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Effect of innervation zones in estimating biceps brachii force–EMG relationship during isometric contraction
Journal of Electromyography and Kinesiology 22 (2012) 80–87
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Effect of innervation zones in estimating biceps brachii force–EMG relationship during isometric contraction Timo Rantalainen a,b,c,d,⇑, Adam Kłodowski b, Harri Piitulainen c,e a
Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia Department of Mechanical Engineering, Lappeenranta University of Technology, Finland c Neuromuscular Research Center, Department of Biology of Physical Activity, University of Jyväskylä, Finland d Department of Health Sciences, University of Jyväskylä, Finland e Brain Research Unit, Low Temperature Laboratory, School of Science, Aalto-University, Espoo, Finland b
a r t i c l e
i n f o
Article history: Received 17 May 2011 Received in revised form 30 August 2011 Accepted 22 September 2011
Keywords: High-density EMG Modeling Neuromuscular Validation Innervation zone
a b s t r a c t Measuring muscle forces in vivo is invasive and consequently indirect methods e.g., electromyography (EMG) are used in estimating muscular force production. The aim of the present paper was to examine what kind of effect the disruption of the physiological signal caused by the innervation zone has in predicting the force/torque output from surface EMG. Twelve men (age 26 (SD ±3) years; height 179 (±6) cm; body mass 73 (±6) kg) volunteered as subjects. They were asked to perform maximal voluntary isometric contraction (MVC) in elbow flexion, and submaximal contractions at 10%, 20%, 30%, 40%, 50% and 75% of the recorded MVC. EMG was measured from biceps brachii muscle with an electrode grid of 5 columns 13 rows. Force–EMG relationships were determined from individual channels and as the global mean value. The relationship was deemed inconsistent if EMG value did not increase in successive force levels. Root mean squared errors were calculated for 3rd order polynomial fits. All subjects had at least one (4–52) inconsistent channel. Two subjects had inconsistent relationship calculated from the global mean. The mean root mean squared error calculated using leave one out method for the fits of the individual channels (0.33 ± 0.17) was higher (P < 0.001) than the error for the global mean fit (0.16 ± 0.08). It seems that the disruption of the physiological signal caused by the innervation zone affects the consistency of the force–EMG relationship on single bipolar channel level. Multichannel EMG recordings used for predicting force overcame this disruption. Crown Copyright Ó 2011 Published by Elsevier Ltd. All rights reserved.
1. Introduction Musculoskeletal loading, apart from a few notable exeptions, is not directly measurable in vivo. Direct measurement of muscle forces in vivo is invasive and limited to few muscles with extensive outer tendons (e.g., quadriceps femoris (Finni et al., 2000) and triceps surae muscle groups (Finni et al., 1998; Komi et al., 1992)). The situation is very similar for measuring bone strains in vivo, being invasive and limited to superficial bone sites (Lanyon et al., 1975; Hoshaw et al., 1997; Milgrom et al., 2000). Detailed musculoskeletal loading information would be useful on multiple occasions, such as in planning surgical interventions (Delp and Loan, 1995).
⇑ Corresponding author at: School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia. Tel.: +61 3 925 17247; fax: +61 3 924 46017. E-mail addresses: [email protected], [email protected] (T. Rantalainen).
Estimating musculoskeletal loading depends on determining muscular forces. Due to the redundancy problem of the muscles as actuators of the body, the forces cannot be directly derived from inverse kinematics and kinetics and consequently optimization procedures are required. The optimisation procedures, in turn, introduce error into the muscle force estimates. Another feasible approach for estimating muscle forces indirectly is electromyography (EMG) (Disselhorst-Klug et al., 2009; Staudenmann et al., 2010), which has actually been used to validate inverse kinematics based muscular force estimates in musculoskeletal models (Anderson et al., 1996; Nazer et al., 2008). Furthermore, representing muscle activation and ensuing muscular force production with EMG has meaningful applications in clinical practice e.g., in controlling prostheses (Triolo and Moskowitz, 1989) and during rehabilitation (Holtermann et al., 2010). The EMG recordings are often measured using standard bipolar surface EMG (sEMG) with the recording electrodes positioned according to general guidelines, such as the SENIAM recommendations (Hermens et al., 2000). Successful models for predicting muscle forces from sEMG have been developed for both the isometric
1050-6411/$ - see front matter Crown Copyright Ó 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2011.09.012
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(Fuglevand et al., 1993; Staudenmann et al., 2006; de Oliveira and Menegaldo, 2010; Wheeler et al., 2010) and the dynamic condition (Lloyd and Besier, 2003). Force–EMG relationship has been studied extensively and the reader is directed to recent reviews for physiological and methodological considerations (Disselhorst-Klug et al., 2009; Staudenmann et al., 2010). Surface EMG varies spatially over the muscle belly (Holtermann et al., 2005; Staudenmann et al., 2005, 2006, 2010; Holtermann and Roeleveld, 2006; Piitulainen et al., 2009) and accordingly, improvements in predicting muscle forces have been obtained by applying multichannel electrodes thereby increasing the representability of the measured surface EMG-signal (Staudenmann et al., 2005, 2006, 2010). However, it is often neglected in force– EMG relationship that electrode location with respect to various anatomical structures (e.g., innervation zones and tendon region) may disrupt the physiological information of the sEMG signal (Piitulainen et al., 2009). The aim of the present paper was to examine what kind of effect the disruption of the physiological signal caused by the innervation zone has in predicting the force/torque output from surface EMG. It was speculated that the disruption caused by innervation zones weakens the association between force/torque and EMG. 2. Methods A convenience sample of data from 12 male subjects (age 26 (SD ±3) years; height 179 (±6) cm; body mass 73 (±6) kg; subcutaneous tissue thickness in biceps brachii 2.3 (±0.6) mm) was used in the present study. No subject had any known symptoms of neuromuscular disorders. The subjects were non-smokers, did not drink any caffeine rich drinks 12 h before the measurements, and avoided strenuous exercise two days prior to the measurements. The study was conducted in agreement with the Helsinki declaration with the approval of the local ethical committee. Written informed consent was obtained from all participants. 2.1. Study protocol The subjects were allowed to warm-up with three sets of submaximal isometric elbow flexions lasting 8 s each. Maximal voluntary contraction (MVC) was then determined in the elbow flexors of the dominant arm (all subjects were right handed) for each subject. The highest value of three MVC trials with less than 5% difference from the second highest value was accepted as the true MVC value. The subjects then performed intermittent sub-maximal voluntary isometric contractions in the following order, at 10%, 20%, 30%, 40%, 50% and 75% of MVC (duration of 8 s). Between each contraction, subjects were allowed a minimum of 2 min rest, to minimize the possibility of metabolic fatigue in the muscle. All submaximal isometric contractions were performed twice. 2.2. Force measurements Elbow flexor force was measured while subjects were seated, and their dominant arm was attached to a force transducer in a custom made chair (University of Jyväskylä, Finland) by applying straps to the wrist. The forearm was half supinated, elbow angle was set at 120° (180° corresponds to full extension) and an arm support was used. The chair dimensions were adjusted for each subject individually. 2.3. EMG recordings sEMG signals were recorded at 2048 Hz sampling frequency in a monopolar acquisition mode with a semi-disposable grid of 64
