Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Mechanomyogram for Muscle Function Assessment: A Review

  • Md. Anamul Islam ,

    Affiliation AI-Rehab Research Group, Universiti Malaysia Perlis (UniMAP), Kompleks Pauh Putra, Arau, Perlis, Malaysia

  • Kenneth Sundaraj,

    Affiliation AI-Rehab Research Group, Universiti Malaysia Perlis (UniMAP), Kompleks Pauh Putra, Arau, Perlis, Malaysia

  • R. Badlishah Ahmad,

    Affiliation AI-Rehab Research Group, Universiti Malaysia Perlis (UniMAP), Kompleks Pauh Putra, Arau, Perlis, Malaysia

  • Nizam Uddin Ahamed

    Affiliation AI-Rehab Research Group, Universiti Malaysia Perlis (UniMAP), Kompleks Pauh Putra, Arau, Perlis, Malaysia

Mechanomyogram for Muscle Function Assessment: A Review

  • Md. Anamul Islam, 
  • Kenneth Sundaraj, 
  • R. Badlishah Ahmad, 
  • Nizam Uddin Ahamed



Mechanomyography (MMG) has been extensively applied in clinical and experimental practice to examine muscle characteristics including muscle function (MF), prosthesis and/or switch control, signal processing, physiological exercise, and medical rehabilitation. Despite several existing MMG studies of MF, there has not yet been a review of these. This study aimed to determine the current status on the use of MMG in measuring the conditions of MFs.

Methodology/Principal Findings

Five electronic databases were extensively searched for potentially eligible studies published between 2003 and 2012. Two authors independently assessed selected articles using an MS-Word based form created for this review. Several domains (name of muscle, study type, sensor type, subject's types, muscle contraction, measured parameters, frequency range, hardware and software, signal processing and statistical analysis, results, applications, authors' conclusions and recommendations for future work) were extracted for further analysis. From a total of 2184 citations 119 were selected for full-text evaluation and 36 studies of MFs were identified. The systematic results find sufficient evidence that MMG may be used for assessing muscle fatigue, strength, and balance. This review also provides reason to believe that MMG may be used to examine muscle actions during movements and for monitoring muscle activities under various types of exercise paradigms.


Overall judging from the increasing number of articles in recent years, this review reports sufficient evidence that MMG is increasingly being used in different aspects of MF. Thus, MMG may be applied as a useful tool to examine diverse conditions of muscle activity. However, the existing studies which examined MMG for MFs were confined to a small sample size of healthy population. Therefore, future work is needed to investigate MMG, in examining MFs between a sufficient number of healthy subjects and neuromuscular patients.


Researchers are exploring to set suitable methods to examine muscles' activities noninvasively; these methods for example, include surface electromyogram (sEMG) [1], [2], sonomyogram (SMG) [3], [4], tensiomyogram (TMG) [5], [6], and mechanomyogram (MMG) [7], [8].

Although the widely used sEMG has attracted attention for decades as a reliable tool for the assessment of skeletal muscles, it has some drawbacks. sEMG is sensitive to external noise and interference, which limits its operating environment and range of application [9]. In addition, sEMG sensor requires low noise and a very stable signal component [9]. Furthermore, signal processing and analysis is complex [9], [10]. It is also expensive, since it requires three electrodes for differential recordings [9].

Conversely, MMG has been proposed as another tool to study muscle mechanical activity [11]. The term mechanomyography represents a technique by which the mechanical activity of muscle is detected using specific transducers to record muscle surface oscillations due to mechanical activity of the motor units [12]. MMG signals can be detected using several types of transducers including piezoelectric contact sensors (PIZ) [13][15], microphones (MIC) [7], [16], [17], accelerometers (ACC) [18][22] and laser distance sensors (LDS) [23][25].

MMG can provide some notable advantages over sEMG [26]. First, due to its propagating property through the muscle tissue, the placement of MMG sensors is not required to be precise or specific [27]. Second, MMG is a mechanical signal; thus, it is not influenced by the change in the skin impedance due to sweating [9], [26]. Further, MMG can be used in conjunction with sEMG to examine neuromuscular function [28].

However, with the aid of MMG, many researchers have continued to explore its application aspects. There are many examples where MMG has been applied to characterize muscle activity. These applications include for example, the characterization of MFs [22], [29][31], the development of prosthesis and/or switch control [32], [33], studying activity of motor unit [34][36], evaluating muscles during sports and exercises [37][39], monitoring neuromuscular blockade [40], and development of a suitable model for studying the motor unit activity [41]. In addition, we have found several studies which conducted research on MMG sensor development [6], [15], [42], simultaneously testing multiple sensors and comparing their effects on muscles' [43], [44]. We also have found several studies that evaluated the sensor placement effect on muscles [19], [45]. Several works on MMG signal processing for muscle characterization [16], [46], [47] have already been reported.

However, we only found three MMG related review articles [28], [48], [49], which examined MMG amplitude and frequency responses [49], as well as motor unit recruitment strategies [28] and motor unit firing rates [48] during dynamic muscle actions. To our knowledge, there has not been an article which presents a review of MMG for monitoring MFs. This gap motivates the present study. Therefore, the purpose of this review was to determine the current status on the use of MMG in assessing MF. Another objective was to use this review outcome to identify priority area(s) for future research.



