- Email: [email protected]
Walking speed effects on the lower limb electromyographic variability of healthy children aged 7–16 years
Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
Contents lists available at SciVerse ScienceDirect
Journal of Electromyography and Kinesiology journal homepage: www.elsevier.com/locate/jelekin
Walking speed effects on the lower limb electromyographic variability of healthy children aged 7–16 years Oren Tirosh a,c,⇑, Morgan Sangeux a,b, Matthew Wong a, Pam Thomason b, H. Kerr Graham a,b a
Murdoch Childrens Research Institute, Royal Children’s Hospital, Parkville 3052, Victoria, Australia Hugh Williamson Gait Analysis Laboratory, Royal Children’s Hospital, Parkville 3052, Victoria, Australia c Institute of Sport, Exercise and Active Living (ISEAL), Victoria University, Footscray, Victoria, Australia b
a r t i c l e
i n f o
Article history: Received 6 December 2012 Received in revised form 13 March 2013 Accepted 8 June 2013 Available online xxxx Keywords: Gait Electromyography Gait variability Children Aging
a b s t r a c t The evaluation of surface electromyography (sEMG) is commonly performed in children with cerebral palsy (CP) and reliable interpretation necessitates knowledge of the variability in age-matched, typically developing (TD) children. Variance ratio was calculated for inter-trial sEMG linear envelope (LE) and the Instantaneous Mean Frequency (IMNF) variability in the lower limb muscle in TD children, in three different age groups during slow, comfortable speed, and fast walking. Significantly greater variability was found in the 7–9 group compared to the 13–16 years. Variability during both slow and fast walking was significantly greater compared to comfortable speed walking and was profound in the 7–9 year age group. Variability of the IMNF was significantly greater than LE in the Tibialis-Anterior, Biceps-Femoris (BF), Vastus-Lateralis (VL), and Rectus-Femoris (RF). Clinical implications are that children under 10 years are more variable than older children when walking either slower or faster than self-selected walking speed. This suggests that muscle activation patterns in gait mature at a later stage of childhood than do kinematic gait patterns. Greater precaution, therefore, is needed when comparing sEMG patterns of less than 10 years of age patient and TD children. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Surface electromyography (sEMG) is used to record lower limb muscle activity to provide information about the timing of muscle activation. In clinical gait analysis, diagnosis and decision making are based on a diagnostic matrix which may include comparing a patient’s sEMG patterns to those of a typically developing population. One example is interpretation of the sEMG pattern to assess the outcome of Rectus Femoris transfers for the treatment of stiff knee gait in children with Cerebral palsy (CP) (Miller et al., 1997). The evaluation of sEMG is commonly performed in children with CP and reliable interpretation necessitates knowledge of the variability in age-matched, typically developing children. The linear envelope (LE) waveform of the sEMG signal is the most common processing technique to illustrate muscle activation patterns during the gait cycle. The LE is generated by smoothing the rectified signal with a low pass filter to describe the muscle’s time domain characteristics, as reflected in changes to the curve’s amplitude. New advanced signal processing techniques using time–frequency wavelet analysis of sEMG were recently (Lauer ⇑ Corresponding author. Address: Gait Laboratory & Orthopaedics Research Group, Murdoch Childrens Research Institute, Royal Children’s Hospital, Flemington Road, Parkville 3052, Victoria, Australia. Tel.: +61 3 93455354. E-mail address: [email protected] (O. Tirosh).
et al., 2007b) utilized to measure both signal amplitude and frequency. Time–frequency characteristics of muscle activation quantified by the Instantaneous Mean Frequency (IMNF) have been suggested to be more reflective of changes in gait kinematics (Lauer et al., 2007b) and have greater sensitivity to the muscle’s function following hamstring surgery in children with CP (Lauer et al., 2007a). This methodology can be useful in children with CP to determine sensitivity to spasticity, muscle weakness, and fatigue. To use this methodology as a diagnostic tool in gait analysis by comparing to typical developed gait sEMG template it is important to examine the inter-individual variability of ensemble averaged IMNFs. An important research question is, therefore, which signal processing method, LE or IMNF, has the most reduced interindividual variability and, as a consequence, is more clinically appropriate for the diagnosis and evaluation of gait disorders in children with CP. The variance ratio (VR) is most commonly employed to examine the repeatability of sEMG LE waveforms over a given number of identical gait cycles (Granata et al., 2005; Kadaba et al., 1985). As proposed by Hershler and Milner (1978), the VR quantifies waveform variability as the ratio of the mean square between repetitions to the total sum of squares. A VR of zero represents no variability while a VR of one represents maximum variability. The VR is independent of peak amplitude, providing a good measure of repeatability in the overall wave shapes. The VR is,
1050-6411/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jelekin.2013.06.002
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
2
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
furthermore, insensitive to mean sEMG amplitude and the degree of smoothing applied to the data (Gabel and Brand, 1994). While variability in lower limb sEMG in adult’s gait is well documented there has been little research with children. Adult LE patterns were shown to be repeatable between walking trials in the order of 0.21VR (Granata et al., 2005; Kadaba et al., 1985). The one study of variability in children’s’ LE waveforms is Granata et al. (2005), who found that children (6.5 ± 2.3 years) had a between trial mean VR of 0.46, approximately double that of adults (Granata et al., 2005). The natural gait cycle variability of the sEMG frequency components generated by the IMNF curve has not been previously investigated. Age and walking speed effects are important when interpreting sEMG waveforms. As reflected in kinematic and kinetic variables, walking has been suggested to be ‘‘mature’’ by 5–8 years (Chester et al., 2006; Ganley and Powers, 2005; Grieve and Gear, 1966; Hillman et al., 2009; Ounpuu et al., 1991; Rose-Jacobs, 1983; Vaughan et al., 2003). Gait maturation as indicated by muscle activity is less well documented and the few previous studies are inconclusive (Chang et al., 2007; Okamoto et al., 2003; Shiavi et al., 1987b; Sutherland et al., 1980). Sutherland et al. (1980) found that in children of 1–7 years there was reduced activation time in the Gluteus Maximus, Vastus Medialis, and Tibialis Anterior as age progressed. A later study comparing the sEMG patterns of children aged 4 and 7 years with a group 8–11 years, revealed significant changes in the intensity and phases of activity (Shiavi et al., 1987b) in the Rectus Femoris and the Hamstrings but not the Tibialis Anterior. Detrembleur et al. (1997) found that muscle activation onset and duration during comfortable walking in children 4–7 years was not different to that of a group aged 8–11 years. Chang et al. (2007) also noted that the timing and duration of sEMG activity is poorly correlated with age (groups between 3 and 6, 7 and 11, and 12 and 18 years). The authors concluded that inconsistent age effects were due to the high variability (standard deviation) in all muscle groups with respect to maximum peak timing and baseline activity. Walking speed has also been found to influence LE waveform amplitude (Detrembleur et al., 1997; Hof et al., 2002; Rose-Jacobs, 1983; Schwartz et al., 2008; Shiavi et al., 1987a). Schwartz et al. (2008), for example, reported amplification of peak values with increased walking speed. Walking speed was also shown to affect the variability of muscle LE patterns in children (Shiavi et al., 1987b). Using the coefficient of variation Shiavi et al. (1987b) found that LEs in all muscles tended to become more consistent as speed increased. The Peroneus Longus, Rectus Femoris, and Lateral Hamstring were highly variable at slower speeds but the lower leg muscles (Tibialis Anterior, Medial Gastrocnemius, and Soleus) were shown to be more consistent. The aim of this experiment was to investigate the effect of age and walking speed on between-trial variability in LE and IMNF waveforms.
(10–12 years), and Group III (13–16 years), see Table 1. All participant’s guardians signed a consent form approved by the institute Ethics Committee (HREC29108) and completed a screening questionnaire to confirm the absence of musculoskeletal, neurological or other medical conditions that might have suggested exclusion. All participants had three-dimensional instrumented gait analysis undertaken by a physiotherapist and a human movement scientist, both experienced in clinical gait analysis. Participants were requested to walk under three conditions, slow, comfortable, and fast walking speed. Testing began with the comfortable walking condition followed by either slow or fast walking conditions presented in randomised order. In the fast walking condition participants were instructed to walk as fast as possible while in the slow walking condition they were requested to walk slower than comfortable. Walking speed was not controlled from trial-to-trial within each condition as this potentially can cause modifications to the gait pattern (Bertram and Ruina, 2001; Schwartz et al., 2008). Five minutes rest was given between walking speed conditions. Reflective markers attached to the participant’s foot were sampled using Vicon MX system (Vicon, Oxford, UK) at 100 Hz to identify the gait cycle events, heel-contact and toe-off. Muscle activation data were sampled simultaneously via a ZeroWire EMG system (Aurion s.r.l.) using fixed 20 mm inter-electrode (silver/silver chloride) spacing (Myotronics Inc. kent WA), input impedance of 10 MOhms, a common mode rejection ratio of 90 dB, and signal to noise ratio >50 db. The sEMG raw signals were sampled at 1000 Hz and amplified with a gain of 1000 (Vicon Nexus software). Electromyographic activity of the lower left and right extremities was recorded from the Tibialis-Anterior (TA), Medial-Gastrocnemius (MG), Biceps-Femoris (BF), Vastus-Lateralis (VL), and Rectus-Femoris (RF). Following the SENIAM guidelines (Hermens and Freriks, 1999) electrodes were located on the midline of the muscle belly, with the detection surface oriented perpendicular to the long axis of the muscle fibres. Once the electrode position was marked shaving, gentle abrasion with sandpaper and cleansing with alcohol prepared the skin. 2.2. Processing the sEMG signals Using MatlabÒ software (The Mathworks Inc.) the sEMG were first bandpass filtered (20–500 Hz). To generate the LE the bandpass filtered sEMG signal was rectified and low passed filtered at 6 Hz using a zero-lag forth-order Butterworth filter. The IMNF waveform is generated by applying a continuous wavelet transform (CWT) to each sEMG signal using morlet ‘‘mother’’ function (Lauer et al., 2007b). The output of the CWT analysis is a threedimensional scalogram that is reduced to a time–frequency curve by calculating the mean frequency for each 0.1% gait cycle interval. All walking trials LE and IMNF data for a subject were time normalized to a percentage of stride duration.
