SciELO - Scientific Electronic Library Online

vol.29 issue4Prevalence of postural deviations and associated factors in children and adolescents: a cross-sectional studyMicrovascular oxygen extraction during maximal isometric contraction in patients with chronic obstructive pulmonary disease author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand




Related links


Fisioterapia em Movimento

On-line version ISSN 1980-5918

Fisioter. mov. vol.29 no.4 Curitiba Out./Dec. 2016 

Artigos originais

Electromyographic normalization of vastus lateralis and biceps femoris co-contraction during gait of elderly females

Normalização eletromiográfica da co-contração do vasto lateral e bíceps femoral durante a marcha de idosas

Katy Andrade Monteiro Zacarona  b 

João Marcos Domingues Diasb  c 

Mariana Asmar Alencarb  d 

Luane Landim de Almeidae 

Carlos Alberto Mourão-Júniora 

Rosângela Correa Diasb  c 

aUniversidade Federal de Juiz de Fora (UFJF), Juiz de Fora, MG, Brazil

bUniversidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG, Brazil

cUniversidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil

dUniversidade Fundação Mineira de Educação e Cultura (FUMEC), Belo Horizonte, MG, Brazil

eFaculdade de Ciências Médicas e da Saúde de Juiz de Fora (FCMS/JF), Juiz de Fora, MG, Brazil



Analyze muscle co-contraction using electromyographic signals, which are normalized to compare individuals, muscles and studies. Maximum voluntary isometric contraction (MVIC) and peak electrical activity (PEA) during movement are the most widely used forms of normalization.


Compare inter-subject variability and investigate the association between the co-contraction indices of the vastus lateralis and biceps femoris during gait, normalized by MVIC and PEA. Methods: Thirty elderly women, aged 70.33 ± 3.69 years took part. Electrical muscle activity during MVIC and gait was recorded using a Biopac MP100 electromyograph. MVIC was performed in a Biodex isokinetic dynamometer. For normalization, the signals were divided by the Root Mean Square values of MVIC and PEA of gait.


The coefficient of variation of non-normalized data was 69.3%, and those normalized by PEA and MVIC were 30.4% and 48.9% respectively. Linear regression analysis resulted in a prediction model: PEA = 0.04 + 0.16 x MVIC. The goodness of fit of the regression model was statistically significant (p=0.02). The confidence interval (95% CI) for the intercept was between 0.02 and 0.29 and for MVIC between 0.03 and 0.06.


The data normalized by PEA showed less variation than those normalized by MVIC. A 100% variation in data normalized by MVIC resulted in a 16% variation in data normalized by PEA, while variation in normalization by MVIC accounts for 17% of the variation in normalization by PEA and vice versa.

Keywords: Muscle Contraction; Isometric Contraction; Reproducibility of Results; Muscle Strength Dynamometer; Electromyography



Analisa-se co-contração muscular através dos sinais eletromiográficos, os quais são normalizados para permitir comparação entre indivíduos, músculos e estudos. A contração voluntária máxima isométrica (CVMi) e o pico da atividade elétrica (PAE) durante o ato motor são as formas de normalização mais utilizadas. Objetivos: Comparar a variabilidade inter-sujeitos e investigar a associação entre os índices de co-contração, do vasto lateral e bíceps femoral durante a marcha, normalizados pela CVMi e PAE.


Participaram 30 idosas, idade 70,33 ± 3,69 anos. A atividade elétrica dos músculos durante a CVMi e na marcha foi registrada pelo eletromiógrafo Biopac MP100. A CVMi foi realizada no dinamômetro isocinético Biodex. Para normalização, os sinais foram divididos pelos valores do Root Mean Square da CVMi e pelo PAE da marcha.


O coeficiente de variação dos dados não normalizados foi 69,3%, e dos normalizados pelo PAE e CVMi foram 30,4% e 48,9% respectivamente. A análise de regressão linear produziu o modelo de predição: PAE = 0,04 + 0,16 x CVMi. O ajuste do modelo de regressão foi estatisticamente significante (p=0,02). O intervalo de confiança (IC95%) para o intercepto foi de 0,02 a 0,29 e para a CVMi foi de 0,03 a 0,06.


