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Throughout the literature, different observations of motor unit firing behavior during muscle fatigue have been reported and explained with varieties of conjectures. The disagreement amongst previous studies has resulted, in part, from the limited number of available motor units and from the misleading practice of grouping motor unit data across different subjects, contractions, and force levels. To establish a more clear understanding of motor unit control during fatigue, we investigated the firing behavior of motor units from the vastus lateralis muscle of individual subjects during a fatigue protocol of repeated voluntary constant force isometric contractions. Surface electromyographic decomposition technology provided the firings of 1,890 motor unit firing trains. These data revealed that to sustain the contraction force as the muscle fatigued, the following occurred: 1) motor unit firing rates increased; 2) new motor units were recruited; and 3) motor unit recruitment thresholds decreased. Although the degree of these adaptations was subject specific, the behavior was consistent in all subjects. When we compared our empirical observations with those obtained from simulation, we found that the fatigue-induced changes in motor unit firing behavior can be explained by increasing excitation to the motoneuron pool that compensates for the fatigue-induced decrease in muscle force twitch reported in empirical studies. Yet, the fundamental motor unit control scheme remains invariant throughout the development of fatigue. These findings indicate that the central nervous system regulates motor unit firing behavior by adjusting the operating point of the excitation to the motoneuron pool to sustain the contraction force as the muscle fatigues.

Synchronous motor unit firing instances have been attributed to anatomical inputs shared by motoneurons. Yet, there is a lack of empirical evidence confirming the notion that common inputs elicit synchronization under voluntary conditions. We tested this notion by measuring synchronization between motor unit action potential trains (MUAPTs) as their firing rates progressed within a contraction from a relatively low force level to a higher one. On average, the degree of synchronization decreased as the force increased. The common input notion provides no empirically supported explanation for the observed synchronization behavior. Therefore, we investigated a more probable explanation for synchronization. Our data set of 17,546 paired MUAPTs revealed that the degree of synchronization varies as a function of two characteristics of the motor unit firing rate: the similarity and the slope as a function of force. Both are measures of the excitation of the motoneurons. As the force generated by the muscle increases, the firing rate slope decreases, and the synchronization correspondingly decreases. Different muscles have motor units with different firing rate characteristics and display different amounts of synchronization. Although this association is not proof of causality, it consistently explains our observations and strongly suggests further investigation. So viewed, synchronization is likely an epiphenomenon, subject to countless unknown neural interactions. As such, synchronous firing instances may not be the product of a specific design and may not serve a specific physiological purpose. Our explanation for synchronization has the advantage of being supported by empirical evidence, whereas the common input does not.

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. The increased signal complexity introduced by the changing shapes of the MUAPs due to relative movement of the electrodes and the lengthening/shortening of muscle fibers was managed by an incremental approach to enhancing our established algorithm for decomposing sEMG signals obtained from isometric contractions. We used machine-learning algorithms and time-varying MUAP shape discrimination to decompose the sEMG signal from an increasingly challenging sequence of pseudostatic and dynamic contractions. The accuracy of the decomposition results was assessed by two verification methods that have been independently evaluated. The firing instances of the motor units had an accuracy of ∼90% with a MUAP train yield as high as 25. Preliminary observations from the performance of motor units during cyclic contractions indicate that during repetitive dynamic contractions, the control of motor units is governed by the same rules as those evidenced during isometric contractions. Modifications in the control properties of motoneuron firings reported by previous studies were not confirmed. Instead, our data demonstrate that the common drive and hierarchical recruitment of motor units are preserved during concentric and eccentric contractions.

Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061 motor-unit action-potential trains (MUAPTs), obtained by decomposing surface EMG (sEMG) signals recorded during human voluntary contractions. Decomposition errors were classified into two general categories: location errors representing variability in the temporal localization of each motor-unit firing instance and identification errors consisting of falsely detected or missed firing instances. To mitigate these errors, we developed an error-reduction algorithm that combines multiple decomposition estimates to determine a more probable estimate of motor-unit firing instances with fewer errors. The performance of the algorithm is governed by a trade-off between the yield of MUAPTs obtained above a given accuracy level and the time required to perform the decomposition. When applied to a set of sEMG signals synthesized from real MUAPTs, the identification error was reduced by an average of 1.78%, improving the accuracy to 97.0%, and the location error was reduced by an average of 1.66 ms. The error-reduction algorithm in this study is not limited to any specific decomposition strategy. Rather, we propose it be used for other decomposition methods, especially when analyzing precise motor-unit firing instances, as occurs when measuring synchronization.