2. Methods 2.3. Data analysis 2.1. Subjects and experimental design Thirty-six TD children without known gait pathology aged 11.6 ± 3.1 years participated in the experiment. They were assigned to one of three age groups; Group I (7–9 year), Group II
2.3.1. Participants characteristics and walking speed Means and standard deviation (SD) for participants’ age, weight, and height were compared using One-way Analysis of Variance (ANOVA). Walking speed (v) was rendered dimensionless by
Table 1 Means (SD) for participants’ age, weight, and height. Group
n
Gender (M/F)
Age range
Age (years)
Weight (kg)
Height (cm)
I II III
12 12 12
8/4 6/6 7/5
7–9 10–12 13–16
8.3 (0.9) 10.7 (0.8) 14.7 (1.4)
29.3 (6.1) 37.5 (6.5) 56.9 (14.5)
130.7 (7.5) 143.9 (8.3) 164.4 (10.1)
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
scaling to the participant’s leg length (Lleg) using a formula proposed by Hof (1996):
gLleg
2
where g is acceleration due to gravity (9.81 m/s ). Repeated measures ANOVAs were conducted to confirm that there were no dimensionless speed differences between absolute walking speed conditions and age-groups. An a significance level of 0.05 was used. 2.3.2. Variance ratio Between trial variability was quantified using the variance ratio (VR) method described by Hershler and Milner (1978):
Pm Pn
2
i¼1
j¼1 ðEij
Ei Þ =mðn 1Þ
i¼1
j¼1 ðEij
EÞ =ðmn 1Þ
VR ¼ P P m n
2
ð1Þ
where m is the number of temporal points (one point per % stride); n is the number of strides (one for each walking trial) over which VR is evaluated; Eij is the value of sEMG waveform j at time epoch i; Ei is the average of the EMG values at time epoch i over j strides; and E is the grand mean average of the EMG signal (E) determined by: m 1X Ei : m i¼1
3. Results 3.1. Subject characteristics
m
v ¼ pffiffiffiffiffiffiffiffiffiffi
E¼
3
ð2Þ
The VR score ranges from 0 to 1, where 0 indicates no variability and 1 denotes maximum variability. For each muscle the Analysis of variance (ANOVA) with Bonferroni test for Post Hoc analyses was used to examine the effect of age (Groups I, II, and III), walking speed (slow, comfortable, and fast), and processing method (LE and IMNF) on VR. To ensure that there is no differences between left and right leg sEMG, leg side was also included as the fourth category having a 4-way (3 3 2 2) ANOVA design with 4 categorical predictors. All statistical analyses were performed using STATISTICA (StatSoft, Inc.). An a significance level of 0.0125 was used to adjust for the Bonferroni test (alpha level of 0.05/ 4 categories).
Means and SD for participants’ age, weight, and height are presented in Table 1. Significant differences in age, weight, and height were noted between the three age groups (F2,33 = 46.00, F2,33 = 105.53, F2,33 = 26.58, respectively, p < .001).
3.2. Walking speed Walking speed across the three conditions was successfully modulated (Fig. 1) with both decreased and increased speed reflected in a condition effect (F2,66 = 400.48, p < .001). Compared to comfortable walking the slow condition was 35% slower and the fast condition was 42% faster. The dimensionless speed mean (v⁄ = 0.49) and SD (0.055) at comfortable speed was similar to previous studies, such as Schwartz et al. (2008) who reported a mean freely-chosen dimensionless speed of (v⁄ = 0.43) and SD (0.068). A speed-age interaction was also found (F4,66 = 5.12, p < .01) with the children 7–9 years walking with a higher dimensionless speed in the fast condition compared to the two older age groups.
3.3. Surface electromyography No significant differences in any muscle activation measures were found between left and right legs. Table 2 presents the VR of each muscle for the three age groups across walking speed and processing method. The ANOVA results for VR are included in Table 3. Fig. 2 illustrates left leg LE waveform variability for one representative participant from each age group across the 3 walking speeds. Fig. 3 illustrates left leg IMNF waveform variability for one representative participant from each age group across the 3 walking speeds. The LE waveforms were found to be strongly affected by speed with greater amplitudes at increased speed but the IMNF waveforms did not show speed effects on the frequency component. Age effects on the shape of the LE waveform were most evident in the thigh muscles. Group I showed little RF activity in the initial stance phase compared to group III. Similarly, group I activated the BF less at initial stance compared to group II and group III.
Fig. 1. Dimensionless walking speed across the three walking conditions for Group I (diamond dot line), Group II (circle solid line), and Group III (square dash line). Decreased and increased speed reflected in significant condition effect (F2,66 = 400.48, p < .001). Vertical bars denote 0.95 confidence intervals. Group I are children aged between 7 and 9 years, Group II are children aged between 10 and 12 years, and Group III are children aged between 13 and 16 years of age.
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
4
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
Table 2 Means (standard deviation) of the variance ratio calculated for each muscle for the three age groups (young – I, middle – II, and older – III), walking speed (slow, comfortable, and fast), and sEMG processing method (LE and IMNF). In the Group column, variance ratio for each age group represents the group means of collapsed data from all speeds and processing method (LE and IMNF). In the Speed column, variance ratio for each speed represents the speed means of collapsed data from all age groups and processing method (LE and IMNF). In the Processing method column, variance ratio for each processing method represents the processing method means of collapsed data from all age groups and speed. Group
TA MG RF VL BF a b c
Speed
I
II
0.40a (0.17) 0.26a (0.14) 0.47 (0.21) 0.36 (0.19) 0.47 (0.19)
0.31 0.18 0.53 0.34 0.42
(0.14) (0.11) (0.22) (0.19) (0.18)
Processing method
III
Slow
Normal
Fast
ENV
0.27 (0.12) 0.18 (0.14) 0.47 (0.23) 0.38 (0.24) 0.38a (0.18)
0.37 (0.16) 0.23 (0.16) 0.56b (0.22) 0.44b (0.22) 0.49 (0.18)
0.25b 0.18b 0.41b 0.30b 0.36b
0.35 (0.17) 0.21 (0.13) 0.50b (0.22) 0.34b (0.18) 0.41 (0.19)
0.31 0.22 0.40 0.31 0.40
(0.10) (0.12) (0.21) (0.18) (0.15)
IMNF (0.14) (0.13) (0.18) (0.17) (0.17)
0.35c (0.17) 0.20 (0.14) 0.58c (0.22) 0.41c (0.22) 0.45c (0.19)
ANOVA with Post Hoc Bonferroni test between age groups (I, II, and III) at each muscle p < 0.0125. ANOVA with Post Hoc Bonferroni test between walking speeds (slow, normal, fast) at each muscle p < 0.0125. ANOVA with Post Hoc Bonferroni test between processing methods (ENV and IMNF) at each muscle p < 0.0125.
Table 3 ANOVA results for the Variance Ratio analysis of each muscle for the three age groups (Group) across walking speed (Speed), processing method (Analysis), and leg side (Side). Main effects and interaction are presented. Muscle
F
p
TA
Intercept Group Speed Analysis Side GroupSpeed GroupAnalysis SpeedAnalysis Error
Type III Sum of Squares 47.38977 1.26087 1.20792 0.16340 0.00435 0.43572 0.02884 0.27751 7.25114
1 2 2 1 1 4 2 2 396
47.38977 0.63044 0.60396 0.16340 0.00435 0.10893 0.01442 0.13876 0.01831
2588.056 34.429 32.983 8.924 0.238 5.949 0.787 7.578
0.000000 0.000000 0.000000 0.002990 0.626210 0.000117 0.455750 0.000589
MG
Intercept Group Speed Analysis Side GroupSpeed GroupAnalysis SpeedAnalysis Error
19.28967 0.54067 0.16619 0.03288 0.00176 0.22819 0.00102 0.11551 7.23354
1 2 2 1 1 4 2 2 396
19.28967 0.27034 0.08310 0.03288 0.00176 0.05705 0.00051 0.05776 0.01827
1056.012 14.800 4.549 1.800 0.096 3.123 0.028 3.162
0.000000 0.000001 0.011135 0.180478 0.756265 0.015059 0.972428 0.043419
RF
Intercept Group Speed Analysis Side GroupSpeed GroupAnalysis SpeedAnalysis Error
2877.706 3.873 20.742 96.588 0.001 2.987 2.932 6.128
0.000000 0.021596 0.000000 0.000000 0.978992 0.018893 0.054441 0.002393
VL
Intercept Group Speed Analysis Side GroupSpeed GroupAnalysis SpeedAnalysis Error
57.16946 0.06874 1.53070 1.08971 0.04570 0.83232 0.19667 0.21274 14.24935
1 2 2 1 1 4 2 2 396
57.16946 0.03437 0.76535 1.08971 0.04570 0.20808 0.09833 0.10637 0.03598
1588.781 0.955 21.270 30.284 1.270 5.783 2.733 2.956
0.000000 0.385628 0.000000 0.000000 0.260430 0.000157 0.066268 0.053167
BF
Intercept Group Speed Analysis Side GroupSpeed GroupAnalysis SpeedAnalysis Error
78.43041 0.64742 1.20128 0.26254 0.00123 0.30584 0.00476 0.50052 11.72281
1 2 2 1 1 4 2 2 396
78.43041 0.32371 0.60064 0.26254 0.00123 0.07646 0.00238 0.25026 0.02960
2649.402 10.935 20.290 8.869 0.042 2.583 0.080 8.454
0.000000 0.000024 0.000000 0.003079 0.838412 0.036811 0.922833 0.000254
105.7571 0.2846 1.5245 3.5497 0.0000 0.4391 0.2155 0.4504 14.5532
Degrees of freedom
1 2 2 1 1 4 2 2 396
As shown in Fig. 4 the variability in slow and fast walking was greater than comfortable walking and more noticeable in the youngest participant. There was a trend of lower variability at the distal muscles compared to the proximal muscles, RF, VL, and BF. For example, at all walking speeds MG had approximately 50% less variability than RF, VL, and BF (Fig. 4).