Os dados normalizados pelo PAE apresentaram menor variação que os normalizados pela CVMi. Uma variação de 100% nos dados normalizados pela CVMi resulta em 16% de variação nos dados normalizados pelo PAE, enquanto a variação da normalização pela CVMi é responsável por 17% de variação da normalização pelo PAE e vice-versa.

Palavras-chave: Contração Muscular; Contração Isométrica; Reprodutibilidade dos Testes; Dinamômetro de Força Muscular; Eletromiografia


Muscle co-contraction is the simultaneous contraction of two or more antagonist muscles around a joint 1,2,3. This phenomenon has been used to qualitatively and quantitatively assess human motor behavior in different situations such as gait, reach, jump and stability disorders 4,5.

Advancing age prompts greater muscle co-contraction. One possible explanation for this finding is that co-contraction is a form of compensation for the reduction in muscle strength and atrophy of muscle fibers that accompany aging 6,7.

Surface electromyography (EMG), an efficient tool to assess muscle activity, is also used for quantitative and qualitative evaluation of the co-contraction. The signal captured and recorded by this instrument corresponds to the sum of action potentials of motor units generated by voluntary and reflex muscle action, captured by electrodes placed on the surface of muscles 4,5,6,7,8,9.

One of the greatest obstacles to using this technique is the natural variability of electrical signals between individuals and muscles. Normalization, involving the expression of EMG data in relation to percentage of a reference value during a standardized and reproducible condition 8,10,11, is a prerequisite for reducing the intrinsic and extrinsic factors that contribute to signal variability. This procedure makes it possible to compare EMG data between individuals, muscles, different collection days and different studies 8,11,12,13,14,15.

Reflecting the unease in the scientific community in relation to the topic, several techniques for normalizing electromyographic signals during static and dynamic activity have been extensively debated and thoroughly reviewed 12,16,16,17,18,19,20,21,22,23,24,25,26,27. Of these, the most frequently investigated are maximum voluntary isometric contraction (MVIC) 10,11 and peak electrical activity (PEA) during the specific motor act.

MVIC, the most widely used in research 12,15,20,28,29, is the normalization method suggested by the guide entitled Surface Electromyography for the Non-Invasive Assessment of Muscles (SENIAM) 10. It allows us to determine the percentage of muscle activation in relation to its maximum capacity (100%) 12,30. However, MVIC depends on the maximum recruitment of all the motor units which, in turn, depends on a number of factors, such as the individual's training level, ability to activate the muscle 12,30, understanding, motivation and pain level 22,28,31. Furthermore, MVIC is performed in a condition of isometry, while dynamic activities encompass cycles of concentric and eccentric activations that require specialized equipment, and prolonged collection times 12,18,21,32,33,34,35.

For this reason, recent years have seen considerable investment in incorporating MVIC normalization as the predominant, with a number of studies published on alternative models to MVIC normalization in dynamic activities, including average or submaximal activations during specific dynamic activities 12,16,17,18,20,22,23,26,32,35, submaximal isometric contractions 26, as well as maximal and isokinetic submaximal contractions 19,26,32.

By contrast, normalization by peak electrical activity (PEA) during the specific motor act has been suggested by some investigators as an alternative for normalizing electromyographic signals during analysis of a dynamic event 12,16,17,18. Normalization by PEA also reduces collection time 21 and makes normalization possible in studies with children and populations unable to perform MVIC owing to cognitive impairment, neurological or musculoskeletal disease 12,17,18.

Given the importance of measuring co-contraction in the study of human movement and establishing uniformity in the procedures used in electromyographic studies, it is necessary to determine the association between different normalization techniques, in order to confirm the comparisons made between studies using different techniques. Therefore, this study aimed to compare inter-subject variability between co-contraction indices of the vastus lateralis (VL) and biceps femoris (BF) muscles, normalized by MVIC and PEA, during gait in the elderly, and investigate the association between these indices.


This cross-sectional exploratory study was approved by the research ethics committee of the Federal University of Minas Gerais (protocol no. 045/01).