We have developed and evaluated several dynamical machine-learning algorithms that were designed to track the presence and severity of tremor and dyskinesia with 1-s resolution by analyzing signals collected from Parkinson's disease (PD) patients wearing small numbers of hybrid sensors with both 3-D accelerometeric and surface-electromyographic modalities. We tested the algorithms on a 44-h signal database built from hybrid sensors worn by eight PD patients and four healthy subjects who carried out unscripted and unconstrained activities of daily living in an apartment-like environment. Comparison of the performance of our machine-learning algorithms against independent clinical annotations of disorder presence and severity demonstrates that, despite their differing approaches to dynamic pattern classification, dynamic neural networks, dynamic support vector machines, and hidden Markov models were equally effective in keeping error rates of the dynamic tracking well below 10%. A common set of experimentally derived signal features were used to train the algorithm without the need for subject-specific learning. We also found that error rates below 10% are achievable even when our algorithms are tested on data from a sensor location that is different from those used in algorithm training.

Muscles are composed of groups of muscle fibers, called motor units, each innervated by a single motoneuron originating in the spinal cord. During constant or linearly varying voluntary force contractions, motor units are activated in a hierarchical order, with the earlier-recruited motor units having greater firing rates than the later-recruited ones. We found that this normal pattern of firing activation can be altered during oscillatory contractions where the force oscillates at frequencies ≥2 Hz. During these high-frequency oscillations, the activation of the lower-threshold motor units effectively decreases and that of the higher-threshold motor units effectively increases. This transposition of firing activation provides means to activate higher-threshold motor units preferentially. Our results demonstrate that the hierarchical regulation of motor unit activation can be manipulated to activate specific motoneuron populations preferentially. This finding can be exploited to develop new forms of physical therapies and exercise programs that enhance muscle performance or that target the preferential atrophy of high-threshold motor units as a result of aging or motor disorders such as stroke and amyotrophic lateral sclerosis.

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.

It has been documented that concurrently active motor units fire under the control of a common drive. That is, the firing rates show high correlation with near-zero time lag. This degree of correlation has been found to vary among muscles and among contractions performed at different force levels in the same muscle. This study provides an explanation indicating that motor units recruited during a contraction cause an increase in the variation (SD) and a decrease in the degree (amplitude) of the correlation of the firing rates. The degree of correlation is lower in muscles having greater spindle density. This effect appears to be mediated by the proprioceptive feedback from the spindles and possibly the Golgi tendon organs. Muscle spindles in particular respond to the mechanical excitation of the nonfused muscle fibers and provide a discordant excitation to the homonymous motoneurons, resulting in a decrease in the correlation of the firing rates of motor units. The implication of this work is that the decreased correlation of the firing rates in some muscles is not necessarily an indication of a decreased common drive from the CNS, but rather an inhibitory influence of the proprioceptive feedback from the peripheral nervous system. This explanation is useful for understanding various manifestations of the common drive reported in the literature.

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.

Exercise-induced muscle fatigue has been shown to be the consequence of peripheral factors that impair muscle fiber contractile mechanisms. Central factors arising within the central nervous system have also been hypothesized to induce muscle fatigue, but no direct empirical evidence that is causally associated to reduction of muscle force-generating capability has yet been reported. We developed a simulation model to investigate whether peripheral factors of muscle fatigue are sufficient to explain the muscle force behavior observed during empirical studies of fatiguing voluntary contractions, which is commonly attributed to central factors. Peripheral factors of muscle fatigue were included in the model as a time-dependent decrease in the amplitude of the motor unit force twitches. Our simulation study indicated that the force behavior commonly attributed to central fatigue could be explained solely by peripheral factors during simulated fatiguing submaximal voluntary contractions. It also revealed important flaws regarding the use of the interpolated twitch response from electrical stimulation of the muscle as a means for assessing central fatigue. Our analysis does not directly refute the concept of central fatigue. However, it raises important concerns about the manner in which it is measured and about the interpretation of the commonly accepted causes of central fatigue and questions the very need for the existence of central fatigue.

to the editor: We are compelled to clarify some points made by Farina et al. ([1][1]). In a previous exchange of letters Farina et al. sought that we “decompose a set of synthetic surface EMG signals that we [Farina et al.] generate with a model” to provide a convincing validation of our sEMG

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 ≤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.

