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.

Sunday, September 6, 2026

Counting twitches: automated mechanomyography of muscle fatigue in healthy adults

 Do your stroke medical 'professionals' have two functioning brain cells that will  be used to objectively determine your fatigue and then measure the recovery you get from their EXACT RECOVERY PROTOCOLS?

NO? So you have blithering idiots in charge! You'll never get recovered with them! GET THEM FIRED!

Counting twitches: automated mechanomyography of muscle fatigue in healthy adults

    We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply.

    Abstract

    Background

    Assessing skeletal muscle fatigue is essential for diagnosing neuromuscular impairment, but conventional methods rely on maximal or tetanic contractions that can be impractical or uncomfortable in clinical populations. Surface mechanomyography (MMG) provides a non-invasive alternative; however, current MMG-based fatigue protocols require time-consuming manual peak-to-peak analysis. This study evaluated an automated algorithm for extracting MMG-derived fatigue metrics from electrically evoked muscle twitches.

    Methods

    Eighteen healthy adults completed a standardized fatigue protocol on the wrist extensors and ankle dorsiflexors using electrical stimulation at 2, 4, and 6 Hz over 9 min. A triaxial accelerometer captured MMG signals from > 2,100 contractions per muscle (approximately 4,320 per participant across the two muscles). A custom algorithm automatically extracted peak-to-peak values and computed the endurance index; the mean contraction amplitude was derived from peak and trough points identified manually by the research team. Repeated-measures ANOVAs assessed differences across stimulation frequencies and muscle groups.

    Results

    Endurance index declined significantly across the fixed ascending 2-, 4-, and 6-Hz stimulation sequence (p < 0.001) and was lower in ankle dorsiflexors than wrist extensors (p = 0.010), averaging approximately 6% points lower across frequencies. Mean contraction amplitude was significantly lower in the dorsiflexors (p < 0.001) and greater at 6 Hz than at 2–4 Hz (p < 0.001).

    Conclusion

    This study demonstrates the feasibility of an automated peak-to-peak extraction algorithm for deriving the MMG-based endurance index, enabling rapid processing of > 2,100 contractions per muscle. The mean contraction amplitude reported here was measured manually, and extending automated extraction to that measure remains to be implemented. The combination of the endurance index and the mean contraction amplitude provides a dual-metric approach to characterizing muscle performance. As a feasibility study in healthy adults, it does not establish clinical validity. Future work should validate the algorithm against manual methods and test it in clinical populations(Like stroke), including patients with ICU-acquired weakness.

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