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electrodes (8 mm inter-electrode distance in both directions, model ELSCH064, designed by LISiN at Politecnico di Torino and manufactured by OT Bioelectronica, Torino, Italy). The electrode grid consisted of five longitudinal columns of 13 electrodes except the first column (most lateral one) which consisted of 12 electrodes. The electrode grid was placed over the mid-line of the short head of biceps brachii, 2 cm proximal to the distal tendon. The columns of the electrode grid were placed parallel to the longitudinal axis of biceps brachii. A movable array electrode was used for determining the longitudinal axis. The orientation of the array electrode was varied and the orientation was defined as being parallel to the longitudinal axis, when action potential propagation was optimally observed with the array (for further details please see (Piitulainen et al., 2009)). 2.4. Signal processing The sEMG signals were digitally band-pass zero-lag filtered at a bandwidth of 20–450 Hz (4th order Butterworth filter). The monopolar signals were post-processed to bipolar channels (in total 59 channels) in longitudinal direction of each electrode column, and adjacent channels in each of the five columns were plotted in MatLab (R2007a, ver. 7.4.0.287, The MathWorks Inc., MA, USA) to determine the location of the main innervation zone. Innervation zone location was determined individually for each column by visual inspection of the plots (Fig. 1). This was done to all isometric contraction force levels. The criteria used for determining the location of the innervation zone(s) were based either on a reversal in signal polarity in two adjacent channels (IZ was located between those channels), or on the lowest amplitude in a single channel (IZ was located on that channel). Channels with high interference (signal to noise ratio <20, deemed noisy channels) were excluded from all further analysis. EMG amplitude was determined as root mean square (RMS) value of a 1000 ms epoch during the most stable part (smallest variation of the force signal) of each isometric contraction separately. Furthermore, the accepted epoch had a maximum fluctuation of less than ±5% of the force signal in all isometric contractions. 2.5. Individual channel and global mean analysis RMS was calculated separately for all the accepted bipolar channels (Fig. 2) and these ‘‘raw’’ values are referred as ‘‘individual channels with innervation zone’’ in the text. In addition, a global mean of RMS value was calculated from these accepted channels including the channels belonging to innervation zone. Another approach was chosen to estimate the effect of innervation zone region on force–EMG relationships on individual channel and global level, where all channels overlying the main innervation zone, were excluded from further analysis. These values are referred as ‘‘individual channels without the innervation zone’’ in the present paper. Thus in this case, the global mean value for RMS was calculated as the average of all individual channels with the innervation zones excluded. 2.6. Innervation zone tracking Possible shift of innervation zone due to increase in the contraction force and consequent change in muscle geometry was tracked to avoid consequent bias in RMS values. In such case, the unbiased RMS value was acquired as the average RMS of five parallel channels (each corresponding to one of the five longitudinal columns) in the next channel distal to the channel where innervation zone was detected in the corresponding column. If innervation zone was located in between two adjacent channels, the more distal channel was defined as the location of the innervation zone. Since
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Fig. 1. Innervation zone(s) were determined by visual inspection. The decision of innervation zone location was based either on a reversal in signal polarity in two adjacent channels (IZ was located between those channels), or on the lowest amplitude in a single channel. A representative sample of 0.25 s window of the EMG signals at different force levels from one subject.
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Fig. 2. Example of the effect of the innervation zone (highlighted with empty black circle) on the spatial distribution of the RMS amplitude of EMG in subjects 2 (upper row) and 11 (lower row). The values of the bipolar channels on top of the innervation zone are markedly different from the adjacent bipolar channels. Large shift of innervation zone is seen in column 3 from 10% to 20% MVC in subject 2, possibly due to recruitment of new motor unit or medial shift of the muscle. Noisy channels removed from the analysis and the empty channel in the most lateral column (column one, channel one) are in white color.
path of the main innervation zone was not always transverse in medial–lateral direction of the muscle, the averaged tracked channels were not necessarily on the same row of the electrode grid.
2.7. Force–EMG relationship The relationship between EMG RMS amplitude and force was visualized by normalizing the force level from 10% to 100% and RMS amplitude to the value measured during the isometric MVC. In order to produce a meaningful force–EMG relationship, the amplitude of EMGs needs to increase in successive contraction force levels. If this was not the case, force–EMG relationship at the respective subject or sEMG channel was deemed inconsistent. In addition, the proportion of inconsistent innervation zone chan-
nels was compared to the proportion of inconsistent non-innervation zone channels with two-tailed paired T-test. Furthermore, for each subject third order polynomials were fitted on his force–EMG relationships derived from (1) global mean without innervation zones, (2) global mean with innervation zones, (3) mean of the five tracked unbiased channels adjacent to the channel(s) corresponding to the innervation zone, (4) mean of five channels directly over the innervation zone and (5) each of the individual bipolar channel separately. Then the root mean squared errors of the third order polynomial fits using leave one out method (Stone, 1974) for force levels 10–75% MVC (0% and 100% were always included) were compared between these five conditions using repeated measure analysis of variance (ANOVA). Paired T-tests were used for post hoc testing. P 6 0.05 was chosen to indicate statistically significant difference.
Fig. 3. Force–EMG relationship of the global RMS amplitude of EMG without innervation zone channels (left pane) and from tracked RMS amplitude of EMG (right pane) for the whole group (n = 12). Apparently the relationship is relatively linear on a group level in the present population. RMS amplitude is normalized to the RMS amplitude value during isometric maximal volumetric contraction and all channels overlaying innervation zone are excluded.
