| | Does the frequency content of the surface mechanomyographic signal reflect motor unit firing rates? A brief reviewReceived 22 August 2005; received in revised form 8 December 2005; accepted 18 December 2005. published online 23 February 2006. Abstract The purpose of this review is to examine the literature that has investigated the potential relationship between mechanomyographic (MMG) frequency and motor unit firing rates. Several different experimental designs/methodologies have been used to address this issue, including: repetitive electrical stimulation, voluntary muscle actions in muscles with different fiber type compositions, fatiguing and non-fatiguing isometric or dynamic muscle actions, and voluntary muscle actions in young versus elderly subjects and healthy individuals versus subjects with a neuromuscular disease(s). Generally speaking, the results from these investigations have suggested that MMG frequency is related to the rate of motor unit activation and the contractile properties (contraction and relaxation times) of the muscle fibers. Other studies, however, have reported that MMG mean power frequency (MPF) does not always follow the expected pattern of firing rate modulation (e.g. motor unit firing rates generally increase with torque during isometric muscle actions, but MMG MPF may remain stable or even decrease). In addition, there are several factors that may affect the frequency content of the MMG signal during a voluntary muscle action (i.e. muscle stiffness, intramuscular fluid pressure, etc.), independent of changes in motor unit firing rates. Despite the potential influences of these factors, most of the evidence has suggested that the frequency domain of the MMG signal contains some information regarding motor unit firing rates. It is likely, however, that this information is qualitative, rather than quantitative in nature, and reflects the global motor unit firing rate, rather than the firing rates of a particular group of motor units. 1. Background  As early as the 1800s, scientists were aware that contracting skeletal muscle generates low frequency sound waves that are largely inaudible to the human ear, but can be detected at the surface of the skin [67], [70], [76], [87]. The exact origin(s) of the sound, however, was not clearly understood, and research in the area was limited primarily by the inability to adequately detect the signal and describe its properties. The advent of electronic sensors (hydrophones, condenser microphones, piezoelectric contact sensors, and accelerometers) and digital computers in the early 1980s greatly improved the ability to record, quantify, and process the muscle sound signal [67], and a number of studies were conducted to examine the characteristics of the sound waves produced by different muscles under a variety of conditions. Eventually, the term “mechanomyography” (MMG) was adopted to adequately describe the mechanical nature of muscle sound and avoid confusion regarding the various terminologies, such as soundmyography, phonomyography, acousticmyography, and vibromyography, that had been used in previous studies [67, p. 237]. One of the most important initial research questions dealt with identifying the specific physiological mechanism(s) that generates the MMG signal. Barry [4] and Frangioni et al. [38] were among the first investigators to demonstrate that during an electrically-stimulated isometric twitch, isolated frog gastrocnemius muscle oscillates laterally in directions perpendicular to its long axis. Additional research [5] indicated that the first oscillation was usually the largest in amplitude, followed by progressively smaller oscillations that occurred at the resonant frequencies of the muscle. Stokes and Cooper [82] extended these results to in vivo skeletal muscle and reported that when stimulated to contract, the whole muscle oscillates laterally as a single unit. These findings [4], [5], [38], [82] confirmed that during electrically stimulated twitches of both isolated and intact skeletal muscle, the MMG signal is generated by two primary mechanisms: (a) a slow bulk movement of the muscle at the initiation of the contraction, and (b) smaller subsequent lateral oscillations occurring at the resonant frequencies of the muscle. Under voluntary conditions, however, the asynchronous motor unit activities generate pressure waves that contribute to the muscle surface oscillations underlying the MMG signal [67], [70], [71]. In particular, Orizio et al. [71, p. 480] reported that the mechanical activities of individual motor units are summated at the skin surface over the muscle, and, therefore, the surface MMG can be considered a “compound” signal that is generated by many motor units. Recent studies [16], [78] have provided support for this hypothesis by demonstrating that individual motor units can be extracted from the MMG signal recorded during a voluntary isometric muscle action. The contribution of each motor unit appears, however, to be influenced by the degree to which its twitches are fused [13], [14], [89]. Specifically, Bichler [13] reported that no MMG signal is observed during a fully fused tetanic contraction, which suggested that a specific motor unit’s contribution to the MMG signal may decrease as its twitches become more fused. Collectively, these findings [13], [16], [71], [78] have indicated that during voluntary muscle actions, the MMG signal is generated by the mechanical activities of the unfused, activated motor units, and, therefore, may contain information regarding motor control strategies (i.e. relative contributions of recruitment and firing rate). Despite the advancements that have been made in describing the origin(s)/characteristics of the MMG signal, there are many questions that have yet to be answered conclusively. One of the most intriguing issues involves the relationship between the frequency content of the MMG signal and motor unit firing rate. Although not directly verified [3], many previous studies [1], [31], [54], [69], [70], [72], [88], [89] have suggested that the MMG power density spectrum may contain information regarding the global firing rates and contractile properties of the unfused activated motor units. Specifically, increases in the firing rates of individual motor units may result in an increase in the global motor unit firing rate, thereby resulting in a higher frequency MMG signal [3]. Furthermore, Orizio [67, p. 227] suggested that recruitment of fast-twitch muscle fibers with short contraction times could result in “…shorter MUSS [motor unit sound spikes],” that would increase MMG frequency. In many respects, the potential relationship between MMG frequency and motor unit firing rates could distinguish MMG from other biological signals that have been used to examine muscle function, such as surface and intramuscular electromyography (EMG). For example, the frequency content of the surface EMG signal contains very limited information regarding motor unit firing rates (i.e. only between approximately 0 and 40 Hz) [7]. Beyond 40 Hz, the EMG power density spectrum is influenced primarily by the shapes of the motor unit action potentials [25], [40], [51], [52]. Thus, Akataki et al. [1, pp. 22–23] stated “the MPF [mean power frequency] of the EMG is influenced strongly by the shape of the action potential because the power is greater and frequency content is higher compared with those of the firing rate. The MMG power spectrum is also related to the shape of the elementary MMG due to single MUs [motor units] and the firing rate of the recruited MUs. The duration of the elementary MMG (100 ms in biceps brachii muscles) is much longer than that of the electrical action potential. The two components of the MMG are considered to overlap each other in the power spectrum. Thus, it is possible that the MPF is indicative of the firing rate of the MUs.” In addition, although previous studies [17], [50] have examined motor unit firing rates by measuring interspike intervals with indwelling EMG electrodes, there are drawbacks to using this technique during maximal muscle actions, such as the potential for electrode movement, deformation, or breakage within the muscle [45]. Furthermore, when used to measure interspike intervals, intramuscular EMG provides information regarding the activity of only a few motor units at most. Bellemare et al. [12] indicated that motor unit firing rates ranged from 12 to 60 Hz for the biceps brachii during an isometric maximum voluntary contraction (MVC) of the forearm flexors, and Søgaard et al. [81] reported firing rates between 7 and 43 Hz for the flexor carpi radialis during an isometric muscle action of the wrist flexors at 60% MVC. Thus, it is unlikely that all motor units are firing at the same frequency, even during an isometric muscle action at a steady torque level. In addition, the fact that indwelling electrodes are typically used to record the activities of just a few motor units has important implications for experiments in which the intramuscular EMG and surface MMG signals are recorded simultaneously. Namely, it is likely that intramuscular EMG and surface MMG provide unique information regarding motor unit firing rates, particularly at high torque levels when many motor units are active. For example, Barry et al. [6] reported a close relationship between the intramuscular EMG and surface MMG signals from the vastus lateralis muscle, but only at very low torque levels when a single motor unit could be identified in both signals. Furthermore, Orizio et al. [71] found that in the extensor digitorum communis muscle, the mechanical activities of two separate motor units were summated linearly in the surface MMG when one motor unit was stimulated at 3 Hz and the other motor unit was stimulated at 8 Hz. When the motor units were stimulated at 9 and 20 Hz, however, their mechanical activities were summated nonlinearly, indicating that the resulting MMG signal was not the sum of two separate MMG signals generated by motor units that were acting separately. In addition, Cescon et al. [16] reported that during voluntary isometric muscle actions of the abductor digiti minimi and first dorsal interosseous, the mechanical activities from individual motor units were summated nonlinearly, even when the muscle actions were performed at relatively low torque levels [approximately 2% and 5% of the maximum voluntary contraction (MVC)]. There is also evidence to suggest that the timing of the mechanical activities from separate motor units could affect MMG frequency. Specifically, it has been suggested that synchronization of motor unit firing rates may influence the shape of the MMG power density spectrum by increasing the amount of power present in the MMG signal at frequencies below approximately 20 Hz [66]. Collectively, these findings [6], [16], [66], [71] suggested that during most voluntary muscle actions, the frequency content of the MMG signal does not reflect the activity of just one particular motor unit (or a few motor units), and any potential information regarding motor unit firing rates could be qualitative, rather than quantitative in nature. Although many questions still exist regarding the information contained in the surface MMG signal, there are several studies that have supported the contention that the MMG power density spectrum is influenced by motor unit firing rates. Conversely, there is also evidence that the frequency content of the MMG signal may reflect some other characteristic of muscle function. Thus, a definitive relationship between MMG frequency and motor unit firing rate cannot be determined at the present time. Future studies, in which mathematical models are developed to examine the influence(s) of various nonphysiological factors (i.e. motor unit depth and the characteristics of the tissue between the muscle and the MMG sensor) on the MMG signal may be useful for identifying the limitations involved with inferring motor control strategies from the MMG power density spectrum (Table 1). The purpose of this review, however, is to systematically examine the studies that have used different experimental designs/methodologies to investigate the potential relationship between the frequency content of the MMG signal and motor unit firing rates, and draw some general conclusions regarding the nature of their findings. | | |  | Factor | Effect on MMG amplitude | Effect on MMG frequency |  |