This is a non-probability convenience sample in which sampling size was not calculated. Sampling criteria were accepting to take part and meeting inclusion and exclusion criteria. A total of 30 community-dwelling elderly women, aged between 65 and 80 years, and able to walk without an assistive gait device, participated in the study. They were recruited from the Physical Education in the Elderly Project (Projeto Educação Física na 3a idade) conducted at the School of Physical Education, Physical Therapy and Occupational Therapy of the Federal University of Minas Gerais (UFMG). All participants met the following inclusion criteria: age greater than or equal to 65 years, absence of acute neurological or rheumatic diseases, no history of hip or knee surgery, absence of cognitive impairment that compromises understanding of the tests, hip range of motion of at least 90º, 100º knee flexion and 5º extension and absence of lower limb pain.

All participants gave their informed consent to take part in the study.


An MP150WSW electromyograph (Biopac Systems(r), Goleta, California) was used to monitor the electrical activity of vastus lateralis (VL) muscles, consisting of the knee extensors 16,33,34, and biceps femoris (BF), consisting of the knee flexors 16,29. This device has two amplifiers connected to a microcomputer. The amplifiers have inlet impedance of 2 mega Ohms (MΩ) and a Common Mode Rejection capacity of 1000 MΩ. Two active surface electrodes (Ag/AgCl - 11.4mm diameter - TSD-150A - Biopac System), with bipolar configuration were placed 3cm between the detection surfaces.

A Biodex System 3 Pro isokinetic dynamometer (Biodex Medical Systems, Shirley, New York), operated by a previously trained assessor, was used to obtain MVIC of the BF and VL muscles.


The participants were instructed to appear for testing, conducted on a single day, with clean skin, free of oil or moisturizers.

To characterize the sample, subjects were assessed for anthropometric and clinical aspects including age, body weight, height, body mass index and physical activity level, according to the American College of Sports Medicine (ACSM) 36, which considers as active those who engage in moderate physical activity for at least 30 minutes on most days of the week. To warm up, participants pedaled a stationary bicycle (Ergo-Fit 167, Baujahr, 2001) for 5 minutes at a comfortable speed 17.

After participants' skin was cleaned using cotton soaked in alcohol, the active surface electrode pairs were placed in accordance with Criswell 13. The grounding electrode was placed on the head of the ipsilateral fibula 13. The data were collected only on the dominant lower limb, defined as that which the individual uses to kick a ball.

To familiarize the subjects, they were asked to walk comfortably at a normal self-selected speed for a distance of 6 m, in a straight line on a flat even surface. After familiarization, electrical muscle activity was collected during gait using EMG.

Immediately following collection of muscle activity, participants were seated on the isokinetic dynamometer for MVIC tests of the VL and BF with their trunk, pelvis and thighs stabilized and legs dangling, with a distance of 5 cm between the edge of the chair and popliteal fossa of 5 cm. The rotational axis of the device was aligned with the lateral epicondyle of the femur and the arm of the lever fixed above the lateral malleolus. To obtain data for normalization of the electromyographic signals, subjects underwent MVC of the VL and BF muscles, in the isometric mode of the dynamometer, with the knee flexed at 20 and 100°, respectively. Four 6-second VL contractions were performed 1 minute apart. The first of these was submaximal, aimed at familiarization. After 2 minutes, the same procedure was conducted for BF muscles. Repetitions were monitored using EMG and, of the three maximal contractions, that which generated the greatest electromyographic activity of each muscle was analyzed.

The isokinetic device was calibrated before each test according to manufacturer`s instructions.

Data Reduction

Acqknowledge(r) software (Biopac Systems. Goleta, California) was used to process and analyze electromyographic activity. Electromyographic signals were collected at 1000Hz with 20 Hz high-pass filters and 450 Hz low-pass filters 19,33,37,38, then rectified and filtered (Butterworth - linear envelope) with a 20 Hz low-pass 11,37.