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3. Results 3.1. Golbal sEMG Whole group. On the group level, force–EMG relationships calculated either from global sEMG including innervation zones or global sEMG where innervation zones were excluded turned out to be linear in the studied population. Fig. 3 shows the mean force–EMG relationship for the whole group plotted from the global sEMG. 3.2. Individual subjects Force–EMG relationships from the subjects’ individual global sEMG showed inconsistent force–EMG relationships in 2 out of 12 subjects (Fig. 4). The result was the same whether or not the channels overlaying the innervation zone were excluded. The inconsistent steps occurred at lower (from 30% to 40% of MVC) contraction levels.
12 ± 13 channels (28% of all accepted ones) showed inconsistent force–EMG relationship, while at least one channel was inconsistent on 11 of 12 subjects (range 0–38). In the case of the channels directly overlaying the main innervation zone, total of 14.3 (±1.9) channels (51% of all channels) showed inconsistent force–EMG relationship (Fig. 5). This was a significantly higher proportion of inconsistent channels when compared to channels outside the main innervation zone region (P = 0.002). 3.5. Innervation zone tracking Despite of innervation zone tracking, the force–EMG relationship remained inconsistent in 2 out of 12 subjects (Fig. 4). None of the tracked channels were removed due to noise. The proportion of inconsistent channels was similar to average results over all accepted channels without innervation zone region channels since, 28% of the individual tracked channels showed inconsistent force– EMG relationship.
3.3. Individual bipolar channels with innervation zone 3.6. Polynomial fits On average 57 ± 3 individual bipolar channels were accepted to the analysis on the whole group level (noisy channels were removed). In total 18 ± 16 channels (32% of all accepted ones) produced inconsistent force–EMG relationship. Furthermore, all 12 subjects had at least one inconsistent channel (range 4–52). In addition, one subject showed inconsistent force–EMG relationship in all of the bipolar channels. 3.4. Individual bipolar channels without innervation zone On average 43 ± 3 individual bipolar channels were included to the analysis after removal of noisy channels and the channels overlaying the innervation zone on the whole group level. In this case
The root mean squared errors differed between the five different 3rd order force–EMG fits (P = 0.010). The mean root mean squared error of individual channels (0.33 ± 0.17) was significantly higher (P < 0.001) than the ones for the other four fits (1) global mean without innervation zones 0.16 ± 0.10, (2) global mean with innervation zones 0.16 ± 0.08, (3) mean of the five tracked unbiased channels 0.16 ± 0.06 and (4) the five innervation zone channels which were tracked 0.16 ± 0.07. No improvement was achieved by tracking the main innervation zone with increase in isometric contraction level, when compared to the other multichannel approaches, global means or even to mean of the five channels directly over innervation zone.
Fig. 4. Typical examples of the force–EMG relationships from subjects’ individual global mean RMS amplitude of EMG without innervation zone channels (left pane) and from subjects? individual tracked RMS amplitude of EMG (right pane). Inconsistent force–EMG relationships is seen on subject 2. The trendlines are 3rd order polynomial fits. EMG amplitudes and force levels are normalized to the respective values during maximal voluntary contraction and channels overlaying innervation zones are excluded.
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Fig. 5. Spatial distribution of inconsistent force–EMG relationships. Rows of the matrix run from proximal to distal, while columns run from lateral to medial. Innervation zone locations at force levels of 10% of MVC and at MVC of the individual subjects are highlighted with light gray lines. The channels proximal to the innervation zone appear to exhibit the least number of inconsistent channels. In addition, the medial portion of the matrix appears to have lower number of inconsistent channels than the lateral portion. In this data set the innervation zones were not excluded. The empty channel in most lateral column (column one, channel one) is in white color.
4. Discussion The main finding of the study was that the force–EMG relationship was inconsistent relatively often, at single bipolar channel level especially in case of channels overlaying the innervation zone, even under the highly controlled paradigm of isometric testing. Furthermore, with unfortunate bipolar electrode positioning, it was possible to record inconsistent force–EMG relationship from all of the subjects in the present study. As recently reviewed (Disselhorst-Klug et al., 2009; Staudenmann et al., 2010), because of physiological and anatomical reasons, force–EMG relationship is not necessarily linear nor is there a need for linearity for modeling purposes (i.e. non-linear predictions can be made). Furthermore, the relationship depends on muscle activation history, contraction type and velocity and therefore force prediction in dynamic situations is particularly challenging (Disselhorst-Klug et al., 2009; Staudenmann et al., 2010). Yet, with meticulous approach, reasonable results may be obtained (e.g. (de Oliveira and Menegaldo, 2010)). In agreement with previous studies (Staudenmann et al., 2005; Staudenmann et al., 2006), use of multiple sEMG channels appeared to improve the determination of force–EMG relationship as the prediction error was smaller in multichannel analyses compared to the analyses at individual channel level. Consequently, it is argued that the disruption of the physiological signal caused by the innervation zone affects the precision of the force–EMG relationship. Apparently multichannel sEMG recordings of only five channels may compensate for anatomical and physiological constraints, such as innervation zone region when estimating force–EMG relationship and somewhat surprisingly, regardless of the location of electrodes used for the relationship determination. The question needs to be raised, whether the results obtained in the present paper are caused by physiology or by methodology. Muscle force output is controlled by recruiting motor units and by modulating the firing rate of already active motor units (Adrian and Bronk, 1929). Therefore, while non-linear relationship is to be expected, in theory, the force–EMG relationship should be consistent (Metral and Cassar, 1981). On the other hand, we have rela-
