several reports have 
 indicated that the energy cost needed for gait is associated with gait stability2 , 3 . 
 increased gait stability reduces the energy needed by the muscles involved in postural 
 control during gait2 .
gait is a complex exercise pattern created by neuromuscular , biomechanical , and 
 kinesiological activities4 .
furthermore 
 gait is achieved by the concerted action of muscles located throughout the body5 .
these muscles not only produce or dissipate 
 the work required for each movement but also redistribute the work among different body 
 segments6 .
thus , many muscles are 
 necessary for a coordinated motion , even if their primary functions do not appear to support 
 gait stability .
muscle synergy represents the enhanced activity of muscles working at the 
 same time and can simplify coordination in complex movements by activating muscles 
 appropriate for the intended movement . for example , 5 muscle synergies account for the 
 majority of variance in the surface emg signals recorded from 32 muscles during walking7 , 8 and 
 running7 .
therefore , changes in gait 
 stability that occur due to prolonged exercise may result from changes in muscle synergy 
 patterns .
the function of individual muscles is largely determined by their architecture , moment 
 arms , and fiber - type composition11 . as 
 the pennation angle of a muscle increases ,
the cross - sectional area ( area perpendicular to 
 the fiber direction ) increases , and it is typically accompanied by a decrease in the ratio 
 of the muscle fiber length to the whole muscle length .
when the same work is performed , synchronous contraction of 
 anatomic muscles leads to a decrease in muscle activity12,13,14 .
external muscle activity 
 results in a strong contraction and dynamic movement of a joint16 .
therefore , we speculated that increased muscle synergy of 
 anatomic muscle groups leads to increased gait stability .
nevertheless , little is known 
 about the relationship between gait stability and muscle synergies .
the purpose of this 
 study was to determine whether changes in muscle synergies affect gait stability and muscle 
 activity .
twelve healthy male subjects volunteered for this study ( age range : 2127  years , mean age : 
 23.0  2.0  years , body mass : 64.3  6.8  kg , height : 172.1  6.0  cm ) .
this study conformed to 
 the declaration of helsinki , and informed consent was received from all the participants 
 following the protocol of the ethics committee of the seirei christopher university .
subjects walked on a level treadmill ( autorunner ar-200 , minato , japan ) while wearing a 
 safety harness that allowed a natural arm swing .
subjects initially walked at 4.5  km / h until 
 they were accustomed to the treadmill .
next , they walked at the same speed for 1  min as a 
 control .
finally , the subjects continued to walk on the treadmill at 4.5  km / h for 90  min , 
 and data of the first and last 1  min were recorded .
electromyographic ( emg ) activity was recorded of 7 muscles in the subject s right leg : the 
 tibialis anterior ( ta ) , vastus medialis ( vm ) , rectus femoris ( rf ) , soleus ( sol ) , medial 
 gastrocnemius ( mg ) , semitendinosus ( st ) , and gluteus medius ( gm ) .
emg activity was recorded 
 using active electrodes ( blue sensor m , m-00-s , mets , japan ) , which were applied to lightly 
 abraded skin over the respective muscle belly .
the signals were amplified , filtered ( 20450  hz , 
 tele myo g2 , em-601 , noraxon inc . ,
, japan ) was clipped tightly 
 on a band tightened around the subjects waist .
it was located approximately at the center 
 of the spine at the l3 level to measure acceleration of the back and waist in mediolateral 
 ( ax ) , vertical ( ay ) , and anteroposterior ( az ) directions10 .
the gait cycle was defined by consecutive foot contacts , as determined by signals from a 
 foot contact switch .
the emg data were rectified and subsequently interpolated to 100 evenly 
 spaced values for each instance of the foot switch being triggered .
next , emg datum traces 
 were normalized by their largest value for each step and by the mean value at each of the 
 100 time points within each step .
emg data recorded during the walking trials were examined using a principal component 
 analysis ( pca ) to identify coordinated patterns among the 7 test muscles17 .
pca is a technique in which a large set 
 of multivariate data is reduced to a smaller set of common variables . in this case , each 
 stride was represented by 700 values ( from the 7 muscles and 100 time points per stride ) . 

pca reduced these 700 values to 3 principal components ( pcs ) that were further analyzed . 

