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Relation between differences in electromyographic adaptations during static contractions and the muscle function
Journal of Electromyography and Kinesiology 9 (1999) 253–261 www.elsevier.com/locate/jelekin
Relation between differences in electromyographic adaptations during static contractions and the muscle function Veerle Hermans *, Arthur J. Spaepen, Marc Wouters Laboratory of Ergonomics and Occupational Biomechanics, Faculty of Physical Education and Physiotherapy, Katholieke Universiteit Leuven, Tervuursevest 101, B-3001, Leuven, Belgium Received 22 October 1996; received in revised form 23 July 1998; accepted 14 September 1998
Abstract This study determines whether changes in the EMG values of two important muscles of the shoulder and neck region, the anterior deltoid and the upper trapezius, are due to changes in torque production or due to fatigue processes during sustained activity. Contractions at 20, 40, 60, 80 and 100% MVC were performed during a flexion of the arm in the sagittal plane at 90°, to examine the relation between torque and EMG. A sustained contraction at 20% MVC was performed to endurance point in the same position. RMS, a new parameter called activity, (ACT), and MPF of the deltoid anterior and the upper trapezius were analysed. The amplitude values correlated highly with increasing torque production, both for the deltoid muscle (range r ⫽ 0.95–0.96), and the trapezius muscle (range r ⫽ 0.83–0.87), whereas no significant difference was found for MPF. For the endurance task, the decrease in MPF was far more pronounced for the deltoid than for the trapezius, whereas the opposite occurred with RMS (P ⱕ 0.01). Furthermore, there was no significant difference over time for the ACT values of the deltoid, whereas there were significant increases in ACT for the trapezius (P ⱕ 0.01). The RMS/ACT ratio correlated highly (r ⫽ 0.81) with the MPF. Regression coefficients of these parameters differed significantly for the trapezius muscle but not for the deltoid muscle. Therefore, the RMS/ACT ratio may be extremely important in analysing the fatigue effects during sustained efforts, independent of torque variations, which can influence indicators of fatigue. 1999 Elsevier Science Ltd. All rights reserved. Keywords: Electromyography; Muscle fatigue; Muscle force; Signal analysis; Shoulder–neck
1. Introduction Many studies have proved that the surface electromyographic (EMG) signal changes due to fluctuations in torque production and due to fatigue processes during a sustained contraction. To quantify signal changes, values are determined from raw EMG to represent amplitude, such as the root-mean-square values (RMS), and frequency such as mean power frequency (MPF). Linear or quasi-linear increases are frequently reported [2,15,24] for the relation between the torque produced at a joint and the corresponding EMG amplitude. Other studies mention a number of influencing factors so that a nonlinear relation is found, e.g. kinematics of the movement, processing methods and acquisition
* Corresponding author. Tel.: ⫹ 32-16-3291-07; fax: ⫹ 32-163291-96; e-mail: [email protected].
procedure [24,46]. For changes in the frequency the behaviour in relation to the load is not so clear. Some studies have shown increasing frequency values at loads up to 25 and 80% of the maximal voluntary contraction (MVC), above which frequency becomes independent of the force [12–15,17,30]. However, other authors claim that the spectrum is insensitive to varying contraction intensity [11,25,37]. During a sustained force till endurance, significant changes in the EMG signal are also found. The increases in amplitude are attributed to the recruitment of fresh motor units, which compensate for the reduced performance of motor units that have been working since the beginning of the contraction [3,22,31]. The signal has decreased frequency which is predominantly caused by the reduced conduction velocity of the muscle fibres [29]. The validity of the above-mentioned EMG parameters as indicators of muscle fatigue has recently been questioned because changes in force production can occur
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simultaneously with the fatigue process during a sustained contraction [34–36]. This study presents a method which differentiates between the two processes. This method is applied for two muscles acting at the same joint but with different function. The purpose of the analysis is to explain the difference of the EMG parameters in relation to the difference of the muscle function. Firstly, the relation between the parameters and torque is investigated, followed by the analysis of changes in the EMG signal during a sustained contraction. The rationale for this is that in a work environment, fluctuations in muscle force and arm positions are present and influence the EMG signal. This interferes with the identification of EMG changes due to sustained efforts only, and is perhaps the main reason why several studies in the work environment have failed to demonstrate fatigue patterns in EMG [20,32,38].
2. Materials and methods 2.1. Subject group 10 females with a mean age of 20 yr (SD 1.7), mean height of 168.7 cm (SD 5.9) and mean weight of 61.2 kg (SD 6.8), gave their informed consent to participate in the study, and were all healthy with no previous complaints of the neck–shoulder region. 2.2. Mechanical recording Seated on a chair with the trunk of the body fixed to avoid extreme changes in posture during the tests, the subjects pushed upwards against a lever arm with the right arm extended horizontally (90° flexion in the sagittal plane, palm facing downwards). The axis of the lever arm was mounted to coincide with the shoulder joint axis. A torque meter connected to the lever arm measured torque production (Lebow). Percentages of the net torque at the joint were calculated taking into account both the weight of the subject’s arm and the weight of the handle. An oscilloscope in front of the subject indicated effort and the target torque, which was also presented on a multi meter. Subjects pushed upwards against the handle until the indicator on the oscilloscope monitor coincided with the line showing the target value. 2.3. Electrical recording EMG signals were recorded from two muscles: the anterior deltoid muscle as the prime mover of a forward flexion of the arm, and the upper trapezius muscle as an important stabiliser of the shoulder joint. Bipolar surface electrodes (Nikomed, silver–silver chloride, Denmark) were positioned according to standard procedures [47].
The 10 mm electrodes were attached 15 mm apart and the reference electrode was placed on the seventh cervical vertebra. The Telemetric EMG Measuring Processing System device (TEMPS, K.U.Leuven) served as an EMG amplifier (CMRR 100 dB, input impedance 300 M⍀). The signals were filtered using a band pass filter (5–250 Hz) and sampled at 500 Hz. Both the torque and EMG devices were connected to a computer by an ADconvertor (Labmaster), so that signals could be monitored simultaneously. An overview of the equipment is presented in Fig. 1. 2.4. Procedures Subjects were requested to perform two maximal voluntary contractions (MVC) pushing upwards, of which the highest was used for further calculations, were followed by a random sequence of 20, 40, 60 and 80% MVC. Each contraction lasted 5 s with intervening rest periods of 3 min. After 10 min, the subjects performed an endurance test at 20% MVC, until they were no longer able to maintain the load (deviation of 2.5% of the MVC allowed maximally), or until changes in the test position were noted. A third and final MVC test followed immediately. 2.5. Data analysis The EMG and torque data were analysed off-line using specifically designed software. For the MVC task, peak torque and EMG parameters were calculated for a 1.024 s time period. For the other short-term tasks, EMG parameters were determined over a 2.048 s time period in the middle of the 5 s torque production. During the sustained exercise, values were measured for 2.048 s every 10 s. The frequency spectrum and Mean Power Frequency of the EMG signal were calculated using a Fast Fourier Transformation; Root-Mean-Square values (RMS) were calculated to represent the amplitude, and a fourth parameter further referred to as ‘activity’ (ACT) was determined to distinguish between the torque and fatigue processes. 2.6. Rationale of the ACT parameter The ACT parameter was originally developed to estimate an EMG parameter proportional with isometric force, similar to RMS or integrated EMG values [42], but with several advantages compared with these methods, such as a lower sensitivity to a slowly changing baseline, and a lower dependence on the time interval. The recursive formula to calculate muscular activity (ACTi) from a series of raw EMG samples (Ei) at equally spaced time instances ti, can be summarized as follows: The algorithm computes the activity at time ti ⫹ 1 (ACTi ⫹ 1) from a known activity at time ti (ACTi) and
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Fig. 1.