We performed a comprehensive literature search for MMG of MF assessment in the Elsevier, PubMed, IEEE, SpringerLink, and Google Scholar electronic databases for relevant articles published from 2003 to 2012. We deliberately set our search strategy (the details of the search strategy may be obtained from Table S1) to be broad in the combined search of: # 1 (mechanomyography) AND # 2 (systematic review AND review) AND # 3 (muscle-function) AND # 4 (muscle-assessment) NOT # 5 (electromyography). There were no language and category restrictions during databases searching. Journal articles, conferences, books, letters, and clinical reports were examined for potentially eligible studies. In addition, we checked the reference lists of all important articles that were retrieved in the search.

Study selection

The title and abstract of studies identified by the search were screened for potential relevance. The full text of all potentially relevant studies was reviewed to determine if they fulfilled the eligible criteria. We included studies that described a theoretical or practical of only MMG for MF assessment. Two authors (MAI and NUA) independently screened the results of the electronic searches to select potentially relevant citations based on the title and abstract according to the criteria defined above. The studies those meet the most relevant criteria best were included for this review. The studies those were written in a language other than English, and articles those examined animal muscles were eliminated. In addition, we excluded articles that lacked in-depth discussions, without proper data presentation, and with unclear or vague descriptions of the protocol used.

Data extraction

Two authors (MAI and KS) abstracted data individually using an MS-Word structured data extraction form specially created for this review (Table S2). The extracted data were compared and discussed by the two authors before being compiled as final information. Information extracted from each article included: i) name of the studied muscle and its contraction type, ii) sensor type and model, iii) MFs and subject types, iv) measured parameters, algorithms and hardware, v) main results, vi) author comments, vi) recommended applications and vii) priority for future research.

Validity assessment

Three authors (MAI, KS and BA) analyzed the data extracted from the potentially relevant articles. After analysis we decided to depend on information extracted from the most relevant studies; those that were organized with proper data presentation, clearly verified selection of protocols, and through demonstration of research methodologies, to reduce risk of bias.

Quantitative data synthesis

There was a significant number of MMG driven MF assessment studies across areas of muscle fatigue, strength, muscle balance, muscle movement activities, and effects on muscle conditions for practicing exercises. We described these studies qualitatively and present detailed results in tabulated form (Table 1Table 7).

Table 1. An overview of MMG driven fatigue test from the biceps brachii muscle.


Flow of included study

The comprehensive literature search returned a total of 36 articles (Figure 1). Of these, 16 met the inclusion criteria as muscle fatigue, three met as strength, one met the inclusion criteria as muscle balance, whereas seven met as muscle movement activities, and the remaining nine met the inclusion criteria as muscle exercise or stretching.

Study characteristics

Among the 36 relevant studies, 30 studies were peer-reviewed journal articles, six studies were conference papers. Nine [13], [15], [21], [42], [50][54] involved fatigue assessment from upper arm (biceps brachii, BB) muscles, five studies [14], [29], [30], [55], [56] were related to fatigue assessment of leg muscles (quadriceps), one [57] conducted fatigue investigation of the masseter muscle and another [58] presented fatigue measurements of both BB and vastus lateralis (VL) muscles. Three studies [59][61] incorporated strength tests from VL, rectus femoris (RF), and BB muscles. Another study [31] included muscle balance investigation of quadriceps muscles. Seven studies [7], [22], [33], [62][65] involved muscle activities of movements from different muscles, and the rest nine [8], [37][39], [44], [66][69] were related to investigation of MFs of diverse exercises from quadriceps muscles.

Fatigue assessment

Table 1 presents the main details of fatigue measurement of nine relevant studies using MMG from BB muscle. Gregori et al. [42] developed a new surface composite probe for differential MMG and sEMG recording in order to measure muscle fatigue from BB. A similar non-differential MMG sensor was used for comparisons. The new sensor recorded muscular activity more efficiently than the non-differential probe and could therefore be useful in studying fatigue and neuromuscular diseases [42]. Tanaka et al. [15] developed a PIZ to monitor muscle fatigue for the biceps and triceps brachii muscles. The authors reported that they were able to measure muscle fatigue using the developed sensor [15].

Beck et al. [13] performed a comparison between the fast Fourier transform and the discrete wavelet transform for determining MMG and sEMG centre frequency patterns during fatiguing isokinetic muscle actions of the BB muscle. The authors suggested that Fourier-based methods were acceptable for determining the patterns for normalized MMG and sEMG centre frequency during fatiguing dynamic muscle actions at moderate velocities [13].

Madeleine et al. [54] analyzed the trends of the first three power spectral moments of the MMG signals recorded from the BB muscle using a MIC and an ACC during sustained fatiguing contractions at 30% of maximum voluntary contractions (MVC). For both the MIC and ACC, absolute and normalized root mean square (RMS) values and power spectral variance increased while mean frequency (MNF) and skewness decreased with contraction time [54]. Feng et al. [51] reported an experimental study of MIC-based MMG signal intensity against force of contraction and muscle fatigue during cyclic contractions of the BB muscle. They observed that MMG signal intensity decreased with the increase of onset of muscle fatigue [51]. Furthermore, Krizaj et al. [53] claimed maximum displacement and half relaxation time as parameters to measure muscle fatigue for the BB muscle.

Xie et al. [21] explored fatigue MMG signals during static contractions by applying nonlinear dynamic analysis methods for the BB muscle. The results advocated the use of nonlinear dynamics theory (Volterra–Wiener–Korenberg model and noise titration approach) for analysis and modelling of fatiguing MMG [21]. Again, Xie et al. [52] investigated linear and non-linear properties of MMG signal detected from the BB muscle during fatiguing isometric contractions. They reported that MMG signal during fatigue was nonlinear from all subjects [52].

