Open Journal of Biophysics, 2013, 3, 178-190
http://dx.doi.org/10.4236/ojbiphy.2013.33021 Published Online July 2013 (http://www.scirp.org/journal/ojbiphy)
The Influence of Window Length Analysis on the Time and
Frequency Domain of Mechanomyographic and
Electromyographic Signals of Submaximal
Fatiguing Contractions
Guilherme Nogueira-Neto1,2, Eduardo Scheeren2,3, Eddy Krueger3, Percy Nohama1,2,3,
Vera Lúcia S. N. Button1
1Departamento de Engenharia Biomédica/CEB, Universidade Estadual de Campinas-UNICAMP, Campinas, Brazil
2Laboratório de Engenharia de Reabilitação, Pontifícia Universidade Católica do Paraná-PUCPR, Curitiba, Brazil
3CPGEI, Universidade Tecnológica Federal do Paraná-UTFPR, Curitiba, Brazil
Received May 1, 2013; revised June 7, 2013; accepted June 15, 2013
Copyright © 2013 Guilherme Nogueira-Neto et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
Mechanomyography (MMG) acquires the oscillatory waves of contracting muscles. Electromyography (EMG) is a tool
for monitoring muscle overall electrical activity. During muscle contractions, both techniques can investigate the
changes that occur in the muscle properties. EMG and MMG parameters have been used for detecting muscle fatigue
with diverse test protocols, sensors and filtering. Depending on the analysis window length (WLA), monitoring physio-
logical events could be compromised due to imprecision in the determination of parameters. Therefore, this study inves-
tigated the influence of WLA variation on different MMG and EMG parameters during submaximal isometric contrac-
tions monitoring MMG and EMG parameters. Ten male volunteers performed isometric contractions of elbow joint.
Triaxial accelerometer-based MMG sensor and EMG electrodes were positioned on the biceps brachii muscle belly.
Torque was monitored with a load cell. Volunteers remained seated with hip and elbow joint at angles of 110˚ and 90˚,
respectively. The protocol consisted in maintaining torque at 70% of maximum voluntary contraction as long as they
could. Parameter data of EMG and the modulus of MMG were determined for four segments of the signal. Statistical
analysis consisted of analyses of variance and Fisher’s least square differences post-hoc test. Also, Pearson’s correlation
was calculated to determine whether parameters that monitor similar physiological events would have strong correlation.
The modulus of MMG mean power frequency (MPF) and the number of crossings in the baseline could detect changes
between fresh and fatigued muscle with 1.0 s WLA. MPF and the skewness of the spectrum (μ3), parameters related to
the compression of the spectrum, behaved differently when monitored with a triaxial MMG sensor. The EMG results
show that for the 1.0 s and 2.0 s WLAs have normalized RMS difference with fatigued muscle and that there was strong
correlation between parameters of different domains.
Keywords: Mechanomyography; Electromyography; Window Length Analysis; Local Muscle Fatigue
1. Introduction
During fatiguing muscle contractions, a reduction in
maximum voluntary contraction (MVC) occurs due to
the inability of myofibrils to produce more force [1] and
to the reduction in muscle contraction velocity as well [2].
Electromyography (EMG) has been a useful tool for
studying muscle overall electrical activity, function and
fatigue [3]. Alternatively, the acquisition of muscle os-
cillatory response can be useful in monitoring muscle
condition [4]. Different types of transducers, from piezo-
electric to laser-based distance sensors, can record oscil-
lations non-invasively. Such waves have been measured
using accelerometers [5] and the technique is defined as
mechanomyography (MMG).
MMG is a non-invasive monitoring technique that can
assist in investigating mechanical properties of muscle
voluntary contraction and muscle fatigue [6]. Previous
studies demonstrated that MMG can provide different
information from that obtained with EMG recordings [7],
especially muscle fatigue [8], and both could provide
C
opyright © 2013 SciRes. OJBiphy

G. N. NOGUEIRA-NETO ET AL. 179
valuable information about motor unit (MU) recruitment
and firing rate [9]. A muscle hypothermia study [10]
suggested that the spectral analysis of MMG signals
could precisely identify firing rates below the tetanic
frequency of activated MUs during muscle contraction,
independently of the changes in intrinsic contractile pro-
perties. These findings support the idea that MMG is a
reliable method for observing muscle contractile proper-
ties in different physiological conditions.
