Inter-individual variability and pattern recognition of surface electromyography in front crawl swimming

Accepted Manuscript Inter-individual variability and pattern recognition of surface electromyography in front crawl swimming Jonas Martens, Daniel Daly, Kevin Deschamps, Filip Staes, Ricardo J. Fernandes PII: DOI: Reference:

S1050-6411(16)30170-5 http://dx.doi.org/10.1016/j.jelekin.2016.08.016 JJEK 2013

To appear in:

Journal of Electromyography and Kinesiology

Received Date: Revised Date: Accepted Date:

17 March 2016 29 July 2016 31 August 2016

Please cite this article as: J. Martens, D. Daly, K. Deschamps, F. Staes, R.J. Fernandes, Inter-individual variability and pattern recognition of surface electromyography in front crawl swimming, Journal of Electromyography and Kinesiology (2016), doi: http://dx.doi.org/10.1016/j.jelekin.2016.08.016

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Inter-individual variability and pattern recognition of surface electromyography in front crawl swimming

Jonas Martens1*, Daniel Daly1, Kevin Deschamps2 , Filip Staes2, Ricardo J. Fernandes3

1

Department of Kinesiology, KU Leuven, Leuven, Belgium

2

Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium

3

Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport,

University of Porto and Porto Biomechanics Laboratory, University of Porto, Porto, Portugal

Keywords: variability, wireless electromyography, crawl swimming, cluster analysis, statistical parametric mapping

* Corresponding author Jonas Martens Tervuursevest 101, box 1500 3001, Leuven BELGIUM Telephone & fax: +32 16 32 91 04 E-mail: [email protected]

1

Introduction Since the pioneer study of myoelectrical signals in swimming in 1964 [Ikai et al, 1964], amplitude analysis of electromyography (EMG) has been used to evaluate swimming technique (cf. [Martens et al, 2015b] for an overview), together with subjective video analysis and/or kinematic and kinetic measurements. However, the variability of EMG recordings is a complex phenomenon rarely examined in swimming. Only two studies evaluating the intraindividual variability have been performed [Lewillie, 1976] [Martens et al, 2015a] and nothing was found focusing on inter-individual variability. Nevertheless, research conclusions describing muscle activity patterns of a single group of participants or comparing patterns between diverse groups are based on the assumption of a single general muscle activity pattern (the mean activation pattern of all participants). These studies however report high variability reflected in the standard deviations [Martens et al, 2015b]. In one study on the activation pattern of a series of shoulder muscles during front crawl in “20 collegiate and master level competitive swimmers”, SDs expressed as a percentage of the maximal voluntary isometric contraction (MVIC) ranged from 33 to 52% [Pink et al, 1991]. In a much more homogenous group of swimmers in both expertise and age (n=9), dissimilar (latissimus dorsi) and even contradictory (anterior deltoid) patterns of mean amplitude were found [Rouard et al, 1995] when compared to the study cited above. Inter-individual variability of the EMG amplitude reported as SD was again high. Since there are no reference publications on inter-individual variability in swimming, information was sought in other cyclic movements such as gait [Burden et al, 2003], running [Guidetti et al, 1996] and cycling [Bolgla et al, 2007], [Hug et al, 2008], [Hug et al, 2010], [Rouffet et al, 2008]. In these studies, in addition to mean and SD, additional measures of EMG variability were employed. In running, the mean value of the coefficient of variation (CV) of the rectified EMG amplitude signal was presented as a one dimensional measure (one 2

number summarizing the variability of the total running cycle), as well as CV (per time) expressed over the running cycle as a two dimensional variability measure [Guidetti et al, 1996]. However, CV is influenced by the mean EMG value (the denominator in the CV formula) and might overestimate variability in the sectors in which the muscle is inactive or its activity is weak [Hug et al, 2008]. To counter this shortcoming, in addition to mean and SD, the variance ratio (VR) was proposed [Hershler et al, 1978] and applied in the analysis of inter-individual variability in cycling [Hug et al, 2008], [Hug et al, 2010], [Rouffet et al, 2008]. When EMG waveforms are similar, the VR (equation 4 in methods) tends toward zero and when the waveforms are dissimilar, the VR tends toward one [Knutson et al, 1994]. This one dimensional measure is independent of peak amplitude, providing a good measure of repeatability in the overall wave shapes [Tirosh et al, 2013]. Furthermore, it is insensitive to mean surface EMG (sEMG) amplitude and the degree of smoothing applied to the data [Gabel et al, 1994]. It is therefore generally accepted as an excellent way to document the interindividual variability [Kamen et al, 2010]. A final one dimensional measure of variability (the mean deviation, MD) was proposed in a study on the inter-individual variability in cycling [Hug et al, 2008]. Since there is limited knowledge of the inter-individual variability in swimming EMG patterns, the main purpose of this study was to investigate inter-individual variability in front crawl swimming using variability measures proposed in the study of other cyclic movements. A previous study on the current group of swimmers showed that the intra-individual variability was lower compared to other cyclic movements, with VR ranging from 0.34 to 0.46 [Martens et al, 2015a]. It was hypothesized that inter-individual variability in a homogenous group of highly skilled swimmers would be low and that there was one general muscle activation pattern for each of the studied muscles. This implies that timing and amplitude of activation of each muscle separately would by very similar across this homogenous group of participants. A 3