Mean Square
105.7571 0.1423 0.7623 3.5497 0.0000 0.1098 0.1078 0.2252 0.0368
3.4. Effect of processing method on VR Both LE and IMNF analyses showed greater inter trial variability of the proximal muscles compared to the smaller distal muscles. These differences were 1.5-fold and 2-fold for RF, VL, and BF compared to the TA and MG muscles, respectively. All muscles except
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
5
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
One representative subject in In GROUP I
One representative subject in GROUP II
One representative subject in GROUP III
TA
MG
RF
VL
BF
Gait Cycle (%)
Gait Cycle (%)
Gait Cycle (%)
Fig. 2. Ensemble average of the 6 trials at each speed; slow (dotted), comfortable (solid bold), and fast (solid) walking from one representative subject in each group. Group I are children aged between 7 and 9 years, Group II are children aged between 10 and 12 years, and Group III are children aged between 13 and 16 years of age. All walking trials for a subject were time (100% of one gait cycle duration) and amplitude normalized. Amplitude normalization was performed using the ‘dynamic maximum’ method described by Den Otter et al. Gait & Posture 2004 19, 270–278. For each muscle, signals were amplitude normalized relative to the peak amplitude of all walking speed condition (slow, comfortable and fast), resulting with ranges from 0 to 1, with 1 the dynamic maximum. Normalization was performed to allow illustration of sEMG data across subjects. It is recognized that this approach emphasizes activation patterns at the expense of signal magnitude.
the MG showed significant (F1,2070 = 98.976, p < .01) greater VR for IMNF analysis compared to LE (Table 2).
3.5. Effect of age on VR Significant age main effect was found (F2,2070 = 21.104, p < .01) with post hoc analysis revealing significant greater variability (30%) for group I compared to the older groups II and III only for MG and TA muscles. Significant lower variability was found in group III compared to group I only for BF muscle. No significant differences in VR measurements were found between group II and group III for any of the tested muscles.
3.6. Effect of walking speed on VR Table 2 shows that at comfortable walking speed VR was significantly lower than at slow and fast speeds (0.30 ± 0.18, 0.42 ± 0.22, and 0.37 ± 0.20, respectively, F2,2070 = 90.730, p < .01). In addition, slow walking had significant greater VR compared to fast walking.
3.7. Walking speed/age interaction A significant walking speed by age interaction (F4,2070 = 12.572, p < .01) indicated that significant VR age and walking speed differences were due to greater VR in the 7–9 years group during slow and fast walking (Fig. 4). The average VR for all muscles at comfortable walking in group I was 0.29 ± 0.14, while at slow and fast walking it was 0.46 ± 0.19 and 0.43 ± 0.20, respectively. Group II and group III also showed significant greater VR at slow compared to comfortable walking (0.41 ± 0.23 and 0.31 ± 0.18, respectively for group II, and 0.39 ± 0.23 and 0.31 ± 0.20, respectively for group III, F4,2070 = 12.572, p < .01), but this was less clear during fast walking, specifically in group III where VR of MG and VL muscles was smaller compared to comfortable walking (0.15 ± 0.08 and 0.18 ± 0.13, respectively for MG, and 0.27 ± 0.15 and 0.37 ± 0.23, respectively for VL). 4. Discussion The aim of this study was to extend earlier findings on sEMG variability in children by considering muscle activity patterns in
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
6
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
One representative subject in In GROUP I
One representative subject in GROUP II
One representative subject in GROUP III
TA (Hz)
MG (Hz)
RF (Hz)
VL (Hz)
BF (Hz)
Gait Cycle (%)
Gait Cycle (%)
Gait Cycle (%)
Fig. 3. IMNF (Hz) of muscle activity at slow, comfortable and fast walking (dotted, solid bold, and solid lines, respectively) presented for one representative subject in each group. Group I are children aged between 7 and 9 years, Group II are children aged between 10 and 12 years, and Group III are children aged between 13 and 16 years of age. All walking trials for a subject were time normalized (100% of one gait cycle duration).
a range of children age when walking at a range of speeds. Mature gait as indicated by kinematic and kinetic analysis has been reported to emerge between 5 and 8 years (Beck et al., 1981; Ounpuu et al., 1991; Sutherland et al., 1980). Analysis of muscle activation patterns in gait suggests, however, that fully developed walking patterns are established later. Children with a mean age of 6.5 ± 2.3 years, for example, show twice the variability of adults, VR = 0.47 and 0.21, respectively (Granata et al., 2005). While in our study the age of group I was similar to that of Granata et al. (2005), lower variability values were noted (0.39 compare to 0.47). Our comparison of Group I (7–9 years) with the older groups II and III (9–16 years) showed significantly greater variability in TA and MG muscles. Similar pattern of differences between young and older children were reported earlier (Shiavi et al., 1987b) when the authors observed inconsistent MG activity in children 4–7 years compared to a cohort 8–11 years. It is, therefore, suggested that activation patterns of distal muscles when walking mature relatively late, after approximately 10 years. The control of stable walking involves a combination of distal ankle and proximal hip muscle activation strategies. Proximal muscles are reported to have greater stride to stride variability than the distal muscles, possibly due to their dual role of supporting and correcting posture of the upper body mass associated with the head, trunk and hands (Winter and Yack, 1987). Our study found a similar trend, with greater variability of the proximal
muscles (RF, VL, and BF) compared to the distal muscles (TA and MG) across all ages, independent of walking speed. The effect of walking speed on muscle activation has been previously documented. Using amplitude and time domain measures from LE curves it was shown that faster walking increased amplitude but the shape remained essentially unchanged (Detrembleur et al., 1997; Schwartz et al., 2008; Yang and Winter, 1985). In TD children changes from slow to fast walking reflect decreased variability (Shiavi et al., 1987b). Our study partially supports Shiavi et al. (1987b) findings showing that sEMG variability in both slow and fast walking is significantly greater than comfortable walking. For MG and VL, however, sEMG variability was found to be less regular in fast walking. Our results further indicate that greater variability in slow and fast walking is primarily associated with children under 10 years. Greater variability during slow walking may suggest that the CNS is modulating the movement pattern to accommodate changes in walking speed (Hamill et al., 1999; Harbourne and Stergiou, 2009). Slower gait may be more challenging with respect to neuromuscular control and is more pronounced in younger children. Greater variability in muscular control during walking faster or slower than preferred has been linked to uncertainty (Sanger, 2010), needed practice (Nakayama et al., 2010), and/or gait instability (Hausdorff et al., 2001; Herman et al., 2005). It is possible that the greater muscular variability found in the youngest group of children at non-preferred speeds could be
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
7
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
MG
Group I
TA
Group II
Group III
Group I
RF
Group I
Group II
Group II
Group III
VL
Group III
Group I
Group II
Group III
BF
Group I
Group II
Group III
Fig. 4. Means and 95% confidence intervals (vertical bars) of the calculated variance ratio of each muscle for the three age groups (Group I = 7–9 years, Group II = 10–12 years, and Group III = 13–16 years), and walking speed (slow-diamond, comfortable-cross, and fast-triangle).
due to these factors. This suggests that children between 7 and 9 years demonstrate maturity in muscular control at comfortable speed walking but maturation to accommodate faster or slower walking is only achieved later, at approximately 10 years of age. The final aim was to compare variability outcomes generated by two processing methods, LE and IMNF. The LE is the most common processing method in clinical research; it represents sEMG signal amplitude over time to characterize the recruitment of motor units and changes in firing rate. Recently, time–frequency characteristics of muscle activation quantified by Instantaneous Mean Frequency have been suggested to be more reflective of the resultant changes in gait kinematics (Lauer et al., 2007a). Spectral content of the sEMG can be related to recruitment of additional motor units that include fast twitch muscle fibers to generate increased force at higher mean firing frequency, and synchronizing the firing rate of the motor units currently in use reducing the frequency spectrum of the sEMG signal (Ricard et al., 2005; Wakeling, 2009). This study found that the variability of the IMNF compared to LE tended to be greater, with significant differences for TA, RF, VL and BF, but more profound differences in the knee extensors RF and VL. The increase in sEMG amplitude
might represent motor unit recruitment and/or motor unit firing frequency modulation, whereas the increase in mean frequency of the power spectrum might represent the additional recruitment of superficial high threshold motor units (Moritani and Muro, 1987). The variability in median frequency was also suggested to cause variation in mean motor unit action potential peak-peak time and synchronization (Hermens et al., 1992). It is possible that the variation suggested by Hermens et al. (1992) is responsible for the greater frequency component variability, indicated by IMNF analysis, found in this study. 5. Conclusion The variability of muscle activation in children between 7 and 16 years of age increases when walking at non-preferred speed. Muscle activation patterns in children under 10 years are more variable than for older children only when walking either slower or faster than preferred, suggesting that muscle activation patterns in gait mature at a later stage of childhood. Frequency waveforms of muscle activation have greater variability than amplitude LE waveforms.
