The mechanomyography of persons after stroke during isometric voluntary contractions

Hu, Xiao; Tong, Kai-Yu; Li, Le

doi:10.1016/j.jelekin.2006.01.015

Cited by 37 publications

(42 citation statements)

References 44 publications

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“…Finally, MMG is a recording of muscle vibration that is generated by dimensional changes in active muscle fibers and has been utilized for diverse applications, which include prosthesis control [31], muscle fatigue assessment [25], and neuromuscular diagnosis [15]. Although MMG is influenced by many factors of muscle morphology [22] and the physical milieu, such as intra-muscular pressure, muscle stiffness, and osmotic pressure, and although the sensor placement [26] and movement artifacts also affect the amplitude and spectral feature of the MMG signal, as the control signals for HMIs, MMG can provide some notable advantages over EMG [36].…”

Section: Introductionmentioning

confidence: 99%

Estimation of elbow flexion force during isometric muscle contraction from mechanomyography and electromyography

Youn

Kim

2010

Med Biol Eng Comput

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Mechanomyography (MMG) is the muscle surface oscillations that are generated by the dimensional change of the contracting muscle fibers. Because MMG reflects the number of recruited motor units and their firing rates, just as electromyography (EMG) is influenced by these two factors, it can be used to estimate the force exerted by skeletal muscles. The aim of this study was to demonstrate the feasibility of MMG for estimating the elbow flexion force at the wrist under an isometric contraction by using an artificial neural network in comparison with EMG. We performed experiments with five subjects, and the force at the wrist and the MMG from the contributing muscles were recorded. It was found that MMG could be utilized to accurately estimate the isometric elbow flexion force based on the values of the normalized root mean square error (NRMSE = 0.131 ± 0.018) and the cross-correlation coefficient (CORR = 0.892 ± 0.033). Although MMG can be influenced by the physical milieu/morphology of the muscle and EMG performed better than MMG, these experimental results suggest that MMG has the potential to estimate muscle forces. These experimental results also demonstrated that MMG in combination with EMG resulted in better performance estimation in comparison with EMG or MMG alone, indicating that a combination of MMG and EMG signals could be used to provide complimentary information on muscle contraction.

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Section: Introductionmentioning

confidence: 99%

Estimation of elbow flexion force during isometric muscle contraction from mechanomyography and electromyography

Youn

Kim

2010

Med Biol Eng Comput

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“…Therefore, it is important to consider the effects of fat when adopting MMG as a tool in applications such as prostheses control (Orizio et al, 2003), monitoring of muscular fatigue (Orizio et al, 2003;Scheeren et al, 2010;Tarata, 2003), neuromuscular diseases diagnosis (Hu et al, 2007), respiratory muscle work (Sarlabous et al, 2014), among others. Subcutaneous fat, as demonstrated in this study, interferes on the analysis.…”

Section: Discussionmentioning

confidence: 99%

“…MMG is used in many applications, including: prostheses control (Orizio et al, 2003); indication of muscle activation degree (Scheeren et al, 2010); monitoring of muscle fatigue (Orizio et al, 2003;Tarata, 2003); neuromuscular diseases diagnosis (Hu et al, 2007); obtainment of signals for the study of muscle strength gradation mechanisms (Akataki et al, 2001;Matta et al, 2005;Madeleine et al, 2001;Nogueira-Neto et al, 2013); evaluation of respiratory muscle work (Sarlabous et al, 2014); changes in MU activation strategies, which can occur with aging, neuromuscular diseases, endurance training programs, and care of injuries (Cooper et al, 2014); and Parkinson's disease (Marusiak et al, 2009;Malek and Coburn, 2012). In applications with comparisons between the MMG signal and the force response the biceps brachii (Orizio et al, 1989) and rectus femoris (Krueger et al, 2016;Shin et al, 2016) are the muscles more used due its easy accessibility and comparison capability.…”

Section: Introductionmentioning

confidence: 99%

Influence of subcutaneous fat on mechanomyographic signals at three levels of voluntary effort

et al. 2017

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Introduction: This study aims to assess the influence of different skinfold thicknesses (ST) and their relation to the attenuation of the mechanomyographic (MMG) signal at different force levels (maximal voluntary contraction -MVC, 40% of MVC and 70% of MVC) of the rectus femoris muscle. Methods: Fifteen volunteers were divided in two groups: ST lower than 10mm (G<10) (8 participants) and ST higher than 35mm (G>35) (7 participants). Student t tests were employed to investigate differences between G<10 and G>35 regarding MMG analysis parameters (acceleration root mean square -aRMS, zero crossing -ZC, and median frequency -MDF), for the X, Y and Z axes, as well as for the modulus of these three axes.

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“…Band-pass filtering was applied to the EMG signals (from 10 to 500 Hz). The root mean squared (RMS) [15] values were calculated. Forthe study, participants were instructed to perform a maximal voluntary contraction (MVC) of knee flexion and extension, ankle plantarflexion, and dorsiflexion for 5seconds, and the maximum value of the torque for this movement was set as 100% of the MVC contraction level.…”

Section: Emg Acquisition and Data Analysismentioning

confidence: 99%

“…Increased neural drives can be reflected by increases in the EMG root mean squared (RMS), and the increasing neural stimulation from the neural system after a stroke will cause an obvious variation in EMG RMS [15]. Normalized RMS values were used in this study to reflect muscle activation intensity.…”

Section: Emgmentioning

confidence: 99%

Improved walking ability with wearable robot-assisted training in patients suffering chronic stroke

Chen

Mao

et al. 2015

BME

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Abstract. Wearable robotic devices provide safe and intensive rehabilitation, enabling repeated motions for motor function recovery in stroke patients. The aim of this small case series was to demonstrate the training effects of a three-week robotic leg orthosis, and to investigate possible mechanisms of the sensory-motor alterations and improvements by using gait analysis and EMG. Three survivors of chronic strokes participated in robot-assisted gait therapy for three weeks. EMG signals from the rectus femoris (RF), tibialis anterior (TA), biceps femoris (BF), and medial gastrocnemius (MG), as well as kinetics and kinematics data of the lower limb, were recorded before and after the training. The normalized root mean squared (RMS) values of the muscles, the joint moments, joint angles, and the results of two clinical scales (Berg Balance scale, BBS, and the lower extremity subscale of Fugl-Meyer assessment, LE-FMA) were used for analysis. All participants experienced improved balance and functional performances and increased BBS and LE-FMA scores. The EMG results showed there was an increase of the normalized RMS values of the MG and BF on the affected side. Additionally, EMG activities of the agonist and antagonist pair (i.e. RF and BF) appeared to return to similar levels after training. The peak moment of hip flexor, knee extensor, and plantar flexor, which all contributed to push-off power, were found to have increased after training. In summary, the three-week training period using the wearable RLO improved the three participants' gait performance by regaining push-off power and improved muscle activation and walking speed.

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The mechanomyography of persons after stroke during isometric voluntary contractions

Cited by 37 publications

References 44 publications

Estimation of elbow flexion force during isometric muscle contraction from mechanomyography and electromyography

Estimation of elbow flexion force during isometric muscle contraction from mechanomyography and electromyography

Influence of subcutaneous fat on mechanomyographic signals at three levels of voluntary effort

Improved walking ability with wearable robot-assisted training in patients suffering chronic stroke

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