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 | 1. Muscle stiffness | Increased muscle stiffness may result in a decrease in MMG amplitude [8], [18], [27], [67]. | Increased muscle stiffness may result in an increase in MMG frequency [5]. |  |  | 2. Intramuscular fluid pressure | Increased intramuscular fluid pressure may result in a decrease in MMG amplitude [8], [18], [27], [67]. | The effect of increased intramuscular fluid pressure on MMG frequency is unknown. |  |  | 3. Thickness of the tissue between the muscle and the MMG sensor | Increased thickness of the tissue between the muscle and the MMG sensor results in a decrease in MMG amplitude [67]. | Increased thickness of the tissue between the muscle and the MMG sensor results in a decrease in MMG frequency [43]. |  |  | 4. Muscle temperature | An increase in muscle temperature resulted in an increase in MMG amplitude for isolated frog skeletal muscle [4]. The effect on in vivo muscle is unknown. | The effect of increased muscle temperature on MMG frequency is unknown. |  |  | 5. Muscle length | During electrically-stimulated twitches of isolated frog skeletal muscle, the amplitude of the MMG signal was greatest when the muscle was held at a length corresponding to approximately 90% of the optimal length for force production. MMG amplitude decreased when the muscle was held at lengths greater or shorter than this [4]. | During electrically-stimulated twitches of isolated frog skeletal muscle, the frequency of the MMG signal increased when the muscle was held at longer lengths [38]. |  | | | |
2. Studies supporting a relationship between MMG frequency and motor unit firing rates  2.1. Repetitive electrical stimulation studies There are several studies that have demonstrated that during repetitive electrical stimulation of both isolated and intact skeletal muscle, the mean power frequency (MPF) or peak frequency of the surface MMG signal is very similar to the stimulation rate, but only for unfused tetani [14], [15], [41], [82], [83], [84], [88]. This has suggested that unlike EMG, MMG reflects the mechanical response of the muscle to electrical activation, and this response is largely dependent on the contractile characteristics (i.e. contraction and relaxation times) of the muscle [88], [89]. For example, both surface and intramuscular EMG measure the electrical currents (action potentials) that travel along the muscle fiber membranes, and during most voluntary muscle actions, the action potentials from different motor units are not synchronized [7]. During repetitive electrical stimulation, however, the EMG signal is generated by the electrical currents from the stimulator and, therefore, its frequency content matches the stimulation rate [47]. This is not always the case for MMG. For example, Yoshitake and Moritani [88] reported that during repetitive electrical stimulation of the tibial nerve, MMG MPFs for the medial gastrocnemius and soleus muscles nearly matched the stimulation rate of 5 Hz. When the stimulation rate was increased to 20 Hz, MMG MPF corresponded closely with the stimulation rate for the medial gastrocnemius, but not for the soleus [88]. It was suggested [88] that a greater proportion of slow-twitch muscle fibers in the soleus than in the medial gastrocnemius allowed its twitches to be fused at a lower stimulation rate. Furthermore, Bichler and Celichowski [14] reported that when compared to fast-twitch motor units, slow-twitch motor units in the rat medial gastrocnemius muscle had longer contraction and half-relaxation times, and, at a given stimulation rate, their twitches were more fully fused and had lower MMG amplitude (peak-to-peak) values. Thus, these findings [14], [88] suggested that the MMG signal may contain information regarding muscle-specific differences in the mechanical response to a given rate of electrical stimulation. Interestingly, the characteristics of the MMG signal appear to be more closely related to the fluctuations in force that occur during repetitive electrical stimulation than to the net force that is produced by the muscle. Specifically, Stokes and Cooper [82] reported that during electrical stimulation of the adductor pollicis muscle at frequencies between 10 and 20 Hz, the amplitudes of the MMG and force oscillation signals decreased similarly with an increase in stimulation rate, but the net force produced by the muscle increased. In addition, Kimura et al. [47] examined the effects of experimentally-induced hypothermia on contractile function in the medial gastrocnemius and soleus muscles during electrical stimulation of the tibial nerve at 10 Hz. The authors [47] reported that when the muscles were cooled with ice packs to a temperature of 24 °C, there were significant increases in electrically-stimulated plantar flexion force, but decreases in the amplitudes of the MMG signals from both muscles, as well as in the amplitude of the force fluctuation signal. The peak frequencies of the MMG signals from the medial gastrocnemius and soleus, however, nearly matched the stimulation rate in both the control and hypothermic conditions [47]. It was suggested that increases in contraction and relaxation times during hypothermia resulted in greater fusion of motor unit twitches, thereby influencing the amplitudes of both the MMG and force fluctuation signals [47]. In addition, Vaz et al. [84, p. 118] found that during repetitive electrical stimulation of isolated cat soleus muscle, each electrical impulse resulted in a “distinct vibratory signal” from the muscle. The amplitude of the MMG signal decreased, however, when the muscle was held at longer lengths. It was suggested that changes in muscle length may affect the active and passive properties of muscle contraction, thereby influencing the characteristics of the surface MMG signal [84]. Furthermore, Vaz et al. [83] used a similar experimental design with isolated cat soleus muscle, and reported that increases in the stimulation rate resulted in increases in MMG median frequency and decreases in MMG amplitude. It was suggested [83] that the muscle vibrations that generate the MMG signal are produced by the force fluctuations of unfused contractions of motor units. An important distinction must be made, however, between the MMG signals recorded during repetitive electrical stimulation and voluntary muscle actions. In particular, during supramaximal electrical stimulation, all muscle fibers are activated simultaneously, and the muscle oscillates laterally as a single unit [82]. This situation is very different from a voluntary muscle action, where the mechanical motor unit activities, which are typically not synchronized, are summated at the skin surface [67], [70], [71]. Nevertheless, the findings from electrical stimulation studies have provided two important pieces of evidence that support the use of MMG for examining changes in motor unit firing rates: (a) the surface MMG is a mechanical signal [82] that is generated by motor unit responses to electrical activation [71], and (b) the frequency of the MMG signal increases with the stimulation rate, but only when the twitches are not completely fused [47], [88]. 2.2. Muscle fiber type studies Several studies [57], [61], [62], [75] have reported that the frequency of the MMG signal is higher for muscles composed of a large percentage of fast-twitch fibers than in those that consist primarily of slow-twitch fibers. For example, Mealing et al. [61] recorded surface MMG signals from the biceps brachii and soleus during voluntary isometric muscle actions at 50% MVC. The authors [61, p. 29] reported that for the biceps brachii muscle, the MMG power density spectrum was “distinctly bimodal,” with significant signal power between 10 and 25 Hz. The MMG signal from the soleus muscle, however, was dominated by frequencies between 5 and 10 Hz, with only a single peak in the power spectrum at approximately 6 Hz [61]. In addition, the authors [61] reported that some individuals demonstrated MMG signals with wide frequency bands for both the soleus and biceps brachii muscles. It was suggested that the frequency content of the MMG signal may reflect the greater proportion of fast-twitch motor units in the biceps brachii when compared to the soleus [44], as well as differences in fiber type composition between individuals [61]. In addition, Mealing and McCarthy [62] reported similar results when the MMG power density spectra from the orbicularis oris and soleus muscles were compared. Specifically, the MMG signals from the orbicularis oris muscle contained a wide range of frequencies, with a MPF of approximately 22 Hz [62]. The soleus muscle, however, demonstrated MMG signals that consisted of one “predominant frequency” of approximately 10.8 Hz [62, p. 0948]. It was hypothesized that the higher frequency MMG signals for the orbicularis oris than for the soleus may have been due to a greater number of fast-twitch motor units with higher firing rates [62]. In addition, potential differences in fiber type composition may also be reflected in the MMG signals recorded during electrically-stimulated isometric twitches. For example, Marchetti et al. [57] found that when the vastus lateralis and soleus muscles were electrically stimulated with single supramaximal pulses, the vastus lateralis demonstrated higher MMG median frequency values than the soleus. Thus, it was suggested that MMG may also contain information regarding the overall contraction speed of the muscle [57]. Orizio and Veicsteinas [75] provided perhaps the most compelling evidence that MMG frequency is influenced by muscle fiber type composition. The authors [75] examined the patterns of response for MMG MPF from the vastus lateralis muscle during a sustained maximal isometric leg extension in sprinters, long distance runners, and sedentary subjects. For all subjects, MMG MPF followed a “bilinear trend” that was characterized by a rapid decrease during approximately the first 30 s of the fatigue test, followed by a slower decline to exhaustion [75, p. 598]. The initial rapid decrease in MMG MPF occurred in approximately 65%, 40%, and 35% of the total contraction time for the sprinters, sedentary subjects, and long distance runners, respectively [75]. In addition, the sprinters demonstrated more power in the high frequency (i.e. 15–60 Hz) region of the MMG power density spectrum than the sedentary subjects and long distance runners at all time points during the fatigue test [75]. It was suggested that the duration (expressed as a percentage of the total contraction time) of the initial rapid decrease in MMG MPF may be related to the percentage of fast-twitch muscle fibers in the vastus lateralis [75]. In addition, the high frequency (i.e. 15–60 Hz) portion of the MMG power density spectrum could potentially contain information regarding the contribution of fast-twitch motor units to the surface MMG signal [75]. Thus, it is apparent that during voluntary muscle actions, the frequency content of the surface MMG signal is influenced, at least partially, by the fiber type composition of the muscle being investigated. This has important implications in terms of the potential relationship between MMG frequency and motor unit firing rates because previous investigations have reported that high threshold fast-twitch motor units have higher firing rates than low threshold slow-twitch motor units and require greater stimulation rates to achieve complete fusion of motor unit twitches [14], [17], [39]. Theoretically, both of these factors could contribute to the muscle-specific differences in MMG frequency that have been reported in previous studies [57], [61], [62], as well as to the unique MMG MPF patterns demonstrated during fatiguing exercise in individuals with different athletic backgrounds [75]. 