Hendrix et al. [50] examined fatigue threshold from the BB muscle by comparing the critical torque threshold, electromyographic mean power frequency fatigue threshold (sEMG MPFFT), and mechanomyographic mean power frequency fatigue threshold (MMG MPFFT). They suggested that there were no significant differences between these fatigue thresholds, and the mean torque values from the three fatigue thresholds were significantly intercorrelated at r = 0.94–0.96. Thus, activated motor units may be examined by using non-invasive methods like the MMG MPFFT test [50].

However, five relevant studies focused on fatigue assessment from the quadriceps muscles (Table 2). Ebersole and Malek [14] examined the relationship between fatigue and electromechanical efficiency (EME) for the VL and vastus medialis (VM) muscles and found 58% and 66% decreases in EME for the VM and VL respectively, concurrently with a 47% decline in torque production. They concluded that in healthy muscles, the EME of both muscles decreased concurrently with a decrease in torque [14].

Table 2. An overview of fatiguing MMG assessment for quadriceps muscles.

Faller et al. [55] studied using triaxial-based ACC for MMG signal from the RF muscle to assess fatigue during the execution of neuromuscular electrical stimulation (NMES) protocol, which is used widely for rehabilitation in the physical therapy of fatigue caused by excessive voluntary contraction. They confirmed that the RMS value of MMG correlated with torque but mean power frequency (MPF) of MMG did not significantly correlate for the present NMES protocol [55].

Al-Zahrani et al. [56] investigated the reliability in assessing RF muscle fatigue within-day and between-days using triaxial ACC-assisted MMG. They found poor reliability [56] in between-days for fatigue assessment. The poor between-days reliability found in this study suggests caution in using MMG RMS, MPF, median frequency (MDF) and their corresponding regression slopes in assessing muscle fatigue due to the high number of smallest detectable difference (SDD) values [56].

Hendrix et al. [30] tested MMG MPFFT from the VL, VM and RF muscles during each fatiguing isometric muscle action. They determined that there were no significant differences among the MMG MPFFT values for the three muscles. Hence, the MMG MPFFT test may provide a non-invasive method to examine the fatiguing effects during isometric muscle actions [30].

Armstrong [29] studied the intensity analysis of Morlet wavelets of MMG signal as an alternative to power spectral analysis for the evaluation of postural control strategy during the single-legged stance and to examine the effects of fatigue over the VL, soleus and VM muscles. He found that the intensity of MMG signals increased with increasing fatigue [29]. Furthermore, he mentioned that intensity analysis is a useful tool for exploring posture control and fatigue study [29].

One study by Gobbo et al. [58] verified twitching torque and MMG peak-to-peak (MMGp-p) amplitude from both dominant BB and VL muscles by inducing fatiguing stimulation to investigate muscle fatigue. Another relevant study by Ioi et al. [57] showed supporting evidence of using EME as a parameter to measure masseter muscle fatigue. Table 3 presents an overview of the two relevant studies of fatigue assessment.

Strength assessment

Three studies examined muscle strength [59][61] (Table 4). Matta et al. [60] addressed the temporal (RMS) and spectral components of MMG signals from the BB muscle of males and females during different levels of contraction to characterize muscle strength. Another study by Marek et al. [61] focused on examining the short-term effects of static and proprioceptive neuromuscular facilitation stretching on muscle strength and output power. They concluded that both static and proprioceptive neuromuscular facilitation stretching caused similar deficits in strength, power output, and muscle activation at both slow (60°/s) and fast (300°/s) velocities [61]. In addition, Ryan et al. [59] examined the inter-individual variability for the patterns of responses of MMG amplitude and MPF versus isometric torque from the VL muscle in lower-strength and higher-strength individuals. The authors indicated that the composite MMG amplitude versus isometric torque relationship was best fit with a linear model for the lower-strength group and a cubic model for the higher-strength group [59]. They also found that the composite MMG MPF versus isometric torque relationships was best fit with linear models for both the groups [59].

Muscle balance assessment

One study included muscle balance measurement (Table 5). Armstrong et al. [31] evaluated the reliability of a protocol for using a microelectromechanical high-resolution ACC to measure centre of mass accelerations in the three cardinal planes (vertical, medial/lateral and anterior/posterior) and uniaxial ACC to measure MMG for the purpose of assessing balance and postural control. High resolution ACC and MMG offered reliable information pertaining to balance, and may have application in evaluating postural control and stability [31].

