New York: Plenum Kogi, K. Reports of the Physiology Lab. Institute of Science and Labour 60 , 27—41 Lippold, O. Ergonomics 3 , — Lloyd, A. Messier, R. Milner-Brown, H. Mortimer, J. Ortengren, R. Ergonomics 18 , — Petrofsky, J. Physiologist 18 , a. Piper, H. Berlin: Springer Plonsey, R.
Saltin, B. Stalberg, E. In summary, the results of this study indicate that RMS values of the hamstrings muscles tend to increase nonlinear whereas force with the number of isometric ramp contractions performed. Since these responses are characteristic of neuromuscular fatigue, the test described here may be useful for identifying muscle fatigue in ramp isometric contraction test.
With this feature researches, in future studies, propose mathematical models to identify the turning point of the concavity of the sigmoid adjustment for the analysis and identification of the electromyographic fatigue.
Therefore, in order to develop a new protocol for the identification of fatigue could be observed electromyographic initial characteristics of the sigmoid curve which is a slow increase over time data, which has an exponential characteristic.
With these parameters can ascertain exponential models as the inflection point of the curve for possible identification of neuromuscular fatigue. Understanding the importance of digital signal processing, in this case the surface electromyographic signal, the mathematical adjustment mathematical modeling , presents itself as a tool to direct future research related to bioengineering, which may direct future investigations from the area of instrumentation to the development of new systems of man-machine synchronization.
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Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Downloaded: Introduction From the many joints exposed to muscle-skeletal injuries, the knee joint is the one that more suffers consuming in the daily life, for both athletes and non-athletes [ 1 ], once for the maintenance of the corporal stability, it is necessary for the muscles of this joint to be the strongest as possible [ 2 ].
Subject Twenty female healthy adults age Signal processing The sEMG signals were amplified with gain More Print chapter. How to cite and reference Link to this chapter Copy to clipboard. Available from:. Over 21, IntechOpen readers like this topic Help us write another book on this subject and reach those readers Suggest a book topic Books open for submissions.
More statistics for editors and authors Login to your personal dashboard for more detailed statistics on your publications. The test is used to help detect neuromuscular abnormalities. During the test, one or more small needles also called electrodes are inserted through the skin into the muscle.
The EMG trace changes as we add weights on the arm as because of load the contraction of the muscle get increased to support the extra weight also and as we go on increasing the weight on the same muscle the contraction and tension of the muscle also increases now but the level of contraction is less as compared to. The force generated by a muscle is a function of its velocity. Historically, the force -velocity relationship has been used to define the dynamic properties of the cross-bridges which cycle during muscle contraction.
Experimentally, a muscle is allowed to shorten against a constant load. Electrical activity is directly related to the strength of the muscle contraction , so a stronger contraction will cause a higher spike of activity in the graphical results Muscle Physiology Lab.
So, based on the information above, muscles with strong contractions will have a greater amount of electrical activity. Abnormal EMG results can show up in two ways. First, the muscle may show electrical activity at rest.
On the other hand, the muscle may show abnormal electrical activity during contraction. This shows up as an abnormal action potential pattern with changes in the size or shape of the wave. Signal characteristics of EMG during fatigue. AMUP showed sensitivity to fatigue with increase in amplitude, rise time, and number of spikes counted. PSDF was also easily affected by fatigue so that the total power density curve was shifted towards lower frequencies with a high frequency decay.
Explain why this arm fatigued faster than the other arm. The nondominant arm. This arm fatigued faster because it because harder to recruit more muscle fibers in a short amount of time which causes the readings togo down. Moreover, the kinematics of the muscle over which the electrode is placed must be considered: Are the relative lengths and velocities of the muscles in question comparable, and how is electrode position relative to each muscle's innervation zone changing?
One should be wary when attempting to compare excitation patterns of different muscles, especially during dynamic efforts Farina, If signals are properly normalized and appropriate considerations are made Table 1 , one may infer relative muscle excitation from within-subject, between-muscle sEMG studies.
For example, such studies may provide insight as to how differences in strength arise following verbal cueing Lohse et al. Other studies have utilized sEMG to compare which muscles experience the greatest excitation during different exercises Santana et al.