2.3. MMG MPF versus isometric and dynamic torque relationships One of the most promising applications of MMG is its potential use as a tool for investigating motor control strategies. This was recognized relatively quickly by many researchers, and by the mid-1990s, several studies had examined the MMG amplitude and/or MPF versus torque relationships during dynamic or isometric muscle actions [24], [60], [69], [72], [74]. Orizio et al. [72] were among the first investigators to report that MMG MPF increased with isometric torque production. In particular, the authors [72] found that MMG MPF for the biceps brachii muscle remained relatively stable with increases in forearm flexion torque from 10% to 20% MVC, and then increased steadily from 20% to approximately 80% MVC. From 60% to 100% MVC, however, the MMG signal demonstrated an increase in the amount of power present at higher frequencies (i.e. 15–45 Hz; Fig. 1), and MMG MPF increased rapidly from approximately 80% to 100% MVC. It was suggested that the increases in MMG MPF from 20% to approximately 80% MVC likely reflected recruitment of motor units with progressively higher firing rates [72]. In addition, the changes in the shape of the MMG power density spectrum, and the rapid increases in MMG MPF at higher torque levels (i.e. above 60–80% MVC) may have been due to a progressively greater reliance on increases in motor unit firing rates for generating additional torque [72]. This motor control strategy is typical for large limb muscles (such as the biceps brachii), in which isometric torque is increased primarily through concurrent modulation of the number of active motor units and their firing rates [29]. At very high torque levels, however, increasing motor unit firing rates becomes more important for generating additional torque when almost all motor units have been recruited [50]. In addition, Akataki et al. [3] recently reported different torque-related patterns for MMG MPF from the biceps brachii and first dorsal interosseous during isometric ramp muscle actions from 5% to 70% MVC. Specifically, the authors [3] found that for the biceps brachii muscle, MMG MPF increased from 5% to approximately 53% MVC, decreased slightly from 53% to approximately 62% MVC, and then increased rapidly from 62% to 70% MVC. For the first dorsal interosseous muscle, however, MMG MPF increased linearly with torque from 5% to 70% MVC [3]. It was suggested that the differences between the MMG MPF patterns from the biceps brachii and first dorsal interosseous muscles may have been due to the unique motor control strategies that are used to increase isometric torque in the two muscles [3]. Specifically, the biceps brachii muscle relies heavily on motor unit recruitment, and, to a lesser extent, on increases in firing rates for modulating isometric torque [17], [50]. For the first dorsal interosseous muscle, however, all motor units are recruited at approximately 50% MVC, and, therefore, firing rate modulation is the primary method for increasing torque from 50% to 100% MVC [26]. In addition, recent studies have reported no changes in MMG MPF for the biceps brachii [8] and vastus medialis [19] during submaximal to maximal concentric isokinetic muscle actions. It was suggested that recruitment, with little change in motor unit firing rates, may be the primary motor control strategy for increasing concentric torque production [8], [19]. This hypothesis is consistent with the results from previous studies, in which motor unit firing rates (detected with intramuscular EMG electrodes) for the biceps brachii muscle remained relatively stable with increases in concentric forearm flexion torque [48], [53]. In addition, recent studies have reported that during eccentric isokinetic muscle actions, MMG MPF increased linearly with torque for both the biceps brachii [10] and vastus medialis [20] muscles. These findings were consistent with those from previous investigations that have suggested that unlike concentric muscle actions, torque production during eccentric muscle actions may be modulated by changes in firing rates, in addition to motor unit recruitment [29], [48], [53]. Collectively, the results from these studies indicated that MMG MPF patterns may provide information regarding the potential differences that exist within a muscle, as well as between muscles, for the motor control strategies that are used to modulate torque production during isometric, concentric, and eccentric muscle actions [3], [8], [10], [19], [20], [72]. In particular, the MMG power density spectrum appears to be influenced primarily by the “global” motor unit firing rate [70, p. 329]. Thus, increases in MMG MPF may reflect recruitment of fast-twitch motor units, which have higher initial firing rates than slow-twitch motor units [17], [39], [70], and/or increases in motor unit firing rates. 2.4. MMG frequency domain responses during fatiguing activities Fatiguing exercise provides one of the best situations for examining the potential relationship between MMG frequency and motor unit firing rates. Specifically, it has been hypothesized [58] that during fatiguing activities, the muscle provides proprioceptive feedback to the central nervous system regarding increases in muscle fiber relaxation times. The central nervous system then decreases motor unit firing rates to allow for optimal fusion of motor unit twitches [58]. This concept was termed “muscular wisdom” as a motor control strategy that “minimizes fatigue during prolonged effort” [58, p. 169]. In addition, it has been suggested [77] that during sustained high intensity exercise, some motor units may stop firing (i.e. be de-recruited) prior to the end of the task. Thus, it is possible that muscular wisdom and/or motor unit de-recruitment could result in decreases in MMG frequency (Fig. 2), and several studies have examined the patterns for MMG MPF or median frequency during sustained [32], [42], [55], [56], [66], [73], [85], [86] or repeated [49] isometric muscle actions, as well as during repeated concentric isokinetic muscle actions [9]. For example, Orizio [66] reported that during sustained isometric muscle actions of the forearm flexors at 80% and 100% MVC, the MMG MPF pattern for the biceps brachii was dependent on the intensity of the contraction. Specifically, when the muscle action was performed at 80% MVC, MMG MPF increased throughout approximately the first 30% of the total contraction time (TCT), and then decreased from 30% to 100% TCT [66]. When the muscle action was performed at 100% MVC, however, MMG MPF decreased from 0% to 100% TCT [66]. It was suggested that at 80% MVC, the increase in MMG MPF from 0% to 30% TCT may have been due to increases in motor unit firing rates to sustain the torque output [66]. Furthermore, the decreases in MMG MPF from 30% to 100% TCT, as well as during the sustained isometric muscle action at 100% MVC, may have reflected fatigue-induced decreases in motor unit firing rates (i.e. muscular wisdom) [58], [66]. In addition, Kouzaki et al. [49] examined the MMG median frequency responses from the rectus femoris, vastus lateralis, and vastus medialis muscles during 50 consecutive isometric MVCs of the leg extensors. Each MVC was 3 s in duration, followed by a 3-s relaxation period [49]. The authors [49] reported that MMG median frequencies for the rectus femoris, vastus lateralis, and vastus medialis muscles decreased during the first 25 repetitions of the fatigue test and then remained relatively stable during muscle actions 25–50. The initial decreases in MMG median frequency were much greater, however, for the rectus femoris than for the vastus lateralis and vastus medialis muscles [49]. It was suggested [49, p. 14] that the decreases in MMG median frequencies for each muscle may have reflected a fatigue-induced “dropout from recruitment” of fast-twitch muscle fibers, which are more abundant in the rectus femoris than in the vastus lateralis and vastus medialis [44]. In addition, Esposito et al. [32] recently hypothesized that MMG MPF patterns may provide information regarding fatigue-induced alterations in the motor control strategies that are used during sustained muscle actions. Specifically, the authors [32] reported that during a sustained isometric muscle action of the forearm flexors at 80% MVC, MMG MPF for the biceps brachii increased during the first 6–8 seconds of the muscle action and then decreased to exhaustion. Immediately following this initial fatigue test, the subjects rested for 10 min and then performed fatiguing exercise that consisted of repetitive cycles (6-s contraction followed by 4-s rest) of isometric muscle actions of the forearm flexors at 50% MVC [32]. This exercise was performed until the subjects could no longer produce the required 50% MVC torque [32]. After this exercise, the subjects rested for 9-min, and then performed an isometric MVC of the forearm flexors [32]. Following this strength assessment, the subjects performed another sustained isometric muscle action of the forearm flexors at 80% of the new MVC [32]. The authors [32] reported that unlike the first fatigue test (i.e. before the repetitive fatiguing exercise), MMG MPF decreased from the beginning of the second fatigue test to exhaustion. It was suggested that the increase in MMG MPF during the first fatigue test was likely due to increases in motor unit firing rates to maintain the required torque [32]. During the second fatigue test, however, elongation of motor unit twitches in the biceps brachii muscle may have resulted in proprioceptive feedback to the central nervous system that prevented an increase in motor unit firing rates (i.e. muscular wisdom) [32], [58]. Collectively, these findings suggested that MMG MPF and median frequency patterns may provide information regarding the motor control strategies that are used during fatiguing exercise. In particular, changes in the frequency content of the MMG signal could potentially reflect fatigue-induced decreases in motor unit firing rates [32], [58], [66], and/or de-recruitment of fast-twitch muscle fibers [49], [77], both of which could reduce the global motor unit firing rate. Furthermore, the results from these studies also provided support for two key points that are important in examining the potential relationship between MMG frequency and motor unit firing rates: (a) the surface MMG signal is generated by mechanical motor unit activities, but it provides different information from the torque signal during fatiguing exercise (because MMG MPF can increase or decrease during a sustained isometric muscle action, despite a constant torque level) [32], [66], and (b) decreases in MMG MPF or median frequency may reflect reductions in the global motor unit firing rate, rather than decreases in the firing rates of individual motor units (because motor unit de-recruitment could also reduce MMG MPF or median frequency) [49]. 