Organophosphate (OP) compounds are present in house- hold and agricultural pesticides as well as in nerve agents. The toxic effects of these chemicals result from their anticholinesterase activity, which dis- rupts nerve junctions and parasympathetic effector sites, leading to a variety of symptoms and possible death. When the anticholinesterase agents in OP compounds reach the neuromuscular junction, they cause a disruption in the firing of muscle fiber action potentials. This effect has the potential of altering the time course of the electromyographic (EMG) signal detected by surface electrodes. We investigated the association between OP compound dose, surface EMG changes, and overt signs of OP toxicity. Daily doses of 10 -15 g/kg of diisopropylfluorophosphate (DFP) were injected into the calf muscle of four rhesus monkeys while surface EMG signals were recorded from two thigh muscles bilaterally. With increasing number of doses, the EMG signal presented an increasing number of time gaps. The presence of the gaps was evident prior to any overt symptoms of cholinesterase toxicity. These find- ings can lead to the development of noninvasive technology for indicating the presence of OP compounds in muscle tissue prior to clinical abnormal- ities.

We investigated the firing rate of motor units in the vastus lateralis muscle in five healthy young men (mean = 21.4 yr, SD = 0.9) during a sequence of isometric constant-torque contractions repeated to exhaustion. The contractions were sustained at 20% of the maximal voluntary level, measured at the beginning of the test sequence. Electromyographic (EMG) signals were recorded via quadrifilar fine-wire electrodes and subsequently decomposed into their constituent motor unit action potentials to obtain the motor unit firing times. In addition, we measured the whole muscle mechanical properties during the fatigue task using electrical stimulation. The firing rate of motor units first decreased within the first 10–20% of the endurance time of the contractions and then increased. The firing rate increase was accompanied by recruitment of additional motor units as the force output remained constant. The elicited twitch and tetanic torque responses first increased and then decreased. The two processes modulated in a complementary fashion at the same time. Our data suggest that, when the vastus lateralis muscle is activated to maintain a constant torque output, its motoneuron pool receives a net excitatory drive that first decreases to compensate for the short-lived potentiation of the muscle force twitch and then increases to compensate for the diminution of the force twitch. The underlying inverse relationship between the firing rate and the recruitment threshold that has been reported for nonfatigued contractions is maintained. We, therefore, conclude that the central nervous system control of vastus lateralis motor units remains invariant during fatigue in submaximal isometric isotonic contractions.

Considerable evidence shows that sensation from the feet and ankles is important for standing balance control. It remains unclear, however, to what extent specific foot and ankle sensory systems are involved. This study focused on the role of plantar cutaneous sensation in quasi-static balance control. Iontophoretic delivery of anesthesia was used to reduce the sensitivity of the forefoot soles. In a follow-up experiment, subjects received intradermal injections of local anesthetic into the entire weight-bearing surface of the foot soles. Properties of the center-of-foot-pressure (COP) trajectories and ground reaction shear forces were analyzed using stabilogram-diffusion analysis and summary statistics. Effects of foot-sole anesthesia were generally small and mostly manifested as increases in COP velocity. Magnitude of COP displacement was unaffected by foot-sole anesthesia. Forefoot anesthesia mainly influenced mediolateral posture control, whereas complete foot-sole anesthesia had an impact on anteroposterior control. During bipedal stance, statistically significant effects of foot-sole anesthesia on COP were present only under eyes-closed conditions and included increases in COP velocity (11-12%) and shear force root-mean-square (13%), the latter indicating increases in body center-of-mass accelerations due to the foot-sole anesthesia. Similar effects were seen for unipedal stance in addition to an increase in anteroposterior COP median frequency (36%). Changes in stabilogram-diffusion parameters were confined to the short-term region suggesting that sensory information from the foot soles is mainly used to set a relevant background muscle activity for a given posture and support surface characteristic, and consequently is of little importance for feedback control during unperturbed stance. In general, this study demonstrates that plantar sensation is of moderate importance for the maintenance of normal standing balance when the postural control system is challenged by unipedal stance or by closing of the eyes. The impact of reduced plantar sensitivity on postural control is expected to increase with the loss of additional sensory modalities such as the concomitant proprioceptive deficits commonly associated with peripheral neuropathies.