rejection of this hypothesis would then lead to the assumption that several sub patterns are present and need defining. Several pattern recognition techniques have been used to classify EMG patterns in golfers with and without low back pain [Silva et al, 2015]. Pattern recognition has also been used in clinical environments to classify involuntary leg muscle contractions in individuals with spinal cord injury [Thomas et al, 2014] and to classify forearm motion in patients with chronic hemiparesis [Geng et al, 2013]. Therefore, the secondary purpose of this study was to determine if EMG sub patterns could be found using key features selected with both qualitative and quantitative classification strategies in a cluster analysis. The methods developed to investigate these secondary purposes could be relevant to sport science, as well as in physical medicine and rehabilitation. Methods Participants Data were obtained from 15 adult male competitive swimmers whose descriptive characteristics are shown in Table 1. None of the participants suffered from any type of injury. All participants were informed of the goal and the methods of the study and gave their written consent to conditions approved by the institutional ethics committee (S55199) and in accordance with the Declaration of Helsinki. Participant preparation To keep skin impedance low, the site for electrode placement was prepared by shaving, gently abrading the skin using sandpaper and cleaning with 70% isopropyl alcohol. Surface electrodes were positioned on the left and right deltoideus medialis (DM) and left and right rectus

abdominis

(RA)

following

the

European

Recommendations

for

Surface

Electromyography (SENIAM) [Hermens et al, 1999] and Cram and Kasman [Cram et al, 1998], respectively. Electrodes were placed consistently by the same researcher on each participant and pictures were taken as a control measure. Muscles were chosen based on their 4

function as a prime mover (DM) or a stabilizer (RA) in front crawl swimming [Martens et al, 2015b]. Waterproof taping of the EMG units was achieved by carefully covering the unit with Opsite Flexifix clear film (Smith & Nephew®, London, UK) and then covering the edges with sports tape. Experimental protocol After positioning and waterproofing the EMG electrodes, participants were placed in the muscle testing position [Hislop et al, 2007] and performed two maximum voluntary isometric contractions (MVIC) of 5 s (with 2 min rest interval). For rectus abdominis, the participant was in a horizontal supine position with the hands crossed on the chest and the knees bent. One researcher stabilized the feet, while a second resisted flexion of the trunk by vertically applying full body weight pressure to the shoulders of the participant. For deltoideus medialis, the participant was sitting with the non-tested arm resting (hand on the leg), and the tested arm at side with elbow flexed at 90°. The researcher stood beside the participant and manually resisted the abduction of the arm. Strong verbal encouragement was given and the single maximum value over the two measurements was defined for normalization. A previous study with the same group of participants concluded that no meaningful differences were found in intra-individual EMG variability when using the MVIC or the alternative dynamic peak method [Martens et al, 2015a]. Subsequently, each participant swam 25 m in front crawl with a push-off water start accelerating to maximum speed in the first 12.5 m (breathing allowed) and maintaining speed during the final 12.5 m (without breathing). The swimmers repeated this protocol until three successful trials were achieved collecting EMG recordings of all muscles. A rest interval of 10 min was taken between trials to exclude the effects of fatigue. Data collection EMG was recorded using four of a five channel wireless electromyograph (KINE®, KINE Ltd., Hafnarfjördur, Iceland) with an input impedance of 10 GΩ, a common mode rejection 5