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Overview of the equipment for measuring and analysing torque and electromyographic signals simultaneously.
the corresponding raw EMG values Ei ⫹ 1 and Ei. Because of reasons explained below, ACTi ⫹ 1 is equal to the sum of two terms: ACTi ⫹ 1 ⫽ P ⫹ Q Firstly, it is well known that the force production in a muscle fibre, the twitch force, does not stop abruptly when stimulation is terminated, but continues for a limited time, a digressive exponential aftereffect occurs [9]. An indicator for the rate of force decay can be given by the constant value, P. If P denotes the amount of force following from previous activity, then P ⫽ p*ACTi (p ⬍ 1) indicating that some force production will remain from previous activity even if no further EMG is noticed. In a recursive formula, it is important to notice that p depends on the sampling frequency, which was 500 Hz in this experiment. Secondly, activity obtained from the amount of force resulting from the new EMG activity (Q) is to be added to the previous term. Many other EMG quantification parameters assume that force delivered by a muscle is related to the amount of electrical activity (E). However, force production results from changes in the membrane ionization, which reproduce changes in electrical current (pulse) through a muscle fibre, rather than the value of electrical activity itself. If force production is sustained, an incessant train of pulses is needed, resulting in continuous changes in depolarization current [23]. Therefore, these changes in EMG are presented by a differentiation technique: the absolute value of differences of subsequent samples are calculated. Q ⫽ 兩Ei ⫹ 1 ⫺ Ei兩q A value of q equal to 1 would indicate changes in the
activity proportional to the changes in the EMG signal. If they are not linearly related, other values for q are found. As can be derived from the expression for Q, muscle activity is expressed in arbitrary units. From an initial value of the activity (ACT1) and subsequent EMG samples, A2, A3... An can be calculated, resulting in a time series of activities at the time instances ti (i ⫽ 1... n). It has been demonstrated that the outcome of the computation is independent of the first value of the activity (ACT1) after 0.5 s. To determine the values of p and q, and the initial value of the activity, ACT1, on which all subsequent values of ACTi are based, initial calculations were performed in which different combinations of p and q were tested. The resultant series of ACTi values were compared with the corresponding isometric forces using Pearson correlation coefficients. The following values gave a minimal coefficient of non-determination for a sampling rate of 500 Hz, and thus the highest possible linear relation between EMG and ACT: p ⫽ 0.9875 and q ⫽ 0.5 [41,42]. Relatively high activity values were found, even for a relaxed muscle (e.g. for 17% of the maximal torque production, an ACT value of 40% was calculated, whereas 21% of the maximum RMS). The reason for this is due to the simple normalising function of resizing the EMG differentials by means of a square root. However, from stimulation studies [4,44] it is clear that a more complex sigmoid relation between relative isometric force and the rate of activation in whole [21] muscles as well as in single muscle units should be used. A non-linear force behaviour is found in which the motor unit force varies as a function of the firing rate. For the human toe extensor motor units, low frequencies (5–8 Hz) are reported which produce unfused twitches, intermediate frequencies (10 Hz) produce a steep, near-linear
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occur, a zero-effect in relation to the basic signal results, since the same values compared with the basic signal cancel each other out. For the different torque percentages, the values of the EMG parameters were normalized to the EMG value for 100% MVC. For the sustained test, the average value of the first 2 s recording was used as a reference value for normalization. For reasons which become more clear in the results and discussion, the RMS/ACT ratio was also calculated. 2.7. Statistics
Fig. 2. Simulation of four different sinusoid signals: a basic signal, an increase in amplitude, a decrease in frequency and the combinated signal.
relation between force and frequency, and relatively high frequencies (20–30 Hz) are needed to produce maximal forces [4]. These frequencies can be compared with the motor unit firing rates during submaximal and maximal voluntary contractions. Although this simple approach was taken, high ACT–torque correlations were found. The offset-value for ACT is found by measuring the EMG activity of the relaxed muscle. In this study, the intercept of the ACT–torque regression line (approximately 16% of the MVC ACT value) is used as ACT at zero-torque level and has been subtracted from ACT before further data processing. In order to understand the effect of changes in the raw EMG signal on the ACT, a simulation of a raw EMG signal is presented by a sinusoid. A basic EMG signal is presented, accompanied by a slower EMG signal (freq: 25% frequency decrease) which has often been explained in literature by a decrease in conduction velocity of the fibres active from the onset of a sustained contraction. Also a signal with higher amplitude (ampl: 30% amplitude increase) is shown which may be caused by the recruitment of a larger motor unit. In addition, the combination of these two factors (freq ⫹ ampl) is presented (Fig. 2). In Table 1, the mean steady-state ACT values and standard deviation for these four sinusoids are presented: the decrease in frequency lowers the ACT values, whereas the increase in amplitude increases them. If both Table 1 Calculated ACT values of four different sinusoid signals: a basic signal (Basic), an increase in amplitude (Ampl.), a decrease in frequency (Freq.) and the combinated signal (Freq. ⫹ ampl.)
ACT SD
Basic
Freq.
Ampl.
Freq. ⫹ ampl.
59.94 0.26
68.34 0.29
52.39 0.21
59.75 0.24
Pearson’s correlation coefficients (r) were used as descriptive measures of the association between MPF, RMS, ACT and torque. To evaluate the relation between load and the EMG parameters and the changes in the values over time, linear regression analyses were performed using a statistical model, based on the mathematical considerations of Neter et al. [33]. The calculation procedure takes into account the individual regression lines of each subject and the number of observations, so that a mean intercept and slope with the accompanied variance is obtained. Furthermore, the difference between slopes can be calculated using t-tests. A detailed presentation of this model is presented in Hermans and Spaepen [18]. Pearson’s correlation coefficients (r) were used to examine the relation.
3. Results 3.1. Relation between torque and EMG The mean maximal torque production in the shoulder joint for a forward flexion of the right arm was 46.53 Nm (SD 6.61). The changes in EMG parameters, normalized to the values for 100% MVC, with increasing load are presented in Fig. 3 for deltoid and trapezius. Significant increases were found for ACT and RMS with increasing load (P ⱕ 0.01). These parameters correlated closely with load for the prime mover, the deltoid muscle, (range r ⫽ 0.95–0.96) and for the stabilizer, the trapezius muscle, (range r ⫽ 0.83–0.87). MPF values for both muscles did not change significantly with increasing torque. Important to notice is the fact that MPF and torque correlated only very poorly for both muscles, deltoid range r ⫽ ⫺ 0.17– ⫺ 0.27, trapezius range r ⫽ 0.10–0.19. 3.2. EMG changes during sustained submaximal contraction The mean endurance time for the sustained 20% MVC was 251 s (SD 36.04). In Fig. 4, the mean changes in
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Fig. 4. Changes in EMG parameters (MPF, ACT and RMS) over time for the anterior deltoid and the upper trapezius. Mean values over 10 subjects, normalized to the first recorded values. Fig. 3. Changes in EMG parameters (MPF, RMS and ACT) for the anterior deltoid and the upper trapezius during increasing torque production. Mean values over 10 subjects, relative to the MVC values.