2.5. MMG frequency domain responses in young versus elderly subjects and diseased versus healthy muscle Previous studies [68], [79] have reported that the frequency content of the surface MMG signal may be influenced by neuromuscular disease. For example, Rhatigan et al. [79] found that during an isometric MVC of the forearm flexors, the MMG MPF values for the biceps brachii muscle were lower for subjects with a neuromuscular disorder (such as Myotonic Dystrophy, Spinal Muscular Atrophy, Myasthenia Gravis, Parkinson’s Disease, Friedreich’s Ataxia, Polymyositis, motorneuron diseases, or facioscapulohumeral and limb-girdle disorders) than for healthy individuals. In addition, Orizio et al. [68] reported that during supramaximal electrically-stimulated isometric twitches of the tibialis anterior muscle, subjects with Myotonic Dystrophy demonstrated surface MMG signals that had lower frequency contents than those from healthy individuals. Specifically, the MMG power density spectra from the subjects with Myotonic Dystrophy had peaks at approximately 22 and 42 Hz, but the corresponding spectra for the healthy individuals had peaks at approximately 32 and 48 Hz. In addition, the mean MMG MPF value for the subjects with Myotonic Dystrophy was approximately 21 Hz less than the mean MMG MPF value for the healthy individuals [68]. It was suggested that an impaired ability to generate muscular tension for the subjects with Myotonic Dystrophy may have reduced the resonant frequency of the muscle, resulting in surface MMG signals with lower frequency contents [68]. There is also evidence to suggest that aging may influence MMG frequency. For example, Akataki et al. [2] reported that during an isometric ramp muscle action of the forearm flexors from 10% to 80% MVC, the MMG MPF versus isometric torque relationship for the biceps brachii was slightly different in young and elderly subjects. Specifically, although both the young and elderly subjects demonstrated a general increase in MMG MPF with isometric torque, the absolute MMG MPF values were typically higher in the young subjects than in the elderly subjects [2]. Furthermore, the MMG MPF versus isometric torque relationship for the young subjects consisted of five “regions,” each of which may have reflected different relative contributions of fast- and slow-twitch motor units to the surface MMG signal [2, p. 508]. The elderly subjects, however, demonstrated an MMG MPF versus isometric torque relationship that consisted of only three regions [2]. It was hypothesized [2, p. 510] that the MMG MPF responses in each of these three regions may reflect a “…functional deterioration in the FT-MUs [fast-twitch motor units], perhaps leading to a greater proportion of force production capability by the ST-MUs [slow-twitch motor units] in the elderly.” Interestingly, there is also evidence to suggest that the MMG MPF versus isometric torque relationship may provide information regarding retrieval of fast-twitch motor units following a resistance training program. Specifically, Esposito et al. [30] reported that before a 12 week isokinetic training program for the leg extensors, the MMG power density spectrum for the vastus lateralis muscle in 10 elderly men (age range = 62–78 years) during a maximum isometric leg extension was unimodal, with a well-defined peak at approximately 11 Hz. After the isokinetic training program, however, the MMG power density spectrum was bimodal, with a large peak at about 15 Hz and a second peak at approximately 30 Hz. Esposito et al. [31] also examined the MMG MPF versus isometric torque relationships for the biceps brachii muscle in young and elderly subjects. The authors [31] reported that the absolute MMG MPF values for the young subjects were significantly higher than those for the elderly subjects at several torque levels. In addition, the MMG power density spectrum for the young subjects was bimodal at 80% MVC, with peaks at approximately 12 Hz and 38 Hz [31]. The elderly subjects, however, demonstrated a unimodal MMG power density spectrum at 80% MVC, with a single peak at approximately 12 Hz [31]. It was suggested that when compared to the young subjects, the lower frequency MMG signals for the elderly subjects may have been due to a smaller number of fast-twitch motor units in the biceps brachii muscle [31]. Collectively, the results from these studies [2], [31], [68], [79] have provided indirect evidence that the frequency domain of the surface MMG signal contains information regarding motor unit firing rates. Specifically, the presence of neuromuscular disease could potentially influence MMG frequency by: (a) directly reducing motor unit firing rates, and/or (b) interfering with the processes of contraction and relaxation, resulting in fusion of motor unit twitches at lower firing rates. Theoretically, both of these factors could contribute to lower MMG MPF values. In addition, an age-related decrease in the number of fast-twitch motor units could also influence the MMG power density spectrum because fast-twitch motor units have higher initial firing rates [17], [39] and require greater stimulation rates to achieve complete fusion of motor unit twitches [14] than slow-twitch motor units. 3. Studies that do not support a relationship between MMG frequency and motor unit firing rates  3.1. MMG MPF does not always increase with isometric torque Previous investigations [1], [3], [70], [72] have suggested that rapid increases in MMG MPF with isometric torque from approximately 60% to 100% MVC may reflect increases in motor unit firing rates. Several other studies, however, have found no change [24], [54], [60] or even a decrease [8], [59] in MMG MPF at these same torque levels. For example, Maton et al. [60] reported that MMG MPF for the biceps brachii increased with isometric forearm flexion torque from 10% to approximately 30% MVC, and then plateaued from 30% to 100% MVC. In addition, Dalton and Stokes [24] found that during isometric forearm flexion muscle actions, MMG MPF for the biceps brachii increased with torque from 10% to approximately 75% MVC and then plateaued from 75% to 100% MVC. It was suggested [24] that the plateau in MMG MPF may have been due to inter-subject variability in the motor control strategy that was used to increase isometric torque in the biceps brachii muscle (i.e. for some subjects, firing rate modulation may be important for increasing torque, whereas for others, it may not). Matheson et al. [59] examined the MMG MPF versus isometric torque relationship for the rectus femoris muscle in high (leg extension torque range = 254–330 ft.–lbs.) and low (leg extension torque range = 101–198 ft.–lbs.) force subjects. The authors [59] found that MMG MPF for the rectus femoris muscle increased similarly for the two groups from 20% to 80% MVC. From 80% to 100% MVC, however, MMG MPF for the rectus femoris muscle decreased in the high force group and increased in the low force group [59]. In addition, Beck et al. [8] found that during submaximal to maximal isometric muscle actions of the forearm flexors, MMG MPF for the biceps brachii muscle remained relatively stable from 10% to approximately 50% MVC, increased from 50% to approximately 80% MVC, and then decreased from 80% to 100% MVC. It was suggested that the increase in MMG MPF from 50% to approximately 80% MVC may have reflected recruitment of fast-twitch motor units and/or increases in motor unit firing rates [8]. The decrease in MMG MPF from 80% to 100% MVC, however, may have been due to high levels of muscle stiffness and/or intramuscular fluid pressure [8]. Specifically, muscle stiffness is primarily a function of the number of attached cross bridges [33], and intramuscular fluid pressure increases with isometric torque [80]. Thus, it is possible that at high levels of isometric torque production, muscle stiffness and/or intramuscular fluid pressure could impair the lateral muscle fiber oscillations that generate the MMG signal, thereby influencing MMG frequency [1], [8], [19], [67], [74]. Furthermore, Madeleine et al. [54] reported that there was no significant change in MMG MPF during isometric muscle actions of the first dorsal interosseous from 0% to 100% MVC. The authors [54] hypothesized that in addition to motor control strategies, the frequency content of the MMG signal may also be influenced by the fiber type composition of the muscle being investigated. Thus, the results from these studies [8], [24], [54], [59], [60] suggested that: (a) MMG frequency may not always reflect potential changes in the global motor unit firing rate, (b) factors such as muscle stiffness and/or intramuscular fluid pressure could interfere with a potential relationship between MMG frequency and the global motor unit firing rate, and/or (c) the global motor unit firing rate may not always increase with isometric torque. However, several studies have reported increases in motor unit firing rates with isometric torque, and there is evidence to support a relationship between the frequency content of the MMG signal and the global motor unit firing rate [17], [50], [61], [88]. Thus, it is more likely that in some cases, factors such as muscle stiffness and/or intramuscular fluid pressure could potentially impair the lateral muscle fiber oscillations that generate the surface MMG signal, thereby interfering with a relationship between MMG frequency and the global motor unit firing rate. 3.2. MMG MPF does not always increase with velocity during maximal concentric or eccentric isokinetic muscle actions It has been suggested [37], [65] that during maximal concentric isokinetic muscle actions, there is a velocity-related shift in the contributions of slow-twitch muscle fibers to torque production. Specifically, at low velocities, both slow- and fast-twitch muscle fibers contribute to the torque that is produced by the muscle [37]. With increases in velocity, however, slow-twitch muscle fibers become “unloaded” because they are unable to contract rapidly enough to keep up with the speed of the movement [65, p. 452]. Thus, it is possible that with increases in velocity, the contributions of slow-twitch motor units to the MMG signal