Decomposition of surface EMG signals from cyclic dynamic contractions.

Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait.

High-yield decomposition of surface EMG signals.

Objective: Automatic decomposition of surface electromyographic (sEMG) signals into their constituent motor unit action potential trains (MUAPTs).

Methods: 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 at force levels ranging up to 100% of their maximal level, including those that were covered with more than 1.

A combined sEMG and accelerometer system for monitoring functional activity in stroke.

Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks).

Surface EMG signal decomposition using empirically sustainable biosignal separation principles.

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 components.

Aliasing rejection in Precision Decomposition of EMG signals.

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 action-potential aliasing, whereby the action potential of a motor unit is incorrectly identified in signal data that actually supports the action potential of another motor unit.