There is considerable evidence that lower-limb somatosensation contributes to the control of upright balance. In this study, we investigated the specific role of foot sole cutaneous afferents in the generation of balance corrections following lateral accelerations of the support surface. Participants were subjected to balance perturbations before and after targeted anesthesia of the cutaneous soles induced by intradermal injections of local anesthetic. Subject responses were quantified in terms of net joint torques at the ankles, hips and trunk. Contrary to the conclusions drawn in earlier studies, response torque impulses at the ankles and hips were clearly scaled with the perturbation impulse under both control and anesthetized conditions. Reduced plantar sensitivity produced a relative shift in compensatory torque production from the ankles and trunk to the hips. These findings demonstrate that plantar cutaneous afferents play an important role in the shaping of dynamic postural responses. Furthermore, the results suggest that loss of plantar sensation may be an important contributor to the dynamic balance deficits and increased risk of falls associated with peripheral neuropathies.

Motor-unit firing patterns were studied in the vastus lateralis muscle of five healthy young men [21.4 ± 0.9 (SD) yr] during a series of isometric knee extensions performed to exhaustion. Each contraction was held at a constant torque level, set to 20% of the maximal voluntary contraction at the beginning of the experiment. Electromyographic signals, recorded via a quadrifilar fine wire electrode, were processed with the precision decomposition technique to identify the firing times of individual motor units. In repeat experiments, whole-muscle mechanical properties were measured during the fatigue protocol using electrical stimulation. The main findings were a monotonic decrease in the recruitment threshold of all motor units and the progressive recruitment of new units, all without a change of the recruitment order. Motor units from the same subject showed a similar time course of threshold decline, but this decline varied among subjects (mean threshold decrease ranged from 23 to 73%). The mean threshold decline was linearly correlated ( R2 ≥ 0.96) with a decline in the elicited peak tetanic torque. In summary, the maintenance of recruitment order during fatigue strongly supports the notion that the observed common recruitment adaptations were a direct consequence of an increased excitatory drive to the motor unit pool. It is suggested that the increased central drive was necessary to compensate for the loss in force output from motor units whose muscle fibers were actively contracting. We therefore conclude that the control scheme of motor-unit recruitment remains invariant during fatigue at least in relatively large muscles performing submaximal isometric contractions.

Paraspinal electromyographic (EMG) activity was recorded bilaterally from three lumbar levels during 30-s isometric trunk extensions [40 and 80% of maximum voluntary contraction (MVC)] in 20 healthy men and 14 chronic low back pain patients in pain. EMG parameters indicating neuromuscular fatigue and contralateral imbalances in EMG root-mean-square amplitude and median frequency were analyzed. Patients in pain showed less fatigue than controls at both contraction levels and produced only 55% of their MVC. Patients in pain likely did not produce a “true” maximum effort. A low MVC estimate would mean lower absolute contraction levels and less neuromuscular fatigue, thus explaining lower scores in the patients. Contralateral root-mean-square amplitude imbalances were present in both categories of subjects although such imbalances, when averaged across lumbar levels, were significantly larger in patients. Median frequency imbalances were significantly larger in the patients, at segmental as well as across lumbar levels. These results suggest that the presence of pain in these patients caused a redistribution of the activation behavior between synergistic muscles of the lumbar back.

Muscle force is modulated by varying the number of active motor units and their firing rates. For the past five decades, the notion that the magnitude of the firing rates is directly related to motor unit size and recruitment threshold has been widely accepted. This construct, here named the After-hyperpolarization scheme evolved from observations in electrically stimulated cat motoneurons and from reported observations in voluntary contractions in humans. It supports the assumption that the firing rates of motor units match their mechanical properties to “optimize” force production, so that the firing rate range corresponds to that required for force-twitch fusion (tetanization) and effective graduation of muscle force. In contrast, we have shown that, at any time and force during isometric voluntary constant-force contractions in humans, the relationship between firing rate and recruitment threshold is inversely related. We refer to this construct as the Onion-Skin scheme because earlier-recruited motor units always have greater firing rates than latter-recruited ones. By applying a novel mathematical model that calculates the force produced by a muscle for the two schemes we found that the Onion-Skin scheme is more energy efficient, provides smoother muscle force at low to moderate force levels, and appears to be more conducive to evolutionary survival than the After-hyperpolarization scheme.