decrease, potentially resulting in greater MMG MPF values. Cramer et al. [23] provided tentative support for this hypothesis by demonstrating that during maximal concentric isokinetic leg extensions, MMG MPF for the rectus femoris, vastus lateralis, and vastus medialis muscles was greater at 300° s−1 than at 60, 120, 180, and 240° s−1. Furthermore, Cramer et al. [22] reported that during maximal concentric isokinetic leg extensions at velocities ranging from 60–480° s−1, there were velocity-related increases in MMG MPF for both the rectus femoris and vastus lateralis, but not for the vastus medialis. It was suggested [22] that the muscle-specific differences in the MMG MPF responses may have been due to potential differences in fiber type composition among the rectus femoris, vastus lateralis, and vastus medialis muscles [44]. Ebersole et al. [28], however, reported that during maximal concentric isokinetic leg extensions, there was no change in MMG MPF for the vastus lateralis muscle with an increase in velocity from 60 to 300° s−1. Thus, it is unclear if movement velocity has a significant effect on MMG MPF during maximal concentric isokinetic muscle actions. It is possible, however, that any potential influence could be related to muscle fiber type composition [22]. In addition, it has been suggested that there may be de-recruitment of slow-twitch motor units and selective recruitment of fast-twitch motor units with increases in velocity during eccentric muscle actions [65]. Theoretically, this could also result in a velocity-related increase in MMG MPF. Evetovich et al. [34], however, reported that during maximal eccentric isokinetic muscle actions of the leg extensors at velocities of 60, 120, and 180° s−1, there were no changes in MMG MPF for the vastus lateralis with increases in velocity. Furthermore, Cramer et al. [21] found that MMG MPF for the rectus femoris, vastus lateralis, and vastus medialis actually decreased approximately 12% with an increase in velocity from 60 to 120° s−1 during maximal eccentric isokinetic muscle actions of the leg extensors. In addition, there was no change in MMG MPF with an increase in velocity from 120 to 180° s−1 [21]. It was suggested [21] that during maximal eccentric isokinetic muscle actions, the velocity-related patterns for MMG MPF may be similar to those for eccentric isokinetic peak torque (which did not change with increases in velocity from 60 to 180° s−1). Thus, the results from the studies that have examined the MMG MPF responses during maximal concentric [23], [28] or eccentric [21], [34] isokinetic muscle actions indicated that: (a) there is not always a velocity-related increase in the global motor unit firing rate during maximal isokinetic muscle actions, and/or (b) there is a velocity-related increase in the global motor unit firing rate, but it is not always reflected in MMG MPF. The possibility for slow-twitch muscle fibers to become unloaded during concentric muscle actions [37], or even de-recruited during eccentric muscle actions [65] suggested, however, that in some cases, MMG MPF may not be influenced by changes in the global motor unit firing rate. As hypothesized by Cramer et al. [22], it is possible that any potential relationship between MMG MPF and the global motor unit firing rate may also be influenced by muscle fiber type composition. 4. Summary and conclusions  The results from the studies that were examined in the present review generally supported the hypothesis that the frequency content of the surface MMG signal is related to motor unit firing rates. Although the details of this relationship cannot be determined at the present time, the available evidence does allow some basic conclusions to be drawn. For example, the results from repetitive electrical stimulation studies [14], [47], [71], [82], [88] indicated that the surface MMG signal is generated by the motor unit mechanical responses to electrical activation, and its frequency is very similar to the stimulation rate, but only when the twitches are not completely fused. During voluntary muscle actions, however, the motor units are typically not activated simultaneously, and the contribution of any particular motor unit to the MMG signal is influenced by many factors, including the degree to which its twitches are fused, the depth of the motor unit within the muscle, the thickness of the tissue between the muscle and the MMG sensor, as well as muscle stiffness and intramuscular fluid pressure [14], [43], [67]. Furthermore, the results from studies [57], [61], [62], [75] that have examined the MMG frequency domain responses from various muscles during voluntary muscle actions suggest that MMG frequency is influenced by fiber type composition. Specifically, muscles with a large percentage of fast-twitch fibers typically produce MMG signals that contain higher frequencies than those from muscles composed primarily of slow-twitch fibers. This is important because fast-twitch motor units have higher firing rates than slow-twitch motor units and require greater stimulation rates to achieve complete fusion of motor unit twitches [14], [17], [39]. In addition, the argument that the surface MMG signal may be useful as a tool for investigating motor control strategies was strengthened by the results from studies that examined the MMG MPF versus torque relationships during isometric [3], [72] and dynamic [8], [10], [19], [20] muscle actions, as well as during fatiguing activities [49], [66]. For example, Orizio et al. [72] suggested that increases in MMG MPF for the biceps brachii during isometric muscle actions of the forearm flexors from 20% to approximately 80% MVC may have reflected recruitment of motor units with progressively higher firing rates. The rapid increases in MMG MPF from 80% to 100% MVC, however, may have been primarily due to increases in motor unit firing rates [72]. These findings [72] were very similar to the results from previous studies [17], [50] that have used intramuscular EMG electrodes to examine the motor control strategy that is used to increase isometric torque in the biceps brachii muscle. In addition, it has been suggested [49], [66] that decreases in MMG MPF during a sustained isometric MVC may reflect fatigue-induced decreases in motor unit firing rates and/or de-recruitment of fast-twitch motor units. Collectively, the results from these studies [49], [66], [72] indicated that the frequency content of the MMG signal provides information regarding the global motor unit firing rate (which is influenced by changes in motor unit firing rates and/or motor unit recruitment/de-recruitment). Furthermore, elderly subjects and individuals with a neuromuscular disease demonstrate lower frequency MMG signals than young subjects and healthy individuals, respectively [31], [79]. In particular, neuromuscular disease could potentially influence MMG frequency by directly reducing motor unit firing rates and/or interfering with the processes of muscle fiber contraction and relaxation, resulting in fusion of motor unit twitches at lower firing rates. There are also studies that have suggested that MMG frequency is not influenced by changes in motor unit firing rates. In particular, MMG MPF does not always increase with isometric torque [54], [60], and it is likely that in these cases, muscle stiffness and/or intramuscular fluid pressure could influence any relationship between MMG MPF and motor unit firing rates [1], [8], [19], [67]. In addition, MMG MPF does not always increase with velocity during maximal concentric [23], [28] or eccentric [21], [34] isokinetic muscle actions, and it has been suggested [22] that the fiber type composition of the muscle(s) being investigated may influence the MMG MPF responses with increases in velocity. Despite these discrepancies, the majority of the evidence supports the contention that the MMG power density spectrum contains information regarding motor unit firing rates. This information must be interpreted with caution, however, because there are many issues regarding the relationship between MMG frequency and motor unit firing rates that have yet to be identified. Furthermore, there are also limitations due to the intrinsic mechanical nature of the MMG signal that should be considered. For example, the surface MMG is generated by a summation of the mechanical activities from the unfused [14] activated motor units. This summation is nonlinear, however, throughout most of the range of motor unit firing rates [71] as well as during voluntary isometric muscle actions at relatively low torque levels [16]. Therefore, the contribution of an individual motor unit to the surface MMG is influenced by the degree to which its twitches are fused, as well as the activities of the surrounding motor units. These inherent characteristics of the MMG signal have suggested that any information in the frequency domain regarding motor unit firing rates is probably qualitative, rather than quantitative in nature, and likely reflects the global motor unit firing rate, rather than the firing rate of one particular motor unit (or a few motor units). Since the frequency domain of the MMG signal seems to serve as a global and qualitative indicator of motor unit firing rates, designing studies to further characterize its use in this regard has proven difficult. Based on the existing literature, however, future studies may be able to target two general areas to elucidate the relationship between MMG frequency and motor unit firing rates: (a) modeling the MMG signal and (b) additional indirect studies that examine the MMG patterns of response under various physiological conditions. For example, studies by Farina et al. [35], [36] and Merletti et al. [63], [64] have led, in part, to the precise modeling of the surface EMG signal that has provided useful insight into the abilities and limitations of surface EMG. Similar work with the MMG signal may be equally valuable [46]. In contrast, examining the MMG frequency domain responses under physiological conditions that are known to affect motor unit firing rates may continue to provide valuable evidence. For example, administering certain pharmacological agents (i.e. caffeine) may be useful for characterizing the relationships among the time and frequency domains of the MMG signal and motor unit firing rates. Furthermore, increasingly sophisticated signal processing techniques, such as the continuous wavelet transform [11], may provide more precision when tracking frequency changes in the MMG signal. Thus, continually reassessing the time and frequency domains of the MMG signal may be necessary not only for modeling purposes, but also for extending the findings of existing studies after technological advances. Collectively, the results from these investigations will be useful not only for identifying the exact origin of the MMG signal, but also for assessing the uses/limitations of MMG. References  [1]. [1]Akataki K, Mita K, Watakabe M, Itoh K. Mechanomyogram and force relationship during voluntary isometric ramp contractions of the biceps brachii muscle. Eur J Appl Physiol. 2001;84:19–25. MEDLINE |
CrossRef
[2]. [2]Akataki K, Mita K, Watakabe M, Ito K. Age-related change in motor unit activation strategy in force production: a mechanomyographic investigation. Muscle Nerve. 2002;25:505–512.