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
8
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
Conflict of Interest All authors declare that they have no proprietary, financial, professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled: Walking Speed Effects on the Lower Limb Electromyographic Variability of Healthy Children Aged Seven to Sixteen Years Acknowledgement This study was supported in part by The Hugh Williamson Foundation. References Beck RJ, Andriacchi TP, Kuo KN, Fermier RW, Galante JO. Changes in the gait patterns of growing children. J Bone Joint Surg Am 1981;63:1452–7. Bertram JE, Ruina A. Multiple walking speed–frequency relations are predicted by constrained optimization. J Theor Biol 2001;209:445–53. Chang WN, Lipton JS, Tsirikos AI, Miller F. Kinesiological surface electromyography in normal children: range of normal activity and pattern analysis. J Electromyogr Kinesiol 2007;17:437–45. Chester VL, Tingley M, Biden EN. A comparison of kinetic gait parameters for 3–13 year olds. Clin Biomech (Bristol Avon) 2006;21:726–32. Detrembleur C, Willems P, Plaghki L. Does walking speed influence the time pattern of muscle activation in normal children? Dev Med Child Neurol 1997;39:803–7. Gabel RH, Brand RA. The effects of signal conditioning on the statistical analyses of gait EMG. Electroencephalogr Clin Neurophysiol 1994;93:188–201. Ganley KJ, Powers CM. Gait kinematics and kinetics of 7-year-old children: a comparison to adults using age-specific anthropometric data. Gait Posture 2005;21:141–5. Granata KP, Padua DA, Abel MF. Repeatability of surface EMG during gait in children. Gait Posture 2005;22:346–50. Grieve DW, Gear RJ. The relationships between length of stride, step frequency, time of swing and speed of walking for children and adults. Ergonomics 1966;9:379–99. Hamill J, van Emmerik RE, Heiderscheit BC, Li L. A dynamical systems approach to lower extremity running injuries. Clin Biomech (Bristol, Avon) 1999;14:297–308. Harbourne RT, Stergiou N. Movement variability and the use of nonlinear tools: principles to guide physical therapist practice. Phys Ther 2009;89:267–82. Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in communityliving older adults: a 1-year prospective study. Arch Phys Med Rehabil 2001;82:1050–6. Herman T, Giladi N, Gurevich T, Hausdorff JM. Gait instability and fractal dynamics of older adults with a ‘‘cautious’’ gait: why do certain older adults walk fearfully? Gait Posture 2005;21:178–85. Hermens HJ, Bruggen TA, Baten CT, Rutten WL, Boom HB. The median frequency of the surface EMG power spectrum in relation to motor unit firing and action potential properties. J Electromyogr Kinesiol 1992;2:15–25. Hermens HJ, Freriks B. European recommendations for surface electromyography (SENIAM). In: Development RRa, editor. Enschede, The Netherlands; 1999. Hershler C, Milner M. An optimality criterion for processing electromyographic (EMG) signals relating to human locomotion. IEEE Trans Biomed Eng 1978;25:413–20. Hillman SJ, Stansfield BW, Richardson AM, Robb JE. Development of temporal and distance parameters of gait in normal children. Gait Posture 2009;29: 81–5. Hof AL. Scaling gait to body size. Gait Posture 1996;4:222–3. Hof AL, Elzinga H, Grimmius W, Halbertsma JP. Speed dependence of averaged EMG profiles in walking. Gait Posture 2002;16:78–86. Kadaba MP, Wootten ME, Gainey J, Cochran GV. Repeatability of phasic muscle activity: performance of surface and intramuscular wire electrodes in gait analysis. J Orthop Res 1985;3:350–9. Lauer RT, Smith BT, Shewokis PA, McCarthy JJ, Tucker CA. Time–frequency changes in electromyographic signals after hamstring lengthening surgery in children with cerebral palsy. J Biomech 2007a;40:2738–43. Lauer RT, Stackhouse CA, Shewokis PA, Smith BT, Tucker CA, McCarthy J. A timefrequency based electromyographic analysis technique for use in cerebral palsy. Gait Posture 2007b;26:420–7. Miller F, Cardoso Dias R, Lipton GE, Albarracin JP, Dabney KW, Castagno P. The effect of rectus EMG patterns on the outcome of rectus femoris transfers. J Pediatr Orthop 1997;17:603–7. Moritani T, Muro M. Motor unit activity and surface electromyogram power spectrum during increasing force of contraction. Eur J Appl Physiol Occup Physiol 1987;56:260–5. Nakayama Y, Kudo K, Ohtsuki T. Variability and fluctuation in running gait cycle of trained runners and non-runners. Gait Posture 2010;31:331–5.
Okamoto T, Okamoto K, Andrew PD. Electromyographic developmental changes in one individual from newborn stepping to mature walking. Gait Posture 2003;17:18–27. Ounpuu S, Gage JR, Davis RB. Three-dimensional lower extremity joint kinetics in normal pediatric gait. J Pediatr Orthop 1991;11:341–9. Ricard MD, Ugrinowitsch C, Parcell AC, Hilton S, Rubley MD, Sawyer R, et al. Effects of rate of force development on EMG amplitude and frequency. Int J Sports Med 2005;26:66–70. Rose-Jacobs R. Development of gait at slow, free, and fast speeds in 3- and 5-yearold children. Phys Ther 1983;63:1251–9. Sanger TD. Controlling variability. J Mot Behav 2010;42:401–7. Schwartz MH, Rozumalski A, Trost JP. The effect of walking speed on the gait of typically developing children. J Biomech 2008;41:1639–50. Shiavi R, Bugle HJ, Limbird T. Electromyographic gait assessment, Part 1: Adult EMG profiles and walking speed. J Rehabil Res Dev 1987a;24:13–23. Shiavi R, Green N, McFadyen B, Frazer M, Chen J. Normative childhood EMG gait patterns. J Orthop Res 1987b;5:283–95. Sutherland DH, Olshen R, Cooper L, Woo SL. The development of mature gait. J Bone Joint Surg Am 1980;62:336–53. Vaughan CL, Langerak NG, O’Malley MJ. Neuromaturation of human locomotion revealed by non-dimensional scaling. Exp Brain Res 2003;153:123–7. Wakeling JM. Patterns of motor recruitment can be determined using surface EMG. J Electromyogr Kinesiol 2009;19:199–207. Winter DA, Yack HJ. EMG profiles during normal human walking: stride-to-stride and inter-subject variability. Electroencephalogr Clin Neurophysiol 1987;67:402–11. Yang JF, Winter DA. Surface EMG profiles during different walking cadences in humans. Electroencephalogr Clin Neurophysiol 1985;60:485–91.
Oren Tirosh is senior research officer at the Murdoch Childrens Research Institute in Melbourne, Australia and the Institute of Sport, Exercise and Active Living (ISEAL), Victoria University. His main research area is in clinical gait analysis of typical developing population and population with gait disorder including children with cerebral palsy and adults with stroke. Oren serves as the director of Motion3D, a research-driven company offering biomechanical analysis services to individuals, coaches, and clinicians to reduce injury rates, improve sports performance, and restore functionality.
Morgan Sangeux completed his PhD at the Université Technologique de Compiègne in France in 2006. He then moved to Australia in 2007 and he currently is the senior biomedical engineer at the Hugh Williamson Gait Analysis Laboratory, Melbourne. Morgan’s research interests are kinematics, kinetics modelling and musculo-skeletal modelling of walking.
Matthew Wong received his master degree in Engineering Science from Monash University, Australia in 2002, where he completed a study of lower limb surface EMG for range activities as part of his thesis. He also worked at the university’s Rehabilitation Technology Research Unit, involving in gait analysis and technology evaluation, before joining the Murdoch Childrens Research Institute in 2009. His interest includes innovation in medical and assistive technology.
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx Pam Thomason is the Gait Analysis Services Manager and Senior Physiotherapist at the Hugh Williamson Gait Analysis Laboratory, Royal Children’s Hospital Melbourne. She has worked in paediatric physiotherapy since 1985 in a variety of clinical roles in orthopaedics, rheumatology, neurology and rehabilitation. Pam has worked as a research physiotherapist since 1995 combining this with her clinical roles. She obtained her Masters of Physiotherapy by research from The University of Melbourne in 2004.
9
Kerr Graham is The University of Melbourne Professor of Orthopaedic Surgery, Director of the Hugh Williamson Gait Laboratory and a Consultant Orthopaedic Surgeon at The Royal Children’s Hospital in Melbourne. Professor Graham’s clinical and research interests are principally in the area of cerebral palsy, clinical gait analysis, clinical trials of spasticity management and gait improvement surgery for children with cerebral palsy. He serves as Associate Editor for Developmental Medicine and Child Neurology (DMCN) and is an Editorial Board Member for several journals including DMCN and the Journal of Pediatric Orthopaedics. He was awarded The King James IV Professorship of The Royal College of Surgeons in Edinburgh for 2012/13.