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tively recently demonstrated, that changes in muscle anatomy during contraction (e.g., change in innervation zone location with respect to the surface electrodes on the skin) can disrupt the physiological meaning of the recorded bipolar sEMG even in the highly controlled isometric contraction (Piitulainen et al., 2009) (Fig. 2). In the present study an attempt was made to test, whether this crucial methodological limitation for successful force–EMG relationship could be overcome by following the innervations zone location throughout the experiment and measuring sEMG in relation to the innervation zone. No improvement was observed against other multichannel approaches applied in the present experiment. This may partly be because of methodological limitations concerning the procedure for innervation zone tracking, such as detection resolution which is limited by inter-electrode distance (current resolution was 4 mm). However, innervation zone tracking is also subject to limitations due to anatomy and physiology. Some subjects may show multiple main innervation zones in biceps brachii muscle which are corresponding to different groups of motor units and thus can be activated at different contraction force levels. In some of our subjects, this appeared as large shift of the main innervation zone at successive contraction levels (Fig. 2). Such a large shift cannot be caused simply by shift of the muscle under the electrode grid, but is presumably caused by recruitment of new higher threshold motor units which have their neuromuscular junction in a different location than the motor units recruited at lower force levels. The aforementioned may also be one of the reasons why inconsistencies were predominantly seen in lower force levels (30–50% of MVC) in force–EMG relationship, since it is know that in biceps brachii muscle the recruitment of large and fast motor units begins approximately from 40% of MVC onwards during isometric contractions (Gydikov and Kosarov, 1974). In addition, the innervation zone shift is rather minimal during isometric contractions (Piitulainen et al., 2009) when compared to dynamic ones (Martin and MacIsaac, 2006) where its shift may be in more crucial role. Therefore, the present finding cannot be generalized to dynamic activities. While the confounding effect of placing the electrode on top of the innervation zone was not a major consideration in the present study when multichannel sEMG recordings were applied, it is worth mentioning that sEMG signals are disrupted if positioned on top of the innervation zone (Farina et al., 2001; Hogrel et al., 1998). This effect was especially true when considering that the proportion of inconsistent force–EMG relationships was clearly higher in bipolar channels on top of the innervation zone compared to other channels, although no effect was seen in the estimation error of force–EMG relationship. Some attempts have been made to provide instructions for placement of standard bipolar sEMG electrodes in a way that the main innervation zone is most often avoided (Rainoldi et al., 2004), however the innervation zone location may vary considerably between individuals (Masuda et al., 1985) and in practice determining the location of the main innervation zone on an individual basis is necessary. Moreover, determining the location of innervation zones requires multichannel electrode array, which is usually not a standard sEMG tool in most laboratories. The methodology is, however, becoming gradually more widespread. Another obstacle is that the spatial dependency of sEMG amplitude is two dimensional, e.g., Fig. 5 in which the medial border (channels 4 and 5) appears to have lower number of inconsistent channels among the study group. It is noteworthy that, also lateral shift may occur underneath the electrode, which cannot be observed without the use of two dimensional electrode grid (more than a single array of sEMG electrodes). Concerning the limitations of the present study, EMG was not measured from synergistic and antagonistic muscles. In one subject all channels of the electrode grid showed inconsistent force– EMG relationship, which was somewhat unexpected. However,
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force distribution among synergists may vary between force levels (Finni et al., 2003) (and between tasks (Finni et al., 2003; Nelson and Roberts, 2008; Staudenmann et al., 2009; Holtermann et al., 2009)) as is the case with antagonistic co-activation and this is a likely explanation for the observed inconsistent force–EMG relationship in all channels in case of this particular subject. Obviously, this limitation also pertains to the whole study. Determining innervation zones is labour intensive, although development of automatic detection algorithms can be expected provide a solution for this (Ostlund et al., 2007). Innervation zone channels are more likely to produce inconsistent force–EMG relationship than noninnervation zone channels. However, the inclusion of innervation zone channels in the global sEMG value did not affect the consistency of force–EMG relationships, which indicates that the laborious part of determining innervation zones may not be necessary if high-density sEMG is recorded. Furthermore, the prediction error using 3rd order polynomial fits was unaffected by the proportion of inconsistent channels used for the fitting in the case of the applied multichannel approaches, which indicates that other sources of error are of more importance. It needs to be noted, that marked over fitting was observed with the leave one out method, if the MVC was left out and then predicted with the obtained fit. Hence, we also tested linear fitting, which also indicated no difference in prediction error between the four different multichannel ways of analyzing EMG. Furthermore, the prediction errors were similar between linear and 3rd order fits. In conclusion, since the proportion of inconsistent channels was higher on the innervation zone channels it seems that the disruption of the physiological signal caused by the innervation zone affects the consistency of the force–EMG relationship. Multichannel EMG recordings used for predicting force overcame this disruption. Therefore, applying multichannel array electrode systems in sEMG recordings may have the ability to improve the extraction of reliable force–EMG relationship by improving the sEMG signal quality upon the widely applied standard of bipolar sEMG recordings.
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T. Rantalainen et al. / Journal of Electromyography and Kinesiology 22 (2012) 80–87 Timo Rantalainen received his PhD in biomechanics from University of Jyväskylä, Finland in 2010. His main research interests lie in muscle–bone interaction, specifically in estimating lower body skeletal loading and related skeletal adaptation. He is currently working under Professor Robin Daly’s supervision as a Postdoctoral Research Fellow at the Deakin University, Melbourne, Australia.
Adam Klodowski graduated as a master of science in 2007 from the University of Bielsko-Biala, Poland. Since September 2007 the author has been working at the Lappeenranta University of Technology, Finland, as s junior researcher investigating the use of flexible multibody dynamics methodology in biomechanics.
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Harri Piitulainen received his M.Sc. (2006) and Ph.D. (2010) in Biomechanics from University of Jyväskylä, Finland. He is currently Post-doctoral Researcher in Brain Research Unit (BRU) of Low Temperature Laboratory (LTL) at Aalto University School of Science, Espoo, Finland. His research interests include function and adaptation of human motor control, connection between cortical activity and distal motor actions, cortico-muscular/kinetic coherence and their clinical applications, muscle mechanics and physiology and development and application of multichannel surface electromyography.