Muscle movement assessment

Seven studies [7], [22], [33], [62][65] examined muscle activities due to movements as shown in Table 6. Scheeren et al. [64] investigated the functional movement of RF and VL muscles using MMG between healthy and spinal cord injured (SCI) patients during different functional electrical stimulation (FES) profiles. The authors found that the MMG signal was different between healthy and SCI patients but comparable in the RF and VL muscles per subject [64]. In addition, Krueger et al. [62] made a correlation between MMG signal and passive movements of healthy and SCI patients. The correlation found by the authors was positive for healthy subjects and negative for SCI patients [62]. Tian et al. [63] also observed different sEMG and MMG behaviours accompanied with age-related sarcopenia for elder and younger group collected from the VL muscle during concentric contraction with movement intensities of 45%, 60%, and 75%. The averages MMG RMS between groups were different for all movement intensities while sEMG RMS was indistinguishable between groups. sEMG MNF and MMG MNF increased with movement intensity among both the young and the elderly subjects [63]. The authors suggested that MMG should be used as an important tool in studying muscle contraction in age-related sarcopenia [63]. Yoshimi et al. [65] developed a new system to examine muscle activities and mandibular movement patterns during sleep bruxism (tapping, clenching and grinding). The system consisted of a dual-axis ACC and sEMG to record activities of the masseter muscle. The authors showed that grinding (59.5%) was most common, followed by clenching (35.6%) based on relative activity to MVC, whereas tapping was only 4.9%. They concluded that tapping, clenching, and grinding movements of the mandible could be effectively differentiated by the new system and sleep bruxism was predominantly perceived as clenching and grinding which varied between individuals [65]. Kawakami et al. [7] further investigated MMG and sEMG signals in the human lateral pterygoid muscle during mandibular movements for maximum voluntary clenching. They showed that the activity of the lateral pterygoid muscle could be evaluated by MMG signals recorded in the external ear canal if the jaw closing major muscles do not show active contractions [7]. Furthermore, Alves and Chau [33] designed and tested MMG signals during eyebrow movements to control a binary switch. They showed that the eyebrow movement MMG-driven switch performed with almost perfect sensitivity and specificity for all participants. The performance of their algorithm was robust against typical participant movements [33]. However, Scheeren et al. [22] characterized wrist movements like elbow extension and flexion, ulnar deviation and radial deviation. Their statistical analysis indicated that flexion was different from extension, ulnar and radial deviation, and radial deviation was different from ulnar deviation and flexion [22].

MMG measurement in exercises

Nine studies [8], [37][39], [44], [66][69] reported MMG measurements of exercise. Table 7 depicts an overview of MMG effects examined for exercises. McKay et al. [39] performed recording of MMG signals to determine the effects of graded levels of exercise on ipsilateral and contralateral post-exercise resting RF muscle. They observed that MMG activity was greater when the RF muscle length was shorter (i.e. when the leg was extended versus flexed). This result suggested that less stretched muscles could more freely oscillate, producing higher MMG amplitudes [39]. Again, McKay et al. [69] examined MMG signals from resting muscles before and after resistance exercises. They found that resting MMG amplitudes increased about threefold after vigorous resistance exercise, and that the increase decayed exponentially over time. Conversely, resting muscle sEMG amplitudes doubled after resistance exercises, but their amplitudes were below the resolution of the measuring instrument [69]. Further, Herda et al. [67] utilized MMG signal to discriminate muscle fiber types during three training phases namely, resistance trained (RT), aerobically trained (AT), and sedentary (SED). The authors showed that there were differences in fiber type composition of the VL muscle among AT, RT, and SED persons [67].

However, Cramer et al. [38] investigated the acute effects of static stretching on neuromuscular functions (peak torque, work, joint angle at peak torque, acceleration time, range of motion, sEMG amplitude, and MMG amplitude) during maximal concentric isokinetic leg extensions in men and women. After stretching, the authors found that peak torque, acceleration time, and sEMG amplitude decreased from pre-stretching to post-stretching at 1.04 and 5.23 rad/s; there were no changes in work, joint angle at peak torque, isokinetic range of motion, or MMG amplitude [38]. Moreover, Esposito et al. [37] evaluated the effects of stretching of the MG muscle on sEMG signal, MMG signal, and muscle force. They found that stretching may affect the mechanical properties of the muscle but no significant change was found in case of sEMG [37]. Another work by Taylor et al. [66] described an ACC-based system that can detect and classify small deviation from a correct exercise performance for knee osteoarthritis (joint disorder caused by pain and stiffness). This system allowed the possibility of quick recovery and prevention for the patients by taking full advantage of exercise secondary disorders [66].

Malek et al. [68] examined the MMG amplitude and MPF versus power output relationships for the RF muscle during cycle ergometry (CE) and knee extensor (KE) incremental exercises on the same subject. They demonstrated that the KE model expressed similar patterns of responses (best-fit with linear model) for absolute and normalized MMG amplitude of the composite data in all eight subjects, whereas for the CE exercise, these patterns varied on a subject-to-subject basis. In the analysis of MMG MPF, there were no consistent patterns of responses for CE and KE exercises involving the RF muscle [68]. Further, Malek et al. [44] compared ACC and PIZ MMG sensors for VL and RF muscles during incremental CE. They showed similar patterns of response for MMG amplitude but inconsistent responses for MMG MPF by both the sensors and muscle groups on a subjects-to-subject basis [44]. Malek et al. [8] however examined the effect on MMG responses during incremental CE across innervation zone (IZ) of the VM muscle. They concluded in their findings that MMG signals during dynamic exercise were not influenced by IZ of the VM muscle [8].


This review summarizes the findings of MMG in measuring diverse MFs. The main findings of this review are as follows: First, we find sufficient studies that MMG may be used to measure different conditions of MFs including muscle fatigue, strength, and balance. Second, this review provides sufficient evidence that MMG may be used to examine muscle actions due to movements, even for patients with SCI. Third, we also find sufficient studies to show that MMG is able to monitor muscle activities under various types of exercise paradigms. Fourth, this review reveals an important issue that all of these studies, except one [56] (Table 1 to Table 7), have been conducted on small samples of healthy subjects only. For medical diagnostic purposes, comparisons would be needed with well-defined samples of neuromuscular patients. Finally, since we included studies that had a proper data presentation, clearly verified protocols and thoroughly demonstrated research methodologies, we believe that there is a little risk of bias across studies.

