Effect of electrode location on EMG signal envelope in leg muscles during gait

Journal of Electromyography and Kinesiology 17 (2007) 515–526 www.elsevier.com/locate/jelekin

Effect of electrode location on EMG signal envelope in leg muscles during gait I. Campanini a, A. Merlo a, P. Degola a, R. Merletti b, G. Vezzosi a, D. Farina

c,*

a

c

LAM Laboratorio Analisi Movimento (Dip. Riabilitazione), AUSL di Reggio Emilia, Correggio, Italy b LISiN Laboratorio di Ingegneria del Sistema Neuromuscolare, Politecnico di Torino, Torino, Italy Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7 D-3, DK-9220 Aalborg, Denmark Received 5 January 2006; received in revised form 16 May 2006; accepted 1 June 2006

Abstract The aim of the study was to assess the variability of EMG signal envelope with electrode location during gait. Surface EMG signals were recorded from 10 healthy subjects from the tibialis anterior (TA), peroneus longus (PL), gastrocnemius medialis (GM), gastrocnemius lateralis (GL), and soleus (SO) muscles. From TA, PL, GL and GM, signals were acquired using a two-dimensional grid of 4 · 3 electrodes (10 · 15 mm in size, as used in most gait laboratories) with 20-mm interelectrode distance in both directions. A similar grid of 3 · 3 electrodes was used for SO. EMG envelope was characterized by its peak value, area after normalization by the peak value, and time instant corresponding to the maximum. The maximum relative change in peak value with electrode location, expressed as a percentage of the peak value in the central location, was (mean ± SD) 31 ± 18% for TA, 29 ± 13% for PL, 25 ± 15% for GL, 14 ± 8% for GM, and 26 ± 14% for SO. The maximum relative change in area was 29 ± 13% for TA, 73 ± 40% for PL, 31 ± 23% for GL, 35 ± 20% for GM, 20 ± 13% for SO, and in the position of maximum, computed as distance from the maximum position in the central channel, it was 5 ± 10% of the gait cycle for TA, 26 ± 16% for PL, 3 ± 2% for GL, 3 ± 1% for GM, 3 ± 3% for SO. A crosstalk index, defined on the basis of the expected intervals of muscle activation for healthy subjects, indicated that estimated crosstalk was present between TA and PL, in an amount which depended on electrode location. It was concluded that the estimate of muscle activation intensity during gait from surface EMG is variable with location of the electrodes while timing of muscle activity is more robust to electrode displacement and can be reliably extracted in those cases in which crosstalk is limited. These results are valid for healthy subjects, where the level of muscular activity during gait is much lower than maximum.  2006 Elsevier Ltd. All rights reserved. Keywords: Surface electromyography; Gait; Electrode placement; Crosstalk

1. Introduction Surface electromyography (EMG) is an important tool for clinical gait analysis since it provides information on the relative contribution of the superficial muscles during movement (Basmajian and DeLuca, 1985; Esquenazi and Mayer, 2004). In particular, the assessment of the timing of muscle activation from the surface EMG in neurological patients (Blanc, 1996, 1997; Gage, 2004; Lamontagne et al., *

Corresponding author. Tel.: +45 96358821; fax: +45 98154008. E-mail address: [email protected] (D. Farina).

1050-6411/$ - see front matter  2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2006.06.001

2000; Perry, 1992; Richards and Olney, 1996a) allows, in combination with kinematic and kinetic analysis, the selection of specific therapeutic or surgical treatments (Esquenazi and Mayer, 2004; Mayer, 1996; Richards and Olney, 1996b). However, surface EMG is affected by many methodological factors, such as electrode location, which may vary among experimental sessions (Farina et al., 2004). Since in gait analysis the information extracted from the surface EMG is usually limited to the intervals of muscle activation and shape of the envelope, it is often assumed that these factors play a minor role in the reliability of the results.

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The importance of electrode location in surface EMG recordings has been emphasised in many previous studies (e.g., Blanc, 1997; Blumenstein and Basmajian, 1980; Jensen et al., 1993; Roy et al., 1986). Electrode placement affects the characteristics of the recorded surface EMG due to the dependency of source characteristics with position (e.g., generation of the intracellular action potential vs. propagation) and inhomogeneity of the volume conductor (tissues separating the sources from the recording electrodes). Recommendations for electrode location in a number of human muscles have been recently provided by the European project Surface Electromyography for Non-Invasive Muscle Assessment (SENIAM) (Hermens et al., 1999). However, in all previous studies, the effect of electrode location has been investigated in static contractions. During movement, the muscle shifts with respect to the recording electrodes due to changes in joint angle (Farina et al., 2001). In addition, many muscles are active in different time intervals with potential crosstalk among muscles (Conforto et al., 1999; De Luca and Merletti, 1988; Koh and Grabiner, 1993; Koh and Grabiner, 1992; Mayer, 1996; Morrenhof and Abbink, 1985; Solomonow et al., 1994). When the muscle activation is submaximal, such as during gait of healthy subjects, there may also be non-uniform spatial muscle activation (Holtermann et al., 2005). These factors may affect the estimation of the timing of muscle activation from the surface EMG and the EMG envelope that constitute the most commonly used information from EMG recordings during gait (Perry, 1992). This study aims at analysing EMG envelope features as estimated from multiple electrode locations over the muscle during gait in healthy subjects. We focused on a set of muscles of the leg that are analysed with surface EMG in clinical gait analysis. The type of electrodes, detection configuration, and interelectrode distance applied were those most commonly used in clinical gait analysis (Hermens et al., 1999). It was hypothesised that different electrode locations on the same muscle may lead