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
Contents lists available at SciVerse ScienceDirect
Journal of Electromyography and Kinesiology journal homepage: www.elsevier.com/locate/jelekin
Walking speed effects on the lower limb electromyographic variability of healthy children aged 7–16 years Oren Tirosh a,c,⇑, Morgan Sangeux a,b, Matthew Wong a, Pam Thomason b, H. Kerr Graham a,b a
Murdoch Childrens Research Institute, Royal Children’s Hospital, Parkville 3052, Victoria, Australia Hugh Williamson Gait Analysis Laboratory, Royal Children’s Hospital, Parkville 3052, Victoria, Australia c Institute of Sport, Exercise and Active Living (ISEAL), Victoria University, Footscray, Victoria, Australia b
a r t i c l e
i n f o
Article history: Received 6 December 2012 Received in revised form 13 March 2013 Accepted 8 June 2013 Available online xxxx Keywords: Gait Electromyography Gait variability Children Aging
a b s t r a c t The evaluation of surface electromyography (sEMG) is commonly performed in children with cerebral palsy (CP) and reliable interpretation necessitates knowledge of the variability in age-matched, typically developing (TD) children. Variance ratio was calculated for inter-trial sEMG linear envelope (LE) and the Instantaneous Mean Frequency (IMNF) variability in the lower limb muscle in TD children, in three different age groups during slow, comfortable speed, and fast walking. Significantly greater variability was found in the 7–9 group compared to the 13–16 years. Variability during both slow and fast walking was significantly greater compared to comfortable speed walking and was profound in the 7–9 year age group. Variability of the IMNF was significantly greater than LE in the Tibialis-Anterior, Biceps-Femoris (BF), Vastus-Lateralis (VL), and Rectus-Femoris (RF). Clinical implications are that children under 10 years are more variable than older children when walking either slower or faster than self-selected walking speed. This suggests that muscle activation patterns in gait mature at a later stage of childhood than do kinematic gait patterns. Greater precaution, therefore, is needed when comparing sEMG patterns of less than 10 years of age patient and TD children. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction Surface electromyography (sEMG) is used to record lower limb muscle activity to provide information about the timing of muscle activation. In clinical gait analysis, diagnosis and decision making are based on a diagnostic matrix which may include comparing a patient’s sEMG patterns to those of a typically developing population. One example is interpretation of the sEMG pattern to assess the outcome of Rectus Femoris transfers for the treatment of stiff knee gait in children with Cerebral palsy (CP) (Miller et al., 1997). The evaluation of sEMG is commonly performed in children with CP and reliable interpretation necessitates knowledge of the variability in age-matched, typically developing children. The linear envelope (LE) waveform of the sEMG signal is the most common processing technique to illustrate muscle activation patterns during the gait cycle. The LE is generated by smoothing the rectified signal with a low pass filter to describe the muscle’s time domain characteristics, as reflected in changes to the curve’s amplitude. New advanced signal processing techniques using time–frequency wavelet analysis of sEMG were recently (Lauer ⇑ Corresponding author. Address: Gait Laboratory & Orthopaedics Research Group, Murdoch Childrens Research Institute, Royal Children’s Hospital, Flemington Road, Parkville 3052, Victoria, Australia. Tel.: +61 3 93455354. E-mail address: [email protected] (O. Tirosh).
et al., 2007b) utilized to measure both signal amplitude and frequency. Time–frequency characteristics of muscle activation quantified by the Instantaneous Mean Frequency (IMNF) have been suggested to be more reflective of changes in gait kinematics (Lauer et al., 2007b) and have greater sensitivity to the muscle’s function following hamstring surgery in children with CP (Lauer et al., 2007a). This methodology can be useful in children with CP to determine sensitivity to spasticity, muscle weakness, and fatigue. To use this methodology as a diagnostic tool in gait analysis by comparing to typical developed gait sEMG template it is important to examine the inter-individual variability of ensemble averaged IMNFs. An important research question is, therefore, which signal processing method, LE or IMNF, has the most reduced interindividual variability and, as a consequence, is more clinically appropriate for the diagnosis and evaluation of gait disorders in children with CP. The variance ratio (VR) is most commonly employed to examine the repeatability of sEMG LE waveforms over a given number of identical gait cycles (Granata et al., 2005; Kadaba et al., 1985). As proposed by Hershler and Milner (1978), the VR quantifies waveform variability as the ratio of the mean square between repetitions to the total sum of squares. A VR of zero represents no variability while a VR of one represents maximum variability. The VR is independent of peak amplitude, providing a good measure of repeatability in the overall wave shapes. The VR is,
1050-6411/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jelekin.2013.06.002
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
2
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
furthermore, insensitive to mean sEMG amplitude and the degree of smoothing applied to the data (Gabel and Brand, 1994). While variability in lower limb sEMG in adult’s gait is well documented there has been little research with children. Adult LE patterns were shown to be repeatable between walking trials in the order of 0.21VR (Granata et al., 2005; Kadaba et al., 1985). The one study of variability in children’s’ LE waveforms is Granata et al. (2005), who found that children (6.5 ± 2.3 years) had a between trial mean VR of 0.46, approximately double that of adults (Granata et al., 2005). The natural gait cycle variability of the sEMG frequency components generated by the IMNF curve has not been previously investigated. Age and walking speed effects are important when interpreting sEMG waveforms. As reflected in kinematic and kinetic variables, walking has been suggested to be ‘‘mature’’ by 5–8 years (Chester et al., 2006; Ganley and Powers, 2005; Grieve and Gear, 1966; Hillman et al., 2009; Ounpuu et al., 1991; Rose-Jacobs, 1983; Vaughan et al., 2003). Gait maturation as indicated by muscle activity is less well documented and the few previous studies are inconclusive (Chang et al., 2007; Okamoto et al., 2003; Shiavi et al., 1987b; Sutherland et al., 1980). Sutherland et al. (1980) found that in children of 1–7 years there was reduced activation time in the Gluteus Maximus, Vastus Medialis, and Tibialis Anterior as age progressed. A later study comparing the sEMG patterns of children aged 4 and 7 years with a group 8–11 years, revealed significant changes in the intensity and phases of activity (Shiavi et al., 1987b) in the Rectus Femoris and the Hamstrings but not the Tibialis Anterior. Detrembleur et al. (1997) found that muscle activation onset and duration during comfortable walking in children 4–7 years was not different to that of a group aged 8–11 years. Chang et al. (2007) also noted that the timing and duration of sEMG activity is poorly correlated with age (groups between 3 and 6, 7 and 11, and 12 and 18 years). The authors concluded that inconsistent age effects were due to the high variability (standard deviation) in all muscle groups with respect to maximum peak timing and baseline activity. Walking speed has also been found to influence LE waveform amplitude (Detrembleur et al., 1997; Hof et al., 2002; Rose-Jacobs, 1983; Schwartz et al., 2008; Shiavi et al., 1987a). Schwartz et al. (2008), for example, reported amplification of peak values with increased walking speed. Walking speed was also shown to affect the variability of muscle LE patterns in children (Shiavi et al., 1987b). Using the coefficient of variation Shiavi et al. (1987b) found that LEs in all muscles tended to become more consistent as speed increased. The Peroneus Longus, Rectus Femoris, and Lateral Hamstring were highly variable at slower speeds but the lower leg muscles (Tibialis Anterior, Medial Gastrocnemius, and Soleus) were shown to be more consistent. The aim of this experiment was to investigate the effect of age and walking speed on between-trial variability in LE and IMNF waveforms.
(10–12 years), and Group III (13–16 years), see Table 1. All participant’s guardians signed a consent form approved by the institute Ethics Committee (HREC29108) and completed a screening questionnaire to confirm the absence of musculoskeletal, neurological or other medical conditions that might have suggested exclusion. All participants had three-dimensional instrumented gait analysis undertaken by a physiotherapist and a human movement scientist, both experienced in clinical gait analysis. Participants were requested to walk under three conditions, slow, comfortable, and fast walking speed. Testing began with the comfortable walking condition followed by either slow or fast walking conditions presented in randomised order. In the fast walking condition participants were instructed to walk as fast as possible while in the slow walking condition they were requested to walk slower than comfortable. Walking speed was not controlled from trial-to-trial within each condition as this potentially can cause modifications to the gait pattern (Bertram and Ruina, 2001; Schwartz et al., 2008). Five minutes rest was given between walking speed conditions. Reflective markers attached to the participant’s foot were sampled using Vicon MX system (Vicon, Oxford, UK) at 100 Hz to identify the gait cycle events, heel-contact and toe-off. Muscle activation data were sampled simultaneously via a ZeroWire EMG system (Aurion s.r.l.) using fixed 20 mm inter-electrode (silver/silver chloride) spacing (Myotronics Inc. kent WA), input impedance of 10 MOhms, a common mode rejection ratio of 90 dB, and signal to noise ratio >50 db. The sEMG raw signals were sampled at 1000 Hz and amplified with a gain of 1000 (Vicon Nexus software). Electromyographic activity of the lower left and right extremities was recorded from the Tibialis-Anterior (TA), Medial-Gastrocnemius (MG), Biceps-Femoris (BF), Vastus-Lateralis (VL), and Rectus-Femoris (RF). Following the SENIAM guidelines (Hermens and Freriks, 1999) electrodes were located on the midline of the muscle belly, with the detection surface oriented perpendicular to the long axis of the muscle fibres. Once the electrode position was marked shaving, gentle abrasion with sandpaper and cleansing with alcohol prepared the skin. 2.2. Processing the sEMG signals Using MatlabÒ software (The Mathworks Inc.) the sEMG were first bandpass filtered (20–500 Hz). To generate the LE the bandpass filtered sEMG signal was rectified and low passed filtered at 6 Hz using a zero-lag forth-order Butterworth filter. The IMNF waveform is generated by applying a continuous wavelet transform (CWT) to each sEMG signal using morlet ‘‘mother’’ function (Lauer et al., 2007b). The output of the CWT analysis is a threedimensional scalogram that is reduced to a time–frequency curve by calculating the mean frequency for each 0.1% gait cycle interval. All walking trials LE and IMNF data for a subject were time normalized to a percentage of stride duration.
