Training-related changes in the EMG-moment relationship during isometric contractions: Further evidence of improved control of muscle activation in strength-trained men?

Journal of Electromyography and Kinesiology xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

Journal of Electromyography and Kinesiology journal homepage: www.elsevier.com/locate/jelekin

Training-related changes in the EMG–moment relationship during isometric contractions: Further evidence of improved control of muscle activation in strength-trained men? David Amarantini a,b,⇑, Bertrand Bru c a b c

Inserm, Imagerie cérébrale et handicaps neurologiques UMR 825, Toulouse, France Université de Toulouse, UPS, Imagerie cérébrale et handicaps neurologiques UMR 825, Toulouse, France Codamotion, Victoria Mills, Fowke Street, Rothley LE7 7PJ, UK

a r t i c l e

i n f o

Article history: Received 20 October 2008 Received in revised form 1 April 2015 Accepted 2 April 2015 Available online xxxx Keywords: Knee Neural adaptations Agonist–antagonist co-activation Muscle fibre dominance Long-term strength training

a b s t r a c t The possibility of using electromyography (EMG) to track muscle activity has raised the question of its relationship with the effort exerted by the muscles around the joints. However, the EMG–moment relationship is yet to be fully defined, and increasing knowledge of this topic could contribute to research in motor control and to the development of EMG-based algorithms and devices. With regards the training-related adaptations at the peripheral and central level, the present study investigated the effect of strength training on EMG–moment relationship. Our aim was to clarify its nature and gain further understanding of how morphological and neural factors may affect its form. The EMG–moment relationship was determined during knee flexion and extension isometric contractions performed by strength-trained male athletes and untrained male participants. The results showed that strength training induced linearity of the EMG–moment relationship concomitantly with enhanced maximum force production capacity and decreased co-activation of knee agonist–antagonist muscle pair. These results clarified discordant results regarding the linear or curved nature of the EMG–moment in isometric conditions and suggested that the remarkable linearity of the EMG–moment found in trained participants could indicate improved control of muscle activation. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction The possibility of using surface electromyography (EMG) to track muscle activity as an electrical event has raised the key question of the nature of its relationship with the mechanical effort, i.e. the force or moment, exerted by the muscles around the joints. Knowledge about this topic contributes significantly to research in motor control processes or recruitment strategies (e.g., Solomonow et al., 1990) and to the development of EMG-based algorithms for muscle force estimation (Erdemir et al., 2007), and could be used to improve EMG-based devices. In isometric conditions, previous studies reported linearity of the EMG–force/moment relationship (Bigland and Lippold, 1954; Hof, 1997; Lippold, 1952; Onishi et al., 2000; Pincivero and Coelho, 2000; Shinohara et al., 1998). It was also classified as

⇑ Corresponding author at: Université Paul Sabatier Toulouse 3 (UPS), Faculté des Sciences du Sport et du Mouvement Humain (F2SMH), 118 route de Narbonne, 31062 Toulouse Cedex 9, France. Tel.: +33 (0) 5 61 55 83 82; fax: +33 (0) 5 61 55 82 80. E-mail addresses: [email protected] (D. Amarantini), bertrand. [email protected] (B. Bru).

quasi-linear, non-linear or quadratic (Basmajian and Deluca, 1985; Eloranta, 1989; Marras and Granata, 1997; Metral and Cassar, 1981; Watanabe and Akima, 2009), or even more complex: linear or non-linear depending on the range of force levels (Pincivero and Coelho, 2000; Monod and Flandrois, 2003) or on the force vector in relation to the direction of muscle fibre alignment (Anders et al., 2008). In dynamic conditions, the EMG– force/moment relationship follows either a linear (Bigland and Lippold, 1954; Herman and Bragin, 1967) or a non-linear (Häkkinen and Komi, 1983) relation, but its nature remains much less clear because of the influence of additional factors, such as force–length or force–velocity relationships (Herman and Bragin, 1967; Hof, 1997; Olney and Winter, 1985). Thus, the nature of the relationship between EMG and force/moment is not completely understood and is yet to be fully defined. Previous studies have shown that numerous factors, such as angle (Nourbakhsh and Kukulka, 2003; Worrell et al., 2001), velocity (Herzog et al., 1998), muscle type (Watanabe and Akima, 2009), EMG methodology (Beck et al., 2009) and co-activation (Brown and McGill, 2008) can affect the EMG–force/moment relationship. Many other studies have suggested that muscle fibre type

http://dx.doi.org/10.1016/j.jelekin.2015.04.002 1050-6411/Ó 2015 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Amarantini D, Bru B. Training-related changes in the EMG–moment relationship during isometric contractions: Further evidence of improved control of muscle activation in strength-trained men?. J Electromyogr Kinesiol (2015), http://dx.doi.org/10.1016/ j.jelekin.2015.04.002

