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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.

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This study compared the performance of surface electromyographic (sEMG) sensors for different detection conditions affecting the electro-mechanical stability between the sensor and its contact with the skin. These comparisons were made to gain a better understanding of how specific characteristics of sensor design and use may alter the ability of sEMG sensors to detect signals with high fidelity under conditions of vigorous activity. The first part of the study investigated the effect of different detection surface contours and adhesive tapes on the ability of the sensor to remain in electrical contact with the skin. The second part of the study investigated the effects of different skin preparations and hydrophilic gels on the production of movement artifact resulting from sinusoidal and impact mechanical perturbations. Both parts of the study evaluated sensor performance under dry skin and wet skin (from perspiration) conditions. We found that contouring the detection surface and adding a more adhesive double-sided tape were effective in increasing the forces needed to disrupt the electrical contact between the electrodes and the skin for both dry skin and wet skin conditions. The mechanical perturbation tests demonstrated that hydrophilic gel applied to the detection surface of the sensor produced greater movement artifacts compared to sensors without gel, particularly when the sensors were tested under conditions in which perspiration was present on the skin. The use of a surfactant skin preparation did not influence the amount of movement artifacts that resulted from either the sinusoidal or impact perturbations. The importance of these findings is discussed in terms of their implications for improving sEMG signal fidelity through sensor design modifications and procedures for interfacing them with the skin.

Parkinson’s disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n = 11 patients) and tested (n = 8 patients; n = 4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities.