Journal of Electromyography and Kinesiology 23 (2013) 540–547

Contents lists available at SciVerse ScienceDirect

Journal of Electromyography and Kinesiology

journal homepage: www.elsevier .com/locate / je lek in

Assessment of time patterns of activity and rest in full-shift recordings oftrapezius muscle activity – Effects of the data processing procedure

Kaj Bo Veiersted a,⇑, Mikael Forsman b,c, Gert-Åke Hansson d, Svend Erik Mathiassen c

a National Institute of Occupational Health, Oslo, Norwayb Occupational and Environmental Medicine, Public Health Sciences, Karolinska Institutet, Stockholm, Swedenc Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, Swedend Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden

a r t i c l e i n f o

Article history:Received 2 August 2012Received in revised form 26 November 2012Accepted 21 December 2012

Keywords:ElectromyographyMuscle activityData processingExposureTime variation

1050-6411/$ - see front matter � 2013 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.jelekin.2012.12.004

⇑ Corresponding author. Address: National InstitutBox 8149 Dep., 0033 Oslo, Norway.

E-mail address: bo.veiersted@stami.no (K.B. Veiers

a b s t r a c t

The purpose of this paper was to compare the effects of different data reduction procedures on the valuesof variables characterizing the time pattern of trapezius muscle activity during full work shifts. Surfaceelectromyography (EMG) of the right and left upper trapezius muscles were obtained from 40 young sub-jects in different occupations, mainly electricians, hairdressers and students. The target EMG variableswere gap frequency, muscle rest, and the number and duration of episodes with sustained muscle activity(from 0.13 s to 30 min as minimum duration). These variables were derived from the EMG recordingsusing different Root Mean Square (RMS) windows (from 0.13 to 6.38 s), and discrimination levelsbetween ‘‘activity’’ and ‘‘rest’’ (0.5%, 1% and 2% of maximal EMG).

The results give basis for practical suggestions for EMG analyses of full work shifts. For most variables, adiscrimination level of 0.5% EMGmax showed to be preferable. The time proportion of muscle rest and sus-tained muscle activity should, in general, be preferred over the corresponding frequency measures. Sus-tained muscle activity should be calculated using a RMS window between 1 and 3 s, and preferably bestated in terms of variables describing time proportions of activity. Uninterrupted activity episodeslonger than 10 min proved not to be a useful variable due to limited occurrence in many work shifts.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Trapezius myalgia and more general neck pain are common inoccupations characterized by manual work for extended periodswith low force and little variation (National Research Council andInstitute of Medicine, 2001; Bernard, 1997; Østensvik et al.,2009b). Electromyographic (EMG) analyses of muscle activityduring work have been used extensively in research aiming at abetter understanding of the risks of developing these disorders(Mathiassen et al., 1995; Basmajian and De Luca, 1985). The abso-lute level of electrical activity has been used for about 70 years(Davis, 1939; Lundervold, 1951), but it is only after improvementin equipment and analysis techniques during the 1960- and1970s that EMG has got a wider use for quantifying muscle activityin occupational studies (Cram, 2003). Integrated in this develop-ment was also the introduction of normalization procedures, i.e.that ‘‘raw’’ EMG recordings should be expressed relative to the

ll rights reserved.

e of Occupational Health, PO

ted).

EMG amplitude obtained during a standardized referencecontraction, typically a maximal voluntary contraction; EMGmax

(Mathiassen et al., 1995).Bengt Jonsson introduced the use of percentiles derived from

the Amplitude Probability Distribution Function (APDF) as a datareduction method for long recordings of EMG amplitude (Jonsson,1978, 1982). The ‘‘static’’ level was defined as the 10th percentile ofthe EMG amplitude across the measurement period. While this‘‘static’’ level of muscle activity has been addressed extensivelyin ergonomics research since then, it is still a matter of debatewhether it is associated with the risk of developing neck pain,especially for tasks with low-level force demands (Westgaard,1999).

The 10th percentile of the EMG amplitude distribution givesan overall impression of the lower levels of muscle activity fora certain period of time, but it does not describe temporal varia-tion. Thus, it has been suggested that a lack of association be-tween (low) muscle activity level and disorders may be due tothe importance of temporal aspects of exposure (Mathiassen,2006; Wells et al., 2007). Physiological studies of fatigue have,indeed, shown that the time pattern of loading has a profoundeffect on physiological fatigue responses, most notably endurance

K.B. Veiersted et al. / Journal of Electromyography and Kinesiology 23 (2013) 540–547 541

time (Mathiassen, 1993; Sundelin and Hagberg, 1989; Dul et al.,1994; El ahrache et al., 2006). Also, some epidemiological studieshave found an association between neck pain and the number ofshort interruptions of activity in the upper trapezius muscleduring work, ‘‘EMG gaps’’ (Veiersted et al., 1993; Madeleineet al., 2003) or the proportion of work time when the muscle isat rest (Hägg and Åström, 1997; Sandsjö et al., 2000; Thornet al., 2007).

In accordance with these ideas of predictive features in themuscle activity pattern, data reduction methods focusing on thetemporal structure of activity and rest have been presented, someof which focus on episodes of sustained muscle activity rather thanthe ’’reciprocal’’; i.e. EMG gaps or muscle rest. The Exposure Varia-tion Analysis (EVA; Mathiassen and Winkel, 1991), and even thedata reduction procedure proposed by Linderhed (1991) addresstemporal aspects of EMG recordings. However, both methods pro-duce an overall description of the time structure of the analyzedsignal, and do not, per se, isolate those aspects of muscle activationthat would be most important in relation to the development ofdisorders, even if attempts have been made to construct EVA deriv-atives with this purpose in mind (Anton et al., 2003; Ciccarelliet al., 2011). The ‘‘burst analysis’’ (Kern et al., 2001) focuses onthe number of episodes and total time with muscle activity above2% EMGmax. Subjects spending more than 70% of their workingtime above this level of trapezius muscle activity reported morepain in the neck and shoulders the previous 6 months than sub-jects having less than 50% of their working time in bursts (Morkand Westgaard, 2006). In a similar approach, episodes with SUs-tained (Low-level) Muscle Activity (capitals marking short form’’SULMA-periods’’/episodes) have been defined as episodes withcontinuous activity above 0.5% EMGmax (Østensvik et al., 2009a).The rationale for the SULMA approach is that the duration of epi-sodes with continuous muscle activity may be an important riskfactor in its own right for pain development. In the SULMA analy-sis, the duration of these uninterrupted episodes are categorized,the uppermost bin being uninterrupted episodes of at least20 min. A positive correlation was found between the occurrenceof SULMA episodes longer than 8 min during a working day andneck pain, both at time of recording and 1 year later (Østensviket al., 2009b).

The output of these different analyses of the temporal structureof EMG can be expected to be sensitive to the procedures forrecording and processing the EMG signal, including the samplingfrequency and filtering of the raw EMG signal (Larivière et al.,2005), the method for obtaining the muscle activity time line,e.g. the size of the Root Mean Square (RMS) conversion window,and the subsequent settings leading to the desired EMG variables,for instance the selected discrimination level between ’’activity’’and ‘‘rest’’ in an analysis of muscular rest time (Hansson et al.,1997; Hansson, 2011). While standard RMS windows are in the or-der of 0.1 s, longer windows will eliminate very short episodes ofmuscle rest (EMG gaps) for which the pathophysiological signifi-cance has been questioned (Westgaard, 1999). Thus, EMG signalsfiltered using long RMS windows may, in theory, reflect disorderrisks better than signals processed by standard RMS procedures.

To our knowledge, the sensitivity of temporal EMG analysis pro-cedures to different choices of RMS time windows has not beeninvestigated before. The effect of the level of discrimination be-tween ‘‘activity’’ and ‘‘rest’’ has only been presented for gap andmuscular rest analysis (Hansson et al., 2000; Delisle et al., 2009),while the burst and SULMA analyses have not been addressed inthis respect.

The aim of this study was to investigate the sensitivity of vari-ables describing the temporal structure of occupational trapeziusmuscle activity to the duration of the RMS filtering window andthe level of discrimination between ‘‘activity’’ and ’’rest’’.

2. Materials and methods

2.1. Subjects

Subjects were selected from a population of 420 school studentsinvolved in a research project initiated in 2002 in the greater Osloarea. The subjects, who were followed from the second year atschool into their first jobs, represented trades assumed to involveboth a high mechanical exposure for the neck and shoulders (elec-tricians and hairdressers) and a less pronounced load (art, design,media and communication). A subgroup of 156 subjects was ap-proached, and 45 subjects were willing, and permitted by their em-ployer when necessary, to join our measurement protocol. Twocancelled their appointments, one subject was excluded becausethe day of recording did not represent her normal work tasks,and two recordings were excluded for technical reasons. Thus, 40subjects were included in the present study (14 male electricians,15 female hairdressers, 3 male employees in retail and kindergar-ten, and 5 female students and 3 female employees in retail andhotel). Their median age at the time of the EMG recordings (spring2007) was 21 years (range 21–25 years). The median height for fe-males was 163 cm (range 152–175 cm) and for males 179 cm(range 170–193 cm). Corresponding figures for weight were60 kg (range 44–80 kg) and 69 kg (range 54–96 kg). All participantssigned an informed written consent form, and the study was ap-proved by the Region South Committee for Medical Research Ethicsin Norway.

2.2. Electromyography

2.2.1. Data acquisition and processingFrom each of the 40 subjects, surface electromyographic (EMG)

activity in the upper trapezius muscle was recorded bilaterallyduring a full work shift including breaks and lunch using bipolarAg/AgCl electrodes with gel diameter of 6 mm (E-10-VS, Medico-test A/S, Ølstykke, Denmark). The skin was cleaned with ethyl alco-hol and lightly abraded with fine sand paper, before the electrodeswere placed at 20 mm center-to-center distance in a standardizedlocation 2 cm lateral to the midpoint between the acromion pro-cess and C7 vertebra (Mathiassen et al., 1995; Veiersted, 1991; Jen-sen et al., 1993). The recording equipment was mounted in themorning before actual work started. The signal was amplified,band-pass filtered 10–400 Hz (Hansson et al., 2003), sampled at1024 Hz, using a 12 bit AD-convertor, and stored as ‘‘raw EMG’’on portable loggers (Logger Teknologi HB, Åkarp, Sweden). Themean EMG recording length was 6 h and 22 min (range 3 h39 min–8 h 37 min).

After the recording, the digitized ‘‘raw EMG’’ signal was band-pass (30–400 Hz) and notch (50, 100, 150,. . . Hz) filtered, thus vir-tually eliminating the ECG (Redfern et al., 1993) and the power lineartefacts, before it was Root Mean Square (RMS) converted in con-secutive, non-overlapping epochs (windows) of 0.125 s. In addi-tion, power spectra, mean power frequencies and artefact indiceswere derived according to Hansson et al. (1997), and used for iden-tifying and excluding data with movement artefacts and electro-magnetic interference. Recordings during complete rest wereincluded in the registration, and the noise level – estimated asthe lowest 2.4 s (moving window) RMS value throughout the fulldata-set – was subtracted in a power sense from all data (Hansson,2011). The mean noise level was 1.3 lV (range 0.2–3.7 lV) and1.4 lV (range 0.5–1.8 lV), for the right and left trapezius muscles,respectively. The EMGmax of the trapezius muscle was determinedas the highest 0.5 s (moving window) RMS value of three attemptsof a maximal voluntary contraction, elevating the upper arm in 90�abduction in the scapular plane with the subject in an upright

542 K.B. Veiersted et al. / Journal of Electromyography and Kinesiology 23 (2013) 540–547

position (Hansson et al., 1997). This procedure has been shown toproduce similar results as other procedures for obtaining EMGmax

(Veiersted et al., 2007). The mean EMGmax level was 1401 lV(range 424–3725 lV) and 1450 lV (range 526–4194 lV) for theright and left trapezius muscles, respectively. All six RMS-con-verted recordings for a specific subject were normalized by theEMGmax of that subject.

The RMS-converted signal was then run through rectangularmoving average (MA) filters of the length 0.25, 0.75, 1.625, 3.25and 6.375 s. Although this is an approximation of the RMS win-dows, in the following text, these RMS-MA-windows of differentlengths are termed ‘‘RMS-windows’’, and the lengths are roundedoff to two decimal points (0.125–0.13, 1.625–1.63 and 6.375–6.38).

2.2.2. EMG variablesEach of the normalized EMG recordings were processed to give

the 10th (‘‘static’’), 50th (‘‘median’’) and 90th (‘‘peak’’) percentilesof the amplitude distribution (Jonsson, 1982). Thereafter, theywere processed to give the time-series of ‘‘activity’’ and ‘‘rest’’ se-quences, using each of three discrimination levels, i.e. 0.5%, 1%and 2% of EMGmax. Each of these three time-series was first exam-ined for the number of ‘‘EMG gaps’’ per minute (episodes of muscleactivity below the chosen discrimination level) and ‘‘muscularrest’’ (proportion of total time with an EMG activity below the cho-sen discrimination level). These are widely used descriptors ofmuscle activity in the literature (e.g. Veiersted et al., 1990; Hans-son et al., 2000; Takala and Viikari-Juntura, 1991). In addition,the number of episodes per hour with a muscle activity abovethe chosen discrimination level was assessed, as well as their totalduration as a proportion of the shift, and summarized for differentcriteria as to the minimal required duration (see Fig. 1). These min-imum durations of a sequence to qualify for a count were: 0.125 s,2, 4, 6, 8, 10, 15, 20 and 30 min, termed Dur_01, Dur_2, Dur_4, etc.This approach was based on the analyses of SULMA episodes sug-gested by Østensvik et al. (2009a). An EMG burst has previouslybeen defined as an episode with an amplitude >2% EMGmax andwith a duration >0.1 s (Kern et al., 2001) or >0.2 s (Mork andWestgaard, 2005). As explained above, we used a minimum dura-tion of 0.125 s.

All calculations were performed in MATLAB R2008a (Math-works, Massachusetts).

2.3. Statistics

Results were reported using conventional descriptive statistics.Differences between right and left side measures were tested using

Fig. 1. Surface electromyographic (sEMG) recording during work, exemplified with a 1 hthe activity during maximal voluntary contraction (% EMGmax). In this example the lengtone episode with sustained muscle activity above this level for longer than 10 min is sho3.5 and P90 at 7% EMGmax. The ‘‘gap frequency’’ is 1/min and the time proportion of ‘‘musustained muscle activity (continuously above 1% EMGmax in this example) is 1/h and th

the Wilcoxon signed-rank test for paired samples, and the correla-tion between variables were analyzed using Spearman’s rho. Atwo-tailed probability of p < 0.05 was chosen as the statistical sig-nificance level. The statistical analysis was performed in IBM SPSSStatistics 19 (SPSS Inc. 2010).

3. Results

The peak muscle activity (90th percentile) and the number ofgaps per minute showed a significant difference between rightand left side when all subjects were pooled. No other difference be-tween right and left was statistically significant (Table 1).

Fig. 2 shows the frequency of episodes with EMG activity belowthe three investigated discrimination levels, and the correspondingproportion of time spent below these discrimination levels. The fig-ure includes results for the 0.5% EMGmax discrimination level,which has been used extensively in previous studies on ‘‘EMGgaps’’ and ‘‘muscle rest’’. Both the frequency and the time propor-tion were very sensitive to the discrimination level; both variablesshowed an almost twofold increase at the 2% EMGmax compared tothe 0.5% EMGmax discrimination level for all RMS window lengths.The time proportion with ‘‘muscle rest’’ was considerably less sen-sitive to the RMS window than the frequency variable for all threediscrimination levels. A significant difference between the rightand left trapezius occurred for the frequency of gaps <0.5% EMGmax

for almost all RMS windows, and for the proportion of muscleactivity <2% EMGmax when using long RMS windows; all otherright-left differences were statistically non-significant.

APDF percentiles changed marginally from the values shown inTable 1 when manipulating the RMS window length, and obviouslynot at all when the discrimination level was changed; thus the dataare not reported.

Fig. 3 shows the number of activity episodes (upper panel) andthe relative time (lower panel) above the three discrimination lev-els for the different RMS window lengths, and with different crite-ria for the minimal required duration of activity episodes. Forexample, in the upper left Fig. 3A–a, four activity episodes per hourof 2 min duration or longer (Dur_2) were observed at a discrimina-tion level of 0.5% EMGmax and a RMS window of 0.13 s (marked by#). This number increased to almost six activity episodes per hourwith a RMS window of 1.63 s. The lower right panel, Fig. 3C–b,illustrates for example that the total time proportion with muscleactivity episodes of any length (>0.13 s; Dur_01) above a discrimi-nation level of 2% EMGmax was approximately 65% of the shift(marked by ##). Only a small part of the shift was found to be

segment of activity from the left upper trapezius muscle. The EMG is normalized toh of the RMS window is 3.25 s, discrimination level is 1% EMGmax (broken line) andwn (solid line). This example results in a P10 (10th APDF percentile) at 0.46, P50 atscle rest’’ is 20%. The frequency of episodes with a minimum duration of 10 min ofe time proportion with this kind of activity is 17% of the recording.

Table 1Selected descriptive electromyographic (EMG) metrics. Percentiles (APDF), EMG gaps per min, muscle rest (% of recording time), bursts and episodes with sustained muscleactivity (SULMA episodes) are presented for the study population in total and stratified by occupational group. Each cell reports the mean value and the range. See footnotes forfurther explanations.