Fatigue or strength assessment

As mentioned previously, MMG provides a platform for muscle fatigue, and strength assessment. In most cases, researchers consider MMG amplitude, MPF, torque and contraction force to measure fatigue [15], [51], [54], [55], and strength [59][61]. However, Al-Zahrani et al. [56] left caution of using RMS, MPF, and MDF for fatigue measurement between days due to the low number of smallest detectable difference. They used intraclass correlation coefficients (ICC) instead [56]. Xie et al. [21] explored fatigue MMG signals using embedded and correlation dimension parameters and advocated the use of nonlinear dynamics theory (Volterra–Wiener–Korenberg model and noise titration approach). Armstrong [29] performed intensity analysis using Morlet wavelets of MMG signal to determine postural control and fatigue. However, Krizaj et al. [53] claimed maximum displacement and half relaxation time as parameters to measure muscle fatigue. On the other hand, the authors showed supporting evidence for using EME as a parameter to measure muscle fatigue [14], [57]. The choice of parameters used for muscle fatigue measurement differ possibly due to the various set of protocols considered, use of different MMG sensors or selection of different muscles. Nevertheless, all of these studies agreed that MMG may be implemented as a measurement tool for monitoring muscle fatigue. However, all but Al-Zahrani et al. [56] examined muscle fatigue using a small sample size which were confined to healthy subjects only.

Movement activity or balance assessment

Based on the studies reviewed, MMG appears to be an effective means of measuring muscle balance, and activities due to movement. Researchers in [7], [62][65] allowed MMG RMS amplitude and MNF as the parameters to examine muscle activities of movements. However, Scheeren et al. [22] analyzed zero crossing, peak counting and RMS of MMG signals to characterize wrist movements. The results from their studies suggest that MMG may be capable of measuring muscle movement activities caused by either SCI or age-related sarcopenia, and muscle balance of quadriceps. In addition, MMG may also be useful to differentiate mandible movements during sleep [65], and voluntary clenching tasks [7]. Alves and Chau [33] also detected eyebrow movements using MMG with high accuracy. All of these studies confirm that MMG may be used to examine movement activities of different muscles. On the other hand, Armstrong et al. [31] conceded that ICC besides the commonly used MMGp-p amplitude, could be chosen to characterize muscle balance. However, all these assessments used a small sample size of healthy subjects.

MMG in exercise assessment

We have found studies which include MMG measurement to examine muscle activities of exercises. This review reveals that stretching may affect the response of MMG signals. The authors in [38], [39], for example, clearly showed the effects on MMG amplitude for the RF muscle after stretching. Further, Esposito et al. [37] found that stretching may affect the mechanical properties of the MG muscle. However, Malek et al. [68] demonstrated that there is a linear response between MMG and knee extensor exercises for all participated eight subjects, but the MMG response was influenced on a subject-to-subject basis for CE exercises involving the RF muscle. Again, Malek et al. [44] determined that MMG amplitude responded in similar patterns using PIZ and ACC sensors from VL and RF muscles on subject-to-subject basis. This subject dependent MMG response during CE may be due to varying muscle fiber composition, and different force labels production between subjects. Further, McKay et al. [69] demonstrated that the resting RF muscle affected MMG signals before and after resistance exercises. Furthermore, Taylor et al. [66] implemented and studied the effect of exercises on MMG to monitor the correct amount of exercise required for knee osteoarthritis subjects. However, Herda et al. [67] analyzed the effects on MMG RMS of the VL muscle during AT, RT, and SED and used these to identify muscle fiber types resulting from the three training phases. On the other hand, Malek and Coburn [8] found no influence on MMG response during incremental CE exercises across the IZ of the VM muscle. This result suggests that MMG signal from the VM muscle during CE exercises may be used to study muscle condition without regards to signal contamination by the IZ. Thus, this review reports that MMG may be applied as a useful tool to monitor muscle activities for stretching or exercising. However, all of these studies only conducted the assessments using a small sample size of healthy population.


This review comprises several strong points, including its uniqueness. To date, this is the first study specially designed to retrieve, analyze and critically appraise existing trends of MFs assessment based on MMG. There are nevertheless some limitations in this study. We included studies which still suffer from limited sample size, poor characterization of subjects and the heterogeneous methodology. We also did not include non-English studies in our analysis. On the other hand, lower quality of trials not published in English may also introduce bias [70]. In addition, a small amount of publication bias could also be present due to the bounded time frame (2003–2012) used for article searching. This bias can be neglected because technology was much less advanced in earlier years and can hardly be compared to approaches used in later studies. Overall, the information documented by this article may be useful to future researchers in understanding the existing status on the use of MMG for monitoring MFs.


In summary this review reveals that MMG may be applied as a useful tool to examine muscle fatigue, strength, and balance. We also find sufficient evidence that MMG may be used to evaluate muscle activities of movements. In addition, this review also shows that MMG may be a useful tool to monitor muscle activities during, after or before exercises. However, we observe that of these 36 studies, only two studies performed analyses using neuromuscular patients but still with a small sample size, and only one examined with a reliable sample size that is also confined to a healthy population only. Therefore, future work is needed to examine MMG for MFs in a sufficient number between healthy subjects and neuromuscular patients.

Supporting Information

Table S1.

Keywords and search strategies for MMG in measuring muscle function.


Table S2.