2

D. Amarantini, B. Bru / Journal of Electromyography and Kinesiology xxx (2015) xxx–xxx

composition and motor unit recruitment are some important factors altering the form of the EMG–force/moment relationship (Beck et al., 2009; Beck, 2009; Watanabe and Akima, 2009; Woods and Bigland-Ritchie, 1983). Previous studies reported that muscle fibre type composition and motor unit recruitment affect the force–velocity relationship (Petrofsky and Phillips, 1980). These suggested, firstly, that both the surface EMG features and the capacity for force/moment production differ among fibre types (Beck et al., 2007, 2009; Bottinelli et al., 1996; D’Antona et al., 2006; Pincivero and Coelho, 2000); and secondly that greater motor unit synchronization contributes to an increase in muscle force/moment of contraction (Fling et al., 2009). Interestingly, it was established that strength training directly influences muscle fibre type composition (Fry et al., 2003a,b; Häkkinen et al., 1998), motor unit recruitment (Semmler et al., 2004) and EMG features (Häkkinen and Komi, 1983; Häkkinen et al., 1998; Thorstensson et al., 1976). Strength-trained athletes exhibit specific adaptations on fast-twitch muscle fibres upon which their performance depends with, in particular, a greater proportion of muscle fibre subtypes IIa and IIab (Fry et al., 2003a,b; Häkkinen et al., 1998). Strength training also induces cortical adaptations (Dal Maso et al., 2012; Falvo et al., 2010; Griffin and Cafarelli, 2005) and changes at the level of the corticospinal drive (Aagaard et al., 2002; Carroll et al., 2002; Vila-Chã et al., 2012) that may be directly associated with enhanced motor unit recruitment and synchronization (Aagaard et al., 2002; Adkins et al., 2006; Griffin and Cafarelli, 2005; Sale, 1988; Semmler et al., 2004). In particular, the motor units activate more muscle fibres in strength-trained athletes than in untrained participants (Ryushi and Fukunaga, 1986), although the Henneman size principle of motor unit recruitment is not affected by training (Duchateau and Hainaut, 1981). Therefore, comparing strength-trained with untrained participants could provide a good opportunity to evaluate the EMG–force/moment relationship and investigate the mechanisms underlying the patterns of muscle activation during voluntary contractions. The purpose of the present study was to investigate the effect of strength training on the relationship between surface EMG activity and the net joint moment. Our aim was to clarify the nature of this key relationship and to gain further understanding of how morphological and neural factors, influenced by strength training, may affect its nature. The EMG–moment relationship was investigated during knee flexion and extension isometric contractions ranging from 0% to 100% MVC performed by strength-trained male athletes and untrained male participants. 2. Methods 2.1. Participants Ten male volunteers participated in this study after giving written informed consent. The participants were divided in two age- and mass-matched groups: an untrained participants group (UT; n = 5, age: 21.8 ± 2.28 years; height: 1.78 ± 0.04 m; mass: 75.4 ± 6.15 kg; mean ± SD) and a strength-trained athletes group (ST; n = 5, 24.6 ± 3.25 years; height: 1.79 ± 0.08 m; mass: 75.75 ± 10.75 kg; mean ± SD). ST had been engaged in weight training for a minimum of four training sessions of ninety minutes per week for two years. UT had done less than one hour of sport per week, and had never been involved in weight training exercises. 2.2. Apparatus The coordinates of reflective markers positioned on the subjects’ right side over the head of the fifth metatarsophalangeal