The signals were normalized in two ways: by the Root Mean Square obtained in MVIC 11 and PEA of the same muscle and participant. The mean co-contraction value was obtained using a specific program in MATLAB(r), proposed by Fonseca et al. 3, where the common area between the muscle activity curves of the muscle groups that were in simultaneous contraction was identified and quantified. This area was obtained by overlapping the electromyographic activity proportion curves of the muscles tested.

Statistical Analysis

Normal distribution of the variables was tested and confirmed by the Kolmogorov-Smirnov test.

Descriptive statistical analysis of anthropometric, demographic and co-contraction variables was conducted. In the case of co-contraction, coefficients of variation (CV) were also calculated.

To investigate the association between co-contraction normalized by MVIC and PEA, the data were plotted on a dispersion diagram in order to verify the existence of a tendency to correlation. Based on the tendency to linearity observed, a liner model (simple regression) was fit to estimate the possibility of prediction. In this model, normalization by PEA was considered the dependent variable and by MVIC the independent variable. Analysis of residues was carried out after all the conditions required for regression analysis were met. The coefficient of determination (R2) was also calculated to check the extent to which variation in normalization by MVIC explains the variation in normalization found by PEA.

A significance level of p < 0.05 was adopted. Analyses were performed using the GraphPad Prism 5.0 statistical package.


Of the 64 elderly assessed, 34 did not take part in the study for the following reasons: presence of neurological disease 6, acute rheumatic disease 15, previous hip surgery 3, previous knee surgery 9, and presence of cognitive impairment that compromises the tests 1. Thus, the final sample consisted of 30 elderly women. The anthropometric aspects of the participants are described in table 1.

Table 1 Anthropometric aspects of the sample (n = 30) 

Note: SD: standard deviation; BMI: body mass index. Source: Study data.

According to the ACSM, 53.33% of the participants were classified as active and 46.67% sedentary. Electromyographic data showed no outliers. Descriptive statistical analysis (mean ± SD) and CV of non-normalized co-contraction were 0.13 ± 0.09 (CV = 69.3%). Normalization by PEA and MVIC were 0.054 ± 0.016 (CV = 30.4%) and 0.09 ± 0.044 (CV = 48.9%), respectively. The CV results are illustrated in Figure 1, which shows that the variation in data normalized by PEA was visibly lower than that produced by normalization using MVIC.

Note: Study data.

Figure 1 Variations in vastus lateralis and biceps femoris co-contraction levels during gait, normalized by peak electrical activity (PEA) and maximal voluntary isometric contraction (MVIC) (n = 30). 

Linear regression analysis produced the following prediction model: PEA = 0.04 + 0.16 x MVIC. The goodness of fit of the regression model was statistically significant (p = 0.02). The confidence level (95% CI) for the intercept was from 0.02 to 0.29 and for MVIC from 0.03 to 0.06. These results suggest that an increase of 1 unit in MVIC corresponds to a rise of 0.16 in PEA, that is, a 100% variation in MVIC corresponds to only 16% in PEA. These data are consistent with the coefficient of determination found (R2 = 17%). The dispersion diagram, straight line of regression and the respective 95% confidence intervals are presented in Figure 2.

Note: Study data.

Figure 2 Dispersion diagram and straight line of regression (with the respective 95% confidence intervals) howing the level of co-contraction normalized by peak electrical activity (PEA) and maximum voluntary isometric contraction (MVIC) (n = 30) in gait. 


Given that an absence of adequate familiarization can cause a 20 to 30% loss of MVIC capacity 11, particular care was taken to familiarize the volunteers, eliminating inadequate data.

Since the objective was to collect maximal electrical muscle activity during MVIC, the position adopted in this study aimed at exposing the muscles tested to a condition of shortened active insufficiency, such that the muscle recruited the maximum number of motor units to generate MVIC) 39, ensuring an electromyographic recording that revealed the maximum possible contraction. Rutherford et al. 15, however, in a study aimed at verifying the existence of a difference in the amplitude of electrical activity during a series of maximal contractions at various knee angles and body positions, concluded that the greatest VL activation occurred at an amplitude of 45º knee flexion in the sitting position or 15º knee flexion in the supine position. The same authors concluded that the highest FB activation occurred at an angle of 15º knee flexion in the supine position. The study, however, did not assess positions that placed muscles in shortened active insufficiency. Therefore, the choice of angle and position for MVIC may cause a methodological error, requiring further studies to determine the best position for maximum electrical activation of each muscle.