normalized EMG parameters over time are presented, both for deltoid and trapezius. From Fig. 4 it is clear that the significant decrease in MPF over time was larger for the deltoid (25%) than for trapezius (8%). The opposite occurred for the increase in RMS over time (P ⱕ 0.01) which was larger for trapezius (74%) than for deltoid (28%). The second parameter of the time domain, ACT, revealed a different behaviour dependent on the muscle: for trapezius there was a significant increase of 22% (P ⱕ 0.01), whereas for deltoid there was even a small (though not significant) decrease of 5%. No significant difference for the deltoid was noticed between the slope of the RMS/ACT ratio and the RMS slope, because of the rather constant ACT values, whereas for the trapezius a difference was found (P ⱕ 0.01). The slopes of the RMS/ACT ratio have also been compared with MPF changes. For the deltoid no significant difference between these parameters occurred. However, high correlations (r ⫽ ⫺ 0.82) were found between the RMS/ACT slopes and MPF changes. For the trapezius muscle the slopes of RMS/ACT and MPF were signifi-
cantly different from each other and a very low correlation was found (r ⫽ ⫺ 0.08). The MVC torque value immediately after the fatigue test was 42.28 Nm (SD 7.07), which is 87.71% of the pre-fatigue MVC value (P ⱕ 0.05). Comparing the EMG parameters between the two maximal tests shows that the pre- and post-fatigue EMG parameters of the deltoid differ significantly from each other (MPF P ⱕ 0.001, ACT P ⱕ 0.001, RMS P ⱕ 0.01). However, for trapezius muscle, the relative decreases in the parameters were very low, the pre- and post-fatigue values did not differ significantly from each other (P > 0.1), although the MVC torque was decreased. The relative post-fatigue values (mean values over the subject group and standard deviations) are presented in Table 2.
4. Discussion This study evaluated the relation between isometric force production during a flexion of the arm in the sagittal plane and the corresponding EMG values of two important muscles of the shoulder and neck region, the anterior deltoid and the upper trapezius. Changes in
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Table 2 Torque and EMG values of the mean power frequency (MPF), rootmean-square (RMS) and activity (ACT) for the post-fatigue MVC test. Mean values and standard deviations relative to the pre-fatigue values for deltoid and trapezius
MPF ACT RMS Force
Deltoid
Trapezius
92.5 (4.4) 88.3 (6.7) 82.5 (14.5)
99.4 (5.1) 94.9 (10.8) 90.5 (18.3) 87.7 (12.2)
EMG parameters during a sustained submaximal task were also examined. 4.1. Relation of EMG and torque The very high correlation between the torque production and the EMG amplitude of the deltoid in this study, is confirmed by others [11,19], and is explained by the function of the muscle as the prime mover during flexion of the arm in the sagittal plane. Together with the activation of the lateral deltoid, the biceps and the major pectoralis muscle, the arm can be raised. The action line of the anterior part for performing an anteflexion of the arm corresponds with the anatomic alignment of the deltoid and therefore often only the net torque is measured and assumed to be linear with respect to the agonist muscle of interest. In order to suspend the upper extremity, the muscles of the shoulder girdle are activated, mainly the descending part of the trapezius. Due to the degrees of freedom of the shoulder joint, certain work tasks can be accomplished through a large number of muscle combinations [27] which can considerably influence the relation between force and EMG [40,45]. For this reason, for instance, the MVC task can be regarded as a maximal effort of the prime mover, the deltoid muscle, but certainly not for the stabilizer, the trapezius muscle. This is also illustrated by the fact that the upward force that can be delivered during shoulder elevation is far higher than the upward force produced on the lever arm. Because of the high torque–RMS and torque–ACT correlations it is in the non fatigue test to assume a gradual increase in stabilization force with increasing anteflexion effort, which corroborates several other muscles. The EMG values of the trapezius during the MVC test however, should not be regarded as maximal effort for the muscle. The high correlations between torque and deltoid ACT, r ⫽ 0.95, are in agreement with previous studies on other muscles (knee extensors and trunk muscles) evaluated as prime movers during specific tasks, for which the correlations ranged from r ⫽ 0.94 to 0.99 [41,42]. Furthermore, since several advantages of this parameter were demonstrated compared with other amplitude parameters (e.g. lower sensitivity to slowly
changing baselines), we may conclude that this parameter efficiently represents the force production necessary to perform a static forward flexion of the arm. Regarding the relation between torque and MPF, con¨ flicting results are found in literature. Hagg [16] presented a list of authors reporting a dependence of MPF on contraction level, most pronounced at low levels. The possible cause for this is that when force increases, additional motor units are recruited, characterised by larger fibres and higher action potential velocity. When these fibres are recruited during more forceful contractions, a higher frequency content results. In our experiment, there were no significant differences between the MPF values of succeeding torque productions for both muscles, which is supported by the results of Gerdle et al. [11]. In fact, there was a slight decrease in both studies. Regarding the experiments of Gerdle et al. [11] the question arises of whether this is due to the fact that a ramp contraction of 10 s was performed from 0 to 100% MVC, which can introduce fatigue during the last seconds and therefore a decrease in MPF. The use of ramp calibration for determining the relation between force and EMG is also discussed in literature. [1,28]. In our experiment, short successive contractions were performed separated by rest periods, and were presented to the subjects randomly, so no effect of fatigue was expected. Our results support the hypothesis that MPF values are not significantly influenced by the level of torque production. 4.2. Sustained activity Increasing amplitude and decreasing frequency values during sustained contractions are often considered as indicators of muscle fatigue [7,8] and are found both in laboratory and true working environments, although some studies fail to demonstrate these indicators, despite subjective fatigue scores [6,11,17,34,38]. This questions the use of MPF as a valid indicator of muscle fatigue. In our study a decrease of 8% in the MPF occurred for the trapezius muscle, which is much less than the deltoid decrease (25%), but can ¨ be considered significant according to the studies of Oberg et al. [35]. No significant changes occurred in the ACT parameter for the deltoid muscle, only a small decrease is found, explained by the small torque decrease at the end of the experiment. According to Gamet and Maton this is caused by difficulties in maintaining the initial force level due to an increase of physiological tremor [10]. For the trapezius, the decrease in frequency is much lower but high RMS increases (74%) and a significant ACT increase are found at the end of the task. This increase is explained by the different function of the muscles, as described above. When the arm is elevated, an increased torque is produced both in the glenohumeral joint and for the scapula by the mass of the arm. Accord-
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ing to Hagberg [13], functional changes within the deltoid or transfer of torque performance from the deltoid to other muscles may occur when this task has to be sustained in order to delay the fatigue feeling. This is called trade-off synergism: try to delay exhaustion by regulating muscle activities such that the load can be sustained [39]. Minor position movements can be needed to change the relative distribution of forces during a fatiguing contraction, e.g. slightly elevating the shoulder. This causes an increase in force production of the trapezius, although this muscle originally (mechanically) does not take part in the production of force in arm flexion [34]. This high increase in amplitude is due to the fact that initially the trapezius is only working at a very small percentage of its real maximum for stabilizing the shoulder during a forward flexion. The maximum activation (and thus higher EMG amplitude) of this muscle is only obtained in the position of elevating the shoulder when the arm is hanging vertically. So the 74% RMS increase is still only a small part of the real maximum contraction value. The constant value of the ACT parameter of deltoid during sustained effort and the linear increase with the torque, suggest it is sensitive to force production, but not to