Alla (N = 40) Electricians (N = 14) Hairdressers (N = 15) Other male (N = 3) Other female (N = 8)

APDF-Rb (% EMGmax)10% 0.65 (0.1–5.2) 0.58 (0.1–2.2) 0.99 (0.2–5.2) 0.27 (0.1–0.5) 0.27 (0.1–0.6)50% 4.9 (0.2–11.3) 5 (2.3–9.1) 6.1 (1.2–11.3) 3.3 (2.5–4.6) 3 (0.2–9.7)90% 14.4** (1.6–23.5) 15.4 (8–20.6) 16.2 (7.1–23.5) 10.8 (8.3–13.5) 10.7 (1.6–19.6)

APDF-Lb (% EMGmax)10% 0.68 (0.1–4) 0.65 (0.2–2.1) 1.05 (0.2–4) 0.28 (0.2–0.4) 0.16 (0.1–0.5)50% 4.3 (0.2–10.4) 4.8 (1.2–10.4) 5.6 (1.3–9.4) 3.3 (2–4.2) 1.3 (0.2–4)90% 12.7** (2.3–25) 13.9 (5.4–25) 15.2 (7.7–22) 9.9 (8.6–11) 7 (2.3–15.3)

EMG gaps (min)Right 8.8** (1.3–22) 8.1 (3.1–15.5) 8.2 (1.3–19.2) 9 (4–13) 10.8 (2–21.6)Left 11.7** (1.8–37) 10.4 (3–21) 12.6 (1.8–37) 9.4 (6.1–14.7) 13 (9.1–22)

Muscle rest (%)Right 18 (3.5–63) 15.9 (6.8–27) 13.6 (3.5–30) 19.8 (10.1–28) 29 (8.3–63)Left 19.9 (2.6–59) 16.4 (4.7–29) 11.7 (2.6–25) 19.8 (13.3–30) 42 (14.7–59)

Bursts (per h)c

Right 1146 (273–2065) 1203 (589–1996) 1066 (380–1931) 1315 (839–1729) 1134 (273–2065)Left 1156 (423–2227) 1207 (504–1969) 1070 (423–2227) 1331 (1024–1610) 1162 (701–1822)

Burst time (%)c

Right 64 (14–90) 67 (47–84) 69 (43–90) 63 (55–79) 50 (14–86)Left 60 (18–93) 64 (36–86) 70 (43–93) 62 (48–70) 35 (18–63)

SULMA epis. (per h)d

Right 0.96 (0–2.3) 0.85 (0–2.3) 1.31 (0.3–2) 0.53 (0.1–1.2) 0.64 (0–2.3)Left 0.92 (0–2.1) 0.86 (0.1–2) 1.37 (0.8–2.1) 0.52 (0–0.9) 0.32 (0–1)

SULMA time (%)d

Right 26 (0–80) 21 (0–76) 40 (4.6–80) 9 (2–21) 16 (0–67)Left 25.5 (0–90) 20.3 (2–77) 44.5 (13.2–90) 8.5 (0–13.5) 5.5 (0–15.7)

a A RMS window of 0.13 s length was used for all parameters, except for the SULMA episodes (1.63 s).b APDF = amplitude probability distribution function. R = right, L = left.c Bursts and burst time, defined as number of episodes per hour and relative total time, respectively, with muscle activity >2% EMGmax,d ‘‘SULMA’’-episodes and ‘‘SULMA’’ time, defined as number of episodes per hour and relative total time, respectively, with muscle activity >0.5% EMGmax for longer than

8 min. SULMA = SUstained Low-level Muscle Activity (see text).** p < 0.01, difference between right and left side.

K.B. Veiersted et al. / Journal of Electromyography and Kinesiology 23 (2013) 540–547 543

spent in episodes of 2 min or longer above 2% EMGmax, especiallywhen using short RMS windows.

An increased discrimination level led to a larger frequency ofepisodes irrespective of duration. For the lowest discrimination le-vel 0.5% EMGmax (Fig. 3A–a) and a RMS window of 0.13 s, episodeslonger than 2 min were rare; only 4 of these episodes occurred perhour, while 521 episodes per hour were below 2 min of duration.For sustained muscle activity episodes with a particular minimalduration requirement, an increased discrimination level led to adecrease in both frequency and relative time proportion. For a dis-crimination level of 2% EMGmax, the occurrence of episodes longerthan 4 min was very rare, especially when short RMS windowswere used. As a general result, a longer RMS window length ledto a larger occurrence of episodes longer than 2 min, but fewer epi-sodes in total. Fig. 4 shows that the choice of discrimination leveland RMS window length strongly influenced the number of record-ings in which activity episodes occurred at all, i.e. shifts with anon-zero value. Thus, a short RMS window length and a high dis-crimination level led to many zero counts of long activity periods,i.e. that no such episodes occurred during the shift. For instance, ata discrimination level of 0.5% EMGmax and a RMS window length of1.63 s, 36 (90%) of the analyzed shifts contained episodes of 8 minor longer while the corresponding results for discrimination levels1% and 2% EMGmax were 28 (70%) and 16 (40%) shifts, respectively.

4. Discussion

This methodological paper focuses on EMG variables describingthe temporal pattern of muscle activity during full-shift recordings

including breaks. EMG percentiles (Jonsson, 1978) are only in-cluded for comparison with other studies. The investigated EMGvariables otherwise include well-documented expressions oftime-related exposure characteristics in occupational work, suchas gaps (Veiersted et al., 1990) and muscle rest (Veiersted et al.,1990; Hansson et al., 2000), but also variables that have been re-ported previously but to a lesser extent such as bursts (Kernet al., 2001) and SULMA episodes (Østensvik et al., 2009a,b).

Our study confirms that these EMG variables describing timepatterns of muscle activity are influenced by the choice of data pro-cessing procedures, in casu the window length when RMS-convert-ing the raw signal and the activity level selected to discriminatebetween ‘‘activity’’ and ‘‘rest’’. The frequency and total durationof episodes with sustained muscle activity was highly sensitiveto the RMS window length and the discrimination level, withshorter RMS windows and higher discrimination levels generallyresulting in reduced frequencies and less proportions of time withsustained muscle activity. A large proportion of the investigatedrecordings showed no occurrence of ‘‘long’’ periods with sustainedactivity.

