The Influence of Different Elbow Angles on the Twitch Response of the Biceps Brachii Muscle Between Intermittent Electrical Stimulations

Djordjević, S; Tomažič, Sašo; Zupančič, Gregor Drago; Pišot, Rado; Dahmane, Raja

doi:10.5772/35312

Cited by 3 publications

(6 citation statements)

References 35 publications

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“…It is important to note that the various muscles in our body respond differently when it comes to endurance and fatigue occurrence at different joint angles [1]. While for most muscles it was found that a small joint angle, which gives a longer muscle length, fatigue would occur more quickly [2,3]. However, for the biceps brachii it was found that the RMS value of the MMG signal increased with increases in the elbow angle.…”

Section: Discussionmentioning

confidence: 87%

“…The joint angle affects the muscle length, which again affects the localised fatigue in the contracting muscle. Djordjevic et al [2] found that fatigue occurred faster and at a higher rate in long muscles, which means that the fatigue occurs quicker at small elbow angles. Mamghani et al studied signals from sustained isometric contraction recorded with both MMG and sEMG in time and frequency domains in various upper limb muslces, including the biceps brachii [3].…”

Section: Introductionmentioning

confidence: 99%

“…MMG has also been utilised in studies on muscle activity [1] and prosthetic control [2]. MMG is a mechanical measure of the changes within the surface of a contracting muscle i.e., where the vibration of the muscle fibres that move are recorded orizio.…”

Section: Introductionmentioning

confidence: 99%

“…MMG is a mechanical measure of the changes within the surface of a contracting muscle i.e., where the vibration of the muscle fibres that move are recorded orizio. MMG can be recorded using a variation of sensors, such as hydrophones, condenser microphones, piezoelectric contact sensors, laser distance sensors orizio,orizio2, sonomyography [2], accelerometers [3] and goniometer [3] among others.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Elbow Angle for MMG Signal Classification of Biceps Brachii during Dynamic Fatiguing Contraction

Almulla

Sepúlveda

Suoud

2015

Bioinformatics and Biomedical Engineering

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Abstract. Mechanomyography (MMG) activity of the biceps muscle was recorded from thirteen subjects. Data was recorded while subjects performed dynamic contraction until fatigue. The signals were segmented into two parts (Non-Fatigue and Fatigue), An evolutionary algorithm was used to determine the elbow angles that best separate (using DBi) both Non-Fatigue and Fatigue segments of the MMG signal. Establishing the optimal elbow angle for feature extraction used in the evolutionary process was based on 70% of the conducted MMG trials. After completing twenty-six independent evolution runs, the best run containing the best elbow angles for separation (fatigue and non-fatigue) was selected and then tested on the remaining 30% of the data to measure the classification performance. Testing the performance of the optimal angle was undertaken on eight features that where extracted from each of the two classes (non-fatigue and fatigue) to quantify the performance. Results show that the elbow angles produced by the Genetic algorithm can be used for classification showing 80.64% highest correct classification for one of the features and on average of all eight features including worst performing features giving 66.50%.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Elbow Angle for MMG Signal Classification of Biceps Brachii during Dynamic Fatiguing Contraction

Almulla

Sepúlveda

Suoud

2015

Bioinformatics and Biomedical Engineering

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“…Finally, electrical noise has no effect on MMG. EMG on the other hand, cannot be used concurrently with functional electrical stimulation (FES) [18], accelerometers [19] and goniometers [19] among others. Each sensor has advantages and disadvantages in terms of weight, cost, accuracy, noise and so on.…”

Section: Introductionmentioning

confidence: 99%

The effect of accelerometer mass in mechanomyography measurements

Ahn¹,

Shin²,

Kim³

2016

J. vibroeng.

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Abstract. Mechanomyography (MMG) signals record and quantify low-frequency lateral oscillations of active skeletal muscles. These oscillations reflect the ''mechanical counterpart'' of the motor unit activity measured by electromyography (EMG). Accelerometers have been commonly used to measure MMG. However, the accelerometer mass can affect the MMG signal. The purpose of this paper was to investigate the relationship of the accelerometer mass and the MMG signal. Thirty-two normal volunteers conducted the maximum voluntary contraction of leg extension. MMG signals were obtained from the rectus femoris muscle using an accelerometer. For each subject, the accelerometer mass was varied from 3, 8, 13, 18, 23 and 28 g. The signals were measured for three seconds with a sampling rate of 1kHz. Results showed that the MMG signal amplitude increased as the accelerometer mass increased. However, the median frequency (MF) of the MMG signal decreased with the increased accelerometer mass. When the accelerometer mass increased from 8 g to 13 g, the amplitude of the MMG signal increased the most, and the MF of the MMG signal decreased the most. However, for accelerometers heavier than 13 g, no significant change was observed in both the amplitude and MF. Based on the present study, the mass of the accelerometer is recommended to not exceed 13 g to properly measure MMG signals.

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The Tensiomiography Amplitude Stimulation Influences the Interpretation of the Rectus Femoris Neuromuscular Status After a Repeated Sprint Training

Muñoz-López

Sañudo

Hoyo

2021

Innovation in Physical Activity and Sport

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This study aimed to analyze the relationship between the muscle contractile properties (MCP), before and after performing several bouts of repeated sprints, measured at different neuromuscular electrical stimulation (NMES) amplitudes. Twenty-two male participants participated on this study (age = 23.2 ± 9.8 years, height = 1.75 ± 0.01 m, body mass) 72.7 ± 21.0 kg). Testing consisted of creatine kinase (CK) measurement, tensiomyography (TMG), and maximal voluntary isometric contraction (MVIC). We first collected a fingertip capillary blood sample for the CK determination after 10 min of remaining in a seated position. Secondly, we conducted a TMG test on each limb in the Rectus Femoris (RF). Then, participants performed 6 maximal 30 m repeated sprints (RS) with a 30 s recovery. Pre-intervention, the highest correlations (p < 0.05) were found between muscle displacement stimulated at 40 mA and RS-change (r b = -0.65), RS-Loss (r b = -0.66) and RS-Fatigue (r b = -0.55). Superior NMES amplitude decreased the magnitude of the correlation. After the intervention, the highest correlations were found with an amplitude of 60 mA between muscle displacement and RS-change (r b = -0.61), RS-Loss (r b = -0.57), and RS-Fatigue (r b = -0.50). These results suggest that measuring the TMG until the Dm-max point can lead to misinterpretations of the MCP behavior in response to transient fatigue.

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The Influence of Different Elbow Angles on the Twitch Response of the Biceps Brachii Muscle Between Intermittent Electrical Stimulations

Cited by 3 publications

References 35 publications

Optimal Elbow Angle for MMG Signal Classification of Biceps Brachii during Dynamic Fatiguing Contraction

Optimal Elbow Angle for MMG Signal Classification of Biceps Brachii during Dynamic Fatiguing Contraction

The effect of accelerometer mass in mechanomyography measurements

The Tensiomiography Amplitude Stimulation Influences the Interpretation of the Rectus Femoris Neuromuscular Status After a Repeated Sprint Training

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