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Effect of innervation zones in estimating biceps brachii force–EMG relationship during isometric contraction Timo Rantalainen a,b,c,d,⇑, Adam Kłodowski b, Harri Piitulainen c,e a
Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia Department of Mechanical Engineering, Lappeenranta University of Technology, Finland c Neuromuscular Research Center, Department of Biology of Physical Activity, University of Jyväskylä, Finland d Department of Health Sciences, University of Jyväskylä, Finland e Brain Research Unit, Low Temperature Laboratory, School of Science, Aalto-University, Espoo, Finland b
a r t i c l e
i n f o
Article history: Received 17 May 2011 Received in revised form 30 August 2011 Accepted 22 September 2011
Keywords: High-density EMG Modeling Neuromuscular Validation Innervation zone
a b s t r a c t Measuring muscle forces in vivo is invasive and consequently indirect methods e.g., electromyography (EMG) are used in estimating muscular force production. The aim of the present paper was to examine what kind of effect the disruption of the physiological signal caused by the innervation zone has in predicting the force/torque output from surface EMG. Twelve men (age 26 (SD ±3) years; height 179 (±6) cm; body mass 73 (±6) kg) volunteered as subjects. They were asked to perform maximal voluntary isometric contraction (MVC) in elbow flexion, and submaximal contractions at 10%, 20%, 30%, 40%, 50% and 75% of the recorded MVC. EMG was measured from biceps brachii muscle with an electrode grid of 5 columns 13 rows. Force–EMG relationships were determined from individual channels and as the global mean value. The relationship was deemed inconsistent if EMG value did not increase in successive force levels. Root mean squared errors were calculated for 3rd order polynomial fits. All subjects had at least one (4–52) inconsistent channel. Two subjects had inconsistent relationship calculated from the global mean. The mean root mean squared error calculated using leave one out method for the fits of the individual channels (0.33 ± 0.17) was higher (P < 0.001) than the error for the global mean fit (0.16 ± 0.08). It seems that the disruption of the physiological signal caused by the innervation zone affects the consistency of the force–EMG relationship on single bipolar channel level. Multichannel EMG recordings used for predicting force overcame this disruption. Crown Copyright Ó 2011 Published by Elsevier Ltd. All rights reserved.
1. Introduction Musculoskeletal loading, apart from a few notable exeptions, is not directly measurable in vivo. Direct measurement of muscle forces in vivo is invasive and limited to few muscles with extensive outer tendons (e.g., quadriceps femoris (Finni et al., 2000) and triceps surae muscle groups (Finni et al., 1998; Komi et al., 1992)). The situation is very similar for measuring bone strains in vivo, being invasive and limited to superficial bone sites (Lanyon et al., 1975; Hoshaw et al., 1997; Milgrom et al., 2000). Detailed musculoskeletal loading information would be useful on multiple occasions, such as in planning surgical interventions (Delp and Loan, 1995).
⇑ Corresponding author at: School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia. Tel.: +61 3 925 17247; fax: +61 3 924 46017. E-mail addresses: [email protected], [email protected] (T. Rantalainen).
Estimating musculoskeletal loading depends on determining muscular forces. Due to the redundancy problem of the muscles as actuators of the body, the forces cannot be directly derived from inverse kinematics and kinetics and consequently optimization procedures are required. The optimisation procedures, in turn, introduce error into the muscle force estimates. Another feasible approach for estimating muscle forces indirectly is electromyography (EMG) (Disselhorst-Klug et al., 2009; Staudenmann et al., 2010), which has actually been used to validate inverse kinematics based muscular force estimates in musculoskeletal models (Anderson et al., 1996; Nazer et al., 2008). Furthermore, representing muscle activation and ensuing muscular force production with EMG has meaningful applications in clinical practice e.g., in controlling prostheses (Triolo and Moskowitz, 1989) and during rehabilitation (Holtermann et al., 2010). The EMG recordings are often measured using standard bipolar surface EMG (sEMG) with the recording electrodes positioned according to general guidelines, such as the SENIAM recommendations (Hermens et al., 2000). Successful models for predicting muscle forces from sEMG have been developed for both the isometric
1050-6411/$ - see front matter Crown Copyright Ó 2011 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2011.09.012
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(Fuglevand et al., 1993; Staudenmann et al., 2006; de Oliveira and Menegaldo, 2010; Wheeler et al., 2010) and the dynamic condition (Lloyd and Besier, 2003). Force–EMG relationship has been studied extensively and the reader is directed to recent reviews for physiological and methodological considerations (Disselhorst-Klug et al., 2009; Staudenmann et al., 2010). Surface EMG varies spatially over the muscle belly (Holtermann et al., 2005; Staudenmann et al., 2005, 2006, 2010; Holtermann and Roeleveld, 2006; Piitulainen et al., 2009) and accordingly, improvements in predicting muscle forces have been obtained by applying multichannel electrodes thereby increasing the representability of the measured surface EMG-signal (Staudenmann et al., 2005, 2006, 2010). However, it is often neglected in force– EMG relationship that electrode location with respect to various anatomical structures (e.g., innervation zones and tendon region) may disrupt the physiological information of the sEMG signal (Piitulainen et al., 2009). The aim of the present paper was to examine what kind of effect the disruption of the physiological signal caused by the innervation zone has in predicting the force/torque output from surface EMG. It was speculated that the disruption caused by innervation zones weakens the association between force/torque and EMG. 2. Methods A convenience sample of data from 12 male subjects (age 26 (SD ±3) years; height 179 (±6) cm; body mass 73 (±6) kg; subcutaneous tissue thickness in biceps brachii 2.3 (±0.6) mm) was used in the present study. No subject had any known symptoms of neuromuscular disorders. The subjects were non-smokers, did not drink any caffeine rich drinks 12 h before the measurements, and avoided strenuous exercise two days prior to the measurements. The study was conducted in agreement with the Helsinki declaration with the approval of the local ethical committee. Written informed consent was obtained from all participants. 2.1. Study protocol The subjects were allowed to warm-up with three sets of submaximal isometric elbow flexions lasting 8 s each. Maximal voluntary contraction (MVC) was then determined in the elbow flexors of the dominant arm (all subjects were right handed) for each subject. The highest value of three MVC trials with less than 5% difference from the second highest value was accepted as the true MVC value. The subjects then performed intermittent sub-maximal voluntary isometric contractions in the following order, at 10%, 20%, 30%, 40%, 50% and 75% of MVC (duration of 8 s). Between each contraction, subjects were allowed a minimum of 2 min rest, to minimize the possibility of metabolic fatigue in the muscle. All submaximal isometric contractions were performed twice. 2.2. Force measurements Elbow flexor force was measured while subjects were seated, and their dominant arm was attached to a force transducer in a custom made chair (University of Jyväskylä, Finland) by applying straps to the wrist. The forearm was half supinated, elbow angle was set at 120° (180° corresponds to full extension) and an arm support was used. The chair dimensions were adjusted for each subject individually. 2.3. EMG recordings sEMG signals were recorded at 2048 Hz sampling frequency in a monopolar acquisition mode with a semi-disposable grid of 64
