Deans' stroke musings

Changing stroke rehab and research worldwide now.Time is Brain!Just think of all the trillions and trillions of neurons that DIE each day because there are NO effective 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:

Monday, February 27, 2017

EMG driven musculoskeletal model for robot assisted stroke rehabilitation system

This is an engineering student so I guess going down the failed rehab route was to be expected. You want to have better recoveries and less need for rehab? Then the solution is to stop these 5 causes of the neuronal cascade of death in the first week.
byJauw, Veronica Lestari
Neuro-rehabilitation is a medical process aimed at restoring the sensory and motor functions of the nervous system via repetitive learning and training process. The success of neuro-rehabilitation is simply measured by how fast the patients can recover from the disability. However, the continuous growth of the number of disabled patients and the lack of resources and rehabilitation facilities has then questioned the justification of its success in the future. There are questions about whether the present approaches of neuro-rehabilitation are still efficient to keep up with the future growth of the disabled patients which is growing exponentially. Hence, there is a need for constant advancement in neuro-rehabilitation engineering to improve the efficiency of automated rehabilitation. By doing this, the recovery rate of the patients can be increased, allowing more patients to be rehabilitated even with the lack of resources and rehabilitation facilities. The current study explores the utilization of 9 different upper limb’s muscles to find the best correlation between the electromyography (EMG) signals of the muscles and the individual torque of the upper joints via mathematical models. The EMG signals are acquired in real time and processed by rectification and filtration to eliminate the interference of the signal. These processed signals are then used as the input variables in GA; training the optimized correlation coefficients to minimize the discrepancy between the actual and simulated torque. The best correlation will be used as a control model in the rehabilitative robot’s controller as a decision making. Should the patient require assistance; this model will calculate the amount of assistive force required and supply it to the patient through the servo to complete the rehabilitation cycle. A comparative study, supported by the quantitative data of the simulation results, indicates that the individual torque of the upper limb’s joints is best modeled as the inverse logarithm of the EMG signals of the upper limb muscles with a constant. The estimated torque calculated from this model is the closest to the actual torque among the other mathematical models and thus, it has the lowest discrepancy. The average of 32% discrepancy is calculated throughout 100 generations in ±34 minutes. However, this figure is not attainable upon the integration of the inverse logarithm mathematical model into the controller mainly due to the communication rate between the DAQ system and the server. There is a discrepancy of approximately 10% between the experimental and simulated results which deems to be quite significant.

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