ratio of 110 dB, a signal-to-noise ratio of 60 dB, a differential detection mode and a built in A/D converter of 10 bit with a range of 4 mV, resulting in a sensitivity of 4 µV. The Al/AgCl electrodes (including the reference electrode) were built into a single 26 g unit (56 x 46 x 16 mm) in a fixed triode configuration with a 20 mm center-to-center inter-electrode distance. EMG signals were streamed live to a laptop, but when the electrodes were submerged, live signal was lost. In these cases, the system automatically stored the data in a built in internal memory of maximal 7 min. The data was recollected when live connection was restored after the swimmer left the water following each trial. EMG data was collected live or recollected from the internal memory at a sample rate of 1600 samples per second with KINE Pro software [Martens et al, 2014]. Two underwater stationary 50 Hz video cameras (Sony Handycam HDR-HC9) recorded the swimmers’ movements in their frontal and sagittal planes in the final 12.5 m window of each swim capturing at least two upper limb movement cycles. Cameras were synchronized using Dartfish Software Prosuite 6.0. To synchronize video to EMG, a LED light connected to the EMG equipment illuminating in video view at the start of the EMG recording. Data processing Kinematic data processing EMG was analyzed for two upper limb movement cycles from both left and right sides in the final 12 m (when swimming velocity was maximal) from each of the three swimming trials, resulting in the recording of six full cycles for both upper limbs. Upper limb cycle phases were defined based on angle of upper limb (the line from shoulder to wrist) in relation to the horizontal in the sagittal plane [Persyn, 1974], [Rouard et al, 1990]: from 0 to 45° (entry), from 45 to 90° (pull), from 90 to 135° (push), from 135 to 180° (exit) and from 180 to 360° (recovery) based on the video recordings using Dartfish Prosuite 6.0. The clean swimming

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velocity in each trial was obtained by digitizing the upper rim of the swimsuit at known intervals in the last 12 m of each trial and then averaging. EMG data processing KINE Pro software was used for filtering with a 2nd order Butterworth filter with cut-off frequencies of 20– 800 Hz and slopes of -12 dB/octave. Furthermore, full-wave rectification and integration with an Average Rectified Value (ARV) was done with a moving window of 0.025 s and a step rate of one sample of the raw EMG data. Time normalization As upper limb cycles had different phase timing both within and between swimmers, time normalization was necessary. Multi-event synchronization was performed (left and right separately) using an in-house build Matlab 2012a ® based software framework (ACEPManager). A complete cycle was determined by a start and stop event (hand entry at 0 and 360°, respectively) and then mapped to a 1000 point (promille) time scale using linear interpolation of the original data. This was executed using the interp1 function in Matlab 2012. The technique is similar to the time normalization method used in gait/running analysis [Guidetti et al, 1996], [Martens et al, 2015a]. A 1000 points scale is a % scale with a higher resolution enabling more precise data examination of the high resolution of raw EMG data (in this study 1600 samples per second). In addition, intermediate events (the angles delimiting the cycle phases) were synchronized to the mean timing of that event of all swimmers using the interp1 Matlab 2012 function with the default linear interpolation method. The durations of each complete cycle, as well as each phase, then became equivalent for all cycles collected. Amplitude normalization EMG data was normalized using the MVIC method (the highest value, in micro volts, found during the MVIC was considered 100%) using ACEPManager. 7

Data reduction Compared to other cyclic movements, lower intra-individual variability was found in this group of swimmers. This indicated that a small number of stroke cycles was sufficient for amplitude analysis of muscle activity [Martens et al, 2015a]. The median cycle of the six recorded cycles for each participant was considered most representative and used for analysis, presentation of results and discussion. Using a median rather than a mean avoided smoothing effects of averaging the six cycles [Carson et al, 2001]. To determine the median cycle for each participant, the absolute value of the difference between each single EMG value and the median of six values was calculated. The smallest sum (SS in Eq. 1) of all absolute values for each of the six complete cycles designated the median cycle. The SS was in this study identified as follows and repeated for each cycle of all 15 participants: (1) where k is the number of intervals over the cycle (i.e. 1000), interval for the jth cycle and

is the EMG value at the ith

is the median of the EMG values at the ith time interval over

all j cycles (i.e. 6). Inter-individual variability consideration To study the inter-individual variability, the mean EMG signals and SDs for the median cycle of all 15 participants were calculated for the entire cycle for all four muscles. In addition to mean and SD, one dimensional CV (Eq. 2) was calculated as follows, permitting the comparison of the variability of data sets with a high or a smaller mean and/or SD [Burden et al, 2003]:

(2)

8

where k is the number of intervals over the cycle (i.e. 1000),

is the mean of the EMG values

at the ith interval calculated over the 15 median cycles and

is the SD of the EMG values

about

calculated over the 15 cycles.

When accounting for time, variability was also presented in two dimensions, CV (Eq. 3) at the ith time interval is calculated as: (3) The mean value of 1000

’s was defined as “mean CV”.