Over the past four decades, various methods have been implemented to measure synchronization of motor-unit firings. In this work, we provide evidence that prior reports of the existence of universal common inputs to all motoneurons and the presence of long-term synchronization are misleading, because they did not use sufficiently rigorous statistical tests to detect synchronization. We developed a statistically based method (SigMax) for computing synchronization and tested it with data from 17,736 motor-unit pairs containing 1,035,225 firing instances from the first dorsal interosseous and vastus lateralis muscles—a data set one order of magnitude greater than that reported in previous studies. Only firing data, obtained from surface electromyographic signal decomposition with >95% accuracy, were used in the study. The data were not subjectively selected in any manner. Because of the size of our data set and the statistical rigor inherent to SigMax, we have confidence that the synchronization values that we calculated provide an improved estimate of physiologically driven synchronization. Compared with three other commonly used techniques, ours revealed three types of discrepancies that result from failing to use sufficient statistical tests necessary to detect synchronization. 1) On average, the z-score method falsely detected synchronization at 16 separate latencies in each motor-unit pair. 2) The cumulative sum method missed one out of every four synchronization identifications found by SigMax. 3) The common input assumption method identified synchronization from 100% of motor-unit pairs studied. SigMax revealed that only 50% of motor-unit pairs actually manifested synchronization.

reply: Dr. Kirkwood is correct in indicating that our work on the synchronization of motor unit firing instances since 1993 has progressively questioned the relevance of the common input notion as a cause. However, we suggest that his designation of “opposition” to our opinion on the common

This lecture explores the various uses of surface electromyography in the field of biomechanics. Three groups of applications are considered: those involving the activation timing of muscles, the force/EMG signal relationship, and the use of the EMG signal as a fatigue index. Technical considerations for recording the EMG signal with maximal fidelity are reviewed, and a compendium of all known factors that affect the information contained in the EMG signal is presented. Questions are posed to guide the practitioner in the proper use of surface electromyography. Sixteen recommendations are made regarding the proper detection, analysis, and interpretation of the EMG signal and measured force. Sixteen outstanding problems that present the greatest challenges to the advancement of surface electromyography are put forward for consideration. Finally, a plea is made for arriving at an international agreement on procedures commonly used in electromyography and biomechanics.

STUDY DESIGN A surface electromyographic procedure for evaluating back muscle impairment was studied in patients undergoing rehabilitation for low back pain. OBJECTIVES The results were analyzed to determine whether the electromyographic procedure was able to: 1) distinguish muscle impairment between patients with low back pain and normal subjects, and 2) monitor changes in muscle function after low back pain rehabilitation. METHODS Patients with chronic low back pain (n = 85) were tested to measure the median frequency of the electromyographic signals from six lumbar electrode sites during sustained trunk extensions. A subset (n = 28) of these patients was re-tested after low back pain rehabilitation. A discriminant function for classifying subjects into "low back pain" and "normal" groups was formulated using the electromyographic data from a subset of the patients with low back pain (n = 28) and a normative sample (n = 42). Results for this "learning" sample were compared with results using the same function on the remaining "holdout" sample of patients (n = 57) and an additional normative sample (n = 6). Differences in electromyographic parameters before and after rehabilitation also were analyzed. RESULTS The discriminant function classified subjects into low back pain and normal groups, with 86% and 89% correct classification for the "learning" and "holdout" samples, respectively. These classification results were independent of trunk extensor strength. Changes in median frequency after the rehabilitation program were consistent with improvements in back muscle fatigability. CONCLUSION These findings demonstrate how electromyographic spectral measurements may be used to identify and monitor back muscle impairment in patients undergoing rehabilitation for low back pain.

We developed a model to investigate the influence of the muscle force twitch on the simulated firing behavior of motoneurons and muscle force production during voluntary isometric contractions. The input consists of an excitatory signal common to all the motor units in the pool of a muscle, consistent with the “common drive” property. Motor units respond with a hierarchically structured firing behavior wherein at any time and force, firing rates are inversely proportional to recruitment threshold, as described by the “onion skin” property. Time- and force-dependent changes in muscle force production are introduced by varying the motor unit force twitches as a function of time or by varying the number of active motor units. A force feedback adjusts the input excitation, maintaining the simulated force at a target level. The simulations replicate motor unit behavior characteristics similar to those reported in previous empirical studies of sustained contractions: 1) the initial decrease and subsequent increase of firing rates, 2) the derecruitment and recruitment of motor units throughout sustained contractions, and 3) the continual increase in the force fluctuation caused by the progressive recruitment of larger motor units. The model cautions the use of motor unit behavior at recruitment and derecruitment without consideration of changes in the muscle force generation capacity. It describes an alternative mechanism for the reserve capacity of motor units to generate extraordinary force. It supports the hypothesis that the control of motoneurons remains invariant during force-varying and sustained isometric contractions.