During a task that involves muscle contraction, MMG
time and spectrum characteristics depend on force level
and movement rate. From 70% to 80% of MVC, MMG
amplitude [7] and frequency [11] responses of contract-
ing muscles decreased during fatiguing exercises.
There are many studies involving MMG and fatiguing
muscle contractions as well as investigations using MMG
and EMG simultaneously [6,8,12,13]. The use of differ-
ent MMG methods and sensors can lead to different re-
sults. Variations in the analysis window length (WLA)
could have influence on torque correlation, on physio-
logical effects or it could compromise the precision of
the analysis parameters. A study that monitors signal
descriptors varying WLA and muscle physiological con-
dition can help determine which parameter or WLA is
more sensitive to detect muscle condition variations. Such
parameter and WLA could be useful in control strategies
of functional electrical stimulation (FES) systems where
muscle fatigue is an important limitation [14].
For EMG, Karlsson et al. [22] used wavelet analysis
and determined that WLAs greater than 1.5 s would be
inadequate for monitoring muscle contraction at force
levels above 50% MVC because such intervals would not
allow for locally stationary epochs. In this work; however,
there was the interest of monitoring MMG signals from
short to long WLAs such as 0.2 s and 1.0 s, respectively.
Stulen and De Luca built an analog device to track
muscle fatigue [23]. They monitored EMG using two
parameters: mean power frequency (MPF) and the ratio
between the spectral energies comprised in the low and
high frequency spectrum regions (delimited by the first
MPF calculated with fresh muscle and defined here as
spectral ratio-SR). Merletti et al. have been processing
[24] and comparing algorithms for estimating EMG ac-
tivity [25] and they also suggested experiments with dif-
ferent methods for this purpose.
This work aims to observe the influence of WLA on
root mean square (RMS) and the mean power frequency
(MPF) of MMG and EMG during sustained biceps
brachii (BB) muscle submaximal isometric contractions.
It is important that torque be monitored with energy-re-
lated parameters to assure that EMG and MMG data be
acquired at a sustained force level. Another goal is to
provide novel results using triaxial accelerometer-based
MMG sensors. The reason for studying these parameters
is to investigate the influence of varying WLA on their
ability to detect changes in muscle condition (specifically,
fatigue installation) using the same sensor, protocol and
filtering.
2. Materials & Methods
2.1. Volunteers
Participated in this study ten physically active male in-
dividuals, all college students without neuromuscular
disorders or elbow joint problems (age: 22.6 ± 3.6 years;
weight: 76.5 ± 9.8 kg; height: 1.80 ± 0.10 m). The ex-
periments were approved by the Pontifícia Catholic Uni-
versidade of Parana’s ethics committee (number 2416/08).
All participants were informed in detail about the se-
quence of the test protocol and they agreed to participate
giving written informed consent. After skin preparation
(trichotomy and cleaning) the volunteer stretched out his
elbow joint and the warm up exercise was carried out to
avoid muscle damage and consisted of 30 slow dynamic
contractions (approximately 50˚/s) of the elbow joint
with a load of 0.5 kg.
2.2. Sensors, Electrodes and Adjustable
Flat-to-90˚ Chair
Figure 1(A) shows the MMG sensor using a Freescale
MMA7260Q (USA) triaxial accelerometer with high
sensitivity—800 mV/V@1.5 G (G—acceleration of gra-
vity). The sensor provides three acceleration compo-
nents from which a resultant or overall acceleration can
be obtained, termed here as modulus. The complete as-
semblage weighs about 4 g (grams) and has dimensions
of 14 mm × 16 mm × 2 mm. A separate electronic circuit
allowed 10× amplification and 4 - 40 Hz Butterworth
filtering of MMG signals, focusing the MMG passband
[26]. The sampling frequency was 1 kHz for all signals.
