:Electric Stimulation / Q000379:methods*; D004867:Equipment Design; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology; D009069:Movement Disorders / Q000209:etiology* / Q000534:rehabilitation*; D018482:Muscle, Skeletal / Q000502:physiology; D010291:Paresis / Q000534:rehabilitation; D011597:Psychomotor Performance / Q000502:physiology; D020127:Recovery of Function; D012371:Robotics / Q000379:methods*; D012656:Self-Help Devices; D020521:Stroke / Q000150:complications*; D000071939:Stroke Rehabilitation; D016896:Treatment Outcome; D034941:Upper Extremity / Q000502:physiology; D014584:User-Computer Interface