fatigue. The RMS parameter on the other hand increases with load and fatigue. Therefore, the RMS/ACT seems to relate mainly to fatigue, with the parameters cancelling each other out for the effects of load. Three observations support this hypothesis. Firstly, there was a high correlation (r ⫽ ⫺ 0.81) of the RMS/ACT regression coefficients with the MPF coefficients for the deltoid, and the regression analysis revealed no difference between the slopes of both parameters. Furthermore, because of the relatively low stabilising effort of the trapezius muscle, it is not expected to be affected by fatigue to the same extent as the prime mover, which is confirmed by larger fatigue times in several literature studies [13,43,34]. Analysis of the parameters confirms this hypothesis. The RMS/ACT ratio correlates poorly with MPF (r ⫽ ⫺ 0.08) and the regression coefficients differed significantly from each other. Secondly, the performance of an MVC contraction immediately after the sustained task caused no significant differences in the EMG parameters for the trapezius, compared with the pre-fatigue MVC, whereas for the deltoid there were significant fatigue indications in the EMG parameters. Thirdly, experiments on trunk muscles also revealed a high correlation between the time course of MPF and that of the RMS/ACT ratio during prolonged fatiguing exercise [5]. In conclusion, in this study it is suggested that the RMS/ACT ratio obtained from a surface EMG signal, can be a valuable parameter for discriminating between changes in signal amplitude due to fatigue processes or
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due to force adaptations. Recently, also other methods attempt to discriminate force-related and fatigue-induced EMG changes. Luttmann et al. [26] mention the joint analysis of EMG spectrum and amplitude (the JASA method). They use the median frequency as a measure of the spectral distribution. However, it is mentioned in literature that also the median frequency is influenced by force effects, as the MPF (cf. supra) and that can influence the data results and the interpretation. To validate more our ratio parameter, it is necessary to make comparisons with other methods (e.g. comparisons with measurements of intramuscular pressure and/or with JASA) and applications in working environments are needed. This is particularly important since several studies have failed to demonstrate significant decreases in MPF explained by the fact that variations in limb position and fluctuations in force production influence the EMG signals. References [1] Attebrant M, Mathiassen SE, Winkel J. Normalizing upper trapezius EMG amplitude: comparison of ramp and constant force procedures. J Electromyogr Kinesiol 1995;5:245–50. [2] Basmajian JV, De Luca CJ. Muscles alive. Baltimore: Williams and Wilkins, 1985. [3] Bigland-Ritchie B. EMG/force relations and fatigue of human voluntary contractions. Exerc Sport Sci Rev 1981;9:75–117. [4] Bigland-Ritchie B, Fuglevand AJ, Macefield VG. Force modulation by rate-coding in single motor units of human toe extensor muscles. Soc Neurosci Abstr 1993;19:154. [5] Bunkens L. Back muscle fatigue and chronic low back pain, a quantitative analysis of back muscle responses in persons with and without chronic back disorder. Ph.D. thesis, K.U.Leuven, Belgium, 1996. [6] Christensen H. Muscle activity and fatigue in the shoulder muscles of assembly-plant employees. Scand J Work Envir Health 1986;12:582–7. [7] De Luca CJ. Myoelectrical manifestations of localized muscular fatigue in humans. CRC Crit Rev Biomed Engng 1984;11:251– 79. [8] De Luca CJ. Spectral compression of the EMG signal as an index of muscle fatigue. In: Sargeant AJ, Kernell D, editors. Neuromuscular fatigue. Amsterdam: North-Holland, 1993:44–51. [9] Fuglevand AJ, Winter DA, Patla AE. Models of recruitment and rate coding organization in motor-unit pools. J Neurophysiol 1993;70(6):2470–88. [10] Gamet D, Maton B. The fatigability of two agonistic muscles in human isometric voluntary submaximal contractions. I. Assessment of muscular fatigue by means of surface EMG. Eur J Appl Physiol 1989;58:361–8. [11] Gerdle B, Eriksson N-E, Hagberg C. Changes in the surface electromyogram during increasing isometric shoulder forward flexions. Eur J Appl Physiol 1988;57:404–8. [12] Gerdle B, Eriksson N-E, Brundin L. The behaviour of the mean power frequency of the surface elctromyogram in biceps brachii with increasing force and during fatigue. With special regard to the electrode distance. Electromyogr Clin Neurophysiol 1990;30:483–9. [13] Hagberg M. Work load and fatigue in repetitive arm elevations. Ergonomics 1981;24:543–55. [14] Hagberg M, Ericson BE. Myoelectric power spectrum depen-
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dence on muscular contraction level of elbow flexors. Eur J Appl Physiol 1982;48:147–56. Hagberg C, Hagberg M. Surface EMG amplitude and frequency dependence on exerted force for the upper trapezius muscle: a comparison between right and left sides. Eur J Appl Physiol 1988;58:641–5. ¨ Hagg GM. Interpretation of EMG spectral alterations and alteration indexes at sustained contraction. J Appl Physiol 1992;73:1211–7. ¨ ¨ Hansson G-A, Stromberg U, Larsson B, Ohlsson K, Balogh I, Moritz U. Electromyographic fatigue in neck/shoulder muscles and endurance in women with repetitive work. Ergonomics 1992;35:1341–52. Hermans V, Spaepen AJ. The influence of electrode position on changes in electromyographic parameters of the upper trapezius during submaxima sustained contractions. Eur J Appl Physiol 1997;75:319–25. ¨ Jarvholm U, Palmerud G, Karlsson D, Herberts P, Kadefors R. Intramuscular pressure and electromyography in four shoulder muscles. J Ortho Res 1991;9:609–19. ¨ Jorgensen K, Fallentin N, Sidenius B. The strain on the shoulder and neck muscles during letter sorting. Int J Ind Ergon 1989;3:243–8. Kernell D. Neuromuscular frequency coding and fatigue. In: Gandevia SC, Enoka RM, McComas AJ, Stuart DG, Thomas CK, editors. Fatigue: neural and muscular mechanisms. New York: Plenum Press, 1995:135–46. ¨ Krogh-Lund C, Jorgensen K. Changes in conduction velocity, median frequency, and root mean square-amplitude of the electromyogram during 25% maximal voluntary contraction of the triceps brachii muscle, to limit of endurance. Eur J Appl Physiol 1991;63:60–9. Lamb R, Hobart D. Anatomic and physiologic basis for surface electromyography. In: Soderberg GL, editor. Selected topics in surface electromyography for use in the occupational setting: expert perspectives. National Institute for Occupational Safety and Health, 1992:6–22. Lawrence JH, De Luca CJ. Myoelectric signal versus force relationship in different human muscles. J Appl Physiol 1983;54:1653–9. Lindstrom L, Kadefors R, Petersen I. An electromyographic index for localized muscle fatigue. J Appl Physiol 1977;43:750–4. ¨ ¨ Luttman A, Jager M, Sokeland J, Laurig W. Electromyographical study on surgeons in urology. II. Determination of muscular fatigue. Ergonomics 1996;39:298–313. ¨ Mathiassen SE, Winkel J, Hagg GM. Normalization of surface EMG amplitude from the upper trapezius muscle in ergonomic studies—a review. J Electromyogr Kinesiol 1995;5:197–226. Mathiassen SE. The statistical confidence of load estimates based on ramp calibration of upper trapezius EMG. J Electromyogr Kinesiol 1996;6:59–65. Merletti R, Lo Conto LR, Orizio C. Indices of muscle fatigue. J Electromyogr Kinesiol 1991;1:20–33. Moritani T, Nagata A, Muro M. Electromyographic manifestations of muscular fatigue. Med Sci Sport Exerc 1982;14:198– 202. Moritani T, Muro M. Motor unit activity and surface electromyogram power spectrum during increasing force of contraction. Eur J Appl Physiol 1987;56:260–5. Nakata M, Hagner I-M, Jonsson B. Perceived musculoskeletal discomfort and electromyography during repetitive light work. J Electromyogr Kinesiol 1992;2:103–11. Neter J, Wasserman W, Kutner M. Applied linear statistical models. Boston: Irwin, 1990. ¨ Nieminen H, Hameenoja S. Quantification of the static load
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Veerle Hermans received the licentiate degree in physical education in 1990 and a Ph.D. in 1996 from the Katholieke Universiteit Leuven (Belgium), Faculty of Physical Education and Physiotherapy. Since 1990 she has been affiliated to the Laboratory of Ergonomics and Occupational Biomechanics at this faculty, where she worked on various studies related to the application of electromyography in the laboratory environment and during working tasks. Currently, she is a consultant to the Bureau of Ergonomics Advice of the laboratory. She is also affiliated to the Department of Product Development at the Hogeschool Antwerpen as a scientific researcher. Furthermore, she teaches ergonomics at the Faculty of Psychology at the University of Brussels. She is an effective member of the Belgian Ergonomics Society and of the Editorial Board of the Tijdschrift voor Arbeidsgeneeskunde en Ergonomie.