Two extra one dimensional measures of variability, VR (Eq. 4) [Hershler et al, 1978] and MD (Eq. 5) [Hug et al, 2008] were calculated as follows: (4)

(5) where k is the number of intervals over the cycle (i.e. 1000), n is the number of cycles (i.e. 15), is the EMG value at the ith interval for the jth cycle, the ith time interval over j cycles, and

is the mean of the EMG values at

is the grand mean of the EMG values, i.e.

the SD of the EMG values about

calculated over the 15 cycles.

Finally, the coefficient of quartile variation (CQV in Eq. 6) was calculated as an extra measure of variability in two dimensions, where Q1 is the 25th percentile and Q3 the 75 th percentile of the 15 EMG values at a given time interval [Bonett, 2006]. (6) Following the cluster analysis, explained below, these variability measures were again applied to the different clusters to validate the results of this analysis. Key feature selection

9

In gait analysis qualitative classification methods consider a multitude of clinical expert reasoning and knowledge. Quantitative strategies use objective, systematic and structured approaches to analyze data, but sometimes generate clinically irrelevant groups [Dobson et al, 2007]. Both classification methods were combined to select key features (or key moments) in EMG patterns to examine the secondary purpose of this study, namely to determine if EMG sub patterns could be found. Both a quantitative and a qualitative procedure were applied to define and select the key features. First a group of four experts (three in EMG analysis in swimming, the fourth in EMG waveform analysis and classification in other cyclic movements) was invited to define key features in the patterns of each muscle group. These four researchers were considered experts because they could define critical moments in a muscle activation pattern based on naked eye observation and experience. Key features can be considered as wave forms in specific sections of the EMG curve which the experts consider as differentiating between individual muscle patterns of the same muscle and therefore can be used to potentially classify the EMG patterns in clusters of swimmers. The experts independently screened all 15 median muscle patterns for distinctive congruencies and differences between the patterns in each of the four muscle studies. They then classified the muscle patterns in groups based on their observations. Finally, the main investigator summarized the findings to decide on the selection of the key features as a conclusion of the qualitative procedure. Secondly, in addition to the expert’s knowledge, a quantitative procedure was initiated where key feature selection was guided by performing a linear regression model based on “statistical parametric mapping” (SPM) [Pataky et al, 2013]. This method correlated 38 participant specific discrete parameters (descriptive statistical parameters such as height or arm span and kinematic parameters such as swimming velocity, stroke frequency or duration of the different upper limb phases) from each of the 15

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swimmers with the EMG values from the 15 median cycles of each muscle at each of the 1000 points on the time scale. EMG classification model The key features were subsequently used in a k-means cluster analysis dividing the swimmers into two, three and four clusters, following the k-means ++ algorithm [Arthur et al, 2007]. Considering the explorative nature of the current study and the small sample size, clustering was maximized at four. A clustering at five or more levels created single-subject clusters. The cluster analysis produced silhouette coefficients (SC) expressing the extent to which the clear patterns were distinguished in a cluster. This is an average of coefficients indicating the distance between the position of a single participant to the centroid of a cluster. SC values lower than 0.25 are interpreted as “no substantial structure has been found” and, in the range of 0.26-0.50, 0.51-0.70 and 0.71-1.00 as “the structure is weak and could be artificial”, “a reasonable structure has been found” and “a strong structure has been found”, respectively [Struyf et al, 1997]. Face validity of classification model To investigate the face validity of the classification model, the variability measures described above used on the overall group of 15 swimmers were again applied to explore the interindividual variability per cluster. If valid clusters were formed, variability should drop. Next, SPM [Friston et al, 2007] was used to statistically compare muscle activity between clusters. This tested whether clusters based on specific discriminating time nodes in the muscle activity pattern (the key features) would indeed show different patterns during those time nodes or even other time nodes. An SPM two-tailed two sample t-test was used to compare two clusters and a SPM ANOVA was used to compare three and four clusters (α=0.05). The scalar output statistic, SPM{t}, was calculated separately at each individual time node and is referred to as a Statistical Parametric Map. At this stage it is worth noting that SPM refers to the overall 11