2. Methods 2.3. Data analysis 2.1. Subjects and experimental design Thirty-six TD children without known gait pathology aged 11.6 ± 3.1 years participated in the experiment. They were assigned to one of three age groups; Group I (7–9 year), Group II
2.3.1. Participants characteristics and walking speed Means and standard deviation (SD) for participants’ age, weight, and height were compared using One-way Analysis of Variance (ANOVA). Walking speed (v) was rendered dimensionless by
Table 1 Means (SD) for participants’ age, weight, and height. Group
n
Gender (M/F)
Age range
Age (years)
Weight (kg)
Height (cm)
I II III
12 12 12
8/4 6/6 7/5
7–9 10–12 13–16
8.3 (0.9) 10.7 (0.8) 14.7 (1.4)
29.3 (6.1) 37.5 (6.5) 56.9 (14.5)
130.7 (7.5) 143.9 (8.3) 164.4 (10.1)
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
scaling to the participant’s leg length (Lleg) using a formula proposed by Hof (1996):
gLleg
2
where g is acceleration due to gravity (9.81 m/s ). Repeated measures ANOVAs were conducted to confirm that there were no dimensionless speed differences between absolute walking speed conditions and age-groups. An a significance level of 0.05 was used. 2.3.2. Variance ratio Between trial variability was quantified using the variance ratio (VR) method described by Hershler and Milner (1978):
Pm Pn
2
i¼1
j¼1 ðEij
Ei Þ =mðn 1Þ
i¼1
j¼1 ðEij
EÞ =ðmn 1Þ
VR ¼ P P m n
2
ð1Þ
where m is the number of temporal points (one point per % stride); n is the number of strides (one for each walking trial) over which VR is evaluated; Eij is the value of sEMG waveform j at time epoch i; Ei is the average of the EMG values at time epoch i over j strides; and E is the grand mean average of the EMG signal (E) determined by: m 1X Ei : m i¼1
3. Results 3.1. Subject characteristics
m
v ¼ pffiffiffiffiffiffiffiffiffiffi
E¼
3
ð2Þ
The VR score ranges from 0 to 1, where 0 indicates no variability and 1 denotes maximum variability. For each muscle the Analysis of variance (ANOVA) with Bonferroni test for Post Hoc analyses was used to examine the effect of age (Groups I, II, and III), walking speed (slow, comfortable, and fast), and processing method (LE and IMNF) on VR. To ensure that there is no differences between left and right leg sEMG, leg side was also included as the fourth category having a 4-way (3 3 2 2) ANOVA design with 4 categorical predictors. All statistical analyses were performed using STATISTICA (StatSoft, Inc.). An a significance level of 0.0125 was used to adjust for the Bonferroni test (alpha level of 0.05/ 4 categories).
Means and SD for participants’ age, weight, and height are presented in Table 1. Significant differences in age, weight, and height were noted between the three age groups (F2,33 = 46.00, F2,33 = 105.53, F2,33 = 26.58, respectively, p < .001).
3.2. Walking speed Walking speed across the three conditions was successfully modulated (Fig. 1) with both decreased and increased speed reflected in a condition effect (F2,66 = 400.48, p < .001). Compared to comfortable walking the slow condition was 35% slower and the fast condition was 42% faster. The dimensionless speed mean (v⁄ = 0.49) and SD (0.055) at comfortable speed was similar to previous studies, such as Schwartz et al. (2008) who reported a mean freely-chosen dimensionless speed of (v⁄ = 0.43) and SD (0.068). A speed-age interaction was also found (F4,66 = 5.12, p < .01) with the children 7–9 years walking with a higher dimensionless speed in the fast condition compared to the two older age groups.
3.3. Surface electromyography No significant differences in any muscle activation measures were found between left and right legs. Table 2 presents the VR of each muscle for the three age groups across walking speed and processing method. The ANOVA results for VR are included in Table 3. Fig. 2 illustrates left leg LE waveform variability for one representative participant from each age group across the 3 walking speeds. Fig. 3 illustrates left leg IMNF waveform variability for one representative participant from each age group across the 3 walking speeds. The LE waveforms were found to be strongly affected by speed with greater amplitudes at increased speed but the IMNF waveforms did not show speed effects on the frequency component. Age effects on the shape of the LE waveform were most evident in the thigh muscles. Group I showed little RF activity in the initial stance phase compared to group III. Similarly, group I activated the BF less at initial stance compared to group II and group III.
Fig. 1. Dimensionless walking speed across the three walking conditions for Group I (diamond dot line), Group II (circle solid line), and Group III (square dash line). Decreased and increased speed reflected in significant condition effect (F2,66 = 400.48, p < .001). Vertical bars denote 0.95 confidence intervals. Group I are children aged between 7 and 9 years, Group II are children aged between 10 and 12 years, and Group III are children aged between 13 and 16 years of age.
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
4
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
Table 2 Means (standard deviation) of the variance ratio calculated for each muscle for the three age groups (young – I, middle – II, and older – III), walking speed (slow, comfortable, and fast), and sEMG processing method (LE and IMNF). In the Group column, variance ratio for each age group represents the group means of collapsed data from all speeds and processing method (LE and IMNF). In the Speed column, variance ratio for each speed represents the speed means of collapsed data from all age groups and processing method (LE and IMNF). In the Processing method column, variance ratio for each processing method represents the processing method means of collapsed data from all age groups and speed. Group
TA MG RF VL BF a b c
Speed
I
II
0.40a (0.17) 0.26a (0.14) 0.47 (0.21) 0.36 (0.19) 0.47 (0.19)
0.31 0.18 0.53 0.34 0.42
(0.14) (0.11) (0.22) (0.19) (0.18)
Processing method
III
Slow
Normal
Fast
ENV
0.27 (0.12) 0.18 (0.14) 0.47 (0.23) 0.38 (0.24) 0.38a (0.18)
0.37 (0.16) 0.23 (0.16) 0.56b (0.22) 0.44b (0.22) 0.49 (0.18)
0.25b 0.18b 0.41b 0.30b 0.36b
0.35 (0.17) 0.21 (0.13) 0.50b (0.22) 0.34b (0.18) 0.41 (0.19)
0.31 0.22 0.40 0.31 0.40
(0.10) (0.12) (0.21) (0.18) (0.15)
IMNF (0.14) (0.13) (0.18) (0.17) (0.17)
0.35c (0.17) 0.20 (0.14) 0.58c (0.22) 0.41c (0.22) 0.45c (0.19)
ANOVA with Post Hoc Bonferroni test between age groups (I, II, and III) at each muscle p < 0.0125. ANOVA with Post Hoc Bonferroni test between walking speeds (slow, normal, fast) at each muscle p < 0.0125. ANOVA with Post Hoc Bonferroni test between processing methods (ENV and IMNF) at each muscle p < 0.0125.
Table 3 ANOVA results for the Variance Ratio analysis of each muscle for the three age groups (Group) across walking speed (Speed), processing method (Analysis), and leg side (Side). Main effects and interaction are presented. Muscle
F
p
TA
Intercept Group Speed Analysis Side GroupSpeed GroupAnalysis SpeedAnalysis Error
Type III Sum of Squares 47.38977 1.26087 1.20792 0.16340 0.00435 0.43572 0.02884 0.27751 7.25114
1 2 2 1 1 4 2 2 396
47.38977 0.63044 0.60396 0.16340 0.00435 0.10893 0.01442 0.13876 0.01831
2588.056 34.429 32.983 8.924 0.238 5.949 0.787 7.578
0.000000 0.000000 0.000000 0.002990 0.626210 0.000117 0.455750 0.000589
MG
Intercept Group Speed Analysis Side GroupSpeed GroupAnalysis SpeedAnalysis Error
19.28967 0.54067 0.16619 0.03288 0.00176 0.22819 0.00102 0.11551 7.23354
1 2 2 1 1 4 2 2 396
19.28967 0.27034 0.08310 0.03288 0.00176 0.05705 0.00051 0.05776 0.01827
1056.012 14.800 4.549 1.800 0.096 3.123 0.028 3.162
0.000000 0.000001 0.011135 0.180478 0.756265 0.015059 0.972428 0.043419
RF
Intercept Group Speed Analysis Side GroupSpeed GroupAnalysis SpeedAnalysis Error
2877.706 3.873 20.742 96.588 0.001 2.987 2.932 6.128
0.000000 0.021596 0.000000 0.000000 0.978992 0.018893 0.054441 0.002393
VL
Intercept Group Speed Analysis Side GroupSpeed GroupAnalysis SpeedAnalysis Error
57.16946 0.06874 1.53070 1.08971 0.04570 0.83232 0.19667 0.21274 14.24935
1 2 2 1 1 4 2 2 396
57.16946 0.03437 0.76535 1.08971 0.04570 0.20808 0.09833 0.10637 0.03598
1588.781 0.955 21.270 30.284 1.270 5.783 2.733 2.956
0.000000 0.385628 0.000000 0.000000 0.260430 0.000157 0.066268 0.053167
BF
Intercept Group Speed Analysis Side GroupSpeed GroupAnalysis SpeedAnalysis Error
78.43041 0.64742 1.20128 0.26254 0.00123 0.30584 0.00476 0.50052 11.72281
1 2 2 1 1 4 2 2 396
78.43041 0.32371 0.60064 0.26254 0.00123 0.07646 0.00238 0.25026 0.02960
2649.402 10.935 20.290 8.869 0.042 2.583 0.080 8.454
0.000000 0.000024 0.000000 0.003079 0.838412 0.036811 0.922833 0.000254
105.7571 0.2846 1.5245 3.5497 0.0000 0.4391 0.2155 0.4504 14.5532
Degrees of freedom
1 2 2 1 1 4 2 2 396
As shown in Fig. 4 the variability in slow and fast walking was greater than comfortable walking and more noticeable in the youngest participant. There was a trend of lower variability at the distal muscles compared to the proximal muscles, RF, VL, and BF. For example, at all walking speeds MG had approximately 50% less variability than RF, VL, and BF (Fig. 4).