joint, the lateral malleolus, the lateral epicondyle and the greater trochanter were recorded at 200 Hz using a 8-camera Vicon MX system (Oxford Metrix, Oxford, UK) and filtered at 6 Hz using a 4th-order zero-lag low-pass Butterworth filter. The sagittal angular position of the hip (hH), knee (hK) and ankle (hA) joints were calculated according to the convention shown in Fig. 1. The vertical and the anteroposterior components of the ground reaction force were sampled at 1000 Hz with a 6 component force plate (AMTI, Newton, MA, USA) and filtered at 25 Hz with a 4thorder, zero-lag low-pass Butterworth filter. After suitable skin preparation (Hermens et al., 2000), surface EMG from the right knee muscles was collected at 1000 Hz using a Bagnoli-8 EMG system (DE-2.1, Delsys, Inc., Boston, MA, USA). Following the recommendations from Olney and Winter (1985), Gastrocnemius (GA), Biceps Femoris (BF), Rectus Femoris (RF) and Vastus Medialis (VM) were selected to adequately represent the activity of knee flexor and extensor muscles. 2.3. Protocol The participants were seated with the right foot firmly attached to the force plate, the trunk vertical, thighs horizontal and knees and ankles flexed at 90° (Fig. 1). The first step was to perform three successive maximal isometric knee flexion contractions followed by three successive maximal isometric knee extension contractions while verbal encouragements were given. Each contraction lasted 2 s, separated by a 2 s rest period. For each participant and contraction type, a maximum of three attempts was used to define the true maximum voluntary contraction (MVC) level. After a 3 min rest, the participants were asked to perform a supplementary 5 s isometric MVC in each direction of contraction, with a 30 s rest between contractions, to obtain the data necessary to estimate an indicator of muscle fibre dominance using the EMG-based method by Wretling et al. (1987). For the second step, each participant performed six trials, each consisting of a 10 s knee flexors ramp isometric contraction (0– 100% MVC), followed by a 10 s knee extensors ramp isometric contraction (0–100% MVC). Each contraction was separated by a 10 s rest interval and each trial was separated by a 3 min rest period to avoid the onset of muscular fatigue in both UT and ST. 2.4. Data processing The net moment acting at the knee (MK) was calculated from ground reaction, joint angular positions and body segment parameters using standard link-segment equations in static conditions (Winter, 2005). The sign convention was chosen such that positive moment represented a counterclockwise (extensor) moment acting at the knee, while negative moment represented a clockwise (flexor) moment acting at the knee. The raw EMG signals were band-pass filtered from 10 to 400 Hz (Hermens et al., 2000) using a 4th-order zero-lag Butterworth filter. The filtered EMG signals were then full-wave rectified, and their envelopes were obtained by applying a 4th-order low-pass zero-lag Butterworth filter with a 9 Hz cut-off frequency (Shiavi et al., 1998). For each participant and contraction type, EMG envelopes were normalized to their respective maximal value obtained at 100% MVC across the six muscle contraction trials, and the net moment acting at the knee was normalized to its absolute value at 100% MVC. The normalized EMG–moment relationship was then established by 10% step increases in normalized moment from 100% (flexion) to 100% (extension). As recommended by Kellis et al. (2003), co-activation of knee agonist/antagonist muscle pairs (CoAct) was computed from EMG at each MVC level using a modified version of the method by Falconer and Winter (1985), Winter (2005):



3

8- camera Vicon system Reflective markers

Delsys DE-2.1 EMG Electrodes

θK

θA

Display screen to provide visual feedback of the level of muscle contraction

6 components AMTI Force Plate

Fig. 1. Schematic representation of the experimental protocol (hH = 0 is measured between the horizontal and participant’s thighs).

CoAct ¼ 2 EMGANTAGO =ðEMGAGO þ EMGANTAGO Þ 100%

ð1Þ

where EMGANTAGO and EMGAGO are the mean values of normalized EMG recorded from antagonist and agonist muscles, respectively. 2.5. Statistics For each participant, trial, contraction type and muscle, the EMG–moment relationship was fitted by linear or curvilinear (quadratic) regression. The order of the regression model was considered acceptable only if its determination coefficient (R2) was .95 or greater. Student’s t-tests were conducted to compare the maximum moment developed in flexion and in extension between UT and ST. A two factor Training (UT vs. ST) Direction of contraction (Flexion vs. Extension) ANOVA with repeated measures on the factor of Direction of contraction were conducted on CoAct. A significance level of .05 was used for all statistical comparisons.

Fig. 2. Maximum MVC net joint moment performed at the knee during flexion and extension contractions in strength-trained athletes (ST) and untrained participants (UT). ⁄ Indicates a significant effect of expertise; (p < 0.05).