Data extraction form for MMG in assessing muscle function.



We thank Sebastian Sundaraj, MD, Malaysian Ministry of Health, for his assistance.

Author Contributions

Conceived and designed the experiments: MAI KS. Performed the experiments: MAI KS NUA. Analyzed the data: MAI KS RBA. Contributed reagents/materials/analysis tools: NUA. Wrote the paper: MAI KS.


  1. 1. Cho YJ, Kim JY (2012) The effects of load, flexion, twisting and window size on the stationarity of trunk muscle EMG signals. Int. J. Ind. Ergon. 42: 287–292.
  2. 2. Simoneau EM, Longo S, Seynnes OR, Narici MV (2012) Human muscle fascicle behavior in agonist and antagonist isometric contractions. Muscle Nerve 45: 92–99.
  3. 3. Chen X, Zheng YP, Guo JY, Zhu Z, Chan SC, et al. (2011) Sonomyographic responses during voluntary isometric ramp contraction of the human rectus femoris muscle. Eur. J. Appl. Physiol. 13: 13.
  4. 4. Shi J, Chang Q, Zheng YP (2010) Feasibility of controlling prosthetic hand using sonomyography signal in real time: preliminary study. J. Rehabil. Res. Dev. 47: 87–98.
  5. 5. Šimunic B, Degens H, Rittweger J, Narici M, Mekjavic IB, et al. (2011) Noninvasive Estimation of Myosin Heavy Chain Composition in Human Skeletal Muscle. Med. Sci. Sports Exerc. 43: 1619–1625.
  6. 6. Đorđević S, Stančin S, Meglič A, Milutinović V, Tomažič S (2011) MC Sensor—A Novel Method for Measurement of Muscle Tension. Sensors 11: 9411–9425.
  7. 7. Kawakami S, Kodama N, Maeda N, Sakamoto S, Oki K, et al. (2012) Mechanomyographic activity in the human lateral pterygoid muscle during mandibular movement. J. Neurosci. Methods 203: 157–162.
  8. 8. Malek MH, Coburn JW (2011) Mechanomyographic responses are not influenced by the innervation zone for the vastus medialis. Muscle Nerve 44: 424–431.
  9. 9. Martin Ma Y-E (2009) MMG sensor for muscle activity detection-low cost design, implementation and experimentation [Master's Disserttation]. Auckland: Massey University.
  10. 10. Anderson E, Wybo C, Bartol S (2010) An Analysis of Agreement between MMG vs. EMG Systems for Identification of Nerve Location During Spinal Procedures. Spine J. 10 1S–149S.
  11. 11. Mamaghani KN, Shimomura Y, Iwanaga K, Katsuura T (2001) Changes in Surface EMG and Acoustic myogram Parameters During Static Fatiguing Contractions until Exhaustion: Influence of Elbow Joint Angles. J Physiol Anthropol Appl Human Sci. 20: 131–140.
  12. 12. Orizio C, Gobbo M (2006) Mechanomyography Wiley Encyclopedia of Biomedical Engineering. C. Orizio ed. Brescia: John Wiley & Sons, Inc. pp. 1–23.
  13. 13. Beck TW, Housh TJ, Johnson GO, Weir JP, Cramer JT, et al. (2005) Comparison of Fourier and wavelet transform procedures for examining the mechanomyographic and electromyographic frequency domain responses during fatiguing isokinetic muscle actions of the biceps brachii. J. Electromyogr. Kinesiol. 15: 190–199.
  14. 14. Ebersole KT, Malek DM (2008) Fatigue and the electromechanical efficiency of the vastus medialis and vastus lateralis muscles. J. Athl. Train. 43: 152–156.
  15. 15. TanakaMOkuyamaTSaitoKStudy on evaluation of muscle conditions using a mechanomyogram sensor;. 20119–12Oct. pp 741–745.
  16. 16. Qi L, Wakeling JM, Green A, Lambrecht K, Ferguson-Pell M (2011) Spectral properties of electromyographic and mechanomyographic signals during isometric ramp and step contractions in biceps brachii. J. Electromyogr. Kinesiol. 21: 128–135.
  17. 17. Alves N, Falk TH, Chau T (2010) A novel integrated mechanomyogram-vocalization access solution. Med. Eng. Phys. 32: 940–944.
  18. 18. LeiKFTsaiW-WLinW-YLeeM-YMMG-torque estimation under dynamic contractions; 20119–12Oct. pp 585–590.
  19. 19. Zuniga JM, Housh TJ, Camic CL, Hendrix CR, Mielke M, et al. (2010) The effects of accelerometer placement on mechanomyographic amplitude and mean power frequency during cycle ergometry. J. Electromyogr. Kinesiol. 20: 719–725.
  20. 20. Youn W, Kim J (2010) Estimation of elbow flexion force during isometric muscle contraction from mechanomyography and electromyography. Med. Biol. Eng. Comput. 48: 1149–1157.
  21. 21. Xie H-B, Guo J-Y, Zheng Y-P (2010) Uncovering chaotic structure in mechanomyography signals of fatigue biceps brachii muscle. J. Biomech. 43: 1224–1226.
  22. 22. Scheeren EM, Krueger-Beck E, Nogueira-Neto G, Nohama P, Button VLdSN (2010) Wrist Movement Characterization by Mechanomyography Technique. J. Med. Biol. Eng. 30: 373–380.
  23. 23. Dillon MAB, Beck TW, Jason MD, Matt SS (2011) Mechanomyographic amplitude and mean power frequency versus isometric force relationships detected in two axes. Clin Kinesiol 65: 47 (10).