CrossRef
[3]. [3]Akataki K, Mita K, Watakabe M, Itoh K. Mechanomyographic responses during voluntary ramp contractions of the human first dorsal interosseous muscle. Eur J Appl Physiol. 2003;89:520–525. MEDLINE |
CrossRef
[4]. [4]Barry DT. Acoustic signals from frog skeletal muscle. Biophys J. 1987;51:769–773. MEDLINE |
CrossRef
[5]. [5]Barry DT, Cole NM. Muscle sounds are emitted at the resonant frequencies of skeletal muscle. IEEE Trans Biomed Eng. 1990;37:525–531. MEDLINE |
CrossRef
[6]. [6]Barry DT, Geiringer SR, Ball RD. Acoustic myography: a noninvasive monitor of motor unit fatigue. Muscle Nerve. 1985;8:189–194.
CrossRef
[7]. [7]Basmajian JV, De Luca CJ. Muscles alive: their functions revealed by electromyography. fifth ed.. Baltimore, MD: Williams and Wilkins; 1985;. [8]. [8]Beck TW, Housh TJ, Johnson GO, Weir JP, Cramer JT, Coburn JW, et al. Mechanomyographic amplitude and mean power frequency versus torque relationships during isokinetic and isometric muscle actions of the biceps brachii. J Electromyogr Kinesiol. 2004;14:555–564. Abstract | Full Text |
Full-Text PDF (286 KB)
|
CrossRef
[9]. [9]Beck TW, Housh TJ, Johnson GO, Weir JP, Cramer JT, Coburn JW, et al. Mechanomyographic and electromyographic amplitude and frequency responses during fatiguing isokinetic muscle actions of the biceps brachii. Electromyogr Clin Neurophysiol. 2004;44:431–441. [10]. [10]Beck TW, Housh TJ, Johnson GO, Weir JP, Cramer JT, Coburn JW, et al. Mechanomyographic and electromyographic responses during submaximal to maximal eccentric isokinetic muscle actions of the biceps brachii. J Strength Cond Res, in press. [11]. [11]Beck TW, Housh TJ, Johnson GO, Cramer JT, Weir JP, Coburn JW, et al. Comparison of the fast Fourier transform and continuous wavelet transform for examining mechanomyographic frequency versus eccentric torque relationships. J Neurosci Methods. 2006;150:59–66. MEDLINE |
CrossRef
[12]. [12]Bellemare F, Woods JJ, Johansson R, Bigland-Ritchie B. Motor-unit discharge rates in maximal voluntary contractions of three human muscles. J Neurophysiol. 1983;50:1380–1392. MEDLINE [13]. [13]Bichler E. Mechanomyograms recorded during evoked contractions of single motor units in the rat medial gastrocnemius muscle. Eur J Appl Physiol. 2000;83:310–319. MEDLINE |
CrossRef
[14]. [14]Bichler E, Celichowski J. Mechanomyographic signals generated during unfused tetani of single motor units in the rat medial gastrocnemius muscle. Eur J Appl Physiol. 2001;85:513–520. MEDLINE |
CrossRef
[15]. [15]Bolton CF, Parkes A, Thompson TR, Clark MR, Sterne CJ. Recording sound from human skeletal muscle: technical and physiological aspects. Muscle Nerve. 1989;12:126–134.
CrossRef
[16]. [16]Cescon C, Gazzoni M, Gobbo M, Orizio C, Farina D. Non-invasive assessment of single motor unit mechanomyographic response and twitch force by spike-triggered averaging. Med Biol Eng Comput. 2004;42:496–501. MEDLINE |
CrossRef
[17]. [17]Clamann HP. Activity of single motor units during isometric tension. Neurology. 1970;20:254–260. MEDLINE [18]. [18]Coburn JW, Housh TJ, Cramer JT, Weir JP, Miller JM, Beck TW, et al. Mechanomyographic time and frequency domain responses of the vastus medialis muscle during submaximal to maximal isometric and isokinetic muscle actions. Electromyogr Clin Neurophysiol. 2004;44:247–255. [19]. [19]Coburn JW, Housh TJ, Cramer JT, Weir JP, Miller JM, Beck TW, et al. Mechanomyographic and electromyographic responses of the vastus medialis muscle during isometric and concentric muscle actions. J Strength Cond Res. 2005;19:412–420. MEDLINE |
CrossRef
[20]. [20]Coburn JW, Housh TJ, Weir JP, Malek MH, Cramer JT, Beck TW, et al. Mechanomyographic responses of the vastus medialis to isometric and eccentric muscle actions. Med Sci Sports Exercise. 2004;36:1916–1922. [21]. [21]Cramer JT, Housh TJ, Weir JP, Johnson GO, Berning JM, Perry SR, et al. Mechanomyographic and electromyographic amplitude and frequency responses from the superficial quadriceps femoris muscles during maximal, eccentric isokinetic muscle actions. Electromyogr Clin Neurophysiol. 2002;42:337–346. [22]. [22]Cramer JT, Housh TJ, Weir JP, Johnson GO, Berning JM, Perry SR, et al. Gender, muscle, and velocity comparisons of mechanomyographic and electromyographic responses during isokinetic muscle actions. Scand J Med Sci Sports. 2004;14:116–127. MEDLINE |
CrossRef
[23]. [23]Cramer JT, Housh TJ, Weir JP, Johnson GO, Ebersole KT, Perry SR, et al. Power output, mechanomyographic, and electromyographic responses to maximal, concentric, isokinetic muscle actions in men and women. J Strength Cond Res. 2002;16:399–408. MEDLINE |
CrossRef
[24]. [24]Dalton PA, Stokes MJ. Frequency of acoustic myography during isometric contraction of fresh and fatigued muscle and during dynamic contractions. Muscle Nerve. 1993;16:255–261.
CrossRef
[25]. [25]De Luca CJ. Physiology and mathematics of myoelectric signals. IEEE Trans Biomed Eng. 1979;26:313–325. MEDLINE [26]. [26]De Luca CJ, LeFever RS, McCue MP, Xenakis AP. Behavior of human motor units in different muscles during linearly varying contractions. J Physiol. 1982;329:113–128. MEDLINE [27]. [27]Ebersole KT, Housh TJ, Johnson GO, Evetovich TK, Smith DB, Perry SR. The effect of leg flexion angle on the mechanomyographic responses to isometric muscle actions. Eur J Appl Physiol. 1998;78:264–269.
CrossRef
[28]. [28]Ebersole KT, Housh TJ, Weir JP, Johnson GO, Evetovich TK, Smith DB. The effects of leg angular velocity on mean power frequency and amplitude of the mechanomyographic signal. Electromyogr Clin Neurophysiol. 2000;40:49–55. [29]. [29]Enoka RM, Fuglevand AJ. Motor unit physiology: some unresolved issues. Muscle Nerve. 2001;23:4–17.