4.1. Representativeness

The subjects in this study performed a variety of tasks at low,intermediate and high levels of muscle activity. The mean trape-zius EMG 10th percentile (static level) of the pooled populationin this study is below 1% EMGmax (Table 1), i.e. corresponding toprevious studies of office work (Hansson et al., 2000), and a varietyof health care and retail operations (Mork and Westgaard, 2006).

Right trapezius

0.13 0.25 0.75 1.63 3.25 6.38

EMG

gap

s (n

umbe

r/min

)

0

5

10

15

20

25

30

Discrimination level_05 Discrimination level_1 Discrimination level_2

RMS window (s)0.13 0.25 0.75 1.63 3.25 6.38

Mus

cle

rest

tim

e (%

)

0

10

20

30

40

50

60

Left trapezius

0.13 0.25 0.75 1.63 3.25 6.38

0

5

10

15

20

25

30

RMS window (s)0.13 0.25 0.75 1.63 3.25 6.38

0

10

20

30

40

50

60

*

**

* *

***

*

**

Fig. 2. Frequency of episodes and time proportion of ‘‘muscle rest’’ below discrimination levels of 0.5%, 1% and 2% EMGmax for different RMS window lengths. Results areshown for full work shift recordings from the right and left upper trapezius muscles (mean with SD, N = 40). The result of a Wilcoxon signed ranks test for the differencebetween right and left is marked in the left panel if significant (�p < 0.05, ��p < 0.01).

Discrimination level A : > 0.5% EMGmax

0.13 0.25 0.75 1.63 3.25 6.38

Num

ber o

f act

ivity

per

iods

pe

r hou

r

0

2

4

6

B: > 1% EMGmax

0.13 0.25 0.75 1.63 3.25 6.38

0

2

4

6

C : > 2% EMGmax

0.13 0.25 0.75 1.63 3.25 6.38

0

2

4

6 Dur_01 Dur_2 Dur_4 Dur_6 Dur_8 Dur_10 Dur_15 Dur_20 Dur_30

Dur_01:770 467 201 116 69 43 Dur_01: 1149 686 291 167 102 62Dur_01:525 315 138 81 50 31

#

RMS window (s)0.13 0.25 0.75 1.63 3.25 6.38

Prop

ortio

n of

m

uscl

e ac

tivity

(%)

0

20

40

60

80

100

RMS window (s)0.13 0.25 0.75 1.63 3.25 6.38

0

20

40

60

80

100

RMS window (s)0.13 0.25 0.75 1.63 3.25 6.38

0

20

40

60

80

100

##

(a)

(b)

(a)

(b)

(a)

(b)

Fig. 3. Frequency of activity episodes (upper panels, a) and total activity time proportion (lower panels, b) above discrimination levels of (A) 0.5%, (B) 1%, and (C) 2% EMGmax.Each panel shows results according to the RMS window length and the minimum required duration of the counted episode (e.g. Dur_2 indicates a minimum duration of2 min). The frequency of activity episodes of any duration (>0.125 s, Dur_01) exceeded the scale, and is shown in the text at the top of the upper panel. Mean values based onfull work shift recordings of right upper trapezius muscle (N = 40). See text for explanation of special signs (#).

544 K.B. Veiersted et al. / Journal of Electromyography and Kinesiology 23 (2013) 540–547

Discrimination level A: >0.5% EMGmax

RMS window (s)0.13 0.25 0.75 1.63 3.25 6.38

Non

-zer

o re

cord

ings

(n

umbe

r of p

erso

ns)

0

10

20

30

40

Dur_01 Dur_2 Dur_4 Dur_6 Dur_8 Dur_10 Dur_15 Dur_20 Dur_30

B: >1% EMGmax

RMS window (s)0.13 0.25 0.75 1.63 3.25 6.38

0

10

20

30

40

C: >2% EMGmax

RMS window (s)0.13 0.25 0.75 1.63 3.25 6.38

0

10

20

30

40

Fig. 4. Number of full work shift recordings with a non-zero occurrence of activity episodes. As in Fig. 3, results are shown for activity discrimination levels of (A) 0.5%, (B) 1%,and (C) 2% EMGmax, for the six RMS window lengths, and for the different minimum durations of activity episodes.

K.B. Veiersted et al. / Journal of Electromyography and Kinesiology 23 (2013) 540–547 545

However, some participants in our study, especially electriciansand hairdressers, showed higher EMG levels. The 90th percentile(peak level) reported in Table 1 corresponds to the activity levelduring office work but also varied industrial and non-industrialoccupations (Hansson et al., 2010). These rather low levels ofEMG activity suggest that the investigated subjects experienced alow risk of local muscular fatigue during the work shift (Iridiastadiand Nussbaum, 2006). Since the present recordings include breaks,our data will show lower muscular load levels than the aboveexposure data from periods of work, and the difference betweenoccupational groups will be reduced. The results concerning gapfrequencies and proportion of muscle rest were also similar to var-ied industrial and non-industrial occupations (Hansson et al.,2010). The occurrence of activity episodes >2% EMGmax (‘‘bursts’’)was higher in the present material than in a study of health care,retail and office workers (Mork and Westgaard, 2006); one plausi-ble explanation may be the use of a longer RMS window (0.2 s) inthat study. The total burst time was similar in the two studies. Thenumber of activity episodes >0.5% EMGmax with a duration longerthan 8 min in our study was similar to that in a combined groupof forest machine operators and researchers in forestry (Østensviket al., 2009a).

We therefore believe that our results concerning the effects ofthe RMS window length and the discrimination level have a gen-eral bearing on studies of trapezius EMG.

4.2. Methodological considerations

In order to increase the representativeness of the results, we in-cluded different occupations expected to represent considerabledifferences in trapezius muscle activity patterns. The rather lowlevels of muscle activity – the mean 50% APDF percentile was lessthan 5% EMGmax (range 0.2–11.3) – indicated a low risk of localmuscular fatigue (Iridiastadi and Nussbaum, 2006).