to different characteristics of EMG activity during movement. 2. Materials and methods 2.1. Subjects Ten healthy subjects participated in the study (Table 1). The study was in accordance with the Declaration of Helsinki, approved by the local ethics committee, and written informed consent was obtained from all participants prior to inclusion. 2.2. Surface EMG recordings Surface EMG signals were detected from the tibialis anterior (TA), peroneus longus (PL), gastrocnemius lateralis (GL), gastrocnemius medialis (GM) and soleus (SO) muscles of the right leg (Fig. 1). From TA, PL, GM and GL muscles, signals were acquired using a grid of 4 · 3 pre-gelled electrodes (Spes Medica, model DENIS01520, size 15 · 20 mm, interelectrode distance 20 mm). For SO, a 3 · 3 electrode grid was used, since the available detection surface was smaller than in the other muscles (Fig. 1). EMG signals were acquired in single differential configuration, in the direction of the muscle fibers. Nine bipolar signals were thus obtained from TA, PL, GM, and GL and six from SO (Fig. 2). Signals were amplified by a multi-channel surface EMG amplifier (EMG 16, LISiN – Ottino Bioengineering, Rivarolo-TO, Italy; CMRR >96 dB, noise level referenced to the input <1 lVRMS), band-pass filtered (3 dB bandwidth, 10–500 Hz), sampled at 2048 samples/s, and converted to digital data by a 12-bit A/D converter board (National Instruments). The basographic signal was acquired by an insole (BTS, Milan, Italy) placed under the right foot. Insole data allowed the detection of the heel strike and toe off instants. For each muscle, the center of the electrode grid was placed in the location suggested by Blumenstein and Bas-

Table 1 Subject sample investigated in the study Subject

Age

Height (cm)

Weight (kg)

Gender

Shank length (cm)

Shank circumference at 1/4 proximal (cm)

Shank circumference at 1/3 distal (cm)

CM CS FC FM FMA GB LV MB MF PD

22 22 26 28 21 38 28 35 48 25

168 173 170 167 170 168 164 162 168 170

54 65 72 60 60 68 60 56 67 98

F M M F F M F F M M

35.0 35.5 36.5 34.5 36.0 37.0 36.0 32.5 36.0 36.0

34.0 34.5 41.0 34.0 35.0 39.0 34.0 34.0 38.5 43.0

22.0 23.5 29.0 25.0 25.0 26.0 25.0 22.0 22.5 27.5

Shank length is computed from the tip of the head of the fibula to the lateral malleolus.

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Fig. 1. Electrode location in the five muscles.

Fig. 2. Schematic representation of the 9 bipolar signals obtained with a 4 · 3 electrode grid from TA, PL, GM and GL muscles (A) and of the 6 signals obtained with a 3 · 3 electrode grid from SO muscle.

majian (1980). With respect to the length of the leg, measured from the tip of the head of the fibula to the lateral malleolus, the suggested electrode positions are distributed near the first distal quarter for TA, PL and GM, and at one third distal for SO. The circumference of the leg at these levels was measured for each subject (Table 1). Before electrode placement, the skin was shaved, when necessary, and mildly abraded with abrasive paste (Meditec-Every, Parma, Italy).

2.3. General procedures The five muscles were investigated in separate tests within the same experimental session. Each subject performed a walk at self-selected speed along a 12-m linear path, three times for each muscle, with 1-min rests between trials. After the three repetitions, the electrode-grid was removed and a new grid was placed on the next muscle (muscles were assessed in random order). A short test

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recording was acquired before each task to select the best gain of the EMG amplifier. 2.4. Signal analysis The two first and two last strides were excluded from the analysis due to walking initiation and termination. The time duration and cadence of the stride cycle were computed from the basographic data for each trial in order to verify if the self-selected cadence of the subject sample fell within the normal range since muscle activation is influenced by gait velocity (Hof et al., 2003; Perry, 1992). The coefficient of variation (CV), calculated as the standard deviation normalized by mean value and expressed in percentage, over all strides in the three trials was computed to test the repeatability of these two parameters. A wavelet-based filter (Conforto et al., 1999) was used to remove low-frequency movement artefacts from the surface EMG. Visual analysis was performed to check the quality of the EMG signals after the application of the wavelet filter. When the movement artefacts after filtering were still predominant, the specific stride was excluded from further analysis. The linear envelope of the EMG was estimated by rectification and low-pass filtering (anticausal Butterworth filter of order 2, cutoff frequency 10 Hz (Hermens et al., 1999)). The time axis was normalized on a 0–100 time scale (gait cycle) so that EMG envelopes from each stride could be merged within subject and subsequently used in a between-subject comparison (Perry, 1992). In order to test the repeatability of the envelope time series, the variability ratio (VR) was used (Frigo et al., 1996). VR is similar to the coefficient of variation, but it is suited to time series. Given a collection of time series in a matrix X with size (K, N), where K indicates the number of time series and N the number of samples per time series (100 in this case), a generic sample i of the mean time series is: xi ¼