methodological approach and SPM{t} to the scalar trajectory variable. The calculation of SPM{t} simply indicates the magnitude of the differences between the groups, therefore with this variable alone the null hypothesis cannot be accepted or rejected. To test the null hypothesis, the critical threshold was calculated at which only α % (5%) of smooth random curves would be expected to transvers. Conceptually, a SPM t-test or ANOVA is similar to the calculation and interpretation of a scalar t-test or ANOVA. If the SPM{t} trajectory crosses the critical threshold at any time node, the null hypothesis is rejected. Typically, due to waveform smoothness and the inter-dependence of neighboring points, a series of multiple adjacent points of the SPM{t} curve often exceed the critical threshold and are therefore called “supra-threshold clusters”. In other words, a “supra-threshold cluster” is a time domain in which the curve exceeds the critical threshold. All SPM analyses were implemented using the open-source spm1d code (v.M0.1, www.spm1d.org) in Matlab. Results Fig. 1 shows the mean muscle activity, SD (% of MVIC) and the two dimensional variability measures (CV and CQV) for the median cycles of all 15 swimmers for the four muscles. Maximal SDs of 34.8 (left DM), 39.8 (right DM), 19.6 (left RA) and 22.9 % (right RA) were found. The one-dimensional variability measures are presented in Table 2, column A. In relation to the main purpose of this study, these results indicate that inter-individual variability was high for all muscles and hypothesis one could be rejected. Subsequently, classification of sub patterns with cluster analysis was applied to test the secondary purpose of this study. The key features selected for DM were: (i) mean activity from hand entry (0°) to the moment the regression analysis indicated a correlation between muscle activity amplitude and swimming velocity (207 on the 1000 point time line, or 20.7% of cycle time), thus: the first half of the entry phase (ii) from the moment DM activity exceeded three SD of the relative relaxation 12

period occurring around the upper limb angle 45° to hand entry (360°), thus: late pull, push, exit and recovery phases. For RA, key features were: (i) the moment RA activity exceeded three SD of the relative relaxation period after the entry phase until the moment the SPM regression analysis indicated a correlation between muscle activity amplitude and upper limb recovery duration (466 on the 1000 point time line), thus: from halfway through the entry until the start of the push, (ii) from the moment the regression analysis indicated a correlation between muscle activity amplitude and upper limb recovery duration until the RA activity dropped under three SD of the relative relaxation period in the first part of the recovery, thus: from the push to the early recovery and (iii) from the moment RA activity exceeded three SDs of the relative relaxation period after the first part of the recovery phase until hand entry (360°), thus: the latter part of the recovery phase. In Table 3, the 2, 3 and 4 level clustering of the 15 swimmers (see initials) is presented for each muscle. Typically one lower level cluster was retained in the higher level clusters. The second cluster split. Furthermore, clusters formed for one muscle or one side of the body were not related to clusters in any other muscle including that for the opposite side of the body. Table 4 shows the SC from the k-means cluster analysis which ranged from 0.5558 to 0.7158 indicating that a reasonable structure was found in all three levels of cluster analysis. When clustering the swimmers based on the key features, variability in each cluster dropped in all measures with increasing cluster levels except for VR in left DM, where VR did not change (Table 2, columns B, C and D). Furthermore, overall variability and variability in the clusters in RA was consistently higher than variability in DM on both right and left sides. Fig. 2 presents an example of the outcome of the SPM two-tailed two sample t-test to compare muscle activity of two groups based on the cluster analysis, in this case for the right RA. Muscle activity was higher for group 1 than group 2 for a considerable amount of time (Fig. 2a). Supra-threshold clusters were found in the transition of the pull and push phase and in the 13

upper limb recovery. The critical threshold of 3.554 was exceeded, indicating that this difference was significant during that period of the upper limb cycle (Fig. 2b). The precise probability that a supra-threshold cluster of this size would be observed in repeated random samplings was p<0.001. The null hypothesis that there was no difference in any part of the muscle activity pattern between sub patterns was therefore rejected. When comparing three clusters in this muscle using the SPM ANOVA method, supra-threshold clusters were found when comparing group 1 to group 2 again during the transition of the pull and push phase and during the recovery. When comparing group 2 with group 3, only one supra threshold cluster was found during the pull phase. No differences were found between group 1 and group 3 in the right RA. Finally, when comparing the four clusters, differences were found between group 1 and group 3 immediately following hand entry, near the end of the pull phase and during the recovery and between group 2 and 3 during the recovery. No differences were found between group 1 and 2, 2 and 3, 1 and 4, 2 and 4 and 3 and 4. Similar results were found in the left RA, again indicating significant differences in the recovery phase and around the transition of pull to push phase. In DM, significant differences were found mainly in the entry phase and during the exit and early in the recovery phase. Discussion Before analyzing inter-individual variability in front crawl swimming, the amplitude characteristics of the examined muscles were compared with literature results. Fig.1 panels A1 and B1 show that the highest muscle activity in DM in this group of highly skilled swimmers was found in the late exit and early recovery phase (upper limb angle 180°). This implies that the main function of this muscle is as prime mover [Martens et al, 2015a], acting as an abductor of the upper limb during this section of the stroke cycle [McLeod, 2010]. This corresponds with previous findings on DM [Ikai et al, 1964], [Pink et al, 1991], [Rouard et al, 1990], [Figueiredo et al, 2007], [Clarys, 1985]. Only [Pink et al, 1991] reported on the inter14