Much like comparing between-muscles, there are a number of concerns pertaining to whether or not individuals in a group, especially symptomatic ones, have the ability to maximally excite a muscle during a normalization trial 7. Furthermore, different participants respond differently to different normalization positions and techniques Vera-Garcia et al. In such cases, training exercise experience may still confound maximum M-wave normalized sEMG signals Arabadzhiev et al.
Therefore, comparisons between subjects of starkly different populations trained vs. In more homogeneous populations, one can use sEMG to understand mechanisms for differences in function.
For example, authors have used maximum M-wave normalized sEMG amplitudes to help us understand differences in strength between individuals Trezise et al.
The considerations for between-subject and between-muscle comparisons above both apply to this category, but are amplified due to the larger potential for differences to exist. Moreover, sEMG from several muscles drive muscle synergy analyses, which are showing to be informative for understanding neural control differences in those with cerebral palsy or changes following a stroke Steele et al.
Muscle synergy analyses can also be applied to experimental questions in sport, which could provide insight into motor control strategy differences between populations while carrying out a task e. Longitudinally, one may wish to understand how between-muscle neural control strategies explain differential adaptations to an intervention Erskine et al.
Challenging the applicability of sEMG can be viewed as bittersweet. While it is quite humbling that we do not truly understand what we are measuring or its implications, this also opens the door for high quality, impactful research. Ultimately, longitudinal outcomes are likely of greatest interest to practitioners Halperin et al.
Therefore, there is a tremendous need to evaluate the ability of sEMG to be used as a surrogate endpoint for muscle strengthening and hypertrophy Halperin et al. We, the authors, have pondered potential study designs to investigate this question but have been unable to ideate something that we believe is sufficient to answer the question. While previous investigations may be thinking along the right lines, they do not provide robust evidence Calatayud et al.
However, we encourage readers and fellow scientists to brainstorm and carry out such research, with the end goal of determining 1 if sEMG amplitude can be predictive of strength improvements or hypertrophy, 2 what the minimum difference in sEMG amplitude is for predicting greater strength or hypertrophy adaptations, 3 how generalizable the results are, and 4 how new technologies and more advanced signal processing techniques can be utilized.
Indeed, hypertrophy and strength are likely highly multifactorial and nonlinear, which will make such research tremendously difficult, if not impossible. Similar work has already been carried out as it pertains to muscle protein synthesis and hormone responses to exercise bouts Mitchell et al. Such research will validate or invalidate hundreds, if not thousands, of sEMG studies that were intended to be extrapolated for these longitudinal outcomes.
Factors other than muscular effort influence the myoelectric signal, including muscle length, contraction mode, contraction speed, etc. Comparing sEMG signals between different exercises that do not control for these variables should be avoided. Even when the sEMG signal adequately represents the force of the muscle, caution should be exercised when concluding that a certain exercise will be better for increasing strength or hypertrophy due to other factors that influence these adaptations.
Comparing normalized sEMG values between individuals with and without pain should be viewed cautiously. Changes in the normalized sEMG value over time cannot indicate changes in excitation because the normalized value can be influenced by excitation during the normalization contraction or during the measured exercise.
Normalizing to maximum M-wave amplitude can, to an extent, help diminish such effects. Within-subject, within-muscle comparisons of the sEMG signal across different exercises may be able to provide insight into muscular force production, provided the previously mentioned controls are made.
Researchers wishing to produce applicable research pertaining to longitudinal adaptation should prioritize longitudinal studies rather than acute, cross-sectional sEMG work Halperin et al. Because sEMG has not been validated as a surrogate endpoint for longitudinal measures, readers should be wary of bold conclusions.
Important mechanistic details of sEMG, such as signals being confounded by peripheral factors and data not being representative of a muscle, must be considered when attempting to draw conclusions—even acute, mechanistic ones.
For these points, we wish to stress that the burden of proof is on researchers to show that cross-sectional sEMG findings are practically meaningful for longitudinal outcomes, and until this is shown, discussions and conclusions should not imply that they are. Finally, although this review was expansive, depth was sacrificed for breadth and communicabilty. For readers interested in learning more about some of the topics discussed in this review, recommended texts, chapters, papers, and reviews are provided in Table 2.
