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, November 14, 2024

Enhancement Of Finger State Progress Model for Markerless Virtual Fine Motor Stroke Rehabilitation

I'd have everyone fired here for producing useless predictions rather that delivering EXACT REHAB PROTOCOLS!

 Enhancement Of Finger State Progress Model for Markerless Virtual Fine
Motor Stroke Rehabilitation

Mohd Amir Idzham Iberahim1, Syadiah Nor Wan Shamsuddin2*,
Mokhairi Makhtar2, Yousef A.Baker El-Ebiary3
1Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu (UMT),
Terengganu Malaysia.
2Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA),
Terengganu, Malaysia, syadiah@unisza.edu.my
3Faculty of Informatics and Computing, UniSZA University, Malaysia

The use of machine learning as a tool for analyzing and pattern extraction from the results is widely
applied in various medical applications in stroke rehabilitation. It will help the therapist to make a
consistent and precise evaluation for a viable recommendation for an optimal future exercise to
improve the patient’s progress. The objective of this study is to produce a prediction model to
analyze patient finger rehabilitation progress by comparing four regression classifiers' efficiency.
In this study, we proposed an Enhancement of the Finger State Progress (E-FSP) model to produce
prediction results of progress and performance which also consists of a markerless VR application
using markerless motion sensors and can capture kinematic data through Time-based Simplified
Denavit Heartenberg (TSDH) model and measure the results of rehabilitation exercises through the
integration of Finger State Progress (FSP) model. 30 patients have undergone rehabilitation
sessions using VR applications in the Kuala Nerus Rehabilitation and Hemodialysis Health
Organization. The study shows the result of an optimum evaluation is the RandomForest classifier
which has the lowest Mean Absolute Error (MAE) value of 8.26 and Root Mean Square Error
(RMSE) value of 12.38. In conclusion, The VR application and machine learning can produce a
very promising combination of attractive visual and viable prediction analysis for virtual fine motor
stroke rehabilitation.
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