individual variability of the group of swimmers tested solely by reporting SDs. For DM, the highest SD found by Pink et al. was 37% of the MVIC which is similar to the highest SDs found in present study. Less information on muscle activity and variability is available for RA, as it is one of the least frequently examined muscles in swimming [Martens et al, 2015b]. Piette and Clarys reported two peaks of activity during front crawl, one during the pull-push phases and one during the recovery phase, again congruent with the findings of the present study (Fig. 1, panels C1 and D1). As these authors did not assess inter-individual variability, no comparison is possible although they mentioned for RA that “the interindividual pattern was variable” [Piette et al, 1979]. Since previous publications on swimming EMG were limited in reporting variability, the first aim of the present study was undertaken to analyze inter-individual variability further. The two dimensional variability measures (CV, Fig.1 panels A2, B2, C2 and D2 and CQV, Fig. 1 panels A3, B3, C3 and D3) clearly indicated that the highest variability for DM occurred at hand entry and at 90° and for RA at 45°. CQV appears to be a better two dimensional variability measure since SD and mean are not necessarily the most meaningful estimators of spread and location in skewed distributions such as EMG. Therefore, when sampling from non-normal distributions, CQV may be preferred [Bonett, 2006]. The one dimensional variability measures (Table 2) used in this study were compared to the findings for other cyclic movements and other muscles since there was no comparable literature available in swimming. When considering mean CV, the results of this study indicated a higher inter-individual variability in swimming (range 70.84 - 96.81 %) as compared to running (range 68 - 90 %) [Guidetti et al, 1996] and cycling (range 55 - 77 %) [Bolgla et al, 2007]. Burden et al. defined CV in an alternative way (Eq. 2) in their study on

15

variability in gait. They found CV’s in the range of 0.71 - 0.77 [Burden et al, 2003] which was higher than the values found in the present study for the DM, but lower for RA. In a study on the inter-individual variability in cycling using MD [Hug et al, 2008], the results were presented as a %, which was not congruent with the method of calculating MD proposed in the method section of that study and are therefore not comparable to our data. Finally, VR found in a study on gait ranged from 0.56 - 0.67 [Burden et al, 2003] and in cycling from 0.64 - 1.09 [sic] [Rouffet et al, 2008], 0.09 - 0.58 at a lower power output [Hug et al, 2008] and 0.05 0.32 at a higher power output [Hug et al, 2010]. Results of the first cycling study can be disregarded since the VR score should only range from 0 to 1, where 0 indicates no variability and 1 denotes maximum variability [Hershler et al, 1978], [Knutson et al, 1994], [Tirosh et al, 2013] while this study reported a VR of 1.09. The results of the present swimming study (range 0.53 - 0.87) indicated overall a higher inter-individual variability reflected by VR as compared to cycling and to a lesser extent gait. Important to note is that since in this study the median cycle of six cycles was selected, the inter-individual variability was reduced since extreme curves were not considered. In the present study the variability in RA was consistently higher than in DM, probably due to the fact that mean activation in RA is lower than in DM and both mean CV and CV are largely dependent on mean activity. VR on the other hand is independent of peak amplitude [Tirosh et al, 2013], but still variability in RA is higher. A supposition explaining this phenomenon is that whereas DM as a prime mover shows clear moments of activity (abduction of the upper limb) and inactivity (pulling of the upper limb), the activity of RA as a stabilizer is much more dependent on the individual swimming style and therefore more variable across swimmers. Future studies could test this hypothesis by measuring the muscle activity of a group of swimmers with a much more heterogeneous performance level.