3. Results 3.1. Net joint moment Results showed a significant effect of Training on maximum net knee joint moment produced during isometric MVC (Fig. 2), with values greater for ST than for UT both in flexion (t8 = 2.65, p < 0.05; 802 ± 222 N m vs. 359 ± 301 N m, respectively) and in extension (t8 = 2.34, p < 0.05; 530 ± 135 N m vs. 340 ± 123 N m, respectively). 3.2. EMG–moment relationship Fig. 3a and b showed representative normalized EMG–moment relationships obtained for VM and GA muscles in ST and UT, respectively. In general, the results clearly indicated that the EMG activity increased with an increase in the net knee joint moment. For ST (see Fig. 3a for VM and GA), results showed that the isometric EMG–moment relationship remained systematically linear in flexion and in extension whatever the muscle and its action type (i.e., agonist or antagonist to the net joint moment). The quality of the linear fit was characterized by high R2, with

values ranging from 0.95 for the Gastrocnemius muscle in extension to 0.99 for the Biceps Femoris muscle in flexion (overall mean R2 of the linear fit for ST: 0.97 ± 0.01). For UT (see Fig. 3b for VM and GA), the nature of the EMG–moment relationships was different compared with ST: results showed that the isometric EMG– moment relationship was curvilinear (quadratic) for UT whatever the direction of contraction, the muscle and its action during the contraction. R2 of the linear fit was systematically below the minimum threshold of 0.95 (R2 values ranging from 0.77 for the Rectus Femoris muscle in flexion to 9.48 101 for the Biceps Femoris muscle in extension; overall mean R2 of the linear fit for UT: 0.88 ± 0.06), whereas the quadratic fit provided an appropriate description of the EMG–moment relationships with R2 values ranging from 0.97 for the Vastus Medialis muscle in flexion to 9.95 101 for the Gastrocnemius muscle in extension (overall mean R2 of the quadratic fit for UT: 0.99 ± 0.01). Results showed that non-linearity of EMG–moment relationships – i.e., the sum of the squared difference between the observed relationships and the linear fitting – was not significantly related to maximum net knee joint moment values whatever the muscle and the direction of contraction. Regarding the results


4


Normalized knee extensors EMG

(a)

Normalized knee extensors EMG

(b)

1

1

0,6

0,6

Extension torque 40 100

-80

-60

60

80

Extension torque

100

-40

40 -100 -80

Flexion torque

-60

60

80

100

-40

Flexion torque -0,6

-1

Normalized knee flexors EMG

-0,6

-1

Normalized knee flexors EMG

Fig. 3. Representative normalized EMG–torque relationship in strength-trained athletes (a) and untrained participants (b) for Vastus Medialis (VM) and Gastrocnemus (Ga) muscles.

Fig. 4. Mean co-activation of knee agonist/antagonist muscle pairs between strength-trained athletes (ST) and untrained participants (UT) during flexion and extension isometric contractions (CoAct, %). ⁄ Indicates a significant effect of the contraction type; ⁄⁄ indicate a significant effect of expertise; (p < 0.05).

obtained for the VM muscle, linear regression revealed that nonlinearity of EMG–moment relationship was not significantly related to the observed changes on the indicator of muscle fibre dominance (F1,8 = 0.004, p > 0.05; R2 = 4 104). 3.3. Agonist–antagonist co-activation Results showed a significant Training effect on the level of coactivation of knee agonist/antagonist muscle pairs (Fig. 4), with CoAct values greater for UT than for ST (F1,8 = 1.88, p < 0.05; mean CoAct: 63 ± 3% vs. 40 ± 3%, respectively). CoAct was also significantly greater in extension than in flexion (F1,8 = 5.48, p < 0.05; mean CoAct: 59 ± 4% vs. 44 ± 3%, respectively). No significant Training Direction of contraction interaction on CoAct was found. 4. Discussion The present study investigated the effects of strength training on the relationship between electromyography (EMG) and the net joint moment in isometric conditions. Our aim was to clarify

the nature of the EMG–moment relationship and to gain better insights into how morphological and neural factors, especially fibre type composition and motor unit synchronization influenced by strength training, may affect this key relationship. The results indicated that strength training significantly affected the nature of the EMG–moment relationship. For strength-trained participants, the increase in net knee joint moment was typically linearly related to EMG increase, with low level of co-activation between knee agonist and antagonist muscle groups. By contrast, our results revealed that the EMG–moment relationship could be reasonably considered as systematically curvilinear (quadratic) for untrained participants, with two different steps regarding the increase in EMG activity. As observed by Monod and Flandrois (2003), EMG values increased almost linearly with moment values for isometric contraction levels below 40% MVC in flexion and in extension, while EMG increased faster than the net joint moment for greater MVC levels. The remarkable linearity observed in strength-trained participants for all experimental conditions could indicate enhanced control of muscle activation and regulation in voluntary contractions, which may be related to more efficient or greater motor unit synchronization after strength training (Fling et al., 2009; Fukunaga, 1976; Pucci et al., 2006; Semmler and Nordstrom, 1998). Our results on muscle fibre dominance (see Supplementary data and Supplementary Fig. 1) and those on the level of co-activation of knee agonist–antagonist muscle pair appeared to support this assumption. Firstly, we found no significant relationship between muscle fibre type I dominance and the maximum net joint moment produced at the knee joint, and no significant correlation between muscle fibre dominance and non-linearity of the EMG–moment relationship. Without completely excluding the influence of strength training-related adaptations at the peripheral level, these results suggested that the changes in the EMG–moment relationship between untrained and strength-trained participants could be largely explained by the influence of neural adaptations. Thus, the linearity of the EMG–moment relationship observed in strength-trained participants could be associated with cortical adaptations (Dal Maso et al., 2012; Falvo et al., 2010; Griffin and Cafarelli, 2005) and enhanced motor unit recruitment compared with untrained participants (Aagaard et al., 2002; Adkins et al., 2006; Griffin and Cafarelli, 2005; Sale, 1988; Semmler et al., 2004), rather than changes in maximum force generation capacity or in morphological factors such as muscle myotypology (Fry et al.,