AV and TV created the figures. AV and IH constructed the tables. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. For example, the number of cross-bridges established in an active muscle reduces as the muscle shortens from its optimal length.
Therefore, such measurements are not a measure of muscle force, namely due to contraction dynamics. This effect may be amplified with differential recruitment patterns Figure 2. Moreover, in the case of submaximal normalizations, the linear assumption of normalizations may be violated Hug and Tucker, Thus, someone in pain may produce lower excitation during a normalization trial e.
Alkner, B. Sports Exerc. Andersen, L. Neuromuscular activation in conventional therapeutic exercises and heavy resistance exercises: implications for rehabilitation. Arabadzhiev, T.
Influence of motor unit synchronization on amplitude characteristics of surface and intramuscularly recorded EMG signals. The increase in surface EMG could be a misleading measure of neural adaptation during the early gains in strength. Arvidsson, I.
Reduction of pain inhibition on voluntary muscle activation by epidural analgesia. Orthopedics 9, — PubMed Abstract Google Scholar. Aspe, R. Electromyographic and kinetic comparison of the back squat and overhead squat. Strength Cond. Ayotte, N. Electromyographical analysis of selected lower extremity muscles during 5 unilateral weight-bearing exercises. Sports Phys. Buckner, S. Determining strength: a case for multiple methods of measurement. Sports Med. Calatayud, J. Bench press and push-up at comparable levels of muscle activity results in similar strength gains.
Cavanagh, P. Electromyography: its use and misuse in physical education. Health Phys. Recreation 45, 61— Google Scholar. Clarys, J. Electromyography in sports and occupational settings: an update of its limits and possibilities. Ergonomics 43, — Electromyography and the study of sports movements: a review. Sports Sci. Contreras, B. A comparison of two gluteus maximus EMG maximum voluntary isometric contraction positions.
PeerJ 3:e Corlan, A. Damas, F. Resistance training-induced changes in integrated myofibrillar protein synthesis are related to hypertrophy only after attenuation of muscle damage.
Dankel, S. Do metabolites that are produced during resistance exercise enhance muscle hypertrophy? Changes in antagonist muscles' coactivation in response to strength training in older women. A Biol. Vastus lateralis maximum force-generating potential occurs at optimal fascicle length regardless of activation level.
De Luca, C. The use of surface electromyography in biomechanics. Influence of proprioceptive feedback on the firing rate and recruitment of motoneurons. Neural Eng. Behaviour of human motor units in different muscles during linearly varying contractions. Del Vecchio, A. Associations between motor unit action potential parameters and surface EMG features. Desmedt, J. Ballistic contractions in man: characteristic recruitment pattern of single motor units of the tibialis anterior muscle.
Dideriksen, J. Neuromuscular adjustments that constrain submaximal EMG amplitude at task failure of sustained isometric contractions. Dieterich, A. Spatial variation and inconsistency between estimates of onset of muscle activation from EMG and ultrasound. Dimitrova, N. Interpretation of EMG changes with fatigue: facts, pitfalls, and fallacies.
The spatial distribution of ankle muscles activity discriminates aged from young subjects during standing. Earp, J. Inhomogeneous quadriceps femoris hypertrophy in response to strength and power training. Increased hypertrophic response with increased mechanical load in skeletal muscles receiving identical activity patterns. Cell Physiol.
Ema, R. Unique activation of the quadriceps femoris during single- and multi-joint exercises. Inhomogeneous architectural changes of the quadriceps femoris induced by resistance training. Enoka, R. Neuromechanics of Human Movement.
Champaign, IL: Human Kinetics. Inappropriate interpretation of surface EMG signals and muscle fiber characteristics impedes understanding of the control of neuromuscular function. Erskine, R. The contribution of muscle hypertrophy to strength changes following resistance training. Inter-individual variability in the adaptation of human muscle specific tension to progressive resistance training. Escamilla, R. Core muscle activation during Swiss ball and traditional abdominal exercises.
Farina, D. Interpretation of the surface electromyogram in dynamic contractions. Sport Sci. Experimental muscle pain decreases voluntary EMG activity but does not affect the muscle potential evoked by transcutaneous electrical stimulation. Decoding the neural drive to muscles from the surface electromyogram.
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