No filtering was applied to the torque signal and the
EMG was acquired using a commercial device (EMG
System do Brasil/Brazil, 1000× differential amplification,
20 - 500 Hz bandwidth, active bipolar Ag/AgCl Kendall
Medi-Trace 100, 30 mm foam electrodes/USA). Both
the MMG sensor and EMG electrodes were placed on the
mid part of the biceps belly on the line between acromion
and the fossa cubit, approximately one-third of the dis-
tance proximal to fossa cubit [8]. The EMG reference
electrode was placed over the olecranon process of ulna.
The MMG sensor was equidistantly placed between two
active EMG electrodes. The shortest distance that al-
lowed proper operation of the MMG sensor with no me-
chanical interference from the EMG electrodes was 41
mm. Trichotomy and abrasion of the skin reduced inte-
relectrode impedance.
Figure 1(B) shows the load cell (100 kg, 2.0 ± 0.1

G. N. NOGUEIRA-NETO ET AL. 180
mV/V, EMG System do Brasil/Brazil) used for monitor
ing torque. It was adapted to a custom-built chair pre-
sented in Figure 1(C). A steel chain connected the load
cell extremities between the base of the chair and a grip
handle. For every participant, the grip handle had to be
adjusted to the correct height for the required elbow joint
position (90˚). The adjustable backrest was reclined so as
to keep the hips bent at an angle of 110˚.
2.3. Protocol
Volunteers performed isometric MVC of the elbow flex-
ors during 5 s. The highest torque value (peak) was used
to estimate the 70% MVC force level, which was taken
as reference for the fatigue submaximal test, likewise
Vaz et al. [27]. The participants could follow the ac-
quired signals at the target moment of 70% MVC on a
computer screen. This visual feedback allowed them to
check if their torque responses were drifting from the
onscreen 70% MVC reference line thus requiring ad-
justments in the torque intensity. Throughout the exercise,
technicians gave voice commands demanding that vol-
unteers keep the sustained torque level around the refer-
ence line for the longest time they could.
2.4. Data Acquisition
A LabVIEW™ program acquired MMG, EMG and
torque. All signals and volunteer data were saved into
European Data Format (EDF) files. The data acquisition
board was a Data Translation™ DT300 series with a
sampling rate of 1 kHz.
2.5. Time Instants, Segments of Signal and
Parameters
The MMG, EMG and torque signals were analyzed at
three time instants. Preliminary tests showed that torque
values oscillate around the 70% MVC reference level,
showing a high initial peak that should be dismissed. A
Figure 1. Transducers used in the tests. (A) Mechanomyog-
raphy sensor with tri-axial accelerometer on biceps brachii
muscle belly; (B) load cell, and (C) volunteer seated on cus-
tom-built chair.
threshold level was determined after each test and de-
fined as the mean value minus two standard deviations
extracted from a 5 s torque epoch in the beginning of the
signal, soon after the dismissed initial peak. This thresh-
old level helps determine the beginning and end of the
70% MVC test, respectively, when torque crosses this
level for the first and last times. These limits correspond
to the left and right vertical gray lines in Figure 2 where
dark boxes that indicate the segments of the signal to be
analyzed are also illustrated. All segments in Figure 2
can have variable WLA and are defined as: iniSS—seg-
ment that begins at the intersection of the left reference
line and torque; finSS—segment that ends at the inter-
section of the right reference line and torque; midSS—
the segment of the signal that is equidistant from iniSS
and finSS.
The data were normalized by the values obtained from
analyses of MVC level. The RMS and MPF were calcu-
lated for all signals, segments and WLAs. The RMS
represents the quadratic root mean value. MPF is the
frequency that divides the spectrum in two regions of
equal energy and is related to the compression that oc-
curs in the spectrum energy while muscles become more
fatigued. This characteristic affects the ratio between
spectrum regions, spectrum shape and skewness. RMS
and MPF are expressed by the Equations (1) and (2).
2
1
1n
i
i
RMS x
n
 (1)


0
0
in
ii
i
in
i
i
f
Pf
MPF
Pf





 (2)
in the Equations (1) and (2), x is the value of the sample,
n is the number of samples in the WLA, fi is the ith fre-
quency bin, and P(fi) is the spectral density function.
In addition to lateral displacement, Akataki et al. de-
termined that MMG sensors could monitor changes in
the longitudinal shortening of contracting muscles [28].