V. Hermans et al. / Journal of Electromyography and Kinesiology 9 (1999) 253–261 Arthur J. Spaepen graudated in 1973 from the Department of Mechanical Engineering at the Katholieke Universiteit Leuven (Belgium). He worked as a research assistant at the Biomechanics Laboratory and received his Ph.D. in 1980 from the same university. Since 1983 he has been a fully active member of the Faculty of Physical Education and Physiotherapy at KU Leuven, where he started the Laboratory of Ergonomics and Occupational Biomechanics in 1985. Since then, his main topics of research have been related to the quantitative use of EMG in movement analysis in general and more specifically, in occupational tasks, to the forward analysis of human movement (mainly gait) and to the use of assistive technology for persons with a disability. This expertise is also applied in Rehabilitation Technology Center (VLICHT-1989) and
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the Bureau for Ergonomic Advice (BEA-1995), both of which he is the Director. Marc Wouters graduated in 1985 from the Engineering Department of the Katholieke Universiteit Leuven as a mechanical engineer. After his studies he joined the Laboratory of Ergonomics and Occupational Biomechanics as a research assistant. Since 1989 he has worked at VLICHT (Flemish Information and Communication Center for Handicap and Technology) and is still closely related as a technical advisor with the activities of the Laboratory of Ergonomics and Occupational Biomechanics.
Relation between differences in electromyographic adaptations during static contractions and the muscle function Veerle Hermans *, Arthur J. Spaepen, Marc Wouters Laboratory of Ergonomics and Occupational Biomechanics, Faculty of Physical Education and Physiotherapy, Katholieke Universiteit Leuven, Tervuursevest 101, B-3001, Leuven, Belgium Received 22 October 1996; received in revised form 23 July 1998; accepted 14 September 1998
Abstract This study determines whether changes in the EMG values of two important muscles of the shoulder and neck region, the anterior deltoid and the upper trapezius, are due to changes in torque production or due to fatigue processes during sustained activity. Contractions at 20, 40, 60, 80 and 100% MVC were performed during a flexion of the arm in the sagittal plane at 90°, to examine the relation between torque and EMG. A sustained contraction at 20% MVC was performed to endurance point in the same position. RMS, a new parameter called activity, (ACT), and MPF of the deltoid anterior and the upper trapezius were analysed. The amplitude values correlated highly with increasing torque production, both for the deltoid muscle (range r ⫽ 0.95–0.96), and the trapezius muscle (range r ⫽ 0.83–0.87), whereas no significant difference was found for MPF. For the endurance task, the decrease in MPF was far more pronounced for the deltoid than for the trapezius, whereas the opposite occurred with RMS (P ⱕ 0.01). Furthermore, there was no significant difference over time for the ACT values of the deltoid, whereas there were significant increases in ACT for the trapezius (P ⱕ 0.01). The RMS/ACT ratio correlated highly (r ⫽ 0.81) with the MPF. Regression coefficients of these parameters differed significantly for the trapezius muscle but not for the deltoid muscle. Therefore, the RMS/ACT ratio may be extremely important in analysing the fatigue effects during sustained efforts, independent of torque variations, which can influence indicators of fatigue. 1999 Elsevier Science Ltd. All rights reserved. Keywords: Electromyography; Muscle fatigue; Muscle force; Signal analysis; Shoulder–neck
1. Introduction Many studies have proved that the surface electromyographic (EMG) signal changes due to fluctuations in torque production and due to fatigue processes during a sustained contraction. To quantify signal changes, values are determined from raw EMG to represent amplitude, such as the root-mean-square values (RMS), and frequency such as mean power frequency (MPF). Linear or quasi-linear increases are frequently reported [2,15,24] for the relation between the torque produced at a joint and the corresponding EMG amplitude. Other studies mention a number of influencing factors so that a nonlinear relation is found, e.g. kinematics of the movement, processing methods and acquisition
* Corresponding author. Tel.: ⫹ 32-16-3291-07; fax: ⫹ 32-163291-96; e-mail: [email protected].
procedure [24,46]. For changes in the frequency the behaviour in relation to the load is not so clear. Some studies have shown increasing frequency values at loads up to 25 and 80% of the maximal voluntary contraction (MVC), above which frequency becomes independent of the force [12–15,17,30]. However, other authors claim that the spectrum is insensitive to varying contraction intensity [11,25,37]. During a sustained force till endurance, significant changes in the EMG signal are also found. The increases in amplitude are attributed to the recruitment of fresh motor units, which compensate for the reduced performance of motor units that have been working since the beginning of the contraction [3,22,31]. The signal has decreased frequency which is predominantly caused by the reduced conduction velocity of the muscle fibres [29]. The validity of the above-mentioned EMG parameters as indicators of muscle fatigue has recently been questioned because changes in force production can occur
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simultaneously with the fatigue process during a sustained contraction [34–36]. This study presents a method which differentiates between the two processes. This method is applied for two muscles acting at the same joint but with different function. The purpose of the analysis is to explain the difference of the EMG parameters in relation to the difference of the muscle function. Firstly, the relation between the parameters and torque is investigated, followed by the analysis of changes in the EMG signal during a sustained contraction. The rationale for this is that in a work environment, fluctuations in muscle force and arm positions are present and influence the EMG signal. This interferes with the identification of EMG changes due to sustained efforts only, and is perhaps the main reason why several studies in the work environment have failed to demonstrate fatigue patterns in EMG [20,32,38].
2. Materials and methods 2.1. Subject group 10 females with a mean age of 20 yr (SD 1.7), mean height of 168.7 cm (SD 5.9) and mean weight of 61.2 kg (SD 6.8), gave their informed consent to participate in the study, and were all healthy with no previous complaints of the neck–shoulder region. 2.2. Mechanical recording Seated on a chair with the trunk of the body fixed to avoid extreme changes in posture during the tests, the subjects pushed upwards against a lever arm with the right arm extended horizontally (90° flexion in the sagittal plane, palm facing downwards). The axis of the lever arm was mounted to coincide with the shoulder joint axis. A torque meter connected to the lever arm measured torque production (Lebow). Percentages of the net torque at the joint were calculated taking into account both the weight of the subject’s arm and the weight of the handle. An oscilloscope in front of the subject indicated effort and the target torque, which was also presented on a multi meter. Subjects pushed upwards against the handle until the indicator on the oscilloscope monitor coincided with the line showing the target value. 2.3. Electrical recording EMG signals were recorded from two muscles: the anterior deltoid muscle as the prime mover of a forward flexion of the arm, and the upper trapezius muscle as an important stabiliser of the shoulder joint. Bipolar surface electrodes (Nikomed, silver–silver chloride, Denmark) were positioned according to standard procedures [47].