Given that inter-subject CV is inversely related to reproducibility, this index has been widely used in studies on the normalization of EMG data, and as a criterion for selecting a particular normalization method 12,16,17,18,19,20,31,32,35. The inter-subject CV values of the present study showed that both forms of normalization reduced the variation in data when compared with non-normalized data. However, normalization by PEA exhibited a lower CV than that of MVIC. These results are in line with those reported in a number of studies showing less inter-subject variability with normalization by PEA compared with MVIC or other normalization methods 16,17,18,26,32. This led some authors to consider PEA more appropriate in representing data 18, while others report that a lower CV is not necessarily good, since variability is necessary to identify differences 17. Moreover, even though PEA is a viable method for normalizing EMG data of patients with pain and neurological disorders, it tends to produce an electromyographic pattern that may infer unreal group homogeneity by removing real biological variations in EMG files during various activities, including gait 16,17,26,31,32.

Furthermore, since PEA is obtained during the task under study, it does not provide the researcher or specialist with the required degree of muscle activation during gait or some other task, in relation to what the muscle is capable of generating 12,16,17,18. The only methods with the potential to reveal this information are normalization by maximal voluntary isometric or isokinetic contractions 16,17,26,32, but both have exhibited higher CV in studies, primarily the latter 26,32. Although the former conserves the natural biological variability of EMG data, it can cause discomfort, in addition to depending on psychological factors 22,28,31 and training of individuals 12,30. Because of this difficulty in controlling and monitoring the participant`s exertion, it might not generate the maximum activity possible. This will result in higher EMG values than those normalized by PEA and also impacts the CV, which is significantly affected by the way in which MVIC is processed 15,20,26.

The prediction model generated by linear regression showed that a 100% variation in data normalized by MVIC results in only a 16% variation in data normalized by PEA, and that the variation in normalization by MVIC accounts for only 17% of the variation in normalization by PEA and vice versa. These findings demonstrate the need for caution when comparing studies that used different techniques to normalize data 12,18.

No studies were found that clearly described the prediction models used for data normalization. Therefore, it was not possible to compare these results. Nevertheless, we suggest that to make comparisons between EMG data from VL and FB co-contraction normalized by MVIC during gait and those normalized by PEA, conversion must first be performed using the prediction model obtained.


The variation in data normalized by PEA was lower than that produced via normalization by MVIC. Furthermore, a 100% variation in data normalized by MVIC results in only 16% variation in data normalized by PEA, and the variation in normalization by MVIC accounts for only 17% variation in normalization by PEA and vice versa. Caution is therefore recommended when comparing studies that used different techniques to normalize electromyographic data.

It is important to underscore that the results of this study refer to co-contraction of VL and FB muscles during the gait of asymptomatic elderly women. Thus, the conclusions of this study apply only to this scenario and cannot be generalized. Moreover, this investigation conducted measurements on a single day. Therefore, it is recommended that additional studies with the same objective be conducted with different age groups, tasks, muscles and days.


Nardo, FD; Mengarelli, A; Maranesi, E; Burattini, L; Fioretti, S. Assessment of the ankle muscle co-contraction during normal gait: A surface electromyography study. J Electromyogr Kinesiol. 2015;25:347-54. [ Links ]

Lametti, DR; Houle, G; Ostry, DJ. Control of movement variability and the regulation of limb impedance. J Neurophysiol. 2007;98(6):3516-24. [ Links ]

Fonseca, ST; Silva, PLP; Ocarino, JM; Ursine, PGS. Análise de um método eletromiográfico para quantificação de co-contração muscular. Rev Bras Cien e Mov. 2001;9(3):23-30. [ Links ]