16

Summarizing the analysis of inter-individual variability in swimming using several one dimensional variability measures, our results indicated that the inter-individual variability in a group of highly skilled swimmers that were selected to form a homogenous group was higher compared to other cyclic activities and therefore the hypothesis that there was one general muscle activation pattern for the studied muscles was rejected. This finding is in contrast with what has been reported in the previous 50 years of EMG research in swimming. This also suggests that coaches should be very careful to use overall reference EMG information to enhance the swimming technique of their athletes. The present findings suggest that individual characteristics could be of more importance in determining the optimal muscle use pattern from the perspective of increasing performance on one hand, or decreasing the risk of injuries on the other hand. The detection of these crucial individual characteristics could be subject of future studies. Whereas [Guidetti et al, 1996] noted that most inter-individual differences in muscle patterns in running were due to peak location, the results of this swimming study indicated that amplitude was the major factor of inter-individual variability. This could be due to the fact that a very homogenous group of swimmers was tested with a high level of technical skill and therefore differences in shape of the muscle activity pattern were less expected. In future studies, it would be interesting to compare swimmers of a lower level, or even swimmers in a learn-to-swim program using the statistical parametric mapping method presented here. Presumably, peak location and/or shape of the muscle activity pattern might lead to additional differences. Following the fact that inter-individual variability was higher compared to other cyclic movements, the secondary purpose of this study was investigated in an exploratory way to detect if there were strong sub patterns present in the overall muscle activation patterns. A previous study in golf showed that different nervous system strategies and therefore different 17

muscle activation patterns were found in executing a golf swing due to the complexity of the task and the amount of information that is needed to combine eccentric and concentric contractions [Silva et al, 2015]. Indeed, human movement is the result of the actuation of joints by the coordinated excitation of muscles. The multiple muscles spanning a joint reflect the redundancy of the human neuromuscular system in which a prescribed joint motion can be the result of a variety of different muscle excitation strategies [Sartori et al, 2012]. The results of this study showed that based on the key features selected by the expert group and guided by the SPM regression analysis, “reasonable structures” were found for all muscles on both sides of the body which was confirmed by the significant differences in muscle activation patterns found between a part or all of the groups (Fig. 2) and by the fact that inter-individual variability dropped as more clusters were formed (Table 2, columns B, C and D). The statistical SPM analysis performed after the cluster analysis in the present study showed as expected great congruence between the selected key features and the eventual statistical differences since the first was at the base of the latter. This also showed that no other time nodes discriminated between clusters. In fact, when comparing the results of the SPM analysis, it can be concluded that in future front crawl studies the key features could be even more specifically defined. Whereas for DM the expert group combined with the SPM regression analysis specified the examination of the end of pull, together with push, exit and recovery phases, the SPM analysis showed that the exit and early recovery phase was the most discriminating time node. For RA, the entry phase (selected as key feature) did not discriminate sufficiently. The transition from pull to push phase and the recovery phase discriminated between clusters. Table 3 showed that clusters formed for muscle activity on one side of the body had very limited congruence with clusters for the opposite side for the same muscle. Clusters formed for different muscles from the same side of the body also did not correspond well with each other. 18

The overall muscle activation patterns on the other hand did not differ significantly between left and right (Fig. 1). Future studies that aim to subcategorize swimmers based on muscle activation patterns, with the intention of having predictive value for injuries or swimming talent should therefore be careful extrapolating unilateral EMG measurements to the nonmeasured side of the body. Some limitations of this study should be considered. 50 Hz cameras were used as this type of equipment is a standard evaluation tool in current swimming kinematics analysis and were used in the vast majority of EMG swimming studies. The present study therefore did not aim to explain the differences found in muscle activation patterns between the different clustered groups of swimmers. In future research however, high speed cameras and a 3D approach might be needed to obtain more detailed kinematic data. Secondly, as this study ventured into unknown territory in the field of EMG research in swimming, only two muscles were examined, a typical stabilizing muscle and a typical prime mover in front crawl swimming. A larger selection of muscles would have enabled more generalization of the current findings. In conclusion, in front crawl swimming there is not one general activation pattern for DM and RA, but several sub-patterns are present which are statistically different from each other during specific parts of the stroke cycle. This is mainly due to differences in amplitude. Researchers in this field, as well as coaches and trainers should therefore be careful in generalizing previous findings when advising swimmers on technique improvement. A strong individual focus should be adopted when combining video analysis with kinematics and muscle activation patterns for technique evaluation. Acknowledgements

The authors would like to thank Luc Janssens for invaluable help in testing the reliability of the EMG equipment and Dirk Desmet for his contribution in normalizing the data. Funding for 19

this research project was provided by the Department of Kinesiology, Faculty of Kinesiology and Rehabilitation Sciences, KU Leuven.