each of these components contains information from all recorded muscles and time points ; 
 thus , the major components can be used to evaluate the features of the coordinated muscle 
 patterns .
this was achieved by arranging the data into a p  n matrix a , where p = 700 
 values per pattern ( 7 muscle  100 time points for the emg data ) , and n = 360 steps analyzed 
 in both the start and end periods .
the covariance matrix b was calculated from the data in 
 a , and the pc weightings determined from the eigenvectors  covariance matrix b. the 
 importance of each pc was given by the eigenvalues for each eigenvalue  eigenvector pair with 
 the greatest absolute eigenvalues corresponding to the primary pcs .
the proportion of emg 
 patterns explained by each pc was given by  b  , and the loading scores for each pc for 
 the n time points were given by  a. each instantaneous pattern of activity can be 
 reconstructed from the vector product of the pc weightings and the pc loading scores .
the 
 patterns of activity within the anatomic muscle groups ( sol - mg , vm - rf ) were additionally 
 determined by repeating pc analysis using p = 200 , corresponding to whether each anatomic 
 group had 2 muscles , respectively18 .
the 
 co - activation of muscles within anatomic groups was indicated by the contribution ratio .
iemg was calculated from the mean emg data for cycle steps during the 1  min of walking .
emg 
 signals were integrated over the 1  min of walking at the same speed during the start and end 
 periods .
each iemg signal was expressed as a percentage of the iemg reading obtained during 
 the 1  min control period .
these include 
 both external ( e.g. , environmental and/or sensory ) and internal perturbations created by 
 neuromuscular noise .
local dynamic stability , as quantified by the largest lyapunov 
 exponent , refers to the sensitivity of a dynamic system to infinitesimally small 
 perturbations19 .
the lyapunov exponent 
 is the mean exponential rate of divergence of nearby points , and it quantifies local 
 dynamical stability19 , 20 .
we estimated the largest finite - time lyapunov exponent of 
 the down - sampled ( 200  hz ) ax , ay , and az using the algorithm proposed by sano - sawada21 .
the 
 time lag was determined by the time at which the autocorrelation dropped to 1/e of the value 
 at time zero .
the embedding dimension was set to 5 using the result of false nearest 
 neighbor analysis23 .
the largest lyapunov 
 exponent was estimated by calculating the slope of the linear fit to the log - scaled curve of 
 the divergence distance between neighboring trajectories in the reconstructed phase 
 space24 . to exclude the effect of 
 different gait cycle times among the data , the time axis for the divergence curve was 
 rescaled by dividing by the mean gait cycle time .
a decrease in the largest lyapunov 
 exponent corresponds to an increase in gait stability .
contribution ratio , iemg , and the largest lyapunov exponent were compared between the start 
 and end gait periods using the paired t - test .
the significance of all statistical tests was 
 accepted for values of p < 0.05 .
the patterns of activations among muscles can be determined by pca of their time - varying 
 emg activity . in the present study , more than 80% of the emg activity patterns
pc1 partitioned the muscles into anatomic groups , with the 
 triceps surae giving negative weightings , whereas ta , quadriceps , st , and gm gave positive 
 weightings in both the start and end periods ( table 
 1table 1.principal component ( pc ) weightings calculated for the start and end periods to 
 determine the synergies among the 7 muscles tested pc1pc2pc3startendstartendstartendtibialis anterior0.7060.7090.0440.0250.2010.424vastus medialis0.8200.8020.0440.4180.0650.005rectus femoris0.6580.7120.4490.4600.4820.209soleus0.6360.5600.6690.6820.1480.331medial gastrocnemius0.6960.6680.5820.5840.2710.336semitendinosus0.5730.5260.1950.3520.7050.628gluteus medius0.5020.4800.6280.6500.2140.142contribution ratio ( % ) 43.741.723.324.913.013.3the percentage of the contribution ratio explained by each pc is shown . ) .
pc2 partitioned the muscles into groups with ta and st giving negative 
 weightings , whereas the triceps surae , quadriceps , and gm gave positive weightings in both 
 the start and end periods ( table 1 ) .
pc3 
 partitioned the muscles into anatomic groups with rf giving negative weightings , whereas ta , 
 triceps surae , and st , gave positive weightings in both the start and end periods ( table 1 ) .
thus , the pc weighting showed coupling of 
 activity among muscles within anatomic groups ( table 
 1 ) .
each contribution ratio indicated there was no change in pcs between the start 
 and end periods ( table 1 ) .
the triceps surae co - activation during the end period was significantly higher than that 
 observed in the start period ( p < 0.05 ) .
the quadriceps co - activation , however , did not 
 change between the start and end periods ( table 
 2table 2.the contribution ratio , iemg , and the largest lyapunov exponent of the start and 
 end periodsstartendcontribution ratio ( % ) triceps surae92.6  1.994.1  2.6*quadriceps86.6  11.388.8  7.2iemg ( % ) tibialis anterior97.4  3.798.5 
5.3104.8  14.5medial gastrocnemius99.1  3.192.0  12.0*semitendinosus98.0  11.196.1  30.2gluteus medius106.3  20.1128.0  71.3largest lyapunov exponentmediolateral ( ax)0.132  0.0160.124  0.015*vertical
( ay)0.124  0.0080.128  0.013anteroposterior ( az)0.114  0.0090.107  0.006*values are expressed as the mean  sd .
