• EMG can be measured non-invasively with special electrodes placed on the skin overlying the muscle(s) of interest (i.e. surface EMG, sEMG)
• sEMG technology has enabled the use of sEMG signal for medical and sports physiology related muscle performance analysis
• Activation level of the muscle
• Activation levels of specific types of muscle cells
• Muscle fatigue
• Timing of muscle activation(s) in relation to movement
For good training results, it is important to know that the training activates the right muscles to proper activation level. Mpower measures your muscle’s sEMG signal and shows the activation level.
Fast muscle cells are essential for speed, explosive and maximum power production. They also grow more effectively in size but are harder to activate. Slow muscle cell activation is required for endurance training and control of posture and coordination. With Mpower you can measure and monitor online the activation levels of each type of muscle cells and adjust your exercise according to the type and level of desired workout.
Sometimes one side of the body may develop to be stronger than the other. In such cases Mpower helps you to identify the imbalance and monitor your training to ensure the proper development of the weaker side.
For effective training it is beneficial to see muscle fatigue start to develop and how far it is allowed to develop. Mpower analyses the sEMG signal and, based on that, shows the fatigue development.
Strength training increases your power production capability and it has been shown that the increased power production capability is reflected in the EMGsignal.
Neuromechanics of Human Movement, Enoka R., 2002
Changes in cross-sectional area (CSA) of the quadriceps femoris, integrated EMG of vastus lateralis during a maximum contraction and the maximum voluntary contraction (MVC) force during isokinetic training (60 days) and detraining (40 days).
(Adapted from Narici, Roi, Landoni, Minetti and Ceretelli, 1989)
The electrode location and small inter-electrode spacing are important to minimize cross-talk from adjacent muscles
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o Campanini, I., et al. (2007). "Effect of electrode location on EMG signal envelope in leg muscles during gait." Journal of Electromyography and Kinesiology 17(4): 515-526.
o De Luca, C. J., et al. (2012). "Inter-electrode spacing of surface EMG sensors: Reduction of crosstalk contamination during voluntary contractions." Journal of Biomechanics 45(3): 555-561.
For effective sEMG signal measurement, noise should be reduced at the source with well-designed active electrodes
o Clancy, E. A., et al. (2002). "Sampling, noise-reduction and amplitude estimation issues in surface electromyography." Journal of Electromyography and Kinesiology 12(1): 1-16.
o Merletti, R., et al. (2009). "Technology and instrumentation for detection and conditioning of the surface electromyographic signal: State of the art." Clinical Biomechanics 24(2): 122-134.
It has been shown that sEMG signal frequency analysis provides a quite sensitive measure for muscle force estimation. It also has been shown that sEMG signal amplitude analysis based methods suffer from inaccuracies or may even produce contradictory results
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o Staudenmann, D., et al. (2010). "Methodological aspects of SEMG recordings for force estimation - A tutorial and review." Journal of Electromyography and Kinesiology 20(3): 375-387.
An abundance of beneficial information can be identified from sEMG signal with appropriate frequency analysis
1. Muscle cell type composition and cell size
2. Increasing fast muscle cell activation with faster movements
3. Increasing activation rate of the muscle cells when producing more power
4. Fatigue as a shift of frequency spectrum towards lower frequencies
5. Metabolic changes within the muscle visible in the spectrum
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o Wakeling, J. M., et al. (2006). "Muscle fibre recruitment can respond to the mechanics of the muscle contraction." Journal of the Royal Society Interface 3(9): 533-544.
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o Gonzalez-Izal, M., et al. (2010). "EMG spectral indices and muscle power fatigue during dynamic contractions." J Electromyogr Kinesiol 20(2): 233-240.
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o Contessa, P. and C. J. De Luca (2013). "Neural control of muscle force: indications from a simulation model." Journal of Neurophysiology 109(6): 1548-