Mean Square
105.7571 0.1423 0.7623 3.5497 0.0000 0.1098 0.1078 0.2252 0.0368
3.4. Effect of processing method on VR Both LE and IMNF analyses showed greater inter trial variability of the proximal muscles compared to the smaller distal muscles. These differences were 1.5-fold and 2-fold for RF, VL, and BF compared to the TA and MG muscles, respectively. All muscles except
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
5
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
One representative subject in In GROUP I
One representative subject in GROUP II
One representative subject in GROUP III
TA
MG
RF
VL
BF
Gait Cycle (%)
Gait Cycle (%)
Gait Cycle (%)
Fig. 2. Ensemble average of the 6 trials at each speed; slow (dotted), comfortable (solid bold), and fast (solid) walking from one representative subject in each group. Group I are children aged between 7 and 9 years, Group II are children aged between 10 and 12 years, and Group III are children aged between 13 and 16 years of age. All walking trials for a subject were time (100% of one gait cycle duration) and amplitude normalized. Amplitude normalization was performed using the ‘dynamic maximum’ method described by Den Otter et al. Gait & Posture 2004 19, 270–278. For each muscle, signals were amplitude normalized relative to the peak amplitude of all walking speed condition (slow, comfortable and fast), resulting with ranges from 0 to 1, with 1 the dynamic maximum. Normalization was performed to allow illustration of sEMG data across subjects. It is recognized that this approach emphasizes activation patterns at the expense of signal magnitude.
the MG showed significant (F1,2070 = 98.976, p < .01) greater VR for IMNF analysis compared to LE (Table 2).
3.5. Effect of age on VR Significant age main effect was found (F2,2070 = 21.104, p < .01) with post hoc analysis revealing significant greater variability (30%) for group I compared to the older groups II and III only for MG and TA muscles. Significant lower variability was found in group III compared to group I only for BF muscle. No significant differences in VR measurements were found between group II and group III for any of the tested muscles.
3.6. Effect of walking speed on VR Table 2 shows that at comfortable walking speed VR was significantly lower than at slow and fast speeds (0.30 ± 0.18, 0.42 ± 0.22, and 0.37 ± 0.20, respectively, F2,2070 = 90.730, p < .01). In addition, slow walking had significant greater VR compared to fast walking.
3.7. Walking speed/age interaction A significant walking speed by age interaction (F4,2070 = 12.572, p < .01) indicated that significant VR age and walking speed differences were due to greater VR in the 7–9 years group during slow and fast walking (Fig. 4). The average VR for all muscles at comfortable walking in group I was 0.29 ± 0.14, while at slow and fast walking it was 0.46 ± 0.19 and 0.43 ± 0.20, respectively. Group II and group III also showed significant greater VR at slow compared to comfortable walking (0.41 ± 0.23 and 0.31 ± 0.18, respectively for group II, and 0.39 ± 0.23 and 0.31 ± 0.20, respectively for group III, F4,2070 = 12.572, p < .01), but this was less clear during fast walking, specifically in group III where VR of MG and VL muscles was smaller compared to comfortable walking (0.15 ± 0.08 and 0.18 ± 0.13, respectively for MG, and 0.27 ± 0.15 and 0.37 ± 0.23, respectively for VL). 4. Discussion The aim of this study was to extend earlier findings on sEMG variability in children by considering muscle activity patterns in
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
6
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
One representative subject in In GROUP I
One representative subject in GROUP II
One representative subject in GROUP III
TA (Hz)
MG (Hz)
RF (Hz)
VL (Hz)
BF (Hz)
Gait Cycle (%)
Gait Cycle (%)
Gait Cycle (%)
Fig. 3. IMNF (Hz) of muscle activity at slow, comfortable and fast walking (dotted, solid bold, and solid lines, respectively) presented for one representative subject in each group. Group I are children aged between 7 and 9 years, Group II are children aged between 10 and 12 years, and Group III are children aged between 13 and 16 years of age. All walking trials for a subject were time normalized (100% of one gait cycle duration).
a range of children age when walking at a range of speeds. Mature gait as indicated by kinematic and kinetic analysis has been reported to emerge between 5 and 8 years (Beck et al., 1981; Ounpuu et al., 1991; Sutherland et al., 1980). Analysis of muscle activation patterns in gait suggests, however, that fully developed walking patterns are established later. Children with a mean age of 6.5 ± 2.3 years, for example, show twice the variability of adults, VR = 0.47 and 0.21, respectively (Granata et al., 2005). While in our study the age of group I was similar to that of Granata et al. (2005), lower variability values were noted (0.39 compare to 0.47). Our comparison of Group I (7–9 years) with the older groups II and III (9–16 years) showed significantly greater variability in TA and MG muscles. Similar pattern of differences between young and older children were reported earlier (Shiavi et al., 1987b) when the authors observed inconsistent MG activity in children 4–7 years compared to a cohort 8–11 years. It is, therefore, suggested that activation patterns of distal muscles when walking mature relatively late, after approximately 10 years. The control of stable walking involves a combination of distal ankle and proximal hip muscle activation strategies. Proximal muscles are reported to have greater stride to stride variability than the distal muscles, possibly due to their dual role of supporting and correcting posture of the upper body mass associated with the head, trunk and hands (Winter and Yack, 1987). Our study found a similar trend, with greater variability of the proximal
muscles (RF, VL, and BF) compared to the distal muscles (TA and MG) across all ages, independent of walking speed. The effect of walking speed on muscle activation has been previously documented. Using amplitude and time domain measures from LE curves it was shown that faster walking increased amplitude but the shape remained essentially unchanged (Detrembleur et al., 1997; Schwartz et al., 2008; Yang and Winter, 1985). In TD children changes from slow to fast walking reflect decreased variability (Shiavi et al., 1987b). Our study partially supports Shiavi et al. (1987b) findings showing that sEMG variability in both slow and fast walking is significantly greater than comfortable walking. For MG and VL, however, sEMG variability was found to be less regular in fast walking. Our results further indicate that greater variability in slow and fast walking is primarily associated with children under 10 years. Greater variability during slow walking may suggest that the CNS is modulating the movement pattern to accommodate changes in walking speed (Hamill et al., 1999; Harbourne and Stergiou, 2009). Slower gait may be more challenging with respect to neuromuscular control and is more pronounced in younger children. Greater variability in muscular control during walking faster or slower than preferred has been linked to uncertainty (Sanger, 2010), needed practice (Nakayama et al., 2010), and/or gait instability (Hausdorff et al., 2001; Herman et al., 2005). It is possible that the greater muscular variability found in the youngest group of children at non-preferred speeds could be
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
7
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
MG
Group I
TA
Group II
Group III
Group I
RF
Group I
Group II
Group II
Group III
VL
Group III
Group I
Group II
Group III
BF
Group I
Group II
Group III
Fig. 4. Means and 95% confidence intervals (vertical bars) of the calculated variance ratio of each muscle for the three age groups (Group I = 7–9 years, Group II = 10–12 years, and Group III = 13–16 years), and walking speed (slow-diamond, comfortable-cross, and fast-triangle).
due to these factors. This suggests that children between 7 and 9 years demonstrate maturity in muscular control at comfortable speed walking but maturation to accommodate faster or slower walking is only achieved later, at approximately 10 years of age. The final aim was to compare variability outcomes generated by two processing methods, LE and IMNF. The LE is the most common processing method in clinical research; it represents sEMG signal amplitude over time to characterize the recruitment of motor units and changes in firing rate. Recently, time–frequency characteristics of muscle activation quantified by Instantaneous Mean Frequency have been suggested to be more reflective of the resultant changes in gait kinematics (Lauer et al., 2007a). Spectral content of the sEMG can be related to recruitment of additional motor units that include fast twitch muscle fibers to generate increased force at higher mean firing frequency, and synchronizing the firing rate of the motor units currently in use reducing the frequency spectrum of the sEMG signal (Ricard et al., 2005; Wakeling, 2009). This study found that the variability of the IMNF compared to LE tended to be greater, with significant differences for TA, RF, VL and BF, but more profound differences in the knee extensors RF and VL. The increase in sEMG amplitude
might represent motor unit recruitment and/or motor unit firing frequency modulation, whereas the increase in mean frequency of the power spectrum might represent the additional recruitment of superficial high threshold motor units (Moritani and Muro, 1987). The variability in median frequency was also suggested to cause variation in mean motor unit action potential peak-peak time and synchronization (Hermens et al., 1992). It is possible that the variation suggested by Hermens et al. (1992) is responsible for the greater frequency component variability, indicated by IMNF analysis, found in this study. 5. Conclusion The variability of muscle activation in children between 7 and 16 years of age increases when walking at non-preferred speed. Muscle activation patterns in children under 10 years are more variable than for older children only when walking either slower or faster than preferred, suggesting that muscle activation patterns in gait mature at a later stage of childhood. Frequency waveforms of muscle activation have greater variability than amplitude LE waveforms.
