Muscle-joint unit transfer function derived from torque and surface mechanomyogram in humans using different stimulation protocols

Orizio, Claudio; Solomonow, Moshe; Diemont, B.; Gobbo, Massimiliano

doi:10.1016/j.jneumeth.2008.05.012

Cited by 30 publications

(18 citation statements)

References 28 publications

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“…Six studies [4], [29], [36], [59], [88], [89] were found related to muscle torque recorded by MMG (Table VII). Beck et al [89] used a piezoelectric contact sensor to determine MMG amplitude and MPF versus torque (or force) relationships during isokinetic and isometric muscle actions of the BB muscle.…”

Section: Muscle Characterizations Using Mmg 1) Fatigue and Strengtmentioning

confidence: 98%

“…MMG signals can be detected using several types of transducers including piezoelectric contact sensors [24]- [26], microphones, [8], [27], [28] accelerometers [29]- [33] and laser distance sensors [34]- [36]. The laser distance sensor trace displays the gross muscle dimensional changes, and the accelerometer represents the result of the summation of the unfused dimensional changes of the fibers of the recruited MUs [18].…”

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confidence: 99%

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Mechanomyography Sensor Development, Related Signal Processing, and Applications: A Systematic Review

et al. 2013

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Mechanomyography (MMG) is extensively used in the research of sensor development, signal processing, characterization of muscle activity, development of prosthesis and/or switch control, diagnosis of neuromuscular disorders, and as a medical rehabilitation tool. Despite much existing MMG research, there has been no systematic review of these. This paper aims to determine the current status of MMG in sensor development, related signal processing, and applications. Six electronic databases were extensively searched for potentially eligible studies published between 2003 and 2012. From a total of 175 citations, 119 were selected for full-text evaluation and 86 potential studies were identified for further analysis. This systematic review initially reveals that the development of accelerometers for MMG is still in the initial stage. Another important finding of this paper is that sensor placement location on muscles may influence the MMG signal. In addition, we observe that the majority of research processes MMG signals using wavelet transform. Time/frequency domain analysis of MMG signals provides useful information to examine muscle. In addition, we find that MMG may be applied to diagnose muscle conditions, to control prosthesis and/or switch devices, to assess muscle activities during exercises, to study motor unit activity, and to identify the type of muscle fiber. Finally, we find that the majority of the studies use accelerometers as sensors for MMG measurements. We also observe that currently MMG-based rehabilitation is still in a nascent stage. In conclusion, we recommend further improvements of MMG in the areas of sensor development, particularly on accelerometers, and signal processing aspects, as well as increasing future applications of the technique in prosthesis and/or switch control, clinical practices, and rehabilitation.

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Section: Muscle Characterizations Using Mmg 1) Fatigue and Strengtmentioning

confidence: 98%

mentioning

confidence: 99%

Mechanomyography Sensor Development, Related Signal Processing, and Applications: A Systematic Review

et al. 2013

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“…It is difficult to explain the MMG mechanism in a parallel muscle, in which the contraction of the muscle causes the expansion of its diameter. Orizio et al derived the transfer function of the surface MMG by applying sinusoidal electrical stimulation at various frequencies [9]. The transfer function corresponded to that of a whole muscle because the sinusoidal amplitude modulation changed the number of recruited motor units.…”

Section: Introductionmentioning

confidence: 99%

System identification of the mechanomyogram from single motor units during voluntary isometric contraction

Uchiyama

Hashimoto

2011

Med Biol Eng Comput

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A mechanomyogram (MMG) from single motor units of the anconeus muscle in voluntary isometric contraction was recorded from seven subjects using a spike-triggered averaging technique. The MMG system, in which the input was an ideal impulse and the output was the MMG detected with an acceleration sensor, was identified as the fifth-order model by the subspace-based state-space model identification method. The transfer function of the MMG system was factorized to the second- and the first-order models. The second-order model was compared to the standard form of the second-order model, and its resonance frequency was calculated. The resonance frequencies of the second-order models were 166 ± 61 and 93 ± 27 Hz, which were within the range of the values estimated from mechanical impedance in the literature. The equivalent mechanical model of the MMG system of the single motor unit was proposed on the basis of the fifth-order model. The model might be useful to evaluate the visco-elastic properties of the anconeus muscle.

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“…These properties have previously been investigated using mechanical disturbances such as random perturbations [1], [2] or ramp stretch [3]. Recently, a system identification method has been used that can estimate the transfer function between electrical stimulation and a mechanomyogram (MMG) [4]- [8]. Measurement of an MMG induced by electrical stimulation is easy and applicable to various muscles.…”

Section: Introductionmentioning

confidence: 99%

System Identification of Mechanomyogram at Various Levels of Motor Unit Recruitment

Uchiyama

Tamura

2014

SICE Journal of Control, Measurement, and System Integration

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: The mechanomyogram from a single motor unit and the induced mechanomyogram at various levels of recruitment were measured with an acceleration sensor. The transfer functions between motor unit action potential (or electrical stimulation) and the mechanomyogram were identified using the singular value decomposition method. The purpose of this study is to clarify how the model order of the transfer function depends on the recruitment level. The second-to tenth-order transfer functions were calculated, and the difference between the observed and the estimated mechanomyograms using the transfer function, the fitness, was calculated. The relationship between the model order and the fitness was tested using the Holm-Bonferroni multiple comparison. At low levels (single motor unit, 20, and 40%) of recruitment, there were significant differences between the fourth-and higher-order models, but there were no significant differences between the fifth-and higher-order models. In contrast, at high levels (60, 80, and 100%) of recruitment, the fourth-order model did not show significant differences between the fifth-or higher-order models. As a result, the fifthand fourth-order models were appropriate at low and high recruitment levels, respectively. The differences in the order might be caused by interactions between active and resting motor units.

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Muscle-joint unit transfer function derived from torque and surface mechanomyogram in humans using different stimulation protocols

Cited by 30 publications

References 28 publications

Mechanomyography Sensor Development, Related Signal Processing, and Applications: A Systematic Review

Mechanomyography Sensor Development, Related Signal Processing, and Applications: A Systematic Review

System identification of the mechanomyogram from single motor units during voluntary isometric contraction

System Identification of Mechanomyogram at Various Levels of Motor Unit Recruitment

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