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electrodes (8 mm inter-electrode distance in both directions, model ELSCH064, designed by LISiN at Politecnico di Torino and manufactured by OT Bioelectronica, Torino, Italy). The electrode grid consisted of five longitudinal columns of 13 electrodes except the first column (most lateral one) which consisted of 12 electrodes. The electrode grid was placed over the mid-line of the short head of biceps brachii, 2 cm proximal to the distal tendon. The columns of the electrode grid were placed parallel to the longitudinal axis of biceps brachii. A movable array electrode was used for determining the longitudinal axis. The orientation of the array electrode was varied and the orientation was defined as being parallel to the longitudinal axis, when action potential propagation was optimally observed with the array (for further details please see (Piitulainen et al., 2009)). 2.4. Signal processing The sEMG signals were digitally band-pass zero-lag filtered at a bandwidth of 20–450 Hz (4th order Butterworth filter). The monopolar signals were post-processed to bipolar channels (in total 59 channels) in longitudinal direction of each electrode column, and adjacent channels in each of the five columns were plotted in MatLab (R2007a, ver. 7.4.0.287, The MathWorks Inc., MA, USA) to determine the location of the main innervation zone. Innervation zone location was determined individually for each column by visual inspection of the plots (Fig. 1). This was done to all isometric contraction force levels. The criteria used for determining the location of the innervation zone(s) were based either on a reversal in signal polarity in two adjacent channels (IZ was located between those channels), or on the lowest amplitude in a single channel (IZ was located on that channel). Channels with high interference (signal to noise ratio <20, deemed noisy channels) were excluded from all further analysis. EMG amplitude was determined as root mean square (RMS) value of a 1000 ms epoch during the most stable part (smallest variation of the force signal) of each isometric contraction separately. Furthermore, the accepted epoch had a maximum fluctuation of less than ±5% of the force signal in all isometric contractions. 2.5. Individual channel and global mean analysis RMS was calculated separately for all the accepted bipolar channels (Fig. 2) and these ‘‘raw’’ values are referred as ‘‘individual channels with innervation zone’’ in the text. In addition, a global mean of RMS value was calculated from these accepted channels including the channels belonging to innervation zone. Another approach was chosen to estimate the effect of innervation zone region on force–EMG relationships on individual channel and global level, where all channels overlying the main innervation zone, were excluded from further analysis. These values are referred as ‘‘individual channels without the innervation zone’’ in the present paper. Thus in this case, the global mean value for RMS was calculated as the average of all individual channels with the innervation zones excluded. 2.6. Innervation zone tracking Possible shift of innervation zone due to increase in the contraction force and consequent change in muscle geometry was tracked to avoid consequent bias in RMS values. In such case, the unbiased RMS value was acquired as the average RMS of five parallel channels (each corresponding to one of the five longitudinal columns) in the next channel distal to the channel where innervation zone was detected in the corresponding column. If innervation zone was located in between two adjacent channels, the more distal channel was defined as the location of the innervation zone. Since
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Fig. 1. Innervation zone(s) were determined by visual inspection. The decision of innervation zone location was based either on a reversal in signal polarity in two adjacent channels (IZ was located between those channels), or on the lowest amplitude in a single channel. A representative sample of 0.25 s window of the EMG signals at different force levels from one subject.
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Fig. 2. Example of the effect of the innervation zone (highlighted with empty black circle) on the spatial distribution of the RMS amplitude of EMG in subjects 2 (upper row) and 11 (lower row). The values of the bipolar channels on top of the innervation zone are markedly different from the adjacent bipolar channels. Large shift of innervation zone is seen in column 3 from 10% to 20% MVC in subject 2, possibly due to recruitment of new motor unit or medial shift of the muscle. Noisy channels removed from the analysis and the empty channel in the most lateral column (column one, channel one) are in white color.
path of the main innervation zone was not always transverse in medial–lateral direction of the muscle, the averaged tracked channels were not necessarily on the same row of the electrode grid.
2.7. Force–EMG relationship The relationship between EMG RMS amplitude and force was visualized by normalizing the force level from 10% to 100% and RMS amplitude to the value measured during the isometric MVC. In order to produce a meaningful force–EMG relationship, the amplitude of EMGs needs to increase in successive contraction force levels. If this was not the case, force–EMG relationship at the respective subject or sEMG channel was deemed inconsistent. In addition, the proportion of inconsistent innervation zone chan-
nels was compared to the proportion of inconsistent non-innervation zone channels with two-tailed paired T-test. Furthermore, for each subject third order polynomials were fitted on his force–EMG relationships derived from (1) global mean without innervation zones, (2) global mean with innervation zones, (3) mean of the five tracked unbiased channels adjacent to the channel(s) corresponding to the innervation zone, (4) mean of five channels directly over the innervation zone and (5) each of the individual bipolar channel separately. Then the root mean squared errors of the third order polynomial fits using leave one out method (Stone, 1974) for force levels 10–75% MVC (0% and 100% were always included) were compared between these five conditions using repeated measure analysis of variance (ANOVA). Paired T-tests were used for post hoc testing. P 6 0.05 was chosen to indicate statistically significant difference.
Fig. 3. Force–EMG relationship of the global RMS amplitude of EMG without innervation zone channels (left pane) and from tracked RMS amplitude of EMG (right pane) for the whole group (n = 12). Apparently the relationship is relatively linear on a group level in the present population. RMS amplitude is normalized to the RMS amplitude value during isometric maximal volumetric contraction and all channels overlaying innervation zone are excluded.
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3. Results 3.1. Golbal sEMG Whole group. On the group level, force–EMG relationships calculated either from global sEMG including innervation zones or global sEMG where innervation zones were excluded turned out to be linear in the studied population. Fig. 3 shows the mean force–EMG relationship for the whole group plotted from the global sEMG. 3.2. Individual subjects Force–EMG relationships from the subjects’ individual global sEMG showed inconsistent force–EMG relationships in 2 out of 12 subjects (Fig. 4). The result was the same whether or not the channels overlaying the innervation zone were excluded. The inconsistent steps occurred at lower (from 30% to 40% of MVC) contraction levels.