Considering that the frequency content of surface EMG is lim-ited to the band 10–500 Hz a sampling frequency of 1 kHz is suffi-cient, provided that a relevant analog anti-aliasing filter is used(the Nyquist–Shannon sampling theorem). Larivière et al. showedthat commonly used summary measures were insensitive tochoosing a lower sampling frequency of 512 Hz (Larivière et al.,2005). Considering the motor unit firing rate of 8–12 Hz in the tra-pezius muscle, a window (epoch) length of 0.125 s is a good choicefor gap detection (Hansson et al., 2000).

Increasing the RMS window length corresponds to averagingthe initially calculated RMS-values, i.e. a further low-pass filteringof the muscle activity time-series. Thus rapid changes in muscularactivity, e.g. short gaps in the recordings, will disappear; leading toa decreasing gap frequency with increasing window length (Fig. 2).Similarly, the likelihood for interrupting a SULMA episode by

spurious RMS window(s) below the discrimination level willdecrease; thus, the time proportion of muscle activity and thefrequency of SULMA episodes show a monotonic increase withincreasing RMS window length, except for the lowest discrimina-tion level in combination with short episode durations (Fig. 3).The latter phenomenon is due to the fact that the longer RMSwindows favor the concatenating of two or more episodes of shortduration into one episode of longer duration, thus decreasing thenumber of short SULMA episodes and increasing the number ofSULMA episodes of longer duration; this is a similar effect as thatof longer RMS windows on gap frequency (Fig. 2).

4.3. Muscle rest and gap analyses

The proportion of muscle rest has been used widely in occupa-tional studies to describe opportunities for recovery in-betweenmuscle activity periods (Nordander et al., 2000; Escorpizo andMoore, 2007; Delisle et al., 2009; Hansson et al., 2010). It was obvi-ous in the present study that the proportion of muscle rest is highlysensitive to choice of discrimination level, but less sensitive to thechoice of RMS window length (Fig. 2). This means that studiesusing the same discrimination level for muscle rest may be com-pared even if they have not used the same RMS window length.

The frequency of gaps was originally introduced in an attemptto describe temporal aspects of muscle activity (Veiersted et al.,1990). However, it has not been used to any large extent, probablybecause it is highly correlated with the corresponding muscle restvariable (Spearman’s correlation coefficient (rho) in the presentdata set: 0.72), the latter having the advantage of beneficial metricproperties (Hansson et al., 2000). Still, gap frequency and relativemuscle rest describe conceptually different aspects of the muscleactivity pattern. The frequency parameter describes a frequencyaspect of variation (Mathiassen, 2006), while relative muscle restexpresses an aspect of the activity level. Both may therefore beconsidered when quantifying muscle activity patterns in occupa-tional settings, e.g. in intervention studies, and applied accordingly,depending on which aspect is in focus of the intervention.

4.4. Episodes with sustained muscle activity

Attempts to quantify muscle activity have a long history (Cram,2003), and this applies even to some extent to sustained muscleactivity. Mork and Westgaard (2005) analyzed the frequency ofepisodes and the total time proportion of activity above 2% EMGmax

(’’bursts’’ and burst time). The variables used by Mork and Westg-aard give mean values during the analyzed period of work. Thus,they do not address the duration of each uninterrupted episode.This aspect may be an important risk factor in its own right fordevelopment of muscle pain, as suggested by the Cinderella

546 K.B. Veiersted et al. / Journal of Electromyography and Kinesiology 23 (2013) 540–547

hypothesis (Hägg, 1991), and it also contributes to a general under-standing of the pattern of muscle activity variation (Mathiassen,2006). The approach to quantify the duration of episodes withuninterrupted exposure has, to our knowledge, been brought uponly in two previous studies using full work shift measurements;one addressing upper arm postures among male manual workers(Svendsen et al., 2004) and another concerning trapezius muscleactivity among male forest machine operators (Østensvik et al.,2009a,b). The latter study used a RMS window length of 1.6 s,and reported the frequency of activity episodes of certain durationsas measures of muscle activity, from very short (1.6–5 s) to longerthan 20 min. The study suggested an association between theoccurrence of episodes 8 min or longer of right trapezius muscleactivity above 0.5% EMGmax and neck pain at the same side(Østensvik et al., 2009b). Using the same discrimination level andRMS window length in our population, four subjects had no activ-ity episodes longer than 8 min (Fig. 4). This particular result maybe acceptable for statistical analysis of, e.g. differences betweengroups or conditions, but if the duration criterion for counting anactivity episode is tightened up, the number of work shifts withzero episodes may increase substantially. For instance, 75% of allshifts would show a zero value if episodes were required to belonger than 30 min. This sets out a warning for using very long epi-sodes of sustained activity as an exposure variable, especially if theactivity discrimination level is 2% EMGmax.

4.5. Practical guidance

The results of this study lead to a number of suggestions as tothe selection of RMS window length, discrimination level between‘‘activity’’ and ‘‘rest’’, and minimal activity episode length in stud-ies assessing variables related to muscular rest and sustained mus-cle activity.

(1) Use 0.5% EMGmax as the discrimination level when assessingactivity vs. rest.

(2) Use mainly relative muscular rest as a measure of the oppor-tunity for muscular recovery. Gap frequency could be addedin studies of highly repetitive work.

(3) Use the time proportion of activity instead of the number ofactivity episodes when assessing sustained muscle activity.For activity variables, the RMS window length should bebetween 1 and 3 s, and the activity discrimination levelshould be 0.5% EMGmax. Avoid using variables measuringthe occurrence of activity episodes longer than 10 min.

Conflict of interest

None declared.

Acknowledgements

This study was supported by a grant from the Confederation ofNorwegian Business and Industry’s Working Environment Fund.The study sponsors had no influence on the study at any stage.We are grateful for the field data collection by Therese N. Hanvold,Liv Berit Hæg, Ada Ingvaldsen, Birgitta Jarmark, Einar Jebens andJorid Thrane Stuenæs.

References

Anton D, Cook TM, Rosecrance JC, Merlino LA. Method for quantitatively assessingphysical risk factors during variable noncyclic work. Scand J Work EnvironHealth 2003;29:354–62.