CrossRef
[30]. [30]Esposito F, Ce E, Gobbo M, Veicsteinas A, Orizio C. Surface EMG and mechanomyogram disclose isokinetic training effects on quadriceps muscle in elderly people. Eur J Appl Physiol. 2005;94:549–557. MEDLINE |
CrossRef
[31]. [31]Esposito F, Malgrati D, Veicsteinas A, Orizio C. Time and frequency domain analysis of electromyogram and sound myogram in the elderly. Eur J Appl Physiol. 1996;73:503–510.
CrossRef
[32]. [32]Esposito F, Orizio C, Veicsteinas A. Electromyogram and mechanomyogram changes in fresh and fatigued muscle during sustained contraction in men. Eur J Appl Physiol. 1998;78:494–501.
CrossRef
[33]. [33]Ettema GJC, Huijing PA. Skeletal muscle stiffness in static and dynamic contractions. J Biomech. 1994;27:1361–1368. MEDLINE |
CrossRef
[34]. [34]Evetovich TK, Housh TJ, Weir JP, Johnson GO, Smith DB, Ebersole KT. Mean power frequency and amplitude of the mechanomyographic signal during maximal eccentric isokinetic muscle actions. Electromyogr Clin Neurophysiol. 1999;39:123–127. [35]. [35]Farina D, Merletti R. A novel approach for precise simulation of the EMG signal detected by surface electrodes. IEEE Trans Biomed Eng. 2001;48:637–646. MEDLINE |
CrossRef
[36]. [36]Farina D, Mesin L, Martina S, Merletti R. A surface EMG generation model with multilayer cylindrical description of the volume conductor. IEEE Trans Biomed Eng. 2004;51:415–426. MEDLINE |
CrossRef
[37]. [37]Faulkner JA, Claflin DR, McCully KK. Power output of fast and slow fibers from human skeletal muscles. In: Jones NL, McCartney N, McComas AJ editor. Human muscle power. Champaign: Human Kinetics Publishers; 1986;p. 81–91. [38]. [38]Frangioni JV, Kwan-Gett TS, Dobrunz LE, McMahon TA. The mechanism of low-frequency sound production in muscle. Biophys J. 1987;51:775–783. MEDLINE |
CrossRef
[39]. [39]Hannerz J. Discharge properties of motor units in relation to recruitment order in voluntary contraction. Acta Physiol Scand. 1974;91:374–385. MEDLINE |
CrossRef
[40]. [40]Hermens HJ, van Bruggen TAM, Baten CTM, Rutten WLC, Boom HBK. The median frequency of the surface EMG power spectrum in relation to motor unit firing and action potential properties. J Electromyogr Kinesiol. 1992;2:15–25.
CrossRef
[41]. [41]Herroun EF, Yeo GF. Note on the sound accompanying the single contraction of skeletal muscle. J Physiol. 1885;6:287–292. MEDLINE [42]. [42]Itoh Y, Akataki K, Mita K, Watakabe M, Itoh K. Time–frequency analysis of mechanomyogram during sustained contractions with muscle fatigue. Syst Comput Japan. 2004;35:26–36. [43]. [43]Jaskólska A, Brzenczek W, Kisiel-Sajewicz K, Kawczynski A, Marusiak J, Jaskolski A. The effect of skinfold on frequency of human muscle mechanomyogram. J Electromyogr Kinesiol. 2004;14:217–225. Abstract | Full Text |
Full-Text PDF (273 KB)
|
CrossRef
[44]. [44]Johnson MA, Polgar J, Weightman D, Appleton D. Data on the distribution of fibre types in thirty-six human muscles: an autopsy study. J Neurol Sci. 1973;18:111–129.
CrossRef
[45]. [45]Jonsson B, Bagge UE. Displacement, deformation and fracture of wire electrodes for electromyography. Electromyography. 1968;8:329–347. MEDLINE [46]. [46]Kaczmarek P, Celichowski J, Kasinski A. Experimentally verified model of mechanomyograms recorded during single motor unit contractions. J Electromyogr Kinesiol. 2005;15:617–630. Abstract | Full Text |
Full-Text PDF (479 KB)
|
CrossRef
[47]. [47]Kimura T, Hamada T, Massako Ueno L, Moritani T. Changes in contractile properties and neuromuscular propagation evaluated by simultaneous mechanomyogram and electromyogram during experimentally induced hypothermia. J Electromyogr Kinesiol. 2003;13:433–440. Abstract | Full Text |
Full-Text PDF (181 KB)
|
CrossRef
[48]. [48]Kossev A, Christova P. Discharge pattern of human motor units during dynamic concentric and eccentric contractions. Electroencephalogr Clin Neurophysiol. 1998;109:245–255. MEDLINE [49]. [49]Kouzaki M, Shinohara M, Fukunaga T. Non-uniform mechanical activity of quadriceps muscle during fatigue by repeated maximal voluntary contraction in humans. Eur J Appl Physiol. 1999;80:9–15.
CrossRef
[50]. [50]Kukulka CG, Clamann PH. Comparison of the recruitment and discharge properties of motor units in human brachial biceps and adductor pollicis during isometric contractions. Brain Res. 1981;219:45–55. MEDLINE |
CrossRef
[51]. [51]Lindström LH, Magnusson RI. Interpretation of myoelectric power spectra: a model and its applications. Proc IEEE. 1977;65:653–674. [52]. [52]Lindström L, Magnusson R, Petersén I. Muscular fatigue and action potential conduction velocity changes studied with frequency analysis of EMG signals. Electromyography. 1970;4:341–356. [53]. [53]Linnamo V, Moritani T, Nicol C, Komi PV. Motor unit activation patterns during isometric, concentric and eccentric actions at different force levels. J Electromyogr Kinesiol. 2003;13:93–101. Abstract | Full Text |
Full-Text PDF (290 KB)
|
CrossRef
[54]. [54]Madeleine P, Bajaj P, Søgaard K, Arendt-Nielsen L. Mechanomyography and electromyography force relationships during concentric, isometric and eccentric contractions. J Electromyogr Kinesiol. 2001;11:113–121. Abstract | Full Text |
Full-Text PDF (384 KB)
|
CrossRef
[55]. [55]Madeleine P, Jørgensen LV, Søgaard K, Arendt-Nielsen L, Sjøgaard G. Development of muscle fatigue as assessed by electromyography and mechanomyography during continuous and intermittent low-force contractions: effects of the feedback mode. Eur J Appl Physiol. 2002;87:28–37. MEDLINE |
CrossRef
[56]. [56]Mamaghani NK, Shimomura Y, Iwanaga K, Katsuura T. Mechanomyogram and electromyogram responses of upper limb during sustained isometric fatigue with varying shoulder and elbow postures. J Physiol Anthropol Appl Human Sci. 2002;21:29–43. MEDLINE |
CrossRef
[57]. [57]Marchetti M, Felici F, Bernardi M, Minasi P, Di Filippo L. Can evoked phonomyography be used to recognize fast and slow muscle in man?. Int J Sports Med. 1992;13:65–68. MEDLINE |
CrossRef
[58]. [58]Marsden CD, Meadows JC, Merton PA. “Muscular wisdom” that minimizes fatigue during prolonged effort in man: peak rates of motoneuron discharge and slowing of discharge during fatigue. In: Desmedt JE editors. Motor control mechanisms in health and disease. New York: Raven Press; 1983;p. 169–211. [59]. [59]Matheson GO, Maffrey-Ward L, Mooney M, Ladly K, Fung T, Zhang YT. Vibromyography as a quantitative measure of muscle force production. Scand J Rehabil Med. 1997;29:29–35. MEDLINE [60]. [60]Maton B, Petitjean M, Cnockaert JC. Phonomyogram and electromyogram relationships with isometric force reinvestigated in man. Eur J Appl Physiol. 1990;60:194–201. [61]. [61]Mealing D, Long G, McCarthy PW. Vibromyographic recording from human muscles with known fibre composition differences. Br J Sports Med. 1996;30:27–31. MEDLINE |
CrossRef
[62]. [62]Mealing D, McCarthy PW. Muscle sound frequency analysis from fast and slow twitch muscle. In: Proceedings of the IEEE-EMBS 13th annual international conference. New York: IEEE; 1991;p. 0948–0949. [63]. [63]Merletti R, Lo Conte L, Avignone E, Guglielminotti P. Modeling of surface myoelectric signals – Part I: Model implementation. IEEE Trans Biomed Eng. 1999;46:810–820. MEDLINE |
CrossRef
[64]. [64]Merletti R, Roy SH, Kupa E, Roatta S, Granata A. Modeling of surface myoelectric signals – Part II: Model-based signal interpretation. IEEE Trans Biomed Eng. 1999;46:821–829. MEDLINE |
CrossRef
[65]. [65]Nardone A, Romanò C, Schieppati M. Selective recruitment of high-threshold human motor units during voluntary isotonic lengthening of active muscles. J Physiol. 1989;409:451–471. MEDLINE [66]. [66]Orizio C. Soundmyogram and EMG cross-spectrum during exhausting isometric contractions in humans. J Electromyogr Kinesiol. 1992;2:141–149.