The 10 mm electrodes were attached 15 mm apart and the reference electrode was placed on the seventh cervical vertebra. The Telemetric EMG Measuring Processing System device (TEMPS, K.U.Leuven) served as an EMG amplifier (CMRR 100 dB, input impedance 300 M⍀). The signals were filtered using a band pass filter (5–250 Hz) and sampled at 500 Hz. Both the torque and EMG devices were connected to a computer by an ADconvertor (Labmaster), so that signals could be monitored simultaneously. An overview of the equipment is presented in Fig. 1. 2.4. Procedures Subjects were requested to perform two maximal voluntary contractions (MVC) pushing upwards, of which the highest was used for further calculations, were followed by a random sequence of 20, 40, 60 and 80% MVC. Each contraction lasted 5 s with intervening rest periods of 3 min. After 10 min, the subjects performed an endurance test at 20% MVC, until they were no longer able to maintain the load (deviation of 2.5% of the MVC allowed maximally), or until changes in the test position were noted. A third and final MVC test followed immediately. 2.5. Data analysis The EMG and torque data were analysed off-line using specifically designed software. For the MVC task, peak torque and EMG parameters were calculated for a 1.024 s time period. For the other short-term tasks, EMG parameters were determined over a 2.048 s time period in the middle of the 5 s torque production. During the sustained exercise, values were measured for 2.048 s every 10 s. The frequency spectrum and Mean Power Frequency of the EMG signal were calculated using a Fast Fourier Transformation; Root-Mean-Square values (RMS) were calculated to represent the amplitude, and a fourth parameter further referred to as ‘activity’ (ACT) was determined to distinguish between the torque and fatigue processes. 2.6. Rationale of the ACT parameter The ACT parameter was originally developed to estimate an EMG parameter proportional with isometric force, similar to RMS or integrated EMG values [42], but with several advantages compared with these methods, such as a lower sensitivity to a slowly changing baseline, and a lower dependence on the time interval. The recursive formula to calculate muscular activity (ACTi) from a series of raw EMG samples (Ei) at equally spaced time instances ti, can be summarized as follows: The algorithm computes the activity at time ti ⫹ 1 (ACTi ⫹ 1) from a known activity at time ti (ACTi) and
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Fig. 1.
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Overview of the equipment for measuring and analysing torque and electromyographic signals simultaneously.
the corresponding raw EMG values Ei ⫹ 1 and Ei. Because of reasons explained below, ACTi ⫹ 1 is equal to the sum of two terms: ACTi ⫹ 1 ⫽ P ⫹ Q Firstly, it is well known that the force production in a muscle fibre, the twitch force, does not stop abruptly when stimulation is terminated, but continues for a limited time, a digressive exponential aftereffect occurs [9]. An indicator for the rate of force decay can be given by the constant value, P. If P denotes the amount of force following from previous activity, then P ⫽ p*ACTi (p ⬍ 1) indicating that some force production will remain from previous activity even if no further EMG is noticed. In a recursive formula, it is important to notice that p depends on the sampling frequency, which was 500 Hz in this experiment. Secondly, activity obtained from the amount of force resulting from the new EMG activity (Q) is to be added to the previous term. Many other EMG quantification parameters assume that force delivered by a muscle is related to the amount of electrical activity (E). However, force production results from changes in the membrane ionization, which reproduce changes in electrical current (pulse) through a muscle fibre, rather than the value of electrical activity itself. If force production is sustained, an incessant train of pulses is needed, resulting in continuous changes in depolarization current [23]. Therefore, these changes in EMG are presented by a differentiation technique: the absolute value of differences of subsequent samples are calculated. Q ⫽ 兩Ei ⫹ 1 ⫺ Ei兩q A value of q equal to 1 would indicate changes in the
activity proportional to the changes in the EMG signal. If they are not linearly related, other values for q are found. As can be derived from the expression for Q, muscle activity is expressed in arbitrary units. From an initial value of the activity (ACT1) and subsequent EMG samples, A2, A3... An can be calculated, resulting in a time series of activities at the time instances ti (i ⫽ 1... n). It has been demonstrated that the outcome of the computation is independent of the first value of the activity (ACT1) after 0.5 s. To determine the values of p and q, and the initial value of the activity, ACT1, on which all subsequent values of ACTi are based, initial calculations were performed in which different combinations of p and q were tested. The resultant series of ACTi values were compared with the corresponding isometric forces using Pearson correlation coefficients. The following values gave a minimal coefficient of non-determination for a sampling rate of 500 Hz, and thus the highest possible linear relation between EMG and ACT: p ⫽ 0.9875 and q ⫽ 0.5 [41,42]. Relatively high activity values were found, even for a relaxed muscle (e.g. for 17% of the maximal torque production, an ACT value of 40% was calculated, whereas 21% of the maximum RMS). The reason for this is due to the simple normalising function of resizing the EMG differentials by means of a square root. However, from stimulation studies [4,44] it is clear that a more complex sigmoid relation between relative isometric force and the rate of activation in whole [21] muscles as well as in single muscle units should be used. A non-linear force behaviour is found in which the motor unit force varies as a function of the firing rate. For the human toe extensor motor units, low frequencies (5–8 Hz) are reported which produce unfused twitches, intermediate frequencies (10 Hz) produce a steep, near-linear
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occur, a zero-effect in relation to the basic signal results, since the same values compared with the basic signal cancel each other out. For the different torque percentages, the values of the EMG parameters were normalized to the EMG value for 100% MVC. For the sustained test, the average value of the first 2 s recording was used as a reference value for normalization. For reasons which become more clear in the results and discussion, the RMS/ACT ratio was also calculated. 2.7. Statistics
Fig. 2. Simulation of four different sinusoid signals: a basic signal, an increase in amplitude, a decrease in frequency and the combinated signal.
relation between force and frequency, and relatively high frequencies (20–30 Hz) are needed to produce maximal forces [4]. These frequencies can be compared with the motor unit firing rates during submaximal and maximal voluntary contractions. Although this simple approach was taken, high ACT–torque correlations were found. The offset-value for ACT is found by measuring the EMG activity of the relaxed muscle. In this study, the intercept of the ACT–torque regression line (approximately 16% of the MVC ACT value) is used as ACT at zero-torque level and has been subtracted from ACT before further data processing. In order to understand the effect of changes in the raw EMG signal on the ACT, a simulation of a raw EMG signal is presented by a sinusoid. A basic EMG signal is presented, accompanied by a slower EMG signal (freq: 25% frequency decrease) which has often been explained in literature by a decrease in conduction velocity of the fibres active from the onset of a sustained contraction. Also a signal with higher amplitude (ampl: 30% amplitude increase) is shown which may be caused by the recruitment of a larger motor unit. In addition, the combination of these two factors (freq ⫹ ampl) is presented (Fig. 2). In Table 1, the mean steady-state ACT values and standard deviation for these four sinusoids are presented: the decrease in frequency lowers the ACT values, whereas the increase in amplitude increases them. If both Table 1 Calculated ACT values of four different sinusoid signals: a basic signal (Basic), an increase in amplitude (Ampl.), a decrease in frequency (Freq.) and the combinated signal (Freq. ⫹ ampl.)
ACT SD
Basic
Freq.
Ampl.
Freq. ⫹ ampl.
59.94 0.26
68.34 0.29
52.39 0.21
59.75 0.24
Pearson’s correlation coefficients (r) were used as descriptive measures of the association between MPF, RMS, ACT and torque. To evaluate the relation between load and the EMG parameters and the changes in the values over time, linear regression analyses were performed using a statistical model, based on the mathematical considerations of Neter et al. [33]. The calculation procedure takes into account the individual regression lines of each subject and the number of observations, so that a mean intercept and slope with the accompanied variance is obtained. Furthermore, the difference between slopes can be calculated using t-tests. A detailed presentation of this model is presented in Hermans and Spaepen [18]. Pearson’s correlation coefficients (r) were used to examine the relation.