Technologies for decomposing the electromyographic (EMG) signal into its constituent motor unit action potential trains have become more practical by the advent of a non-invasive methodology using surface EMG (sEMG) sensors placed on the skin above the muscle of interest (De Luca et al 2006 J. Neurophysiol. 96 1646-57 and Nawab et al 2010 Clin. Neurophysiol. 121 1602-15). This advancement has widespread appeal among researchers and clinicians because of the ease of use, reduced risk of infection, and the greater number of motor unit action potential trains obtained compared to needle sensor techniques. In this study we investigated the influence of the sensor site on the number of identified motor unit action potential trains in six lower limb muscles and one upper limb muscle with the intent of locating preferred sensor sites that provided the greatest number of decomposed motor unit action potential trains, or motor unit yield. Sensor sites rendered varying motor unit yields throughout the surface of a muscle. The preferred sites were located between the center and the tendinous areas of the muscle. The motor unit yield was positively correlated with the signal-to-noise ratio of the detected sEMG. The signal-to-noise ratio was inversely related to the thickness of the tissue between the sensor and the muscle fibers. A signal-to-noise ratio of 3 was found to be the minimum required to obtain a reliable motor unit yield.

OBJECTIVE To compare parameters of muscle energy metabolism in patients with fibromyalgia syndrome (FMS) and sedentary controls. METHODS Thirteen female FMS patients and 13 female sedentary controls underwent a standardized clinical assessment (including dolorimeter measurements of the upper trapezius and tibialis anterior muscles) and a standardized aerobic fitness test including measurement of maximum oxygen uptake (VO2max). Phosphorus (31P) magnetic resonance spectroscopy studies of the upper trapezius and tibialis anterior muscles were then performed in FMS patients and controls, at rest and during and following a muscle-fatiguing exercise protocol. RESULTS FMS patients and controls had similar levels of VO2max and of maximum voluntary contraction (MVC) of the upper trapezius and tibialis anterior muscles. After controlling for VO2max and MVC, measurements of phosphocreatine (PCr), inorganic phosphate (P(i)), and intracellular pH in these muscles were not significantly different in FMS patients versus sedentary controls either at rest, during exercise, or during recovery. In the patients with FMS, no correlation was found between overall or local pain severity and the principal muscle metabolic parameter, PCr/P(i). Inverse correlations between dolorimeter scores at 2 muscle sites and tibialis anterior PCr/P(i) were found both in patients and in controls. CONCLUSION This study demonstrates that under the conditions studied, muscle energy metabolism in FMS is no different than that in sedentary controls. These findings do not support the hypothesis that detectable defects in muscle energy metabolism occur in FMS.

The quality of attractor reconstruction using the method of delays is known to be sensitive to the delay parameter, τ. Here we develop a new, computationally efficient approach to choosing τ that quantifies reconstruction expansion from the identity line of the embedding space. We show that reconstruction expansion is related to the concept of reconstruction signal strength and that increased expansion corresponds to diminished effects of measurement error. Thus, reconstruction expansion represents a simple, geometrical framework for choosing τ. Furthermore, we describe the role of dynamical error in attractor expansion and argue that algorithms for determining τ should be considered as attempts at estimating an upper bound to the optimal delay.

For the past five decades there has been wide acceptance of a relationship between the firing rate of motor units and the afterhyperpolarization of motoneurons. It has been promulgated that the higher-threshold, larger-soma, motoneurons fire faster than the lower-threshold, smaller-soma, motor units. This relationship was based on studies on anesthetized cats with electrically stimulated motoneurons. We questioned its applicability to motor unit control during voluntary contractions in humans. We found that during linearly force-increasing contractions, firing rates increased as exponential functions. At any time and force level, including at recruitment, the firing rate values were inversely related to the recruitment threshold of the motor unit. The time constants of the exponential functions were directly related to the recruitment threshold. From the Henneman size principle it follows that the characteristics of the firing rates are also related to the size of the soma. The “firing rate spectrum” presents a beautifully simple control scheme in which, at any given time or force, the firing rate value of earlier-recruited motor units is greater than that of later-recruited motor units. This hierarchical control scheme describes a mechanism that provides an effective economy of force generation for the earlier-recruited lower force-twitch motor units, and reduces the fatigue of later-recruited higher force-twitch motor units—both characteristics being well suited for generating and sustaining force during the fight-or-flight response.