K 1X X i;j k j¼1

Three envelope descriptors were computed: the area of the envelope after normalization with respect to the maximum (envelope area, EA), the maximum value (envelope maximum value, EMV), and the time instant corresponding to the maximum in percent of the gait cycle (envelope maximum position, EMP). These indicators were computed for the envelope of each valid stride, in each electrode location. Their median values across strides were then computed as robust estimate of central tendency and used for further analysis. The percent differences between the median values of envelope descriptors computed on a channel and those obtained from the central channel were used to summarize the effects of a shift in electrode location, as follows: DEMPi ¼ jEMPchi  EMPchref j DEMVi ¼

½% gait cycle

ð3Þ

jEMVchi  EMVchref j  100 EMVchref

½% of the reference channel jEAchi  EAchref j DEAi ¼  100 EAchref ½% of the reference channel where i is the channel considered and ref is the central channel. The averages over all channels and the maxima over the channels of DEMP, DEMV, and DEA are reported in the results. A crosstalk index (CTI) was defined as the ratio between the area of the normalized envelope outside the expected activation phase of the muscle and the area of the entire normalized envelope. For this purpose, it was assumed that plantar flexor muscles were active between 10% and 50% of the gait cycle and dorsi flexors between 55% of the gait cycle and 10% of the next cycle, as reported in the literature for healthy subjects (Gage, 2004; Perry, 1992). CTI was computed for each muscle and electrode location and provided an estimate of the amount of crosstalk, with the limitations discussed below (Section 4.3).

ð1Þ

where i ranges between 1 and 100. The mean value over the duration (the gait cycle) of the standard deviation is: !1=2 N k 1 X 1 X 2 S¼ ðX ij  xi Þ ð2Þ N i¼1 k  1 j¼1 VR was defined as the ratio between the mean standard deviation [Eq. (2)], used as an indicator of spread, and the range of the envelope mean profile (Frigo et al., 1996). VR was computed for assessing two types of variability: (1) VR among all valid strides of the three trials was used to test the within subject repeatability of EMG envelope shape at each electrode location and (2) VR among electrode locations was used to quantify the variability of envelope shape among channels.

2.5. Statistical analysis One way repeated measures analysis of variance (ANOVA) with factor muscle was performed on average and maximum DEMP, DEMV, and DEA. Pair-wise comparisons were performed with post hoc Student–Newman–Keuls (SNK) test when the ANOVA was significant. Significance was set to P < 0.05. 3. Results Fig. 3 shows raw surface EMG signals detected during a stride. Stride duration (mean ± SD, 1.01 ± 0.07 s) and cadence (121 ± 8 steps/min) had CV lower than 5% in all subjects. VR of EMG envelope among trials was not larger than 15% for all muscles and electrode locations. Fig. 4 reports

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519

the envelopes from GL of a representative subject, where VR ranged between 9% and 15% depending on the channel. Part of the variability was probably due to the jitter in peak position. 3.1. Variability of envelope with electrode location

Fig. 3. Example of raw EMG signals acquired from TA, PL, GL, GM and SO during one stride.

Table 2 shows VR among channels over all strides of the three trials and the relative variation of envelope descriptors. VR among channels was lower than 15% for GM in all subjects, in 8 out of 10 subjects for GL and SO, and in 6 subjects for TA. It ranged between 16% and 30% for PL. The value of the envelope peak was in the range 50– 100 lV for all muscles and positions. On average (over all channels) the variability of EMV was limited but in the worst cases it was approximately 30% for all muscles. Average and maximum variability of EMV did not significantly depend on the muscle. Variability of EA was not influenced by the variability of the peak value since EA was computed after normalization. For TA and PL, EA variability was mainly due to crosstalk (see below). For GM, GL and SO, EA in the central channel was sometimes the lowest among the channels, thus leading to high average variability. Average and maximum variability of EA depended on the muscle (F > 7.5,

Fig. 4. Envelope (all strides) and related descriptors (see text for their definition) from GL of one subject in the nine locations. Vertical scale: 0–100 lV.

520

Subject

Gender

Shank circumference at 1/4 proximal (cm)

Shank circumference at 1/3 distal (cm)

Envelope descriptors mean and (max) variation TA

PL

GL

GM

SO

CM

F

34.0

22.0

VR among ch 15% DEMP 0% GC (1%) DEMV 10% (20%) DEA 22% (46%)

VR among ch 26% DEMP 11% GC (39%) DEMV 12% (22%) DEA 26% (76%)

VR among ch 9% DEMP 1% GC (2%) DEMV 26% (42%) DEA 13% (34%)

VR among ch 9% DEMP 2% GC (4%) DEMV 12% (21%) DEA 10% (21%)

VR among ch 10% DEMP 1% GC (2%) DEMV 12% (23%) DEA 7% (17%)

CS

M

34.5

23.5

VR among ch 12% DEMP 1% GC (2%) DEMV 19% (54%) DEA 14% (22%)

VR among ch 24% DEMP 10% GC (34%) DEMV 13% (39%) DEA 15% (47%)

VR among ch 11% DEMP 2% GC (3%) DEMV 12% (35%) DEA 14% (25%)

VR among ch 11% DEMP 1% GC (3%) DEMV 13% (29%) DEA 11% (14%)

VR among ch 27% DEMP 3% GC (5%) DValMax 13% (21%) DEA 32% (53%)