2003a,b; Häkkinen et al., 1998). Secondly, we found lower coactivation of knee agonist–antagonist muscle pair in strengthtrained athletes than in untrained participants for similar levels of maximum voluntary force during both flexion and extension contractions. Even if the accuracy of co-activation could be questioned, this result concurred with previous studies in strengthtrained participants (Dal Maso et al., 2012; Tillin et al., 2011) and complemented those from previous studies showing decreased co-activation after motor learning (Osu et al., 2002) or perturbation training (Chmielewski et al., 2005). This observation could indicate that, for strength-trained athletes, priority is assigned to a form of optimized control of muscle activation and regulation that could contribute to enhance the performance and the energetic efficiency of the muscle contraction (Baratta et al., 1988; Carolan and Cafarelli, 1992; Dal Maso et al., 2012; Häkkinen et al., 2000): compared with untrained participants, the increase in agonist muscles activity in strength-trained athletes closely resembled that of the net joint moment, while the smallest increase in the antagonist muscles activity could correspond to that strictly necessary to assure integrity and active stability of the knee joint related to the increase in the net joint moment. This strength training-related difference in the co-activation of the knee agonist–antagonist muscle pair must also be considered to further understand the linear, or the possibly non-linear, underlying relationship EMG and the net joint moment (Brown and McGill, 2008). The impression of linearity of the EMG–moment relationship between strength-trained athletes vs. untrained participants may be, at least partly, attributable to lower antagonist muscle co-activation which has been associated with specific cortical adaptations induced by regular strength training that could exert a specific encoding of antagonist muscles (Dal Maso et al., 2012). In conclusion, our results showed that long-term strength training induced linearity of the EMG–moment relationship concomitantly with enhanced maximum force production capacity and decreased co-activation of knee agonist–antagonist muscle pair. These results clarified discordant results regarding the linear or curved (quasi-linear or quadratic) nature of the fundamental relationship between EMG and the net joint moment in isometric conditions. Even if our results on muscle fibre dominance should be interpreted with caution because of the limitations inherent in the method proposed by Wretling et al. (1987), our results strongly suggested that the remarkable linearity of the EMG– moment found in trained participants vs. untrained participants may be largely explained by the neural adaptations induced by strength-training and could be interpreted as evidence of improved control of muscle activation in strength-trained men. This finding could have significant implications in strength and conditioning training, may have practical importance to improve the functionality of EMG-based devices and to contribute to the development of EMG-based algorithms for muscle force estimation. Conflict of interests There is no conflict of interests from any of the authors. Acknowledgement The authors thank Catherine Bru (Senior Associate at PwC, Leicester, Leicestershire, UK) for English revision of the manuscript. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jelekin.2015.04. 002.

