Use the labels in the right column to find what you want. Or you can go thru them one by one, there are only 29,387 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.
Sunday, June 2, 2024
Bending induced polarization charges in non-polar porous polymer for stroke rehabilitation
This just monitors movement! It DOES NOTHING directly to get survivors recovered. It could be useful, if the measurements provided point DIRECTLY TO 100% RECOVERY PROTOCOLS!
The transverse flexoelectric coefficient of porous PDMS can reach approximately 2.9 times the counterpart of the bulk one.
•
A
competitive mechanism between the strain gradient and permittivity on
the flexoelectricity of porous polymer is discovered in bending mode for
the first time.
•
An optimal porosity corresponding to the maximum polarization response of porous polymer is found.
•
The
developed intelligence terminal of rehabilitation can successfully
collect and recognize the motions of post-stroke patients in real-time.
Abstract
Owing
to the flexibility, lightweight, long-life and low cost, polymers are
promising candidates to realize electromechanical conversion in wearable
electronics. The flexoelectric effect enables non-piezoelectric
materials to achieve electromechanical coupling, thereby broadening
application ranges of nonpolar polymers. In this work, the porous
samples of polydimethylsiloxane (PDMS) with various pore sizes and
porosities are fabricated by the sacrificial salt template method and
the chemical gas foaming method. The flexoelectric polarization response
can be tuned directly by the structural characters of the porous PDMS.
The transverse flexoelectric coefficient of porous PDMS can reach
approximately 2.9 times the counterpart of the bulk one. The competitive
mechanism between strain gradient and permittivity is proposed on
flexoelectricity of random porous media by dielectric impedance
spectrums and finite element analyses. The present regulation of
flexoelectricity provides an alternative to electromechanical
conversion. With the deep learning technique based on one-dimensional
convolution neural networks, actions of both sick and normal legs of
various stroke patients can be well recognized by the designed device of
rehabilitation. The present rehabilitation monitoring system, which is
an innovative application of the flexoelectricity in the porous
polymers, offers a new approach to support physical therapy for
post-stroke patients.
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