FC

M

41.0

29.0

VR among ch 14% DEMP 1% GC (1%) DEMV 14% (25%) DEA 12% (30%)

VR among ch 24% DEMP 2% GC (3%) DEMV 15% (57%) DEA 49% (168%)

VR among ch 6% DEMP 1% GC (1%) DEMV 10% (20%) DEA 6% (13%)

VR among ch 10% DEMP 1% GC (3%) DEMV 6% (10%) DEA 11% (28%)

VR among ch 11% DEMP 3% GC (8%) DEMV 6% (9%) DEA 6% (13%)

FM

F

34.0

25.0

VR among ch 31% DEMP 6% GC (5%) DEMV 32% (69%) DEA 25% (46%)

VR among ch 30% DEMP 1% GC (4%) DEMV 19% (26%) DEA 34% (88%)

VR among ch 16% DEMP 1% GC (3%) DEMV 5% (11%) DEA 31% (59%)

VR among ch 12% DEMP 0% GC (1%) DEMV 9% (20%) DEA 25% (62%)

VR among ch 14% DEMP 0% GC (1%) DEMV 32% (56%) DEA 12% (20%)

FMA

F

35.0

25.0

VR among ch 16% DEMP 0% GC (1%) DEMV 17% (35%) DEA 19% (48%)

VR among ch 16% DEMP 1% GC (3%) DEMV 16% (35%) DEA 31% (66%)

VR among ch 10% DEMP 1% GC (1%) DEMV 6% (13%) DEA 9% (20%)

VR among ch 9% DEMP 1% GC (1%) DEMV 6% (10%) DEA 10% (25%)

VR among ch 19% DPosMax 0% GC (1%) DValMax 14% (31%) DEA 14% (20%)

CB

M

39.0

26.0

VR among ch 12% DEMP 1% GC (2%) DEMV 7% (13%) DEA 10% (24%)

VR among ch 26% DEMP 12% GC (43%) DEMV 12% (20%) DEA 17% (28%)

VR among ch 16% DEMP 1% GC (2%) DEMV 8% (19%) DEA 14% (22%)

VR among ch 9% DEMP 1% GC (2%) DEMV 6% (16%) DEA 11% (35%)

VR among ch 13% DPosMax 1% GC (2%) DEMV 8% (20%) DEA 6% (10%)

LV

F

34.0

25.0

VR among ch 24% DEMP 21% GC (32%) DEMV 7% (19%) DEA 14% (24%)

VR among ch 20% DEMP 12% GC (40%) DEMV 5% (10%) DEA 23% (37%)

VR among ch 17% 1% GC (2%) DEMP DEMV 10% (23%) DEA 8% (12%)

VR among ch 13% DEMP 1% GC (2%) DEMV 12% (22%) DEA 7% (17%)

VR among ch 13% DEMP 2% GC (7%) DEMV 5% (14%) DEA 11% (27%)

MB

F

34.0

22.0

VR among ch 14% DEMP 1% GC (1%) DEMV 12% (28%) DEA 7% (17%)

VR among ch 26% DEMP 12% GC (35%) DEMV 11% (28%) DEA 29% (59%)

VR among ch 11% DEMP 3% GC (8%) DEMV 23% (55%) DEA 8% (20%)

VR among ch 9% DEMP 1% GC (1%) DEMV 16% (27%) DEA 12% (41%)

VR among ch 10% DEMP 0% GC (1%) DEMV 10% (22%) DEA 6% (9%) (continued on next page)

Table 2 (continued)

I. Campanini et al. / Journal of Electromyography and Kinesiology 17 (2007) 515–526

Table 2 Variability of the envelope descriptors (see text for their definitions) due to a shift of approximately 2–3 cm from the central electrode location

Average and maximum (in brackets) variations among channels are reported. Variability is defined in Eq. (3) as normalized spread from the central electrode location.

VR among ch 15 ± 5% DEMP 1 ± 1% GC DEMV 12 ± 8% DEA 11 ± 8% ch 8% 1 ± 1% GC 9 ± 4% 14 ± 6% ch 12 ± 4% 1 ± 1% GC 11 ± 7% 14 ± 10% Mean values and standard deviations among subjects

VR among DEMP DEMV DEA

ch 17 ± 6% 3 ± 6% GC 14 ± 8% 14 ± 6%

VR among ch 24 ± 4% DEMP 8 ± 4% GC DValMax 10 ± 3% DEA 29 ± 10%

VR among DEMP DEMV DEA

VR among DEMP DEMV DEA

VR among ch 18% DEMP 2% GC (3%) DEMV 12% (43%) DEA 5% (13%) ch 11 ± 2% 2% GC (3%) 6% (13%) 16% (32%) VR among DEMP DEMV DEA ch 14% 1% GC (2%) 5% (12%) 32% (82%) M PD

43.0

27.5

VR among DEMP DEMV DEA

ch 19% 1% GC (3%) 14% (30%) 10% (21%)

VR among ch 28% DEMP 5% GC (35%) DEMV 8% (26%) DEA 32% (97%)

VR among DEMP DEMV DEA

ch 10% 2% GC (5%) 3% (6%) 25% (78%) ch 13% 1% GC (3%) 9% (19%) 7% (21%) M MF

38.5

22.5

VR among DEMP DEMV DEA

ch 11% 1% GC (3%) 6% (14%) 9% (15%)

VR among ch 20% DEMP 4% GC (24%) DEMV 20% (31%) DEA 34% (61%)