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Fig. 1. Mean muscle activity and SD, expressed as a % of the MVIC for the median cycles of all 15 swimmers for the four muscles in panels A1, B1, C1 and D1. Presentation of the two dimensional variability measures, CV (panels A2, B2, C2 and D2) and CQV (panels A3, B3, C3, D3). MVIC = maximal voluntary isometric contraction, SD = standard deviation, CV = coefficient of variation, CQV = coefficient of quartile variation Fig. 2. Comparison of muscle activity level of two groups (two clusters on the K-means 2 level) for the right RA (Fig. 2a). In Fig. 2b, two supra threshold clusters where the critical threshold of 3.554 was exceeded (α = 0.05) are shown. MVIC = maximal voluntary isometric contraction, SPM = statistical parametric mapping

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Fig. 1

Fig. 2

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Table 1. Participant descriptive statistics (n=15). Mean Age (yrs)

SD

21.26

2.24

186.55

5.50

Mass (kg)

79.10

7.98

Adipose tissue (%)

13.49

4.71

193.77

6.87

54.72

1.93

634.13

68.98

11.93

3.24

Height (cm)

Arm span (cm) Best time 100 m front crawl (s) Level (FINA points) Number of years of competitive swimming experience (yrs)

Yrs = years, cm = centimeter, kg = kilogram, s = seconds, FINA = Fédération International de Natation, SD = standard deviation

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Table 2. Inter-individual variability of all 15 swimmers, and per cluster based on k-means cluster analysis. A

B

C

D

all

2 clusters

3 clusters

4 clusters

LDM

70.84

63.64

63.03

60.17

Mean CV

RDM

72.08

65.70

58.03

55.00

(%)

LRA

87.79

81.78

73.82

69.73

RRA

96.81

87.92

83.94

75.59

LDM

0.69

0.66

0.65

0.65

RDM

0.71

0.69

0.66

0.63

LRA

1.09

1.01

0.93

0.88

RRA

1.18

1.05

1.03

0.96

LDM

16.94

15.07

14.30

13.71

RDM

17.21

15.98

14.74

13.61

LRA

6.49

5.89

5.21

4.82

RRA

7.45

6.35

5.84

5.22

LDM

0.55

0.55

0.55

0.55

RDM

0.71

0.61

0.57

0.53

LRA

0.77

0.69

0.66

0.62

RRA

0.87

0.83

0.79

0.74

CV

MD

VR

CV = coefficient of variation, MD = mean deviation, VR = variance ratio, LDM = left deltoideus medialis, RDM = right deltoideus medialis, LRA = left rectus abdominis, RRA = right rectus abdominis.

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Table 3. Clusters of swimmers based on k-means cluster analysis at clusters 2, 3 and 4. LDM 2 clusters

RDM

RRA

AL, KC, MD, ML,

SL, AL, MD, JH, MR,

AL, KC, MD, ML,

SL, KC, MD, ML, JH,

MR, LV, BV, MW,

MV, RV

JH, KV, MR, LV, LB,

KV, MR, LV, LB,

MV, RV, MW, JG

BV, MW, JG

SL, BV

AL, MV, RV

JG

3 clusters

LRA

SL, JH, KV, LB, MV,

KC, ML, KV, LV,

RV

LB, MW, JG

AL, KC, MD, ML,

SL, AL, MD, JH, MR,

KC, MD, JH, KV,

SL, KC, MD, ML, JH,

MR, LV, BV, MW,

MV, BV, RV

MV, MW, JG

KV, MR, LV, LB,

JG

BV, MW

SL, JH, LB, MV, RV

KC, ML, JG

SL, BV

AL, JG

KV

KV, LV, LB, MW

AL, ML, MR, LB, RV MV, RV

AL, KC, MD, ML,

KC, ML, JG

KC, MD, JH, KV, LV, SL, MD, ML, JH, KV,

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4 clusters

MR, LV, BV, MW,

MW

MR, LV, LB, MW

SL, BV

AL, JG

JG SL, LB, RV

SL, AL, MD, JH, MR, MV

KV

KV, LV, LB, MW

AL, MV, JG

MV, RV

JH, MV

BV, RV

ML, MR, LB, RV

KC, BV

LDM = left deltoideus medialis, RDM = right deltoideus medialis, LRA = left rectus abdominis, RRA = right rectus abdominis.

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Table 4. Silhouette coefficients as a result of the k-means cluster analysis at clusters 2, 3 and 4. LDM

RDM

LRA

RRA

2 clusters

0.6543

0.5571

0.5657

0.7158

3 clusters

0.6913

0.5558

0.6628

0.6949

4 clusters

0.6841

0.5989

0.6986

0.6340

LDM = left deltoideus medialis, RDM = right deltoideus medialis, LRA = left rectus abdominis, RRA = right rectus abdominis.

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Jonas Martens is Master in Physical Education; Master in Biomedical and Clinical Engineering Techniques; Ph.D. in Biomedical Sciences at KU Leuven (Belgium) in the field of electromyography and swimming; swimming finalist at the Sydney and Athens Paralympic Games and former World Record holder.

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