CrossRef
[67]. [67]Orizio C. Muscle sound: bases for the introduction of a mechanomyographic signal in muscle studies. Crit Rev Biomed Eng. 1993;21:201–243. [68]. [68]Orizio C, Esposito F, Paganotti I, Marino L, Rossi B, Veicsteinas A. Electrically-elicited surface mechanomyogram in myotonic dystrophy. Italian J Neurol Sci. 1997;18:185–190. [69]. [69]Orizio C, Esposito F, Veicsteinas A. Effect of acclimatization to high altitude (5050 m) on motor unit activation pattern and muscle performance. J Appl Physiol. 1994;77:2840–2844. [70]. [70]Orizio C, Gobbo M, Diemont B, Esposito F, Veicsteinas A. The surface mechanomyogram as a tool to describe the influence of fatigue on biceps brachii motor unit activation strategy. Historical basis and novel evidence. Eur J Appl Physiol. 2003;90:326–336. MEDLINE |
CrossRef
[71]. [71]Orizio C, Liberati D, Locatelli C, De Grandis D, Veicsteinas A. Surface mechanomyogram reflects muscle fibres twitches summation. J Biomech. 1996;29:475–481. Abstract |
Full-Text PDF (1415 KB)
|
CrossRef
[72]. [72]Orizio C, Perini R, Diemont B, Figini MM, Veicsteinas A. Spectral analysis of muscular sound during isometric contraction of biceps brachii. J Appl Physiol. 1990;68:508–512. [73]. [73]Orizio C, Perini R, Diemont B, Veicsteinas A. Muscle sound and electromyogram spectrum analysis during exhausting contractions in man. Eur J Appl Physiol. 1992;65:1–7. [74]. [74]Orizio C, Perini R, Veicsteinas A. Muscular sound and force relationship during isometric contraction in man. Eur J Appl Physiol. 1989;58:528–533. [75]. [75]Orizio C, Veicsteinas A. Soundmyogram analysis during sustained maximal voluntary contraction in sprinters and long distance runners. Int J Sports Med. 1992;13:594–599. MEDLINE |
CrossRef
[76]. [76]Oster G. Muscle sounds. Scient Amer. 1984;250:108–114. [77]. [77]Peters EJD, Fuglevand AJ. Cessation of human motor unit discharge during sustained maximal voluntary contraction. Neurosci Lett. 1999;274:66–70. MEDLINE |
CrossRef
[78]. [78]Petitjean M, Maton B. Phonomyogram from single motor units during voluntary isometric contraction. Eur J Appl Physiol. 1995;71:215–222. [79]. [79]Rhatigan BA, Mylrea KC, Lonsdale E, Stern LZ. Investigation of sounds produced by healthy and diseased human muscular contraction. IEEE Trans Biomed Eng. 1986;33:967–971. MEDLINE [80]. [80]Sejersted OM, Hargens AR, Kardel KR, Blom P, Jensen O, Hermansen L. Intramuscular fluid pressure during isometric contraction of human skeletal muscle. J Appl Physiol. 1984;56:287–295. MEDLINE [81]. [81]Søgaard K, Christensen H, Fallentin N, Mizuno M, Quistorff B, Sjøgaard G. Motor unit activation patterns during concentric wrist flexion in humans with different muscle fibre composition. Eur J Appl Physiol. 1998;78:411–416.
CrossRef
[82]. [82]Stokes MJ, Cooper RG. Muscle sounds during voluntary and stimulated contractions of the human adductor pollicis muscle. J Appl Physiol. 1992;72:1908–1913. [83]. [83]Vaz MA, Herzog W, Zhang YT, Leonard TR, Nguyen H. Mechanism of electrically elicited muscle vibrations in the in situ cat soleus muscle. Muscle Nerve. 1996;19:774–776.
CrossRef
[84]. [84]Vaz MA, Herzog W, Zhang YT, Leonard TR, Nguyen H. The effect of muscle length on electrically elicited muscle vibrations in the in-situ cat soleus muscle. J Electromyogr Kinesiol. 1997;7:113–121. Abstract |
Full-Text PDF (818 KB)
|
CrossRef
[85]. [85]Wee AS, Ashley RA. Vibrations and sounds produced during sustained voluntary muscle contraction. Electromyogr Clin Neurophysiol. 1989;29:333–337. [86]. [86]Weir JP, Ayers KM, Lacefield JF, Walsh KL. Mechanomyographic and electromyographic responses during fatigue in humans: influence of muscle length. Eur J Appl Physiol. 2000;81:352–359. MEDLINE |
CrossRef
[87]. [87]Wollaston WH. On the duration of muscle action. Philos Trans Roy Soc London. 1810;1–5. [88]. [88]Yoshitake Y, Moritani T. The muscle sound properties of different muscle fiber types during voluntary and electrically induced contractions. J Electromyogr Kinesiol. 1999;9:209–217. Abstract | Full Text |
Full-Text PDF (272 KB)
|
CrossRef
[89]. [89]Yoshitake Y, Shinohara M, Ue H, Moritani T. Characteristics of surface mechanomyogram are dependent on development of fusion of motor units in humans. J Appl Physiol. 2002;93:1744–1752.  Travis W. Beck received a BS (2002) degree in Biology from Doane College, Crete, NE, and the M.P.E. (2004) degree in Health and Human Performance from the University of Nebraska-Lincoln. He is currently a doctoral student in Human Sciences at the University of Nebraska-Lincoln and his main research interests include evaluation of muscle function using electromyography and mechanomyography.  Terry J. Housh received a B.A. (1977) degree in Physical Education from Doane College, Crete, NE, and MPE (1979) and Ph.D. (1984) degrees from the University of Nebraska-Lincoln. He is a Fellow of the American College of Sports Medicine, Fellow in the Research Consortium of AAHPERD, and received the 1998 Outstanding Sport Scientist Award from the National Strength and Conditioning Association. Presently, he is a Full Professor in the Department of Nutrition and Health Sciences, Director of the Exercise Physiology Laboratory, and Co-Director of the Center for Youth Fitness and Sports Research at the University of Nebraska-Lincoln. His main areas of research are muscle function, fatigue, and growth and development in young athletes.  Glen O. Johnson received B.S. (1960) and M.S. (1964) degrees from Winona State University, Winona, MN, and a Ph.D. (1972) from the University of Iowa. He is a Fellow in the American College of Sports Medicine and a Fellow in the Research Consortium of AAHPERD. He is currently a Professor of Exercise Science at the University of Nebraska-Lincoln.  Joel T. Cramer received a B.A. (1997) degree in exercise science from Creighton University, Omaha, Nebraska, and MPE (2001) and Ph.D. (2003) degrees from the University of Nebraska-Lincoln under the direction of Dr. Terry J. Housh. He is a member of the American College of Sports Medicine and the National Strength and Conditioning Association. He recently accepted a faculty position at the University of Oklahoma as an Assistant Professor. His research interests focus on the non-invasive assessment of muscle function using surface electromyography and mechanomyography.  Joseph P. Weir is Associate Professor and Research Coordinator in the Division of Physical Therapy at Des Moines University-Osteopathic Medical Center in Des Moines Iowa. He received an undergraduate degree in exercise science from Eastern Washington University in 1987 and a Ph.D. in exercise physiology from the University of Nebraska-Lincoln in 1993. His primary research interests are in the study of neuromuscular aspects of exercise physiology, specifically muscle strength and muscle fatigue, the autonomic nervous system, and digital signal processing. He is a Fellow of the American College of Sports Medicine and a member of the National Strength and Conditioning Association and the American Autonomic Society.  Jared W. Coburn received B.S. (1987) and M.S. (1990) degrees from California State University, Fullerton, and a Ph.D. (2005) in exercise physiology at the University of Nebraska-Lincoln. He is currently an associate professor of Kinesiology at California State University, Fullerton. He is a certified strength and conditioning specialist and a member of the National Strength and Conditioning Association and the American College of Sports Medicine. His research interests include the use of electromyography and mechanomyography to measure muscle function, and the use of resistance training to enhance muscular strength and size.  Moh H. Malek received B.A. (2001) degrees in Biology and Psychology from Pitzer College in Claremont CA, and an M.S. (2002) degree in Exercise Physiology from California State University-Fullerton. He is currently a doctoral student in exercise physiology at the University of Nebraska-Lincoln. He is a member of the American College of Sports Medicine, an ACSM Health/Fitness Instructor, a member of the National Strength and Conditioning Association, and a Certified Strength and Conditioning Specialist. His primary research focus involves parameters of aerobic function as it relates to general and clinical populations. a Department of Nutrition and Health Sciences, Human Performance Laboratory, University of Nebraska-Lincoln, 104K Ruth Leverton Hall, Lincoln, NE 68583-0806, United States b Department of Health and Exercise Science, University of Oklahoma, Norman, OK 73019, United States c Applied Physiology Laboratory, Division of Physical Therapy, Des Moines University Osteopathic Medical Center, Des Moines, IA 50312, United States Corresponding author. Tel.: +1 402 472 2690; fax: +1 402 472 0522.
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