VR among DEMP DEMV DEA

VR among DEMP DEMV DEA

VR among ch 10% DEMP 1% GC (2%) DValMax 7% (25%) DEA 8% (15%)

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P < 0.001), with larger values for PL than all other muscles (P < 0.01). Triceps surae muscles showed minimal variation of maximum peak position with electrode location. In TA, EMP was minimally affected by location in 8 subjects (even if the envelope morphology was different among channels); two subjects showed almost no activity in one of the channels and one subject showed crosstalk from plantar flexors (probably from PL) higher than TA activity (Fig. 5). In PL, only two subjects had no variability in EMP while the remaining showed crosstalk from TA in the medial electrode locations (Fig. 6). Average and maximum EMP depended on the muscle (F > 5.0, P < 0.01), with larger values for PL than for other muscles (P < 0.05). 3.2. Crosstalk index and electrode location Table 3 reports CTI for all electrode locations and the five muscles. CTI lower than 20% may have been due to residual movement artefacts (as observed by visual inspection), while larger values of CTI were clearly due to crosstalk (Fig. 4). For TA, CTI progressively increased from the medial column of the grid to the lateral column, which is close or partially over PL. Accordingly, for PL, CTI increased from the lateral to the medial column of the grid. CTI was on the contrary approximately uniform with electrode location in GM, GL and SO. 4. Discussion The variability of the envelope of the surface EMG signal with position of the electrodes during gait was quantified for five muscles of the leg. These data provide numerical indications of the effect of electrode displacement on estimates of muscle activation in the gait cycle of healthy subjects. 4.1. Intensity of muscle activation Maximum envelope value and area of the envelope reflect the intensity of muscle activation. The first index indicates the maximum activity, the second the modulation of activity along the gait cycle. In this study, these two parameters were independent of each other since area was computed after normalization with respect to the maximum. In this way ‘‘area’’ has the dimension of time (% GC) and provides an indication of ‘‘duration of activity’’. The maximum variation of these two indexes across channels was rather large for all muscles (Table 2) while on average over all channels their variability was limited. The results on peak and area were in agreement with those on VR which was particularly high for TA, PL and SO. The observed variability with electrode location may have been due to many factors. EMG amplitude depends on the relative location of innervation zones and tendons with respect to the electrodes (Farina et al., 2001; Roy et al., 1986). Bipolar electrode systems close to the

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Fig. 5. Envelope (all strides) from TA of one subject in the nine locations. Grey stripes outline the presence of activity in mid and terminal stance, due to signal coming from plantar flexors muscles (PL). Vertical scale: 0–110 lV.

Fig. 6. Envelope (all strides) from PL of one subject in the nine locations. Grey stripes outline the presence of activity due to crosstalk, probably from TA. Vertical scale: 0–110 lV.

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Table 3 Crosstalk index computed for each electrode location for all muscles and subjects investigated Subjects

TA

CM

42

19

16

13

23

60

12

19

9

9

5

7

42

32

23

10

9

17

9

7

6

5

4

5

26

19

17

10

24

57

12

11

9

5

5

7

22

17

18

54

54

55

11

9

8

7

7

8

21

20

21

54

54

54

11

10

9

6

6

7

23

17

18

54

54

60

11

9

10

8

7

6

16

18

16

14

11

30

12

10

9

9

9

7

17

17

18

12

10

10

9

9

9

8

6

5

22

17

15

15

13

37

9

9

9

12

9

10

25

13

13

21

20

48

22

18

20

6

4

5

25

25

21

17

14

22

16

13

15

6

7

4

25

20

15

15

21

47

21

18

17

11

10

8

21

18

16

16

13

26

15

12

13

18

18

18

21

21

21

13

10

13

11

10

9

18

18

18

21

20

21

16

10

19

15

13

12

18

18

18

13

13

13

19

21

47

22

12

12

19

11

9

13

13

13

18

16

27

17

12

10

10

8

10

14

14

13

16

19

46

18

13

16

15

11

10

56

23

12

13

11

38

15

14

14

15

12

10

56

46

31

9

9

22

14

11

11

12

10

11

61

22

15

10

10

32

18

17

12

13

11

11

19

10

11

15

21

54

13

13

12

14

10

10

20

17

15

11

11

18

13

13

13

11

11

11

25

13

11

29

23

52

13

13

13

12

11

11

12

11

11

49

49

49

16

16

158

10

7

8

12

12

12

49

49

49

13

12

12

6

6

6

12

11

11

49

49

49

13

13

14

10

8

8

13

11

11

12

17

45

17

16

15

17

8

6

17

17

14

11

9

14

16

15

11

7

6

5

18 24 13

12 18 7

12 16 4

12 22 16

8 22 16

40 38 16

15 14 4

15 12 3

16 12 3

10 12 5

8 11 6

6 11 5

CS

FC

FM

FMA

GB

LV

MB

MF

PD

Mean Std

PL

GL

GM

SO 16

8

8

11

10

8

13

11

10

27

30

25

12

8

9

13

11

10

23

12

7

15

16

12

14

12

12

20

21

17

17

12

11

15

16

17

12

13

14

13

11

11

12

11

11

12

12

11

11

9

9

11

10

10

22

13

13

16

16

13

15 4

13 5

12 4

The index reflects the presence of EMG activity outside the known intervals of muscle activity in healthy subjects walking at self-selected speed (see text for the definition of the index). The numbers are provided for the electrode locations as defined in Fig. 2.