* p < 0.05 , significant difference between 
 the start and end periods . ) .
the mg iemg in the end period was significantly reduced in the start period 
 ( p < 0.05 ) , but no changes were observed for the other muscles ( table 2 ) . the mediolateral and anteroposterior directions for the 
 largest lyapunov exponent in the end period were significantly reduced from the start period 
 ( p < 0.05 ) but showed no changes in the vertical direction ( table 2 ) .
the aim of the present study was to examine whether a change in muscle synergies affects 
 gait stability or muscle activity .
we found that muscle synergies did not change for the 7 
 muscles tested ; however , triceps surae co - activation was increased by prolonged gait . in 
 addition , decreased mg activity and increased gait stability occurred with increased triceps 
 surae co - activation during gait .
this suggests that anatomic muscle group synergies 
 correlate with an increase in gait stability .
a number of muscle activation patterns , which are termed synergies , occur during walking , 
 and combine to enable the necessary muscle coordination required for locomotion7 .
the pca determines the dominant components 
 of the signal ; therefore , any unbiased or random noise will be included in the lower order 
 components .
this study considered the 3 major pcs of the emg activity patterns ; the 
 remaining components ( accounting for 20% of the emg signal ) were excluded .
pc2 comprised triceps surae and gm , 
 and these muscles were active during mid - stance and heel lift - off .
these findings indicate that the muscle synergies 
 did not change for these 7 muscles .
however , studies reporting the activity of these muscles 
 individually have shown the relative levels of emg activity varies among muscles in 
 prolonged cyclic exercise25 , 26 . in this study ,
this included a decrease in the iemg of mg 
 ( table 2 ) and advances in the timing of sol 
 and mg ( table 2 ) .
prolonged cyclic exercise 
 increases co - activation27 and coincident 
 firing13 in anatomic muscle groups . 

these results raise the possibility that the decrease in mg activity 
 which we observed was the result of increased triceps surae co - activation .
the largest 
 lyapunov exponent influences muscle synergies and muscle activity3 , 10 , 19 .
when the same work is performed , synchronous contraction 
 of anatomic muscles leads to a decrease in muscle activity14 , 28 .
external muscle activity results in a strong contraction and 
 dynamic movement of a joint16 , 29 .
a decrease in external muscle activity leads to an 
 increase in the stability of joint movement and gait .
thus , an increase in the co - activation 
 of the triceps surae would lead to a decrease in the activity of the medial gastrocnemius , 
 which in turn would lead to an increase in gait stability .
we hypothesized that an increase 
 in muscle synergies among anatomic muscle groups would lead to increased gait stability .
our 
 data raise the possibility that an increase in muscle synergies among anatomic muscle groups 
 leads to an increase in gait stability .
 [ purpose ] the purpose of this study was to examine whether changes in muscle synergy 
 could affect gait stability or muscle activity by comparing muscle activity before and 
 after prolonged walking . 
 [ subjects and methods ] twelve healthy male subjects walked on a 
 treadmill for 10  min as a warm - up . 
 data were recorded from the participants during the 
 first and last 1  min during 90  min of walking at 4.5  km / h . 
 electromyographic ( emg ) 
 activity was recorded for 7 leg muscles , and patterns of coordination were determined by 
 principal component analysis ( pca ) . 
 the patterns of activity within the anatomic muscle 
 groups were additionally determined by repeating pca . 
 iemg was calculated using the mean 
 emg for each cycle step during the 1  min walking periods . 
 the largest lyapunov exponent 
 was calculated to quantify each subject s inherent local dynamic stability . 
 [ results ] the 
 patterns for each of the 7 muscles showed no change between the start and end periods . 
 
 however , the end period showed a higher co - activation of the triceps surae , lower iemg of 
 the medial gastrocnemius , and a smaller largest lyapunov exponent of the mediolateral and 
 anteroposterior directions than those observed during the start period . 
 [ conclusion ] the 
 increase in triceps surae co - activation may be associated with gait stability . 
INTRODUCTION
SUBJECTS AND METHODS
RESULTS
DISCUSSION