12 ± 13 channels (28% of all accepted ones) showed inconsistent force–EMG relationship, while at least one channel was inconsistent on 11 of 12 subjects (range 0–38). In the case of the channels directly overlaying the main innervation zone, total of 14.3 (±1.9) channels (51% of all channels) showed inconsistent force–EMG relationship (Fig. 5). This was a significantly higher proportion of inconsistent channels when compared to channels outside the main innervation zone region (P = 0.002). 3.5. Innervation zone tracking Despite of innervation zone tracking, the force–EMG relationship remained inconsistent in 2 out of 12 subjects (Fig. 4). None of the tracked channels were removed due to noise. The proportion of inconsistent channels was similar to average results over all accepted channels without innervation zone region channels since, 28% of the individual tracked channels showed inconsistent force– EMG relationship.
3.3. Individual bipolar channels with innervation zone 3.6. Polynomial fits On average 57 ± 3 individual bipolar channels were accepted to the analysis on the whole group level (noisy channels were removed). In total 18 ± 16 channels (32% of all accepted ones) produced inconsistent force–EMG relationship. Furthermore, all 12 subjects had at least one inconsistent channel (range 4–52). In addition, one subject showed inconsistent force–EMG relationship in all of the bipolar channels. 3.4. Individual bipolar channels without innervation zone On average 43 ± 3 individual bipolar channels were included to the analysis after removal of noisy channels and the channels overlaying the innervation zone on the whole group level. In this case
The root mean squared errors differed between the five different 3rd order force–EMG fits (P = 0.010). The mean root mean squared error of individual channels (0.33 ± 0.17) was significantly higher (P < 0.001) than the ones for the other four fits (1) global mean without innervation zones 0.16 ± 0.10, (2) global mean with innervation zones 0.16 ± 0.08, (3) mean of the five tracked unbiased channels 0.16 ± 0.06 and (4) the five innervation zone channels which were tracked 0.16 ± 0.07. No improvement was achieved by tracking the main innervation zone with increase in isometric contraction level, when compared to the other multichannel approaches, global means or even to mean of the five channels directly over innervation zone.
Fig. 4. Typical examples of the force–EMG relationships from subjects’ individual global mean RMS amplitude of EMG without innervation zone channels (left pane) and from subjects? individual tracked RMS amplitude of EMG (right pane). Inconsistent force–EMG relationships is seen on subject 2. The trendlines are 3rd order polynomial fits. EMG amplitudes and force levels are normalized to the respective values during maximal voluntary contraction and channels overlaying innervation zones are excluded.
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Fig. 5. Spatial distribution of inconsistent force–EMG relationships. Rows of the matrix run from proximal to distal, while columns run from lateral to medial. Innervation zone locations at force levels of 10% of MVC and at MVC of the individual subjects are highlighted with light gray lines. The channels proximal to the innervation zone appear to exhibit the least number of inconsistent channels. In addition, the medial portion of the matrix appears to have lower number of inconsistent channels than the lateral portion. In this data set the innervation zones were not excluded. The empty channel in most lateral column (column one, channel one) is in white color.
4. Discussion The main finding of the study was that the force–EMG relationship was inconsistent relatively often, at single bipolar channel level especially in case of channels overlaying the innervation zone, even under the highly controlled paradigm of isometric testing. Furthermore, with unfortunate bipolar electrode positioning, it was possible to record inconsistent force–EMG relationship from all of the subjects in the present study. As recently reviewed (Disselhorst-Klug et al., 2009; Staudenmann et al., 2010), because of physiological and anatomical reasons, force–EMG relationship is not necessarily linear nor is there a need for linearity for modeling purposes (i.e. non-linear predictions can be made). Furthermore, the relationship depends on muscle activation history, contraction type and velocity and therefore force prediction in dynamic situations is particularly challenging (Disselhorst-Klug et al., 2009; Staudenmann et al., 2010). Yet, with meticulous approach, reasonable results may be obtained (e.g. (de Oliveira and Menegaldo, 2010)). In agreement with previous studies (Staudenmann et al., 2005; Staudenmann et al., 2006), use of multiple sEMG channels appeared to improve the determination of force–EMG relationship as the prediction error was smaller in multichannel analyses compared to the analyses at individual channel level. Consequently, it is argued that the disruption of the physiological signal caused by the innervation zone affects the precision of the force–EMG relationship. Apparently multichannel sEMG recordings of only five channels may compensate for anatomical and physiological constraints, such as innervation zone region when estimating force–EMG relationship and somewhat surprisingly, regardless of the location of electrodes used for the relationship determination. The question needs to be raised, whether the results obtained in the present paper are caused by physiology or by methodology. Muscle force output is controlled by recruiting motor units and by modulating the firing rate of already active motor units (Adrian and Bronk, 1929). Therefore, while non-linear relationship is to be expected, in theory, the force–EMG relationship should be consistent (Metral and Cassar, 1981). On the other hand, we have rela-
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tively recently demonstrated, that changes in muscle anatomy during contraction (e.g., change in innervation zone location with respect to the surface electrodes on the skin) can disrupt the physiological meaning of the recorded bipolar sEMG even in the highly controlled isometric contraction (Piitulainen et al., 2009) (Fig. 2). In the present study an attempt was made to test, whether this crucial methodological limitation for successful force–EMG relationship could be overcome by following the innervations zone location throughout the experiment and measuring sEMG in relation to the innervation zone. No improvement was observed against other multichannel approaches applied in the present experiment. This may partly be because of methodological limitations concerning the procedure for innervation zone tracking, such as detection resolution which is limited by inter-electrode distance (current resolution was 4 mm). However, innervation zone tracking is also subject to limitations due to anatomy and physiology. Some subjects may show multiple main innervation zones in biceps brachii muscle which are corresponding to different groups of motor units and thus can be activated at different contraction force levels. In some of our subjects, this appeared as large shift of the main innervation zone at successive contraction levels (Fig. 2). Such a large shift cannot be caused simply by shift of the muscle under the electrode grid, but is presumably caused by recruitment of new higher threshold motor units which have their neuromuscular junction in a different location than the motor units recruited at lower force levels. The aforementioned may also be one of the reasons why inconsistencies were predominantly seen in lower