However, they compared and combined data from two
monoaxial sensors placed on different parts of the quad-
riceps. If muscles vibrate in more than one direction,
since this study employs triaxial accelerometer-based
MMG sensors, then muscle activity resultant acceleration
(the MMG modulus) can be extracted after combining
information from the three axes.
First, it is necessary to calculate the parameter values
of each axis (X, Y, and Z), then, the modulus is com-
puted as shown for the modulus of RMS and MPF in
Equations (3) and (4), respectively.
22
XY
RMSModRMSRMS RMS
2
Z
(3)
22
XY
RMSMPFMPF MPF MPF
2
Z
(4)

G. N. NOGUEIRA-NETO ET AL. 181
Figure 2. Example of the torque output (gray curve) and mechanomyography (MMG) sensor Z axis (blue curve) for a same
subject. Both signals were simultaneously recorded for biceps brachii muscle during sustained submaximal isetric elbow
an ± standard deviation. Sta-
med using SPSSTM for Win-
uration of the tests was 46 ± 16 s. All data
antly drawn from a normally distributed
investigated in the analysis of torque level
the results are shown in Table 1. Table 1
iniSS, midSS and finSS. Differences between segments
were only found related to aftSS. This implies that vol-
ed. Table 3 summarizes mean ± stan-
owed statistical differences
LAs. Each pair of symbols
om
flexion. iniSS, midSS, finSS and aftSS (initial, middle, final, and after signal segments, respectively) appear in the picture
with brighter colors. Above the signals are the indication of the analysis window lengths (2.0 s, 1.0 s, 0.5 s, 0.3 s, and 0.2 s
WLAs) and the threshold is indicated by a red line.
2.6. Statistical Analysis
All data are presented as me
tistical analyses were perfor
dows version 11.0. One-way analysis of variance
(ANOVA) with Fisher's least square differences (LSD)
post hoc test was used to confirm differences between
data groups. The first analysis investigated differences
between the signal segments (iniSS, midSS and finSS) in
every WLA. The second analysis tried to observe statis-
tical differences between WLAs in every signal segment.
Since some parameters were supposed to behave simi-
larly, there was the interest in determining whether they
would present linear correlation coefficients. Therefore,
Pearson’s correlation coefficients were calculated. All
tests adopted a significance level of p < 0.05.
3. Results
The average d
were signific
population according to Shapiro-Wilk tests. The follow-
ing sections describe the results of ANOVA, Fisher’s
LSD post hoc and Pearson’s correlation tests.
3.1. Torque
The parameter
was RMS and
shows that no statistical differences were found between
unteers performed the protocol correctly regarding the
sustainability of the 70% MVC torque level from iniSS
to finSS. Concerning differences between WLAs, 2.0 s
WLA was different from the others in finSS and aftSS.
This can be explained in terms of wide torque oscilla-
tions during a 2 s segment. As initially expected, RMS
response behaved similarly to AbsInt. From the inspec-
tion of aftSS data, it is noticeable that mean/standard
deviation values increase/decrease. This response is in
accordance with what can be seen in Figure 2, once big-
ger WLAs encompass a larger part of the ramp down in
the torque signal.
3.2. Mechanomyography
Regarding the analyses of MMG signals, only moduli
data were consider
dard deviation of data that sh
between either segments or W
(†, x) represents data that were different in a comparison
between two WLAs. These symbols must be interpreted
by looking at a specific segment and parameter (e.g.
MPF modulus at finSS). Table 2 indicates that few
WLAs presented differences, mainly for µ3 modulus in
aftSS. Few differences were observed between large and
short WLAs, specifically with MPF and SR in finSS and
µ3 in midSS.