Basmajian JV, De Luca CJ. Muscles alive. Their functions revealed byelectromyography. Baltimore: Williams & Wilkins; 1985.

Bernard BP, editor. Musculoskeletal disorders and workplace factors. A criticalreview of epidemiologic evidence for work-related musculoskeletal disorders ofthe neck, upper extremity, and low back. NIOSH; 1997.

Ciccarelli M, Straker L, Mathiassen SE, Pollock C. ITkids Part II: variation of posturesand muscle activity in children using different information and communicationtechnologies. Work 2011;38:413–27.

Cram JR. The history of surface electromyography. Appl Psychophysiol Biofeedback2003;28:81–91.

Davis RC. Patterns of muscular activity during ‘mental work’ and their constancy. JExp Psychol 1939;24:451–65.

Delisle A, Larivière C, Plamondon A, Salazar É. Reliability of different thresholds fordefining muscular rest of the trapezius muscles in computer office workers.Ergonomics 2009;52:860–71.

Dul J, Douwes M, Smitt P. Ergonomic guidelines for the prevention of discomfort ofstatic postures based on endurance data. Ergonomics 1994;37:807–15.

El ahrache K, Imbeau D, Farbos B. Percentile values for determining maximumendurance times for static muscular work. Int J Ind Ergonom2006;36:99–108.

Escorpizo RS, Moore AE. Quantifying precision and speed effects on muscle loadingand rest in an occupational hand transfer task. Int J Ind Ergonom2007;37:13–20.

Hägg GM. Static work loads and occupational myalgia – a new explanation model.In: Anderson PA, Hobart DJ, Danoff JV, editors. Electromyographicalkinesiology. Amsterdam: Elsevier Science Publishers B.V; 1991. p. 141–4.

Hägg GM, Åström A. Load pattern and pressure pain threshold in the uppertrapezius muscle and psychosocial factors in medical secretaries with andwithout shoulder/neck disorders. Int Arch Occup Environ Health1997;69:423–32.

Hansson GÅ. Letter to the Editor: modeling nonlinear errors in surfaceelectromyography due to baseline noise: a new methodology. J Biomech2011;44:1637–8.

Hansson GÅ, Asterland P, Kellerman M. Modular data logger system for physicalworkload measurements. Ergonomics 2003;46:407–15.

Hansson GÅ, Asterland P, Skerfving S. Acquisistion and analysis of whole-dayelectromyographic field recordings. In: Hermens HJ, Hägg G, Freriks B, editors.Proceedings of the second general SENIAM, workshop; 1997. p. 19–27.

Hansson GÅ, Balogh I, Ohlsson K, Granqvist L, Nordander C, Arvidsson I, et al.Physical workload in various types of work: Part II. Neck, shoulder and upperarm. Int J Ind Ergonom 2010;40:267–81.

Hansson GÅ, Nordander C, Asterland P, Ohlsson K, Strömberg U, Skerfving S, et al.Sensitivity of trapezius electromyography to differences between work tasks –influence of gap definition and normalisation methods. J Electromyogr Kinesiol2000;10:103–15.

Iridiastadi H, Nussbaum MA. Muscle fatigue and endurance during repetitiveintermittent static efforts: development of predictive models. Ergonomics2006;49(4):344–60.

Jensen C, Vasseljen O, Westgaard RH. The influence of electrode position on bipolarsurface electromyogram recordings of the upper trapezius muscle. Eur J ApplPhysiol 1993;67:266–73.

Jonsson B. Kinesiology – with special reference to electromyographic kinesiology.In: Cobb WA, van Duijn H, editors. Contemporary clinical neurophysiology (EEGsuppl. no. 34). Amsterdam: Elsevier; 1978. p. 417–28.

Jonsson B. Measurement and evaluation of local muscular strain in the shoulderduring constrained work. J Hum Ergol 1982;11:73–88.

Kern DS, Semmler JG, Enoka RM. Long-term activity in upper- and lower-limbmuscles of humans. J Appl Physiol 2001;91:2224–32.

Larivière C, Delisle A, Plamondon A. The effect of sampling frequency on EMGmeasures of occupational mechanical exposure. J Electromyogr Kinesiol2005;15:200–9.

Linderhed H. A method for analyzing the temporal pattern of EMG. In: Quéinnec Y,Daniellou F, editors. Designing for everyone. Proceedings 11th congress IEA,Paris 1991. London: Taylor & Francis; 1991. p. 45–7.

Lundervold A. Occupation myalgia. Electromyographic investigations. Acta PsychNeurol Scand 1951;26:359–69.

Madeleine P, Lundager B, Voigt M, Arendt-Nielsen L. The effects of neck–shoulderpain development on sensory-motor interactions among female workers in thepoultry and fish industries. A prospective study. Int Arch Occup Environ Health2003;76:39–49.

Mathiassen SE. Variation in shoulder–neck activity. Physiological, psychophysicaland methodological studies of isometric exercise and light assembly work.Thesis. Arbete och Hälsa 1993:7:1–106.

Mathiassen SE. Diversity and variation in biomechanical exposure: what is it, andwhy would we like to know? Appl Ergon 2006;37:419–27.

Mathiassen SE, Winkel J. Quantifying variation in physical load using exposure-vs-time data. Ergonomics 1991;34(12):1455–68.

Mathiassen SE, Winkel J, Hägg GM. Normalization of surface EMG amplitude fromthe upper trapezius muscle in ergonomic studies – a review. J ElectromyogrKinesiol 1995;5:197–226.

Mork PJ, Westgaard RH. Long-term electromyographic activity in upper trapeziusand low back muscles of women with moderate physical activity. J Appl Physiol2005;99:570–8.

Mork PJ, Westgaard RH. Low-amplitude trapezius activity in work and leisure andthe relation to shoulder and neck pain. J Appl Physiol 2006;100:1142–9.

National Research Council, Institute of Medicine. Musculoskeletal disorders and theworkplace. Low back and upper extremities. Washington, DC: NationalAcademy Press; 2001.