5

References Aagaard P, Simonsen EB, Andersen JL, Magnusson P, Dyhre-Poulsen P. Neural adaptation to resistance training: changes in evoked V-wave and H-reflex responses. J Appl Physiol 2002;92(6):2309–18. Adkins DL, Boychuk J, Remple MS, Kleim JA. Motor training induces experiencespecific patterns of plasticity across motor cortex and spinal cord. J Appl Physiol 2006;101(6):1776–82. Anders C, Brose G, Hofmann GO, Scholle HC. Evaluation of the EMG-force relationship of trunk muscles during whole body tilt. J Biomech 2008;41(2): 333–9. Baratta R, Solomonow M, Zhou BH, Letson D, Chuinard R, D’Ambrosia R. Muscular coactivation. The role of the antagonist musculature in maintaining knee stability. Am J Sports Med 1988;16(2):113–22. Basmajian J, Deluca C. Muscles alive: their functions revealed by electromyography. 5th ed. Baltimore: Williams and Wilkins; 1985. Beck TW. An examination of the frequency-specific behavior of the mechanomyographic amplitude versus isometric torque relationship. Electroencephalogr Clin Neurophysiol 2009;49(1):35–42. Beck TW, Housh TJ, Fry AC, Cramer JT, Weir JP, Schilling BK, et al. The influence of muscle fiber type composition on the patterns of responses for electromyographic and mechanomyographic amplitude and mean power frequency during a fatiguing submaximal isometric muscle action. Electromyogr Clin Neurophysiol 2007;47(4–5):221–32. Beck TW, Housh TJ, Cramer JT, Stout JR, Ryan ED, Herda TJ, et al. Electrode placement over the innervation zone affects the low-, not the high-frequency portion of the EMG frequency spectrum. J Electromyogr Kinesiol 2009;19(4):660–6. Bigland B, Lippold OC. The relation between force, velocity and integrated electrical activity in human muscles. J Physiol 1954;123(1):214–24. Bottinelli R, Canepari M, Pellegrino MA, Reggiani C. Force-velocity properties of human skeletal muscle fibres: myosin heavy chain isoform and temperature dependence. J Physiol 1996;495(Pt 2):573–86. Brown SH, McGill SM. Co-activation alters the linear versus non-linear impression of the EMG–torque relationship of trunk muscles. J Biomech 2008;41(3):491–7. Carolan B, Cafarelli E. Adaptations in coactivation after isometric resistance training. J Appl Physiol 1992;73(3):911–7. Carroll TJ, Riek S, Carson RG. The sites of neural adaptation induced by resistance training in humans. J Physiol 2002;544(Pt 2):641–52. Chmielewski TL, Hurd WJ, Rudolph KS, Axe MJ, Snyder-Mackler L. Perturbation training improves knee kinematics and reduces muscle co-contraction after complete unilateral anterior cruciate ligament rupture. Phys Ther 2005;85(8):740–9. Dal Maso F, Longcamp M, Amarantini D. Training-related decrease in antagonist muscles activation is associated with increased motor cortex activation: evidence of central mechanisms for control of antagonist muscles. Exp Brain Res 2012;220(3–4):287–95. D’Antona G, Lanfranconi F, Pellegrino MA, Brocca L, Adami R, Rossi R, et al. Skeletal muscle hypertrophy and structure and function of skeletal muscle fibres in male body builders. J Physiol 2006;570(Pt 3):611–27. Duchateau J, Hainaut K. Adaptation du muscle humain et de ses unités motrices à l’exercice. J de biophysique médecine nucléaire 1981;5:249–53. Eloranta V. Patterning of muscle activity in static knee extension. Electroencephalogr Clin Neurophysiol 1989;29(6):369–75. Erdemir A, McLean S, Herzog W, van den Bogert AJ. Model-based estimation of muscle forces exerted during movements. Clin Biomech 2007;22(2):131–54. Falconer K, Winter DA. Quantitative assessment of cocontraction at the ankle joint in walking. Electromyography Clin Neurophysiology 1985;25(2–3):135–49. Falvo MJ, Sirevaag EJ, Rohrbaugh JW, Earhart GM. Resistance training induces supraspinal adaptations: evidence from movement-related cortical potentials. Eur J Appl Physiol 2010;109(5):923–33. Fling BW, Christie A, Kamen G. Motor unit synchronization in FDI and biceps brachii muscles of strength-trained males. J Electromyogr Kinesiol 2009;19(5):800–9. Fry AC, Schilling BK, Staron RS, Hagerman FC, Hikida RS, Thrush JT. Muscle fiber characteristics and performance correlates of male Olympic-style weightlifters. J Strength Conditioning Res 2003a;17(4):746–54. Fry AC, Webber JM, Weiss LW, Harber MP, Vaczi M, Pattison NA. Muscle fiber characteristics of competitive power lifters. J Strength Conditioning Res 2003b;17(2):402–10. Fukunaga T. Die absolute Muskelkraft und das Muskel-. krafttraining. Dtsch Z Sportmed 1976;27:255–66. Griffin L, Cafarelli E. Resistance training: cortical, spinal and motor unit adaptations. Can J Appl Physiol 2005;30(3):328–40. Häkkinen K, Komi PV. Electromyographic changes during strength training and detraining. Med Sci Sports Exerc 1983;15(6):455–60. Häkkinen K, Newton RU, Gordon SE, McCormick M, Volek JS, Nindl BC, et al. Changes in muscle morphology, electromyographic activity, and force production characteristics during progressive strength training in young and older men. J Gerontol 1998;53(6):B415–23. Häkkinen K, Alen M, Kallinen M, Newton RU, Kraemer WJ. Neuromuscular adaptation during prolonged strength training, detraining and re-strengthtraining in middle-aged and elderly people. Eur J Appl Physiol 2000;83(1):51–62. Herman R, Bragin J. Function of the Gastrocnemius and Soleus Muscles. A preliminary study in the normal human subject. Phys Ther 1967;47(2):105–13.