innervation zone detect amplitude lower than those between the innervation zone and tendon. This effect depends on electrode size, interelectrode distance, and mus-

cle anatomy. During movement, electrode location may change over time with respect to the underlying muscle fibers (Farina et al., 2001). This geometrical effect depends

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on the muscle and on the initial electrode location (Farina et al., 2001). Geometrical factors have an influence on both the maximum value and area of the envelope and may have caused part of the variability observed in this study. The electrode locations chosen in this work were near those adopted in clinical practice and were not based on the position of the innervation zone. The latter criterion should be considered in future work. Crosstalk may also play a role in determining differences among channels since some electrode locations may be closer than others to near active muscles (Blanc, 1997). For TA and PL, crosstalk was rather large for electrodes in between the two muscles and probably was one of the main causes of variability of envelope descriptors. These two muscles are rather narrow and close to each other, thus the relative high amount of crosstalk for some of the electrode locations was expected. For GL, GM, and SO, crosstalk was rather limited and uniform across the electrode grid, thus it probably contributed only a small amount to the observed variability in peak envelope and area. The estimate of intensity of activation may also be influenced by the skin–electrode impedance and noise. Impedance and noise were not measured in this study due to the many muscles and configurations adopted. However, the skin was carefully treated with abrasive paste (Bottin, 2002) in the same way for all locations. Even so, it can not be excluded that contact impedance and noise might have been different among the electrodes and that this was one of the causes of the observed variability. This factor of variability would anyway exist also in practical experimental conditions. From the present results, it is not possible to conclude which of the above factors had a major effect. However, the results indicate that estimates of the intensity of muscle activity from surface EMG may have a large variability in the worst case, thus care should be taken in comparing results within and between subjects and studies. 4.2. Timing of muscle activity The variability in position of the envelope maximum provided an indication of reliability of timing information. The position of the maximum was relatively stable with electrode location with average (among channels) variability smaller than 10% for all muscles (Table 2). However, in the worst case, TA and PL showed large variation in the location of the maximum, which was most likely due to crosstalk (Table 2, Figs. 5 and 6). The presence of additional intervals of activity, probably due to crosstalk, did not affect the position of the maximum (if crosstalk was lower than the peak muscle activity) but significantly affected the estimation of muscle onset/offset and the following clinical inference (as in the medial column for PL and in the lateral column for TA). In this study we did not focus on the variability in estimates of intervals of muscle activation since the detection

of muscle onset/offset may be performed with a number of methods (Merlo et al., 2003; Staude and Wolf, 1999), each of which determines different results. However, from the qualitative analysis of the signal envelopes, it was clear that muscle activation intervals estimated from the surface EMG depended on electrode location for TA and PL (Figs. 4 and 5). This was also indirectly evident from the large variability in envelope area (Table 2). Despite the area and peak value variability, the overall information obtained from triceps surae muscles was less affected by electrode location than in the case of TA and PL. 4.3. Limitations The crosstalk index was defined by assuming the absence of muscle activation in specific time intervals. This choice was based on previous literature of reference data in healthy subjects (Gage, 2004; Perry, 1992). However, the present data do not allow to totally exclude the presence of coactivation in the time intervals adopted to define crosstalk. Unfortunately there are no other methods to assess the amount of crosstalk in experimental conditions. For example, it has been proven that crosscorrelation analysis and high-pass filtering (Winter et al., 1994) can not identify crosstalk (Farina et al., 2004). Low values of the selected index of crosstalk do not necessarily indicate the absence of crosstalk signals in recordings from the specific muscle. Indeed, crosstalk is identified only in intervals in which the muscle under study is not active while if the muscle is active concomitantly with others, the eventual presence of crosstalk is not seen with the proposed index. For example, GL and SO are active in the same time intervals during gait, thus potential crosstalk between these two muscles could not be identified with the proposed index. Thus, the current results do not provide any indication on amount of crosstalk or selectivity of the recording in the calf muscles. Despite the limitation in the crosstalk index used, it is noted that the main result was the comparison across channels rather than the absolute quantification. Quantification of crosstalk among leg muscles was not the aim of the study which focused on spatial variations in EMG envelope. The proposed index was introduced to help in the interpretation of the potential factors underlining the observed spatial variability. 5. Conclusion This study presents for the first time the assessment of variability with electrode location of EMG signal envelope during gait, using electrode types and configurations typical of clinical gait analysis. The intensity of muscle activation, quantified by the peak value and area of the envelope, showed large variability among channels (in the worst case)

