Deans' stroke musings: Iterative learning control for stroke rehabilitation with input dependent muscle fatigue modeling

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Thursday, March 8, 2018

Iterative learning control for stroke rehabilitation with input dependent muscle fatigue modeling

No clue what this means. You are on your own since your doctor will never read this.

A 123 page book on iterative learning control here:

Iterative Learning Control for Electrical Stimulation and Stroke Rehabilitation

The latest here:

Iterative learning control for stroke rehabilitation with input dependent muscle fatigue modeling

Luijten, Fons, Chu, Bing and Rogers, Eric (2018) Iterative learning control for stroke rehabilitation with input dependent muscle fatigue modeling In Proceedings of American Control Conference (ACC) 2018. 6 pp. (In Press).

Record type: Conference or Workshop Item (Paper)

Abstract

The consequences of a stroke is a major and increasing problem world wide. Many people who suffer a stroke are left with permanent impairment but the possibility exists that suitable rehabilitation could increase mobility and, for example, enable independent living. This, in turn, requires effective rehabilitation where it is known that currently available methods are relatively poor and are not well suited to home use, where the latter aspect is critical to improving practice and reducing costs. An accepted method to relearn lost function, such as reaching out to an object, is repeated attempts with learning from previous from those already completed with the application of applied stimulation if required. This requirement is analogous to iterative learning control and much progress, with supporting clinical trials data, has been reported on using this engineering design method to regulate the applied stimulation such that patient improvement in completing the task corresponds to increasing voluntary input and reduced stimulation. The applied stimulation in this application can induce muscle fatigue and this paper gives new result on enhancing the control laws to mitigate this unwanted effect.

Text Iterative learning control for stroke rehabilitation with input dependent - Accepted Manuscript

Restricted to Repository staff only until 30 July 2018.

Available under License University of Southampton Accepted Manuscript Licence.

Request a copy

More information

Accepted/In Press date: 20 January 2018

Venue - Dates: American Control Conference 2018: ACC 2018, Milwaukee, United States, 2018-06-27 - 2018-06-29

Learn more about VLC research