http://www.jneuroengrehab.com/content/9/1/32/abstract
Abstract (provisional)
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.
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