  24. 24. Beck TW, Michael AD, Jason MD, Matt SS (2009) Cross-correlation analysis of mechanomyographic signals detected in two axes. Physiol. Meas. 30: 1465.
  25. 25. Orizio C, Solomonow M, Diemont B, Gobbo M (2008) Muscle-joint unit transfer function derived from torque and surface mechanomyogram in humans using different stimulation protocols. J. Neurosci. Methods 173: 59–66.
  26. 26. Xie H-B, Zheng Y-P, Guo J-Y (2009) Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control. Physiol. Meas. 30: 441.
  27. 27. Alves N, Chau T (2008) Stationarity distributions of mechanomyogram signals from isometric contractions of extrinsic hand muscles during functional grasping. J. Electromyogr. Kinesiol. 18: 509–515.
  28. 28. Malek MH, Coburn JW (2012) The Utility of Electromyography and Mechanomyography for Assessing Neuromuscular Function: A Noninvasive Approach. Phys. Med. Rehabil. Clin. N. Am. 23: 23–32.
  29. 29. Armstrong JW (2011) Wavelet-based intensity analysis of mechanomyographic signals during single-legged stance following fatigue. J. Electromyogr. Kinesiol. 21: 803–810.
  30. 30. Hendrix CR, Housh TJ, Zuniga JM, Camic CL, Mielke M, et al. (2010) A mechanomyographic frequency-based fatigue threshold test. J. Neurosci. Methods 187: 1–7.
  31. 31. Armstrong JW, McGregor SJ, Yaggie JA, Bailey JJ, Johnson SM, et al. (2010) Reliability of mechanomyography and triaxial accelerometry in the assessment of balance. J. Electromyogr. Kinesiol. 20: 726–731.
  32. 32. Beck TW (2010) Applications of Mechanomyography for examining muscle function. In: Beck TW, editor. Technical aspects of surface mechanomyography. Kerala, India: Transworld Research Network. pp. 95–107.
  33. 33. Alves N, Chau T (2010) The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements. J. NeuroEng. Rehabil. 7: 1–10.
  34. 34. Cescon C, Madeleine P, Farina D (2008) Longitudinal and transverse propagation of surface mechanomyographic waves generated by single motor unit activity. Med. Biol. Eng. Comput. 46: 871–877.
  35. 35. Cescon C, Sguazzi E, Merletti R, Farina D (2006) Non-invasive characterization of single motor unit electromyographic and mechanomyographic activities in the biceps brachii muscle. J. Electromyogr. Kinesiol. 16: 17–24.
  36. 36. Reza MF, Ikoma K, Chuma T, Mano Y (2005) Mechanomyographic response to transcranial magnetic stimulation from biceps brachii and during transcutaneous electrical nerve stimulation on extensor carpi radialis. J. Neurosci. Methods 149: 164–171.
  37. 37. Esposito F, Limonta E, Cè E (2011) Time course of stretching-induced changes in mechanomyogram and force characteristics. J. Electromyogr. Kinesiol. 21: 795–802.
  38. 38. Cramer JT, Beck TW, Housh TJ, Massey LL, Marek SM, et al. (2007) Acute effects of static stretching on characteristics of the isokinetic angle-torque relationship, surface electromyography, and mechanomyography. J. Sports Sci. 25: 687–698.
  39. 39. McKay WP, Jacobson P, Chilibeck PD, Daku BL (2006) Effects of graded levels of exercise on ipsilateral and contralateral post-exercise resting rectus femoris mechanomyography. Eur. J. Appl. Physiol. 98: 566–574.
  40. 40. Trager G, Michaud G, Deschamps S, Hemmerling TM (2006) Comparison of phonomyography, kinemyography and mechanomyography for neuromuscular monitoring. Can. J. Anesth. 53: 130–135.
  41. 41. Uchiyama T, Hashimoto E (2011) System identification of the mechanomyogram from single motor units during voluntary isometric contraction. Med. Biol. Eng. Comput. 49: 1035–1043.
  42. 42. Gregori B, Galie E, Accornero N (2003) Surface electromyography and mechanomyography recording: a new differential composite probe. Med. Biol. Eng. Comput. 41: 665–669.
  43. 43. Posatskiy AO, Chau T (2012) The effects of motion artifact on mechanomyography: A comparative study of microphones and accelerometers. J. Electromyogr. Kinesiol. 22: 320–324.
  44. 44. Malek MH, Coburn JW, York R, Ng J, Rana SR (2010) Comparison of mechanomyographic sensors during incremental cycle ergometry for the quadriceps femoris. Muscle Nerve 42: 394–400.
  45. 45. Alves N, Sejdic E, Sahota B, Chau T (2010) The effect of accelerometer location on the classification of single-site forearm mechanomyograms. Biomed. Eng. Online 9: 23.
  46. 46. Alves N, Chau T (2010) Automatic detection of muscle activity from mechanomyogram signals: a comparison of amplitude and wavelet-based methods. Physiol. Meas. 31: 461.
  47. 47. Alves N, Chau T (2008) Vision-based segmentation of continuous mechanomyographic grasping sequences. IEEE Trans. Biomed. Eng. 55: 765–773.