Automatic tracking of movement disorders in patients with Parkinson's disease (PD) is dependent on the ability of machine learning algorithms to resolve the complex and unpredictable characteristics of wearable sensor data. The challenge reflects the variety of movement disorders that fluctuate throughout the day which can be confounded by voluntary activities of daily life. Our approach is the development of multiple dynamic neural network (DNN) classifiers whose application are governed by a rule-based controller within the Integrated Processing and Understanding of Signals (IPUS) framework. Solutions are described for time-varying occurrences of tremor and dyskinesia, classified at 1 s resolution from surface electromyographic (sEMG) and tri-axial accelerometer (ACC) data acquired from patients with PD. The networks were trained and tested on separate datasets, respectively, while subjects performed unscripted and unconstrained activities in a home-like setting. Performance of the classifiers achieved an overall global error rate of less than 10%.

We investigated the influence of inter-electrode spacing on the degree of crosstalk contamination in surface electromyographic (sEMG) signals in the tibialis anterior (target muscle), generated by the triceps surae (crosstalk muscle), using bar and disk electrode arrays. The degree of crosstalk contamination was assessed for voluntary constant-force isometric contractions and for dynamic contractions during walking. Single-differential signals were acquired with inter-electrode spacing ranging from 5 mm to 40 mm. Additionally, double differential signals were acquired at 10 mm spacing using the bar electrode array. Crosstalk contamination at the target muscle was expressed as the ratio of the detected crosstalk signal to that of the target muscle signal. The crosstalk contamination ratio approached a mean of 50% for the 40 mm spacing for triceps surae muscle contractions at 80% MVC and tibialis anterior muscle contractions at 10% MVC. For single differential recordings, the minimum crosstalk contamination was obtained from the 10 mm spacing. The results showed no significant differences between the bar and disk electrode arrays. During walking, the crosstalk contamination on the tibialis anterior muscle reached levels of 23% for a commonly used 22 mm spacing single-differential disk sensor, 17% for a 10 mm spacing single-differential bar sensor, and 8% for a 10 mm double-differential bar sensor. For both studies the effect of electrode spacing on crosstalk contamination was statistically significant. Crosstalk contamination and inter-electrode spacing should therefore be a serious concern in gait studies when the sEMG signal is collected with single differential sensors. The contamination can distort the target muscle signal and mislead the interpretation of its activation timing and force magnitude.

Detecting the presence of chaos in a dynamical system is an important problem that is solved by measuring the largest Lyapunov exponent. Lyapunov exponents quantify the exponential divergence of initially close state-space trajectories and estimate the amount of chaos in a system. We present a new method for calculating the largest Lyapunov exponent from an experimental time series. The method follows directly from the definition of the largest Lyapunov exponent and is accurate because it takes advantage of all the available data. We show that the algorithm is fast, easy to implement, and robust to changes in the following quantities: embedding dimension, size of data set, reconstruction delay, and noise level. Furthermore, one may use the algorithm to calculate simultaneously the correlation dimension. Thus, one sequence of computations will yield an estimate of both the level of chaos and the system complexity.

We present a dynamic neural network (DNN) solution for detecting time-varying occurrences of tremor and dyskinesia at 1 s resolution from time series data acquired from surface electromyographic (sEMG) sensors and tri-axial accelerometers worn by patients with Parkinson's disease (PD). The networks were trained and tested on separate datasets, each containing approximately equal proportions of tremor, dyskinesia, and disorder-free data from 8 PD and 4 control subjects performing unscripted and unconstrained activities in an apartment-like environment. During DNN testing, tremor was detected with a sensitivity of 93% and a specificity of 95%, while dyskinesia was detected with a sensitivity of 91% and a specificity of 93%. Similar sensitivity and specificity levels were obtained when DNN testing was carried out on subjects who were not included in DNN training.

We used surface EMG signal decomposition technology to study the control properties of numerous simultaneously active motor units. Six healthy human subjects of comparable age (21 ± 0.63 yr) and physical fitness were recruited to perform isometric contractions of the vastus lateralis (VL), first dorsal interosseous (FDI), and tibialis anterior (TA) muscles at the 20, 50, 80, and 100% maximum voluntary contraction force levels. EMG signals were collected with a five-pin surface array sensor that provided four channels of data. They were decomposed into the constituent action potentials with a new decomposition algorithm. The firings of a total of 1,273 motor unit action potential trains, 20–30 per contraction, were obtained. The recruitment thresholds and mean firing rates of the motor units were calculated, and mathematical equations were derived. The results describe a hierarchical inverse relationship between the recruitment thresholds and the firing rates, including the first and second derivatives, i.e., the velocity and the acceleration of the firing rates. This relationship describes an “operating point” for the motoneuron pool that remains consistent at all force levels and is modulated by the excitation. This relationship differs only slightly between subjects and more distinctly across muscles. These results support the “onion skin” property that suggests a basic control scheme encoded in the physical properties of motoneurons that responds consistently to a “common drive” to the motoneuron pool.