6


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. Herzog W, Sokolosky J, Zhang YT, Guimarães ACS. EMG-force relation in dynamically contracting cat plantaris muscle. J Electromyogr Kinesiol 1998;8(3):147–55. Hof AL. The relationship between electromyogram and muscle force. Sportverletz Sportschaden 1997;11(3):79–86. Kellis E, Arabatzi F, Papadopoulos C. Muscle co-activation around the knee in drop jumping using the co-contraction index. J Electromyogr Kinesiol 2003;13(3): 229–38. Lippold OC. The relation between integrated action potentials in a human muscle and its isometric tension. J Physiol 1952;117(4):492–9. Marras WS, Granata KP. The development of an EMG assisted model to asses spine loading during whole-body free-dynamic lifting. J Electromyogr Kinesiol 1997;7(4):259–68. Metral S, Cassar G. Relationship between force and integrated EMG activity during isometric anisotonic contraction. Eur J Appl Physiol 1981;46(2):185–98. Monod H, Flandrois R. Physiologie du sport. 5 ed. Edition Masson; 2003. Nourbakhsh MR, Kukulka CG. Relationship between muscle length and moment arm on EMG activity of human triceps surae muscle. J Electromyogr Kinesiol 2003;14(2):263–73. Olney SJ, Winter DA. Predictions of knee and ankle moments of force in walking from EMG and kinematic data. J Biomech 1985;18(1):9–20. Onishi H, Yagi R, Akasaka K, Momose K, Ihashi K, Handa Y. Relationship between EMG signal and force in human vastus lateralis muscle using multiple bipolar wire electrodes. J Electromyogr Kinesiol 2000;10(1):59–67. Osu R, Franklin DW, Kato H, Gomi H, Domen K, Yoshioka T, et al. Short- and longterm changes in joint co-contraction associated with motor learning as revealed from surface EMG. J Neurophysiol 2002;88(2):991–1004. Petrofsky JS, Phillips CA. The influence of recruitment order and fibre composition on the force-velocity relationship and fatiguability of skeletal muscles in the cat. Med Biol Eng Compu 1980;18(4):381–90. Pincivero DM, Coelho AJ. Activation linearity and parallelism of the superficial quadriceps across the isometric intensity spectrum. Muscle Nerve 2000;23(3): 393–8. Pucci AR, Griffin L, Cafarelli E. Maximal motor unit firing rates during isometric resistance training in men. Exp Physiol 2006;91(1):171–8. Ryushi T, Fukunaga T. Influence of subtypes of fast-twitch fibers on isokinetic strength in untrained men. Int J Sports Med 1986;7(5):250–3. Sale DG. Neural adaptation to resistance training. Med Sci Sports Exerc 1988;20(5 Suppl):S135–45. Semmler JG, Nordstrom MA. Motor unit discharge and force tremor in skill- and strength-trained individuals. Exp Brain Res 1998;119(1):27–38. Semmler JG, Sale MS, Meyer FG, Nordstrom MA. Motor-unit coherence and its relation with synchrony are influenced by training. J Neurophysiol 2004; 92(6):3320–31. Shiavi R, Frigo C, Pedotti A. Electromyographic signals during gait: criteria for envelope filtering and number of strides. Med Biol Eng Compu 1998; 36(2):171–8. Shinohara M, Kouzaki M, Yoshihisa T, Fukunaga T. Mechanomyogram from the different heads of the quadriceps muscle during incremental knee extension. Eur J Appl Physiol 1998;78(4):289–95. Solomonow M, Baratta R, Shoji H, D’Ambrosia R. The EMG-force relationships of skeletal muscle; dependence on contraction rate, and motor units control strategy. Electroencephalogr Clin Neurophysiol 1990;30(3):141–52.