3. Results 3.1. Relation between torque and EMG The mean maximal torque production in the shoulder joint for a forward flexion of the right arm was 46.53 Nm (SD 6.61). The changes in EMG parameters, normalized to the values for 100% MVC, with increasing load are presented in Fig. 3 for deltoid and trapezius. Significant increases were found for ACT and RMS with increasing load (P ⱕ 0.01). These parameters correlated closely with load for the prime mover, the deltoid muscle, (range r ⫽ 0.95–0.96) and for the stabilizer, the trapezius muscle, (range r ⫽ 0.83–0.87). MPF values for both muscles did not change significantly with increasing torque. Important to notice is the fact that MPF and torque correlated only very poorly for both muscles, deltoid range r ⫽ ⫺ 0.17– ⫺ 0.27, trapezius range r ⫽ 0.10–0.19. 3.2. EMG changes during sustained submaximal contraction The mean endurance time for the sustained 20% MVC was 251 s (SD 36.04). In Fig. 4, the mean changes in
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Fig. 4. Changes in EMG parameters (MPF, ACT and RMS) over time for the anterior deltoid and the upper trapezius. Mean values over 10 subjects, normalized to the first recorded values. Fig. 3. Changes in EMG parameters (MPF, RMS and ACT) for the anterior deltoid and the upper trapezius during increasing torque production. Mean values over 10 subjects, relative to the MVC values.
normalized EMG parameters over time are presented, both for deltoid and trapezius. From Fig. 4 it is clear that the significant decrease in MPF over time was larger for the deltoid (25%) than for trapezius (8%). The opposite occurred for the increase in RMS over time (P ⱕ 0.01) which was larger for trapezius (74%) than for deltoid (28%). The second parameter of the time domain, ACT, revealed a different behaviour dependent on the muscle: for trapezius there was a significant increase of 22% (P ⱕ 0.01), whereas for deltoid there was even a small (though not significant) decrease of 5%. No significant difference for the deltoid was noticed between the slope of the RMS/ACT ratio and the RMS slope, because of the rather constant ACT values, whereas for the trapezius a difference was found (P ⱕ 0.01). The slopes of the RMS/ACT ratio have also been compared with MPF changes. For the deltoid no significant difference between these parameters occurred. However, high correlations (r ⫽ ⫺ 0.82) were found between the RMS/ACT slopes and MPF changes. For the trapezius muscle the slopes of RMS/ACT and MPF were signifi-
cantly different from each other and a very low correlation was found (r ⫽ ⫺ 0.08). The MVC torque value immediately after the fatigue test was 42.28 Nm (SD 7.07), which is 87.71% of the pre-fatigue MVC value (P ⱕ 0.05). Comparing the EMG parameters between the two maximal tests shows that the pre- and post-fatigue EMG parameters of the deltoid differ significantly from each other (MPF P ⱕ 0.001, ACT P ⱕ 0.001, RMS P ⱕ 0.01). However, for trapezius muscle, the relative decreases in the parameters were very low, the pre- and post-fatigue values did not differ significantly from each other (P > 0.1), although the MVC torque was decreased. The relative post-fatigue values (mean values over the subject group and standard deviations) are presented in Table 2.
4. Discussion This study evaluated the relation between isometric force production during a flexion of the arm in the sagittal plane and the corresponding EMG values of two important muscles of the shoulder and neck region, the anterior deltoid and the upper trapezius. Changes in
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Table 2 Torque and EMG values of the mean power frequency (MPF), rootmean-square (RMS) and activity (ACT) for the post-fatigue MVC test. Mean values and standard deviations relative to the pre-fatigue values for deltoid and trapezius
MPF ACT RMS Force
Deltoid
Trapezius
92.5 (4.4) 88.3 (6.7) 82.5 (14.5)
99.4 (5.1) 94.9 (10.8) 90.5 (18.3) 87.7 (12.2)
EMG parameters during a sustained submaximal task were also examined. 4.1. Relation of EMG and torque The very high correlation between the torque production and the EMG amplitude of the deltoid in this study, is confirmed by others [11,19], and is explained by the function of the muscle as the prime mover during flexion of the arm in the sagittal plane. Together with the activation of the lateral deltoid, the biceps and the major pectoralis muscle, the arm can be raised. The action line of the anterior part for performing an anteflexion of the arm corresponds with the anatomic alignment of the deltoid and therefore often only the net torque is measured and assumed to be linear with respect to the agonist muscle of interest. In order to suspend the upper extremity, the muscles of the shoulder girdle are activated, mainly the descending part of the trapezius. Due to the degrees of freedom of the shoulder joint, certain work tasks can be accomplished through a large number of muscle combinations [27] which can considerably influence the relation between force and EMG [40,45]. For this reason, for instance, the MVC task can be regarded as a maximal effort of the prime mover, the deltoid muscle, but certainly not for the stabilizer, the trapezius muscle. This is also illustrated by the fact that the upward force that can be delivered during shoulder elevation is far higher than the upward force produced on the lever arm. Because of the high torque–RMS and torque–ACT correlations it is in the non fatigue test to assume a gradual increase in stabilization force with increasing anteflexion effort, which corroborates several other muscles. The EMG values of the trapezius during the MVC test however, should not be regarded as maximal effort for the muscle. The high correlations between torque and deltoid ACT, r ⫽ 0.95, are in agreement with previous studies on other muscles (knee extensors and trunk muscles) evaluated as prime movers during specific tasks, for which the correlations ranged from r ⫽ 0.94 to 0.99 [41,42]. Furthermore, since several advantages of this parameter were demonstrated compared with other amplitude parameters (e.g. lower sensitivity to slowly
changing baselines), we may conclude that this parameter efficiently represents the force production necessary to perform a static forward flexion of the arm. Regarding the relation between torque and MPF, con¨ flicting results are found in literature. Hagg [16] presented a list of authors reporting a dependence of MPF on contraction level, most pronounced at low levels. The possible cause for this is that when force increases, additional motor units are recruited, characterised by larger fibres and higher action potential velocity. When these fibres are recruited during more forceful contractions, a higher frequency content results. In our experiment, there were no significant differences between the MPF values of succeeding torque productions for both muscles, which is supported by the results of Gerdle et al. [11]. In fact, there was a slight decrease in both studies. Regarding the experiments of Gerdle et al. [11] the question arises of whether this is due to the fact that a ramp contraction of 10 s was performed from 0 to 100% MVC, which can introduce fatigue during the last seconds and therefore a decrease in MPF. The use of ramp calibration for determining the relation between force and EMG is also discussed in literature. [1,28]. In our experiment, short successive contractions were performed separated by rest periods, and were presented to the subjects randomly, so no effect of fatigue was expected. Our results support the hypothesis that MPF values are not significantly influenced by the level of torque production. 4.2. Sustained activity Increasing amplitude and decreasing frequency values during sustained contractions are often considered as indicators of muscle fatigue [7,8] and are found both in laboratory and true working environments, although some studies fail to demonstrate these indicators, despite subjective fatigue scores [6,11,17,34,38]. This questions the use of MPF as a valid indicator of muscle fatigue. In our study a decrease of 8% in the MPF occurred for the trapezius muscle, which is much less than the deltoid decrease (25%), but can ¨ be considered significant according to the studies of Oberg et al. [35]. No significant changes occurred in the ACT parameter for the deltoid muscle, only a small decrease is found, explained by the small torque decrease at the end of the experiment. According to Gamet and Maton this is caused by difficulties in maintaining the initial force level due to an increase of physiological tremor [10]. For the trapezius, the decrease in frequency is much lower but high RMS increases (74%) and a significant ACT increase are found at the end of the task. This increase is explained by the different function of the muscles, as described above. When the arm is elevated, an increased torque is produced both in the glenohumeral joint and for the scapula by the mass of the arm. Accord-