Fallah-Yakhdani, HR; Abbasi-Bafghi, H; Meijer, OG; Bruijn, SM; van den Dikkenberg, N; Benedetti MG; et al. Determinants of co-contraction during walking before and after arthroplasty for knee osteoarthritis. Clin Biomech. 2012;27(5):485-94. [ Links ]

Lee, HH; Lin, CW; Wu, HW; Wu, TC; Lin, CF. Changes in biomechanics and muscle activation in injured ballet dancers during a jump-land task with turnout. J Sports Sci. 2012;30(7):689-97. [ Links ]

Wang, Y; Watanabe, K; Asaka, T. Age effects on multi-muscle modes during voluntary body sway. Res Sports Med. 2015;23(1):88-101. [ Links ]

Nelson-Wong, E; Appell, R; McKay, M; Nawaz, H; Roth, J; Sigler, R; et al. Increased fall risk is associated with elevated co-contraction about the ankle during static balance challenges in older adults. Eur J Appl Physiol. 2012;112(4):1379-89. [ Links ]

Merletti, R; Aventaggiato, M; Botter, A; Holobar, A; Marateb, H; Vieira, TM. Advances in surface EMG recent progress in detection and processing techniques. Crit Rev Biomed Eng. 2010;38(4):305-45. [ Links ]

Kellis, E; Katis, A. Hamstring antagonist moment estimation using clinically applicable models: Muscle dependency and synergy effects. J Electromyogr Kinesiol. 2008;18:144-53. [ Links ]

Hermens, HJ; Freriks, B; Disselhorst-Klug, C; Rau, G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol. 2000;10(5):361-74. [ Links ]

Standards Standards for reporting EMG. J Electromyogr Kinesiol. 1996;6(2):III-IV. [ Links ]

Albertus-Kajee, Y; Tucker, R; Derman, W; Lambert, M. Alternative methods of normalising EMG during cycling. J Electromyogr Kinesiol. 2010;20(6):1036-43. [ Links ]

Criswell, E. Cram´s introduction to surface electromyography. 2nd ed. Ontario (Canada): Aspen; 2010. [ Links ]

Lehman, GJ; McGill, SM. The importance of normalization in the interpretation of surface electromyography: a proof of principle. J Manipulative Physiol Ther. 1999;22(7):444-6. [ Links ]

Rutherford, DJ; Hubley-Kozey, CL; Stanish, WD. Maximal voluntary isometric contraction exercises: a methodological investigation in moderate knee osteoarthritis. J Electromyogr Kinesiol. 2011;21(1):154-60. [ Links ]

Balshaw, TG; Hunter, AM. Evaluation of electromyography normalisation methods for the back squat. J Electromyogr Kinesiol. 2012;22(2):308-19. [ Links ]

Bolgla, LA; Uhl,TL. Reliability of electromyographic normalization methods for evaluating the hip musculature. J Electromyogr Kinesiol. 2007;17(1):102-11. [ Links ]

Albertus-Kajee, Y; Tucker, R; Derman, W; Lamberts, RP; Lambert, MI. Alternative methods of normalising EMG during running. J Electromyogr Kinesiol. 2011;21(4):579-86. [ Links ]

Ball, N; Sucurr, J. An assessment of the reliability and standardisation of tests used to elicit reference muscular actions for electromyographical normalization. J Electromyogr Kinesiol. 2010;20(1):81-8. [ Links ]

Norcross, MF; Blackburn, JT; Goerger, BM. Reliability and interpretation of single leg stance and maximum voluntary isometric contraction methods of electromyography normalization. J Electromyogr Kinesiol. 2010;20(3):420-5. [ Links ]

Nishijima, Y; Kato, T; Yoshizawa, M; Miyashita, M; Iida, H. Application of the segment weight dynamic movement method to the normalization of gait EMG amplitude. J Electromyogr Kinesiol. 2010;20(3):550-7. [ Links ]

Murley, GS; Menz, HB; Landorf, KB; Bird, AR. Reliability of lower limb electromyography during overground walking: a comparison of maximal- and sub-maximal normalisation techniques. J Biomech. 2010;43(4):749-56. [ Links ]