K.B. Veiersted et al. / Journal of Electromyography and Kinesiology 23 (2013) 540–547 547

Nordander C, Hansson GA, Rylander L, Asterland P, Bystrom JU, Ohlsson K, et al.Muscular rest and gap frequency as EMG measures of physical exposure: theimpact of work tasks and individual related factors. Ergonomics2000;43:1904–19.

Østensvik T, Veiersted KB, Nilsen P. A method to quantify frequency and duration ofsustained low-level muscle activity as a risk factor for musculoskeletaldiscomfort. J Electromyogr Kinesiol 2009a;19:283–94.

Østensvik T, Veiersted KB, Nilsen P. Association between number of long periodswith sustained low-level trapezius muscle activity and neck pain. Ergonomics2009b;52:1556–67.

Redfern MS, Hughes RE, Chaffin DB. High-pass filtering to removeelectrocardiographic interference from torso EMG recordings. Clin Biomech1993;8:44–8.

Sandsjö L, Melin B, Rissén D, Dohns I, Lundberg U. Trapezius muscle activity, neckand shoulder pain, and subjective experiences during monotonous work inwomen. Eur J Appl Physiol 2000;83:235–8.

Sundelin G, Hagberg M. The effects of different pause types on neck and shoulderEMG activity during VDU work. Ergonomics 1989;32(5):527–37.

Svendsen SW, Bonde JP, Mathiassen SE, Stengaard-Pedersen K, Frich LH. Work-related shoulder disorders: quantitative exposure–response relationships withreference to arm posture. Occup Environ Med 2004;61:844–53.

Takala EP, Viikari-Juntura E. Muscular activity in simulated light work amongsubjects with frequent neck–shoulder pain. Int J Ind Ergonom 1991;8:157–64.

Thorn S, Søgaard K, Kallenberg LAC, Sandsjö L, Sjøgaard G, Hermens HJ, et al.Trapezius muscle rest time during standardised computer work – a comparisonof female computer users with and without self-reported neck/shouldercomplaints. J Electromyogr Kinesiol 2007;17:420–7.

Veiersted KB. The reproducibility of test contractions for calibration ofelectromyographic measurements. Eur J Appl Physiol 1991;62:91–8.

Veiersted KB, Wærsted M, Hanvold TN, Hæg LB, Hansson GÅ. A simplified procedureto provoke maximal voluntary contraction (activity) in the upper trapeziusmuscle. In: PREMUS 2007, Sixth international scientific conference onprevention of work-related musculoskeletal disorders, the conference centerat Harvard Medical, Boston, Massachusetts, USA, 27–30 August, 2007, book ofabstracts; 2007. p. 66.

Veiersted KB, Westgaard RH, Andersen P. Pattern of muscle activity duringstereotyped work and its relation to muscle pain. Int Arch Occup EnvironHealth 1990;62:31–41.

Veiersted KB, Westgaard RH, Andersen P. Electromyographic evaluation of muscularwork pattern as a predictor of trapezius myalgia. Scand J Work Environ Health1993;19:284–90.

Wells R, Mathiassen SE, Medbo L, Winkel J. Time – a key issue for musculoskeletalhealth and manufacturing. Appl Ergonom 2007;38:733–44.

Westgaard RH. Effects of physical and mental stressors on muscle pain. Scand JWork Environ Health 1999;25(suppl. 4):19–24.

Kaj Bo Veiersted is senior researcher, MD, completedhis medical traing at Copenhagen University in 1983and obtained his medical doctorate at the University ofOslo in 1995 on muscle activity pattern related to tra-pezius myalgia. His main interest in research is to elu-cidate the relationship between occupational exposures,individual characterstics and musculoskeletal disorders.He is now the head of a group focusing on pain mech-anisms, genetic susceptibility and field studies ofphysically demanding work. Further interests are hand-arm vibration syndrome and electrical injuries. He isworking one day a week as an occupational physician.

Mikael Forsman, Ph.D., is presently a senior researcherat the Unit for Occupational Medicine, IMM Institute ofEnvironmental Medicine, Karolinska Institutet, Stock-holm. He is also affiliated to the Centre for Musculo-skeletal Research, University of Gävle, Sweden. Hereceived his Doctorate in 1995 in Medical Engineeringat Chalmers University of Technology, Göteborg, and issince 1999 Associate Professor in Ergonomics. Hisresearch covers ergonomics exposure assessmentmethods, where he is interested both in improvedobservation methods and in improved methods formeasurements and analyses and interpretation of directmeasurements (as in EMG signal processing). Hisresearch covers both field studies, in which his interestsare physical load variation, biomechanical effects ofproduction strategies, participative interventions and

evaluation of interventions, and experimental studieson mechanisms for work related musculoskeletal dis-orders; comparing EMG and oxygenation in patientsand controls, and comparing EMG responses from dif-ferent visual work loads.

Gert-Åke Hansson is Associate Professor of Ergonomicsat Lund University, Sweden. His main research interestis development and evaluation of methods for ambula-tory measurements at the workplaces, analyzing phys-ical workload and extracting summary measures withrelevance to musculoskeletal disorders. The mainapplications of the methods are for exposure assess-ment in epidemiological research on disorders of neckand upper limbs, and for evaluation of workplaceinterventions. The methods have also been used as goldstandard for validating questionnaires and observationmethods. They have been applied in collaboration withother research groups, and to date, an exposure matrix,regarding postures movements and muscular load hasbeen established for about 40 types of work.

Svend Erik Mathiassen is Professor and researchdirector at the Centre for Musculoskeletal Research,University of Gävle, Sweden. His main interest inresearch is physical variation in working life: how tomeasure ‘‘variation’’, effects of different types of varia-tion on performance, fatigue and disorders, and inter-ventions in working life promoting or obstructingvariation. His interest in exposure variability has alsoled to an extensive research on cost-efficient strategiesfor collecting and analyzing data on physical load. Thus,he has been involved in studies of variation and vari-ability among, for instance, hairdressers, industrialassembly workers, cleaners, flight baggage handlers,office workers, and house painters. He has also beenengaged in a scientific advisory committee formed bythe Swedish government on matters related to the

Swedish work environment policy.