Thorstensson A, Karlsson J, Viitasalo JH, Luhtanen P, Komi PV. Effect of strength training on EMG of human skeletal muscle. Acta Physiol 1976;98(2):232–6. Tillin NA, Pain MT, Folland JP. Short-term unilateral resistance training affects the agonist–antagonist but not the force-agonist activation relationship. Muscle Nerve 2011;43(3):375–84. Vila-Chã C, Falla D, Correia MV, Farina D. Changes in H reflex and V wave following short-term endurance and strength training. J Appl Physiol 2012;112(1):54–63. Watanabe K, Akima H. Normalized EMG to normalized torque relationship of vastus intermedius muscle during isometric knee extension. Eur J Appl Physiol 2009;106(5):665–73. Winter DA. Biomechanics and motor control of human movement. 3rd ed. New York: Wiley; 2005. Woods JJ, Bigland-Ritchie B. Linear and non-linear surface EMG/force relationships in human muscles. An anatomical/functional argument for the existence of both. Am J Phys Med Rehabil 1983;62(6):287–99. Worrell TW, Karst G, Adamczyk D, Moore R, Stanley C, Steimel B, et al. Influence of joint position on electromyographic and torque generation during maximal voluntary isometric contractions of the hamstrings and gluteus maximus muscles. J Orthop Sports Phys Ther 2001;31(12):730–40. Wretling ML, Gerdle B, Henriksson-Larsen K. EMG: a non-invasive method for determination of fibre type proportion. Acta Physiol Scand 1987;131:627–8.

David Amarantini: David Amarantini obtained his Ph.D. degree in Biomechanics from the Joseph Fourier University (Grenoble, France) in 2003. He is currently employed as an Assistant Professor in biomechanics at the Paul Sabatier University of Toulouse, France. His main research interests are estimation of muscle forces and joint moments, multi-muscle and multidegrees of freedom coordination, neuro-biomechanics and biomechanical applications of numerical optimization and signal processing.

Bertrand Bru: Bertrand Bru obtained his Ph.D. degree in biomechanics in 2012 at the ISIR (Institut des Systèmes Intelligents et de Robotique) from Pierre and Marie Curie University of Paris, France and his Master’s degree in Biomechanics in 2007 from Paul Sabatier University (Toulouse, France). During his degrees he worked on biomechanical modelling for muscle moment/force estimation and identification of human body inertial parameters. He is currently employed as a research and development manager at Charnwood Dynamics Ltd. (Codamotion).


The Influence of Vibration on Muscle Activation and Rate of Force Development during Maximal Isometric Contractions.

Improved identification of dystonic cervical muscles via abnormal muscle activity during isometric contractions.

Heat production and chemical changes during isometric contractions of the human quadriceps muscle.

Myocardial energetics during isometric twitch contractions of cat papillary muscle.

Quantifying thigh muscle co-activation during isometric knee extension contractions: within- and between-session reliability.

Caffeine-induced increase in voluntary activation and strength of the quadriceps muscle during isometric, concentric and eccentric contractions.

Effect of muscle mass on the pressor response in man during isometric contractions.

Changes produced by increased hydrostatic pressure in isometric contractions of rat fast muscle.

Evaluation of repetitive isometric contractions on the heads of triceps brachii muscle during grip force exercise.

Muscular reflex stimuli to the cardiovascular system during isometric contractions of muscle groups of different mass.

Spatial reorganisation of muscle activity correlates with change in tangential force variability during isometric contractions.

Isometric and isotonic contractions in airway smooth muscle.

Muscle afferent responses to isometric contractions and relaxations in humans.

Further observations on back-firing in the motor nerve fibres of a muscle during twitch contractions.

Changes of HD-sEMG maps of the upper limb during isometric endurance contractions.

EMG-force relations during isometric contractions of the first dorsal interosseous muscle after stroke.

Mechanical compression during repeated sustained isometric muscle contractions and hyperemic recovery in healthy young males.

External abdominal oblique muscle ultrasonographic thickness changes is not an appropriate surrogate measure of electromyographic activity during isometric trunk contractions.

Energy liberation and chemical change in frog skeletal muscle during single isometric tetanic contractions.

Amplitude of the surface electromyogram during fatiguing isometric contractions.

The interaction between peripheral and central fatigue at different muscle temperatures during sustained isometric contractions.

Recruitment of muscle synergies is associated with endpoint force fluctuations during multi-directional isometric contractions.

Behavior of human gastrocnemius muscle fascicles during ramped submaximal isometric contractions.

Determinants of isometric muscle strength in men of different ages.