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for all muscles while the position of the maximum point of the envelope was stable for GL, GM, and SO and more variable for TA and PL, probably due to crosstalk. It is concluded that the determination of muscle activation intensity during the stride from surface EMG may be critical while timing of muscle activity can be reliably assessed when crosstalk is limited. These results provide reference information that can be used to assist in the interpretation of experimental EMG data in clinical gait analysis. It is suggested: (1) to consider the possibility of mutual crosstalk between TA and PL when abnormal timing patterns appear, especially when the electrode size is big with respect to the size of subject’s leg and (2) to repeat the test with slightly different electrode locations in case of doubt of crosstalk or of innervation zone artifacts. Acknowledgement This work was supported by a grant of Fondazione Pietro Manodori, Italy. References Basmajian JV, DeLuca CJ. Muscles alive: their functions revealed by electromyography. Baltimore: Williams & Wilkins; 1985. Blanc Y. EMG timing errors of pathologic gait. In: Hermens HJ, editor. Proceedings of the first general SENIAM (surface EMG for noninvasive assessment of muscles) workshop, Torino, Italy. Enschede, The Netherlands: Roessingh Research and Development; 1996. p. 183–5. Blanc Y. Surface electromyography (SEMG): a plea to differentiate between crosstalk and co-activation. In: Hermens HJ, Freriks B, editors. The state of the art on sensors and sensor placement procedures for surface electromyography: a proposal for sensor placement procedures deliverable, 5 of the SENIAM project. The Netherlands: Roessingh Research and Development b.v; 1997. p. 96–100. Blumenstein R, Basmajian J. Electrode placement in EMG biofeedback. Baltimore: Williams & Wilkins; 1980. Bottin A. Impedance and noise of the skin–electrode interface in surface EMG recordings. In: Proceedings of the XIVth ISEK congress, Wien; 2002. Conforto S, D’Alessio T, Pignatelli S. Optimal rejection of movement artefacts from myoelectric signals by means of a wavelet filtering procedure. J Electromyogr Kinesiol 1999;9:47–57. De Luca CJ, Merletti R. Surface myoelectric signal cross-talk among muscles of the leg. Electroencephalogr Clin Neurophysiol 1988;69:568–75. Esquenazi A, Mayer NH. Instrumented assessment of muscle overactivity and spasticity with dynamic polyelectromyographic and motion analysis for treatment planning. Am J Phys Med Rehabil 2004; 83(10 Suppl):S19–29. Farina D, Merletti R, Nazzaro M, Caruso I. Effect of joint angle on EMG variables in leg and thigh muscles. IEEE Eng Med Biol Mag 2001;20:62–71. Farina D, Merletti R, Enoka RM. The extraction of neural strategies from the surface EMG. J Appl Physiol 2004;96:1486–95. Frigo C, Crenna P, Jensen LM. Moment–angle relationship at lower limb joints during human walking at different velocities. J Electromyogr Kinesiol 1996;6:177–90. Gage J. The treatment of gait problems in cerebral palsy. London: Mac Keith Press; 2004.

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Hermens HJ, Freriks B, Merletti R, Ha¨gg G, Stegeman DF, Blok J, et al.. European recommendations for surface electromyography, SENIAM. Enschede (NL): Roessingh Research and Development; 1999, vol. 8. Hof AL, Elzinga H, Grimmius W, Halbertsma JPK. Speed dependence of averaged EMG profiles in walking. Gait Posture 2003;16:78–86. Holtermann A, Roeleveld K, Karlsson JS. Inhomogeneities in muscle activation reveal motor unit recruitment. J Electromyogr Kinesiol 2005;15:131–7. Jensen C, Vasseljen O, Westgaard RH. The influence of electrode position on bipolar surface electromyogram recordings of the upper trapezius muscle. Eur J Appl Physiol 1993;67:266–73. Koh TJ, Grabiner MD. Evaluation of methods to minimize cross talk in surface electromyography. J Biomech 1993;26(Suppl. 1):151–7. Koh TJ, Grabiner MD. Cross talk in surface electromyograms of human hamstring muscles. J Orthop Res 1992;10:701–9. Lamontagne A, Richards CL, Malouin F. Coactivation during gait as an adaptive behavior after stroke. J Electromyogr Kinesiol 2000;10(6):407–15. Mayer NH, Esquenazi A, Wannstedt G. Surgical planning for upper motoneuron dysfunction: the role of motor control evaluation. J Head Trauma Rehabil 1996;11:37–56, 1986. Merlo A, Farina D, Merletti R. A fast and reliable technique for muscle activity detection from surface EMG signals. IEEE Trans Biomed Eng 2003;50:316–23. Morrenhof JW, Abbink HJ. Cross-correlation and cross-talk in surface electromyography. Electromyogr Clin Neurophysiol. 1985;25:73–9. Perry J. Gait analysis: normal and pathological function. Thorofare, NJ: SLACK Inc; 1992. Richards CL, Olney SJ. Hemiparetic gait following stroke. Part I: characteristics. Gait Posture 1996a;4:136–48. Richards CL, Olney SJ. Hemiparatic gait following stroke Part II: recovery and physical therapy. Gait Posture 1996b;4:149–62. Roy SH, De Luca CJ, Schneider J. Effects of electrode location on myoelectric conduction velocity and median frequency estimates. J Appl Physiol 1986;61:1510–7. Solomonow M, Baratta R, Bernardi M, Zhou B, Lu Y, Zhu M, et al.. Surface and wire EMG crosstalk in neighbouring muscles. J Electromyogr Kinesiol 1994;4:131–42. Staude G, Wolf W. Objective motor response onset detection in surface mioelectric signals. Med Eng Phys 1999;21(6–7):449–68. Winter DA, Fuglevand AJ, Archer SE. Crosstalk in surface electromyography: theoretical and practical estimates. J Electromyogr Kinesiol 1994;4:15–26. Isabella Campanini: graduated summa cum laude in Physiotherapy at Parma University, Italy, in 1995 and obtained her Master degree in Physiotherapy at University of Chieti in 2003. In 2004 she obtained a Master in Management and Organization of Clinical Structures at the Bioengineering Department of Politecnico of Milano, Italy. From 1996 to 1998 she worked as physiotherapist at the Department of Rehabilitation of Reggio Emilia, Italy, and from 1998 to 2000 she worked in the Intensive Neurological Rehabilitation Ward in Correggio Hospital, AUSL of Reggio Emilia, Italy. In 2000 she was given the task of starting the Movement Analysis Laboratory (LAM) of the Rehabilitation Department, AUSL of Reggio Emilia, in Correggio Hospital, Italy. Since 2001 she is the Director of LAM, with clinical, research, and teaching activities. The main clinical and research activities are in the field of neurological disorders. Since 2002, she is involved in teaching activities at the University of Modena and Reggio Emilia.

