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.

Showing posts with label difficult. Show all posts
Showing posts with label difficult. Show all posts

Thursday, September 14, 2017

Stroke solutions - Impossible, unlikely or difficult?

What does your doctor and hospital think? I know the solutions are out there, difficult but not impossible. Seth Godin again hits the nail on the head.

Impossible, unlikely or difficult? 

Difficult tasks have a road map. With effort, we can get from here to there. It might surprise you to realize that difficult is easy once you have the resources and commitment. Paving a road is difficult, so is customer service and fixing software bugs.
But impossible and unlikely are where we get hung up.
On Tuesday, Apple launched a thousand dollar phone. The engineers and designers had unlimited time (ten years since the first one), unlimited resources, unlimited market power. It's possible that there hasn't been that much unlimited in one place in our entire lives. And, yet, all they could build was an animated emoji machine. A slightly better phone. A series of difficult tasks, mostly achieved.
It's not that another breakthrough is impossible. It's not that we've explored all the edges of human connectivity, of alternative currencies, of education, of personal transformation or generosity. It's not that we've already performed all the leaps in safety, in technology, in identity. Or even productivity. Of course not. It's not impossible to leap again with the magic computer we all have in our pocket.
What tripped up Apple, as it trips up many successful organizations or careers, is that the next leap isn't impossible... it's merely unlikely. It was unlikely that the original iPhone would have actually been transformative, but Steve took a huge leap and got lucky on the other side. It could easily have gone sideways. He tried for something that was unlikely to work, but it did.
That same sort of leap, the one into the unlikely, is available to all of us, at different scales. It's unlikely that our next brave novel, our next breakthrough speech, our next scary but generous project will succeed. Unlikely but worth it.
Unlikely never feels quite the same as difficult, and sometimes it appears impossible. It's neither. It's something risky, and something without a map or a guarantee. We hesitate to do it precisely because it might not work, precisely because it's more than difficult.
Working on an unlikely project takes guts and hubris. It requires us to have the insight to distinguish it from the impossible, and the desire to not merely do the difficult.
What percentage of your time are you spending on the unlikely?

 

Thursday, October 13, 2016

Democratizing Neurorehabilitation: How Accessible are Low-Cost Mobile-Gaming Technologies for Self-Rehabilitation of Arm Disability in Stroke?

Well shit, write up some protocols on use instead of articles in PLOSone. But that would be too difficult and hurt their fee-fees. 

Democratizing Neurorehabilitation: How Accessible are Low-Cost Mobile-Gaming Technologies for Self-Rehabilitation of Arm Disability in Stroke?


PLOS

x


Abstract

Motor-training software on tablets or smartphones (Apps) offer a low-cost, widely-available solution to supplement arm physiotherapy after stroke. We assessed the proportions of hemiplegic stroke patients who, with their plegic hand, could meaningfully engage with mobile-gaming devices using a range of standard control-methods, as well as by using a novel wireless grip-controller, adapted for neurodisability. We screened all newly-diagnosed hemiplegic stroke patients presenting to a stroke centre over 6 months. Subjects were compared on their ability to control a tablet or smartphone cursor using: finger-swipe, tap, joystick, screen-tilt, and an adapted handgrip. Cursor control was graded as: no movement (0); less than full-range movement (1); full-range movement (2); directed movement (3). In total, we screened 345 patients, of which 87 satisfied recruitment criteria and completed testing. The commonest reason for exclusion was cognitive impairment. Using conventional controls, the proportion of patients able to direct cursor movement was 38–48%; and to move it full-range was 55–67% (controller comparison: p>0.1). By comparison, handgrip enabled directed control in 75%, and full-range movement in 93% (controller comparison: p<0.001). This difference between controllers was most apparent amongst severely-disabled subjects, with 0% achieving directed or full-range control with conventional controls, compared to 58% and 83% achieving these two levels of movement, respectively, with handgrip. In conclusion, hand, or arm, training Apps played on conventional mobile devices are likely to be accessible only to mildly-disabled stroke patients. Technological adaptations such as grip-control can enable more severely affected subjects to engage with self-training software.

Monday, May 2, 2016

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Damn it all, if you want to predict recovery you first have to have an objective analysis of the dead and damaged areas. Using external representations like movement or sensation difficulties is stupid. I don't care how difficult this is. 

Computational neurorehabilitation: modeling plasticity and learning to predict recovery


  • David J. ReinkensmeyerEmail author,
  • Etienne Burdet,
  • Maura Casadio,
  • John W. Krakauer,
  • Gert Kwakkel,
  • Catherine E. Lang,
  • Stephan P. Swinnen,
  • Nick S. Ward and
  • Nicolas Schweighofer
Journal of NeuroEngineering and Rehabilitation201613:42
DOI: 10.1186/s12984-016-0148-3
Received: 19 November 2015
Accepted: 13 April 2016
Published: 30 April 2016

Abstract

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.