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. The second principle seeks a local minimum in the coefficient of variation of inter-firing intervals of each separated component. Upon incorporation of these principles into our latest Precision Decomposition system for sEMG signals, 20 to 30 motor unit action potential trains (MUAPTs) were decomposed per experimental sEMG signal from isometric contractions with trapezoidal force profiles. Our new system performs well even as the force generated by a muscle approaches maximum voluntary levels.

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 1.5 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, and occasionally up to 40. The accuracy of all the firings of the MUAPTs was on average 92.5%, at times reaching 97%. Conclusions: 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.

During isometric contractions, the fluctuation of the force output of muscles increases as the muscle fatigues, and the contraction is sustained to exhaustion. We analyzed motor unit firing data from the vastus lateralis muscle to investigate which motor unit control parameters were associated with the increased force fluctuation. Subjects performed a sequence of isometric constant-force contractions sustained at 20% maximal force, each spaced by a 6-s rest period. The contractions were performed until the mean value of the force output could not be maintained at the desired level. Intramuscular EMG signals were detected with a quadrifilar fine-wire sensor. The EMG signals were decomposed to identify all of the firings of several motor units by using an artificial intelligence-based set of algorithms. We were able to follow the behavior of the same motor units as the endurance time progressed. The force output of the muscle was filtered to remove contributions from the tracking task. The coefficient of variation of the force was found to increase with endurance time ( P < 0.001, R2 = 0.51). We calculated the coefficient of variation of the firing rates, the synchronization of pairs of motor unit firings, the cross-correlation value of the firing rates of pairs of motor units, the cross-correlation of the firing rates of motor units and the force, and the number of motor units recruited during the contractions. Of these parameters, only the cross-correlation of the firing rates ( P < 0.01, R2 = 0.10) and the number of recruited motor units ( P = 0.042, R2 = 0.22) increased significantly with endurance time for grouped subjects. A significant increase ( P < 0.001, R2 = 0.16) in the cross-correlation of the firing rates and force was also observed. It is suggested that the increase in the cross-correlation of the firing rates is likely due to a decrease in the sensitivity of the proprioceptive feedback from the spindles.

The authors previously reported speaker-dependent automatic speech recognition accuracy for isolated words using eleven surface-electromyographic (sEMG) sensors in fixed recording locations on the face and neck. The original array of sensors was chosen to ensure ample coverage of the muscle groups known to contribute to articulation during speech production. In this paper we systematically analyzed speech recognition performance from sensor subsets with the goal of reducing the number of sensors needed and finding the best combination of sensor locations to achieve word recognition rates comparable to the full set. We evaluated each of the different possible subsets by its mean word recognition rate across nine speakers using HMM modeling of MFCC and co-activation features derived from the subset of sensor signals. We show empirically that five sensors are sufficient to achieve a recognition rate to within a half a percentage point of that obtainable from the full set of sensors.

The surface electromyographic (sEMG) signal that originates in the muscle is inevitably contaminated by various noise signals or artifacts that originate at the skin-electrode interface, in the electronics that amplifies the signals, and in external sources. Modern technology is substantially immune to some of these noises, but not to the baseline noise and the movement artifact noise. These noise sources have frequency spectra that contaminate the low-frequency part of the sEMG frequency spectrum. There are many factors which must be taken into consideration when determining the appropriate filter specifications to remove these artifacts; they include the muscle tested and type of contraction, the sensor configuration, and specific noise source. The band-pass determination is always a compromise between (a) reducing noise and artifact contamination, and (b) preserving the desired information from the sEMG signal. This study was designed to investigate the effects of mechanical perturbations and noise that are typically encountered during sEMG recordings in clinical and related applications. The analysis established the relationship between the attenuation rates of the movement artifact and the sEMG signal as a function of the filter band pass. When this relationship is combined with other considerations related to the informational content of the signal, the signal distortion of filters, and the kinds of artifacts evaluated in this study, a Butterworth filter with a corner frequency of 20 Hz and a slope of 12 dB/oct is recommended for general use. The results of this study are relevant to biomechanical and clinical applications where the measurements of body dynamics and kinematics may include artifact sources.