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-ofprinciple. 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 1.5 cm of adipose tissue. Decomposition accuracy was measured by a new method wherein a signal is first decomposed and then reconstructed and the accuracy is measured by comparison. Results were confirmed by the more established two-source method.Results-The number of MUAPTs decomposed varied among muscles and force levels and mostly ranged from 20 to 30, with a maximum of 40. The accuracy of all the firings of the MUAPTs was on average 92.5%, at times reaching 97%.Conclusion-Reported technology can reliably perform high-yield decomposition of sEMG signals for isometric contractions up to maximal force levels.Significance-The small sensor size and the high yield and accuracy of the decomposition should render this technology useful for motor control studies and clinical investigations.

This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed knowledge-based Artificial Intelligence framework. In the automatic mode the accuracy ranges from 75 to 91%. An Interactive Editor is used to increase the accuracy to > 97% in signal epochs of about 30-s duration. The accuracy was verified by comparing the firings of action potentials from the EMG signals detected simultaneously by the surface sensor array and by a needle sensor. We have decomposed up to six MU action potential trains from the sEMG signal detected from the orbicularis oculi, platysma, and tibialis anterior muscles. However, the yield is generally low, with typically < or = 5 MUs per contraction. Both the accuracy and the yield should increase as the algorithms are developed further. With this technique it is possible to investigate the behavior of MUs in muscles that are not easily studied by needle sensors. We found that the inverse relationship between the recruitment threshold and the firing rate previously reported for muscles innervated by spinal nerves is also present in the orbicularis oculi and the platysma, which are innervated by cranial nerves. However, these two muscles were found to have greater and more widespread values of firing rates than those of large limb muscles.

We present an algorithmic approach to the design of low-power frequency-selective digital filters based on the concepts of adaptive filtering and approximate processing. The proposed approach uses a feedback mechanism in conjunction with wellknown implementation structures for finite impulse response (FIR) and infinite impulse response (IIR) digital filters. Our algorithm is designed to reduce the total switched capacitance by dynamically varying the filter order based on signal statistics. A factor of 10 reduction in power consumption over fixed-order filters is demonstrated for the filtering of speech signals.

Decomposition of indwelling electromyographic (EMG) signals is challenging in view of the complex and often unpredictable behaviors and interactions of the action potential trains of different motor units that constitute the indwelling EMG signal. These phenomena create a myriad of problem situations that a decomposition technique needs to address to attain completeness and accuracy levels required for various scientific and clinical applications. Starting with the maximum a posteriori probability classifier adapted from the original precision decomposition system (PD I) of LeFever and De Luca (25, 26), an artificial intelligence approach has been used to develop a multiclassifier system (PD II) for addressing some of the experimentally identified problem situations. On a database of indwelling EMG signals reflecting such conditions, the fully automatic PD II system is found to achieve a decomposition accuracy of 86.0% despite the fact that its results include low-amplitude action potential trains that are not decomposable at all via systems such as PD I. Accuracy was established by comparing the decompositions of indwelling EMG signals obtained from two sensors. At the end of the automatic PD II decomposition procedure, the accuracy may be enhanced to nearly 100% via an interactive editor, a particularly significant fact for the previously indecomposable trains.

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). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of <10% for the nonidentification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke. NIH Public Access