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Automatic decomposition of surface electromyographic (sEMG) signals into their constituent motor unit action potential trains (MUAPTs).A small five-pin sensor provides four channels of sEMG signals that are in turn processed by an enhanced artificial intelligence algorithm evolved from a previous proof-of-principle. We tested the technology on sEMG signals from five muscles contracting isometrically...
We introduce the concept of empirically sustainable principles for biosignal separation as a means of addressing the complexities that are practically encountered in the decomposition of surface electromyographic (sEMG) signals. Recently, we have identified two new principles of this type. The first principle places upper bounds on the inter-firing intervals and residual signal energies of the separated...
The use of Artificial Intelligence (AI) methods in Precision Decomposition (PD) of indwelling and surface electromyographic (EMG) signals has led to the recent development of systems that can automatically resolve most instances of complex superposition among action potentials. The remaining errors have to be corrected by a user-interactive editing process. Typically, 25% to 50% of such errors involve...
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