  48. 48. Beck TW, Housh TJ, Cramer JT, Weir JP, Johnson GO, et al. (2005) Mechanomyographic amplitude and frequency responses during dynamic muscle actions: a comprehensive review. Biomed. Eng. Online 4: 67.
  49. 49. Beck TW, Housh TJ, Johnson GO, Cramer JT, Weir JP, et al. (2007) Does the frequency content of the surface mechanomyographic signal reflect motor unit firing rates? A brief review. J. Electromyogr. Kinesiol. 17: 1–13.
  50. 50. Hendrix CR, Housh TJ, Camic CL, Zuniga JM, Johnson GO, et al. (2010) Comparing electromyographic and mechanomyographic frequency-based fatigue thresholds to critical torque during isometric forearm flexion. J. Neurosci. Methods 194: 64–72.
  51. 51. FengYZKumarDKArjunanSPMechanomyogram for identifying muscle activity and fatigue; 20093–6Sept. 2009. pp 408–411.
  52. 52. XieH-BZhengY-PGuoJ-YDetection of chaos in human fatigue mechanomyogarphy signals; 20093–6Sept. 2009 pp. 4379–4382.
  53. 53. Krizaj D, Simunic B, Zagar T (2008) Short-term repeatability of parameters extracted from radial displacement of muscle belly. J. Electromyogr. Kinesiol. 18: 645–651.
  54. 54. Madeleine P, Ge H-y, Jaskólska A, Farina D, Jaskólski A, et al. (2006) Spectral moments of mechanomyographic signals recorded with accelerometer and microphone during sustained fatiguing contractions. Med. Biol. Eng. Comput. 44: 290–297.
  55. 55. Faller L, Nogueira Neto GN, Button VLSN, Nohama P (2009) Muscle fatigue assessment by mechanomyography during application of NMES protocol. Rev. Bras. Fisioter. 13: 422–429.
  56. 56. Al-Zahrani E, Gunasekaran C, Callaghan M, Gaydecki P, Benitez D, et al. (2009) Within-day and between-days reliability of quadriceps isometric muscle fatigue using mechanomyography on healthy subjects. J. Electromyogr. Kinesiol. 19: 695–703.
  57. 57. Ioi H, Kawakatsu M, Nakata S, Nakasima A, Counts AL (2006) Mechanomyogram and electromyogram analyses for investigating human masseter muscle fatigue. Orthodontic Waves 65: 15–20.
  58. 58. Gobbo M, Ce E, Diemont B, Esposito F, Orizio C (2006) Torque and surface mechanomyogram parallel reduction during fatiguing stimulation in human muscles. Eur. J. Appl. Physiol. 97: 9–15.
  59. 59. Ryan ED, Cramer JT, Housh TJ, Beck TW, Herda TJ, et al. (2007) Inter-individual variability in the torque-related patterns of responses for mechanomyographic amplitude and mean power frequency. J. Neurosci. Methods 161: 212–219.
  60. 60. Matta TTd, Perini TA, Oliveira GLd, Ornellas JdS, Louzada AA, et al. (2005) Interpretation of the mechanisms related to the muscular strength gradation through accelerometry. Rev. Bras. Med. Esporte 11: 306–310.
  61. 61. Marek SM, Cramer JT, Fincher AL, Massey LL, Dangelmaier SM, et al. (2005) Acute Effects of Static and Proprioceptive Neuromuscular Facilitation Stretching on Muscle Strength and Power Output. J. Athl. Train. 40: 94–103.
  62. 62. Krueger E, Scheeren EM, Nogueira-Neto GN, Button da SNVL, Nohama P (2011) Correlation between mechanomyography features and passive movements in healthy and paraplegic subjects. IEEE Conf Proc on Eng Med Biol Soc 5: 7242–7245.
  63. 63. Tian S-L, Liu Y, Li L, Fu W-J, Peng C-H (2010) Mechanomyography is more sensitive than EMG in detecting age-related sarcopenia. J. Biomech. 43: 551–556.
  64. 64. Scheeren EM, Nogueira-Neto GN, Krueger-Beck E, Button VL, Nohama P (2010) Investigation of muscle behavior during different functional electrical stimulation profiles using Mechanomyography. IEEE Conf Proc on Eng Med Biol Soc 3: 3970–3973.
  65. 65. Yoshimi H, Sasaguri K, Tamaki K, Sato S (2009) Identification of the occurrence and pattern of masseter muscle activities during sleep using EMG and accelerometer systems. Head Face Med 5: 7.
  66. 66. TaylorPEAlmeidaGJMKanadeTHodginsJKClassifying human motion quality for knee osteoarthritis using accelerometers; 2010Aug31 2010-Sept.42010. pp. 339–343.
  67. 67. Herda TJ, Housh TJ, Fry AC, Weir JP, Schilling BK, et al. (2010) A noninvasive, log-transform method for fiber type discrimination using mechanomyography. J. Electromyogr. Kinesiol. 20: 787–794.
  68. 68. Malek MH, Coburn JW, Tedjasaputra V (2009) Comparison of mechanomyographic amplitude and mean power frequency for the rectus femoris muscle: Cycle versus knee-extensor ergometry. J. Neurosci. Methods 181: 89–94.
  69. 69. McKay WP, Chilibeck PD, Daku BL (2007) Resting mechanomyography before and after resistance exercise. Eur. J. Appl. Physiol. 102: 107–117.
  70. 70. Juni P, Holenstein F, Sterne J, Bartlett C, Egger M (2002) Direction and impact of language bias in meta-analyses of controlled trials: empirical study. Int J Epidemiol 31: 115–123.