force levels (30–50% of MVC) in force–EMG relationship, since it is know that in biceps brachii muscle the recruitment of large and fast motor units begins approximately from 40% of MVC onwards during isometric contractions (Gydikov and Kosarov, 1974). In addition, the innervation zone shift is rather minimal during isometric contractions (Piitulainen et al., 2009) when compared to dynamic ones (Martin and MacIsaac, 2006) where its shift may be in more crucial role. Therefore, the present finding cannot be generalized to dynamic activities. While the confounding effect of placing the electrode on top of the innervation zone was not a major consideration in the present study when multichannel sEMG recordings were applied, it is worth mentioning that sEMG signals are disrupted if positioned on top of the innervation zone (Farina et al., 2001; Hogrel et al., 1998). This effect was especially true when considering that the proportion of inconsistent force–EMG relationships was clearly higher in bipolar channels on top of the innervation zone compared to other channels, although no effect was seen in the estimation error of force–EMG relationship. Some attempts have been made to provide instructions for placement of standard bipolar sEMG electrodes in a way that the main innervation zone is most often avoided (Rainoldi et al., 2004), however the innervation zone location may vary considerably between individuals (Masuda et al., 1985) and in practice determining the location of the main innervation zone on an individual basis is necessary. Moreover, determining the location of innervation zones requires multichannel electrode array, which is usually not a standard sEMG tool in most laboratories. The methodology is, however, becoming gradually more widespread. Another obstacle is that the spatial dependency of sEMG amplitude is two dimensional, e.g., Fig. 5 in which the medial border (channels 4 and 5) appears to have lower number of inconsistent channels among the study group. It is noteworthy that, also lateral shift may occur underneath the electrode, which cannot be observed without the use of two dimensional electrode grid (more than a single array of sEMG electrodes). Concerning the limitations of the present study, EMG was not measured from synergistic and antagonistic muscles. In one subject all channels of the electrode grid showed inconsistent force– EMG relationship, which was somewhat unexpected. However,
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force distribution among synergists may vary between force levels (Finni et al., 2003) (and between tasks (Finni et al., 2003; Nelson and Roberts, 2008; Staudenmann et al., 2009; Holtermann et al., 2009)) as is the case with antagonistic co-activation and this is a likely explanation for the observed inconsistent force–EMG relationship in all channels in case of this particular subject. Obviously, this limitation also pertains to the whole study. Determining innervation zones is labour intensive, although development of automatic detection algorithms can be expected provide a solution for this (Ostlund et al., 2007). Innervation zone channels are more likely to produce inconsistent force–EMG relationship than noninnervation zone channels. However, the inclusion of innervation zone channels in the global sEMG value did not affect the consistency of force–EMG relationships, which indicates that the laborious part of determining innervation zones may not be necessary if high-density sEMG is recorded. Furthermore, the prediction error using 3rd order polynomial fits was unaffected by the proportion of inconsistent channels used for the fitting in the case of the applied multichannel approaches, which indicates that other sources of error are of more importance. It needs to be noted, that marked over fitting was observed with the leave one out method, if the MVC was left out and then predicted with the obtained fit. Hence, we also tested linear fitting, which also indicated no difference in prediction error between the four different multichannel ways of analyzing EMG. Furthermore, the prediction errors were similar between linear and 3rd order fits. In conclusion, since the proportion of inconsistent channels was higher on the innervation zone channels it seems that the disruption of the physiological signal caused by the innervation zone affects the consistency of the force–EMG relationship. Multichannel EMG recordings used for predicting force overcame this disruption. Therefore, applying multichannel array electrode systems in sEMG recordings may have the ability to improve the extraction of reliable force–EMG relationship by improving the sEMG signal quality upon the widely applied standard of bipolar sEMG recordings.
5. Conflicts of interest statement All of the authors have no conflicts of interest. Acknowledgements This study was financially supported by the European Regional Development Fund, the Academy of Finland (grant #138574) and the Finnish Ministry of Education (grant #40/627/2006). Dr. Rantalainen is supported by a grant from Finnish Cultural Foundation given by the Foundations’ Post Doc Pool. References Adrian ED, Bronk DW. The discharge of impulses in motor nerve fibres: Part II. The frequency of discharge in reflex and voluntary contractions. J Physiol 1929;67(2):119–51. Anderson DD, Hillberry BM, Teegarden D, Proulx WR, Weaver CM, Yoshikawa W. Biomechanical analysis of an exercise program for forces and stresses in the hip joint and femoral neck. J Appl Biomech 1996;12:292–312. de Oliveira LF, Menegaldo LL. Individual-specific muscle maximum force estimation using ultrasound for ankle joint torque prediction using an EMG-driven Hilltype model. J Biomech 2010;43(14):2816–21. Delp SL, Loan JP. A graphics-based software system to develop and analyze models of musculoskeletal structures. Comput Biol Med 1995;25(1):21–34. Disselhorst-Klug C, Schmitz-Rode T, Rau G. Surface electromyography and muscle force: limits in sEMG–force relationship and new approaches for applications. Clin Biomech 2009;24(3):225–35. Farina D, Merletti R, Nazzaro M, Caruso I. Effect of joint angle on EMG variables in leg and thigh muscles. IEEE Eng Med Biol 2001;20(6):62–71.
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T. Rantalainen et al. / Journal of Electromyography and Kinesiology 22 (2012) 80–87 Timo Rantalainen received his PhD in biomechanics from University of Jyväskylä, Finland in 2010. His main research interests lie in muscle–bone interaction, specifically in estimating lower body skeletal loading and related skeletal adaptation. He is currently working under Professor Robin Daly’s supervision as a Postdoctoral Research Fellow at the Deakin University, Melbourne, Australia.
Adam Klodowski graduated as a master of science in 2007 from the University of Bielsko-Biala, Poland. Since September 2007 the author has been working at the Lappeenranta University of Technology, Finland, as s junior researcher investigating the use of flexible multibody dynamics methodology in biomechanics.
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Harri Piitulainen received his M.Sc. (2006) and Ph.D. (2010) in Biomechanics from University of Jyväskylä, Finland. He is currently Post-doctoral Researcher in Brain Research Unit (BRU) of Low Temperature Laboratory (LTL) at Aalto University School of Science, Espoo, Finland. His research interests include function and adaptation of human motor control, connection between cortical activity and distal motor actions, cortico-muscular/kinetic coherence and their clinical applications, muscle mechanics and physiology and development and application of multichannel surface electromyography.