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
8
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx
Conflict of Interest All authors declare that they have no proprietary, financial, professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled: Walking Speed Effects on the Lower Limb Electromyographic Variability of Healthy Children Aged Seven to Sixteen Years Acknowledgement This study was supported in part by The Hugh Williamson Foundation. References Beck RJ, Andriacchi TP, Kuo KN, Fermier RW, Galante JO. Changes in the gait patterns of growing children. J Bone Joint Surg Am 1981;63:1452–7. Bertram JE, Ruina A. Multiple walking speed–frequency relations are predicted by constrained optimization. J Theor Biol 2001;209:445–53. Chang WN, Lipton JS, Tsirikos AI, Miller F. Kinesiological surface electromyography in normal children: range of normal activity and pattern analysis. J Electromyogr Kinesiol 2007;17:437–45. Chester VL, Tingley M, Biden EN. A comparison of kinetic gait parameters for 3–13 year olds. Clin Biomech (Bristol Avon) 2006;21:726–32. Detrembleur C, Willems P, Plaghki L. Does walking speed influence the time pattern of muscle activation in normal children? Dev Med Child Neurol 1997;39:803–7. Gabel RH, Brand RA. The effects of signal conditioning on the statistical analyses of gait EMG. Electroencephalogr Clin Neurophysiol 1994;93:188–201. Ganley KJ, Powers CM. Gait kinematics and kinetics of 7-year-old children: a comparison to adults using age-specific anthropometric data. Gait Posture 2005;21:141–5. Granata KP, Padua DA, Abel MF. Repeatability of surface EMG during gait in children. Gait Posture 2005;22:346–50. Grieve DW, Gear RJ. The relationships between length of stride, step frequency, time of swing and speed of walking for children and adults. Ergonomics 1966;9:379–99. Hamill J, van Emmerik RE, Heiderscheit BC, Li L. A dynamical systems approach to lower extremity running injuries. Clin Biomech (Bristol, Avon) 1999;14:297–308. Harbourne RT, Stergiou N. Movement variability and the use of nonlinear tools: principles to guide physical therapist practice. Phys Ther 2009;89:267–82. Hausdorff JM, Rios DA, Edelberg HK. Gait variability and fall risk in communityliving older adults: a 1-year prospective study. Arch Phys Med Rehabil 2001;82:1050–6. Herman T, Giladi N, Gurevich T, Hausdorff JM. Gait instability and fractal dynamics of older adults with a ‘‘cautious’’ gait: why do certain older adults walk fearfully? Gait Posture 2005;21:178–85. Hermens HJ, Bruggen TA, Baten CT, Rutten WL, Boom HB. The median frequency of the surface EMG power spectrum in relation to motor unit firing and action potential properties. J Electromyogr Kinesiol 1992;2:15–25. Hermens HJ, Freriks B. European recommendations for surface electromyography (SENIAM). In: Development RRa, editor. Enschede, The Netherlands; 1999. Hershler C, Milner M. An optimality criterion for processing electromyographic (EMG) signals relating to human locomotion. IEEE Trans Biomed Eng 1978;25:413–20. Hillman SJ, Stansfield BW, Richardson AM, Robb JE. Development of temporal and distance parameters of gait in normal children. Gait Posture 2009;29: 81–5. Hof AL. Scaling gait to body size. Gait Posture 1996;4:222–3. Hof AL, Elzinga H, Grimmius W, Halbertsma JP. Speed dependence of averaged EMG profiles in walking. Gait Posture 2002;16:78–86. Kadaba MP, Wootten ME, Gainey J, Cochran GV. Repeatability of phasic muscle activity: performance of surface and intramuscular wire electrodes in gait analysis. J Orthop Res 1985;3:350–9. Lauer RT, Smith BT, Shewokis PA, McCarthy JJ, Tucker CA. Time–frequency changes in electromyographic signals after hamstring lengthening surgery in children with cerebral palsy. J Biomech 2007a;40:2738–43. Lauer RT, Stackhouse CA, Shewokis PA, Smith BT, Tucker CA, McCarthy J. A timefrequency based electromyographic analysis technique for use in cerebral palsy. Gait Posture 2007b;26:420–7. Miller F, Cardoso Dias R, Lipton GE, Albarracin JP, Dabney KW, Castagno P. The effect of rectus EMG patterns on the outcome of rectus femoris transfers. J Pediatr Orthop 1997;17:603–7. Moritani T, Muro M. Motor unit activity and surface electromyogram power spectrum during increasing force of contraction. Eur J Appl Physiol Occup Physiol 1987;56:260–5. Nakayama Y, Kudo K, Ohtsuki T. Variability and fluctuation in running gait cycle of trained runners and non-runners. Gait Posture 2010;31:331–5.
Okamoto T, Okamoto K, Andrew PD. Electromyographic developmental changes in one individual from newborn stepping to mature walking. Gait Posture 2003;17:18–27. Ounpuu S, Gage JR, Davis RB. Three-dimensional lower extremity joint kinetics in normal pediatric gait. J Pediatr Orthop 1991;11:341–9. Ricard MD, Ugrinowitsch C, Parcell AC, Hilton S, Rubley MD, Sawyer R, et al. Effects of rate of force development on EMG amplitude and frequency. Int J Sports Med 2005;26:66–70. Rose-Jacobs R. Development of gait at slow, free, and fast speeds in 3- and 5-yearold children. Phys Ther 1983;63:1251–9. Sanger TD. Controlling variability. J Mot Behav 2010;42:401–7. Schwartz MH, Rozumalski A, Trost JP. The effect of walking speed on the gait of typically developing children. J Biomech 2008;41:1639–50. Shiavi R, Bugle HJ, Limbird T. Electromyographic gait assessment, Part 1: Adult EMG profiles and walking speed. J Rehabil Res Dev 1987a;24:13–23. Shiavi R, Green N, McFadyen B, Frazer M, Chen J. Normative childhood EMG gait patterns. J Orthop Res 1987b;5:283–95. Sutherland DH, Olshen R, Cooper L, Woo SL. The development of mature gait. J Bone Joint Surg Am 1980;62:336–53. Vaughan CL, Langerak NG, O’Malley MJ. Neuromaturation of human locomotion revealed by non-dimensional scaling. Exp Brain Res 2003;153:123–7. Wakeling JM. Patterns of motor recruitment can be determined using surface EMG. J Electromyogr Kinesiol 2009;19:199–207. Winter DA, Yack HJ. EMG profiles during normal human walking: stride-to-stride and inter-subject variability. Electroencephalogr Clin Neurophysiol 1987;67:402–11. Yang JF, Winter DA. Surface EMG profiles during different walking cadences in humans. Electroencephalogr Clin Neurophysiol 1985;60:485–91.
Oren Tirosh is senior research officer at the Murdoch Childrens Research Institute in Melbourne, Australia and the Institute of Sport, Exercise and Active Living (ISEAL), Victoria University. His main research area is in clinical gait analysis of typical developing population and population with gait disorder including children with cerebral palsy and adults with stroke. Oren serves as the director of Motion3D, a research-driven company offering biomechanical analysis services to individuals, coaches, and clinicians to reduce injury rates, improve sports performance, and restore functionality.
Morgan Sangeux completed his PhD at the Université Technologique de Compiègne in France in 2006. He then moved to Australia in 2007 and he currently is the senior biomedical engineer at the Hugh Williamson Gait Analysis Laboratory, Melbourne. Morgan’s research interests are kinematics, kinetics modelling and musculo-skeletal modelling of walking.
Matthew Wong received his master degree in Engineering Science from Monash University, Australia in 2002, where he completed a study of lower limb surface EMG for range activities as part of his thesis. He also worked at the university’s Rehabilitation Technology Research Unit, involving in gait analysis and technology evaluation, before joining the Murdoch Childrens Research Institute in 2009. His interest includes innovation in medical and assistive technology.
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002
O. Tirosh et al. / Journal of Electromyography and Kinesiology xxx (2013) xxx–xxx Pam Thomason is the Gait Analysis Services Manager and Senior Physiotherapist at the Hugh Williamson Gait Analysis Laboratory, Royal Children’s Hospital Melbourne. She has worked in paediatric physiotherapy since 1985 in a variety of clinical roles in orthopaedics, rheumatology, neurology and rehabilitation. Pam has worked as a research physiotherapist since 1995 combining this with her clinical roles. She obtained her Masters of Physiotherapy by research from The University of Melbourne in 2004.
9
Kerr Graham is The University of Melbourne Professor of Orthopaedic Surgery, Director of the Hugh Williamson Gait Laboratory and a Consultant Orthopaedic Surgeon at The Royal Children’s Hospital in Melbourne. Professor Graham’s clinical and research interests are principally in the area of cerebral palsy, clinical gait analysis, clinical trials of spasticity management and gait improvement surgery for children with cerebral palsy. He serves as Associate Editor for Developmental Medicine and Child Neurology (DMCN) and is an Editorial Board Member for several journals including DMCN and the Journal of Pediatric Orthopaedics. He was awarded The King James IV Professorship of The Royal College of Surgeons in Edinburgh for 2012/13.
Please cite this article in press as: Tirosh O et al. Walking speed effects on the lower limb electromyographic variability of healthy children aged 7– 16 years. J Electromyogr Kinesiol (2013), http://dx.doi.org/10.1016/j.jelekin.2013.06.002