Use the labels in the right column to find what you want. Or you can go thru them one by one, there are only 15712 posts. Searching is done in the search box in upper left corner. I blog on anything to do with stroke.DO NOT DO ANYTHING SUGGESTED HERE AS I AM NOT MEDICALLY TRAINED, YOUR DOCTOR IS, LISTEN TO THEM. BUT I BET THEY DON'T KNOW HOW TO GET YOU 100% RECOVERED. I DON'T EITHER, BUT HAVE PLENTY OF QUESTIONS FOR YOUR DOCTOR TO ANSWER.
Deans' stroke musings
Changing stroke rehab and research worldwide now.Time is Brain!Just think of all thetrillions and trillions of neuronsthateach daybecause there areeffective hyperacute therapies besides tPA(only 12% effective). I have 493 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:
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's quite disgusting that this information is not available from every stroke association and doctors group. My back ground story is here:http://oc1dean.blogspot.com/2010/11/my-background-story_8.html
Friday, September 9, 2016
Model-based variables for the kinematic assessment of upper-extremity impairments in post-stroke patients
So right now your occupational therapist has no clue how to assess your upper extremity impairments. Without that there is no way to map changes in function to the exercises used. You are once again a one person clinical trial, known as a guinea pig, with no way to write up your results without any objective endpoints or measurements. http://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-016-0187-9#Abs1
Common scales for clinical
evaluation of post-stroke upper-limb motor recovery are often
complemented with kinematic parameters extracted from movement
trajectories. However, there is no a general consensus on which
parameters to use. Moreover, the selected variables may be redundant and
highly correlated or, conversely, may incompletely sample the kinematic
information from the trajectories. Here we sought to identify a set of
clinically useful variables for an exhaustive but yet economical
kinematic characterization of upper limb movements performed by
post-stroke hemiparetic subjects.
For this purpose, we pursued a
top-down model-driven approach, seeking which kinematic parameters were
pivotal for a computational model to generate trajectories of
point-to-point planar movements similar to those made by post-stroke
subjects at different levels of impairment.
The set of kinematic variables
used in the model allowed for the generation of trajectories
significantly similar to those of either sub-acute or chronic
post-stroke patients at different time points during the therapy.
Simulated trajectories also correctly reproduced many kinematic features
of real movements, as assessed by an extensive set of kinematic metrics
computed on both real and simulated curves. When inspected for
redundancy, we found that variations in the variables used in the model
were explained by three different underlying and unobserved factors
related to movement efficiency, speed, and accuracy, possibly revealing
different working mechanisms of recovery.
This study identified a set of
measures capable of extensively characterizing the kinematics of upper
limb movements performed by post-stroke subjects and of tracking changes
of different motor improvement aspects throughout the rehabilitation
Upper limb functions are altered in about 80 % of acute stroke survivors and in about 50 % of chronic post-stroke patients .
With the increasing of life expectancy, it has been estimated that
stroke related impairments will be ranked to the fourth most important
causes of adult disability in 2030 , prompting the need to design more effective diagnostic and rehabilitative tools [3, 4].
with more traditional and widely accepted clinical scales in the last
two decades investigators have characterized post-stroke motor recovery
also in terms of kinematic parameters extracted from hand and arm
task-oriented movements [3, 5], which offer more objective measures of motor performance . Indeed, clinical scales, whose reliability has often been questioned [7, 8, 9], may not be sensitive to small and more specific changes  and could be of limited use to distinguish different aspects of motor improvement [11, 12].
robot-assisted clinical and pilot studies have proposed a large set of
kinematic parameters to characterize motor improvements [5, 6, 11].
A few of them focused on finding a significant relationship between
robotic measures collected longitudinally in post-stroke patients and
clinical outcome measures, to increase acceptance of kinematic
evaluation scales in practice [5, 6].
Too little effort, however, has been made to identify the different
aspects of movement improvement, how they can be described by kinematic
robot-based measures , and whether they may dissociate with respect to recovery time course and to training response .
Indeed the range of potential changes in limb trajectory during recovery is not known a priori 
and might not be fully represented by a set of arbitrarily selected
parameters extracted from limb trajectories, even if the parameters were
chosen according to a certain number of study hypotheses or to
significant relationships with clinical scales. Moreover, these
variables can be highly correlated and, thus, redundant. Although
redundancy can be tackled by data reduction algorithms, such as
Principal Component Analysis (PCA) or Independent Component Analysis
(ICA) [5, 6], incomplete representation of information might still remain an overlooked issue.
the present study we aimed at devising a novel method for identifying a
set of kinematic measures potentially capable of fully highlighting and
tracking changes of different aspects of movement performance
throughout the rehabilitation training. Instead of starting from a
certain number of a priori
hypotheses, we sought to find which variables were essential for
modeling trajectories of post-stroke patients and were, thus,
informative of kinematic features of upper limb movements. We then
tested whether the identified kinematic parameters i) were capable of highlighting changes in movement performance, ii) were to some extent redundant, and iii)
were informative of different factors of post-stroke motor impairment,
such as paresis, loss of fractionated movement and somatosensation, and
abnormal muscle tone .