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Andrea Merlo graduated in Electronics Engineering at Politecnico of Torino, Italy, in May 2000. From 2000 to 2001, he was a Fellow of the Laboratory for Neuromuscular System Engineering, Politecnico of Torino, Italy, where he focused his research on standardization of surface EMG recordings from muscles of upper and lower extremities. Since 2002 he has been working at the Movement Analysis Laboratory (LAM) of the Rehabilitation Department of the AUSL of Reggio Emilia, Italy, developing software for data analysis and transferring signal processing techniques and statistical methodologies to the daily clinical routine. Since 2003, he has been involved in teaching activities at the University of Modena and Reggio Emilia. He is currently working as a consultant bioengineer with seven movement analysis laboratories in Italy and Switzerland. He is a Registered Professional Engineer in Italy. Paolo Degola: graduated in Physiotherapy at Modena and Reggio Emilia University, Modena, Italy In 2002, he defended a thesis on multi-channel surface EMG. From 2002 to 2004 he worked as physioteraphist and since 2004 he has been working at the Movement Analysis Laboratory (LAM) of the Rehabilitation Department in Correggio Hospital, AUSL Reggio Emilia, Italy. His main research interests are related to surface EMG and measure of stiffness in patients with upper motor neuron syndrome. Roberto Merletti: is currently tenured Full Professor of Biomedical Engineering at Politecnico di Torino, Torino, Italy, and was Associated Professor and Researcher at the NeuroMuscular Research Center of Boston University from 1989 to 1994. He is the founder and director of the Laboratory for Engineering of the Neuromuscular System at Politecnico di Torino. He has been involved in five EU projects (he coordinated one) and two ESA projects (one as coordinator) in the field of engineering of the neuromuscular system. He is a member of the editorial board of four major biomedical engineering journals and published over 100 papers in international peerreviewed journals in the fields of electrical stimulation and non-invasive electromyography. He is the editor, with Phil Parker, of the textbook ‘‘Electromyography: physiology, engineering and non-invasive applications’’(J. Wiley and IEEE Press, 2004).

Guido Vezzosi: obtained his Medical degree at the University of Bologna, Italy, in 1972 and his specialisation degree in Physical Medicine and Rehabilitation at the same University in 1975. He worked as a medical doctor in the rehabilitation field at the USL of Reggio Emilia (RE), Italy from 1972 to 1982. In 1982 he became the Head of the Outpatient Rehabilitation Unit of the Hospital of Montecchio Emilia, RE, Italy; in 1995 he became also Head physician of the Hospital of Scandiano, RE, Italy, and in 1996 of the Hospital of Castelnovo Monti, RE, Italy. During these years he has organized a new inpatient ward for the rehabilitation of neurological patients in Correggio, RE, Italy. Since 1997 he is the Director of the Department of Rehabilitation of the AUSL of Reggio Emilia, comprehensive of the Intensive Neurological Rehabilitation Ward, the Movement Analysis Laboratory (LAM), and 6 Outpatient Rehabilitation Units, meeting the requirements for the province of Reggio Emilia. Since 1997, Dr Vezzosi is involved in teaching activities at the University of Modena and Reggio Emilia. Dario Farina graduated summa cum laude in Electronics Engineering from Politecnico di Torino, Torino, Italy, in February 1998. During 1998 he was a Fellow of the Laboratory for Neuromuscular System Engineering in Torino. In 2001 and 2002 he obtained the PhD degree in Automatic Control and Computer Science and in Electronics and Communications Engineering from the Ecole Centrale de Nantes, Nantes, France, and Politecnico di Torino, respectively. In 19992004 he taught courses in Electronics and Mathematics at Politecnico di Torino and in 2002-2004 he was Research Assistant Professor at the same University. Since 2004, he is Associate Professor in Biomedical Engineering at the Faculty of Engineering and Science, Department of Health Science and Technology of Aalborg University, Aalborg, Denmark, where he teaches courses on biomedical signal processing, modeling, and neuromuscular physiology. He regularly acts as referee for approximately 20 scientific International Journals, is an Associate Editor of IEEE Transactions on Biomedical Engineering, is on the Editorial Boards of the Journal of Neuroscience Methods, the Journal of Electromyography and Kinesiology, and Medical & Biological Engineering & Computing, and member of the Council ISEK (International Society of Electrophysiology and Kinesiology). His main research interests are in the areas of signal processing applied to biomedical signals, modeling of biological systems, basic and applied physiology of the neuromuscular system, and brain-computer interfaces. Dr. Farina is a Registered Professional Engineer in Italy.