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ing to Hagberg [13], functional changes within the deltoid or transfer of torque performance from the deltoid to other muscles may occur when this task has to be sustained in order to delay the fatigue feeling. This is called trade-off synergism: try to delay exhaustion by regulating muscle activities such that the load can be sustained [39]. Minor position movements can be needed to change the relative distribution of forces during a fatiguing contraction, e.g. slightly elevating the shoulder. This causes an increase in force production of the trapezius, although this muscle originally (mechanically) does not take part in the production of force in arm flexion [34]. This high increase in amplitude is due to the fact that initially the trapezius is only working at a very small percentage of its real maximum for stabilizing the shoulder during a forward flexion. The maximum activation (and thus higher EMG amplitude) of this muscle is only obtained in the position of elevating the shoulder when the arm is hanging vertically. So the 74% RMS increase is still only a small part of the real maximum contraction value. The constant value of the ACT parameter of deltoid during sustained effort and the linear increase with the torque, suggest it is sensitive to force production, but not to fatigue. The RMS parameter on the other hand increases with load and fatigue. Therefore, the RMS/ACT seems to relate mainly to fatigue, with the parameters cancelling each other out for the effects of load. Three observations support this hypothesis. Firstly, there was a high correlation (r ⫽ ⫺ 0.81) of the RMS/ACT regression coefficients with the MPF coefficients for the deltoid, and the regression analysis revealed no difference between the slopes of both parameters. Furthermore, because of the relatively low stabilising effort of the trapezius muscle, it is not expected to be affected by fatigue to the same extent as the prime mover, which is confirmed by larger fatigue times in several literature studies [13,43,34]. Analysis of the parameters confirms this hypothesis. The RMS/ACT ratio correlates poorly with MPF (r ⫽ ⫺ 0.08) and the regression coefficients differed significantly from each other. Secondly, the performance of an MVC contraction immediately after the sustained task caused no significant differences in the EMG parameters for the trapezius, compared with the pre-fatigue MVC, whereas for the deltoid there were significant fatigue indications in the EMG parameters. Thirdly, experiments on trunk muscles also revealed a high correlation between the time course of MPF and that of the RMS/ACT ratio during prolonged fatiguing exercise [5]. In conclusion, in this study it is suggested that the RMS/ACT ratio obtained from a surface EMG signal, can be a valuable parameter for discriminating between changes in signal amplitude due to fatigue processes or
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due to force adaptations. Recently, also other methods attempt to discriminate force-related and fatigue-induced EMG changes. Luttmann et al. [26] mention the joint analysis of EMG spectrum and amplitude (the JASA method). They use the median frequency as a measure of the spectral distribution. However, it is mentioned in literature that also the median frequency is influenced by force effects, as the MPF (cf. supra) and that can influence the data results and the interpretation. To validate more our ratio parameter, it is necessary to make comparisons with other methods (e.g. comparisons with measurements of intramuscular pressure and/or with JASA) and applications in working environments are needed. This is particularly important since several studies have failed to demonstrate significant decreases in MPF explained by the fact that variations in limb position and fluctuations in force production influence the EMG signals. References [1] Attebrant M, Mathiassen SE, Winkel J. Normalizing upper trapezius EMG amplitude: comparison of ramp and constant force procedures. J Electromyogr Kinesiol 1995;5:245–50. [2] Basmajian JV, De Luca CJ. Muscles alive. Baltimore: Williams and Wilkins, 1985. [3] Bigland-Ritchie B. EMG/force relations and fatigue of human voluntary contractions. Exerc Sport Sci Rev 1981;9:75–117. [4] Bigland-Ritchie B, Fuglevand AJ, Macefield VG. Force modulation by rate-coding in single motor units of human toe extensor muscles. Soc Neurosci Abstr 1993;19:154. [5] Bunkens L. Back muscle fatigue and chronic low back pain, a quantitative analysis of back muscle responses in persons with and without chronic back disorder. Ph.D. thesis, K.U.Leuven, Belgium, 1996. [6] Christensen H. Muscle activity and fatigue in the shoulder muscles of assembly-plant employees. Scand J Work Envir Health 1986;12:582–7. [7] De Luca CJ. Myoelectrical manifestations of localized muscular fatigue in humans. CRC Crit Rev Biomed Engng 1984;11:251– 79. [8] De Luca CJ. Spectral compression of the EMG signal as an index of muscle fatigue. In: Sargeant AJ, Kernell D, editors. Neuromuscular fatigue. Amsterdam: North-Holland, 1993:44–51. [9] Fuglevand AJ, Winter DA, Patla AE. Models of recruitment and rate coding organization in motor-unit pools. J Neurophysiol 1993;70(6):2470–88. [10] Gamet D, Maton B. The fatigability of two agonistic muscles in human isometric voluntary submaximal contractions. I. Assessment of muscular fatigue by means of surface EMG. Eur J Appl Physiol 1989;58:361–8. [11] Gerdle B, Eriksson N-E, Hagberg C. Changes in the surface electromyogram during increasing isometric shoulder forward flexions. Eur J Appl Physiol 1988;57:404–8. [12] Gerdle B, Eriksson N-E, Brundin L. The behaviour of the mean power frequency of the surface elctromyogram in biceps brachii with increasing force and during fatigue. With special regard to the electrode distance. Electromyogr Clin Neurophysiol 1990;30:483–9. [13] Hagberg M. Work load and fatigue in repetitive arm elevations. Ergonomics 1981;24:543–55. [14] Hagberg M, Ericson BE. Myoelectric power spectrum depen-
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Veerle Hermans received the licentiate degree in physical education in 1990 and a Ph.D. in 1996 from the Katholieke Universiteit Leuven (Belgium), Faculty of Physical Education and Physiotherapy. Since 1990 she has been affiliated to the Laboratory of Ergonomics and Occupational Biomechanics at this faculty, where she worked on various studies related to the application of electromyography in the laboratory environment and during working tasks. Currently, she is a consultant to the Bureau of Ergonomics Advice of the laboratory. She is also affiliated to the Department of Product Development at the Hogeschool Antwerpen as a scientific researcher. Furthermore, she teaches ergonomics at the Faculty of Psychology at the University of Brussels. She is an effective member of the Belgian Ergonomics Society and of the Editorial Board of the Tijdschrift voor Arbeidsgeneeskunde en Ergonomie.
V. Hermans et al. / Journal of Electromyography and Kinesiology 9 (1999) 253–261 Arthur J. Spaepen graudated in 1973 from the Department of Mechanical Engineering at the Katholieke Universiteit Leuven (Belgium). He worked as a research assistant at the Biomechanics Laboratory and received his Ph.D. in 1980 from the same university. Since 1983 he has been a fully active member of the Faculty of Physical Education and Physiotherapy at KU Leuven, where he started the Laboratory of Ergonomics and Occupational Biomechanics in 1985. Since then, his main topics of research have been related to the quantitative use of EMG in movement analysis in general and more specifically, in occupational tasks, to the forward analysis of human movement (mainly gait) and to the use of assistive technology for persons with a disability. This expertise is also applied in Rehabilitation Technology Center (VLICHT-1989) and
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the Bureau for Ergonomic Advice (BEA-1995), both of which he is the Director. Marc Wouters graduated in 1985 from the Engineering Department of the Katholieke Universiteit Leuven as a mechanical engineer. After his studies he joined the Laboratory of Ergonomics and Occupational Biomechanics as a research assistant. Since 1989 he has worked at VLICHT (Flemish Information and Communication Center for Handicap and Technology) and is still closely related as a technical advisor with the activities of the Laboratory of Ergonomics and Occupational Biomechanics.