Chapman, AR; Vicenzino, B; Blanch, P; Knox, JJ; Hodges, PW. Intramuscular fine-wire electromyography during cycling: repeatability, normalisation and a comparison to surface electromyography. J Electromyogr Kinesiol. 2010;20(1):108-17. [ Links ]

Farina, D; Cescon, C; Merletti, R. Influence of anatomical, physical, and detection-system parameters on surface EMG. Biol Cybern. 2002;86(6):445-56. [ Links ]

Hunter, AM; St Clair, GA; Lambert, M; Noakes, TD. Electromyographic (EMG) normalization method for cycle fatigue protocols. Med Sci Sports Exerc. 2002;34(5):857-61. [ Links ]

Burden, A; Bartlett, R. Normalisation of EMG amplitude: an evaluation and comparison of old and new methods. Med Eng Phys. 1999;21(4):247-57. [ Links ]

Kollmitzer, J; Ebenbichler, GR; Kopf, A. Reliability of surface electromyographic measurements. Clin Neurophysiol. 1999;110(4):725-34. [ Links ]

Ha, SM; Cynn, HS; Kwon, OY; Park, KN; Kim, GM. A reliability of electromyographic normalization methods for the infraspinatus muscle in healthy subjects. J Hum Kinet. 2013;36:69-76. [ Links ]

Branco, VR; Negrão Filho, RF; Padovani, CR; Azevedo FM; Alvez, N; Carvalho, AC. Relação entre a tensão aplicada e a sensação de desconforto nos músculos isquiotibiais durante o alongamento. Rev Bras Fisioter. 2006;10:465-72. [ Links ]

Clarys, JP. Electromyography in sports and occupational settings: an update of its limits and possibilities. Ergonomics. 2000;43(10):1750-62. [ Links ]

Burden, A. How should we normalize electromyograms obtained from healthy participants? What we have learned from over 25 years of research. J Electromyogr Kinesiol. 2010;20(6):1023-35. [ Links ]

Burden, AM; Trew, M; Baltzopoulos, V. Normalisation of gait EMGs: a re-examination. J Electromyogr Kinesiol. 2003;13(6):519-32. [ Links ]

Lamy, JC; Iglesias, C; Lackmy, A; Nielsen, JB; Katz, R; Marchand-Pauvert, V. Modulation of recurrent inhibition from knee extensors to ankle motoneurones during human walking. J Physiol. 2008;586(24):5931-46. [ Links ]

Kuznestsov, S; Popov, DV; Borovik, AS; Vinogradova, OL. Wavelet analysis of m. vastus lateralis surface EMG activity in incremental test till exhaustion using bicycle and knee extension exercises. Fiziol Cheloveka. 2011;37(5):129-36. [ Links ]

Rota, S; Rogowski, I; Champely, S; Hautier, C. Reliability of EMG normalisation methods for upper-limb muscles. J Sports Scien. 2013;31(15):1696-704. [ Links ]

Whaley, MH; Brubaker, PH; Otto, RM; Armstrong, LE. ACSM Guidelines for exercise testing and prescription. 7th ed. Philadelphia: Lippincott Williams & Wilkins; 2006. [ Links ]

Law, LF; Krishnan, C; Avin, K. Modeling nonlinear errors in surface electromyography due to baseline noise: a new methodology. J Biomech. 2011;44(1):202-5. [ Links ]

McDonald, AC; Sanei, K; Keir, PJ. The effect of high pass filtering and non-linear normalization on the EMG-force relationship during sub-maximal finger exertions. J Electromyogr Kinesiol. 2013;23(3):564-71. [ Links ]

Anderson, KG; Behm, DG. Maintenance of EMG activity and loss of force output with instability. J Strength Cond Res. 2004;18(3):637-40. [ Links ]

Received: May 20, 2015; Accepted: December 16, 2015

*KAMZ: MS, e-mail:

JMDD: PhD, e-mail:

MAA: PhD, e-mail:

LLA: BS, e-mail:

CAMJ: PhD, e-mail:

RCD: PhD, e-mail:

Creative Commons License This is an open-access article distributed under the terms of the Creative Commons Attribution License