G. N. NOGUEIRA-NETO ET AL. 182
Table 1. One-way analysis of variance Fisher’s leas
Analysis iniSS midSS
at squares difference.
finSS aftSS WLA (s)
nd
1.00 ± 0.00 ‡ 0.98 ± 0.04 ‡0.94 ± 0.05 x‡0.75 ± 0.23 x c 2.00
1.00 ± 0.00 ‡ 0.97 ± 0.07 ‡1.00 ± 0.04 †‡0.89 ± 0.09 † c 1.00
1.0 0. 1.0 ‡ 0.906 † 0 ± 0.00 ‡ 96 ± 0.07 ‡0 ± 0.04 †1 ± 0.c 0.50
1.00 ± 0.00 ‡ 0.97 ± 0.07 1.00 ± 0.04 †‡0.94 ± 0.04 † c 0.30
RMS
1.00 ± 0.00 ‡ 0.97 ± 0.06 1.00 ± 0.03 †‡0.96 ± 0.02 † c 0.20
1.00 ± 0.00 ‡ 0.98 ± 0.04 ‡0.94 ± 0.06 x‡0.74 ± 0.25 x c 2.00
1.00 ± 0.00 ‡ 0.97 ± 0.07 ‡
‡ AbsInt
1.00 ± 0.04 †‡0.89 ± 0.09 † c 1.00
1.00 ± 0.00 ‡ 0.96 ± 0.07 1.00 ± 0.04 †‡0.91 ± 0.06 † c 0.50
1.00 ± 0.00 ‡ 0.96 ± 0.07 0.99 ± 0.04 †‡0.94 ± 0.04 † c 0.30
1.00 ± 0.00 ‡ 0.97 ± 0.06 1.01 ± 0.03 †‡0.96 ± 0.03 † c 0.20
Post hocmean ± staalizeg iniS midftStial,d agnal sets, re-
spectively) and 2.0 s, 1.0 s, 0.52 s aysis w(WLAs). †—ticaeren(x0.05. ‡e has
statistical difference from cont RMroot bsInt—absol
aftSS WLA (s)
results of ndard deviation of normd torque durinS,SS, finSS and aS (ini middle, final anfter sigmen
s, 0.3 s, and 0.nalindow lengths value has statisl diffce from control ), p < —valu
rol (c), p < 0.05.S—mean square; Aute integral.
Table 2. One-way analysis of variance and Fisher’s least squares difference.
Analysis iniSS midSS finSS
1.00 ± 0.‡ 2.0 00 c 0.96 ± 0.08 0.90 ± 0.08 ‡x0.91 ± 0.07
1.00 ± 0.00 c 0.90 ± 0.13 0.81 ± 0.11 ‡0.85 ± 0.16 ‡ 1.0
1.0 0. 0.8‡0.8
‡
Ms
†
x
0 ± 0.00 c 93 ± 0.18 3 ± 0.14 1 ± 0.10 ‡ 0.5
1.00 ± 0.00 c ‡† ‡
PF Modulu
1.00 ± 0.00 c
0.87 ± 0.15 0.79 ± 0.12 0.83 ± 0.15 0.3
‡ 0.90 ± 0.19 0.78 ± 0.17 0.90 ± 0.17 0.2
1.00 ± 0.00 c ‡ x 1.05 ± 0.19 1.13 ± 0.18 1.08 ± 0.25 2.0
1.00 ± 0.00 c ‡ † 1.10 ± 0.28 1.19 ± 0.36 1.31 ± 0.37 1.0
1.00 ± 0.00 ‡ 1.34 ± 0.50
‡
μ3 Modulus
†
‡c †
1.38 ± 0.77 1.61 ± 0.39 0.5
1.00 ± 0.00 †
1.40 ± 0.69 1.50 ± 0.53 1.45 ± 0.41 0.3
1.00 ± 0.00 † 1.44 ± 0.74 1.61 ± 1.10 1.40 ± 0.73 0.2
1.00 ± 0.00 c 0.89 ± 0.13
‡ ‡ ‡ 0.81 ± 0.14 0.83 ± 0.10 2.0
1.00 ± 0.00 c 0.86 ± 0.17
‡ ‡
‡ ‡
#*
Zero-cross
Modulus
‡ 0.77 ± 0.13 0.81 ± 0.17 1.0
1.00 ± 0.00 c ‡ 0.85 ± 0.17 0.73 ± 0.12 0.80 ± 0.16 0.5
1.00 ± 0.00 c * 0.86 ± 0.17
‡‡‡ 0.71 ± 0.16 0.81 ± 0.13 0.3
1.00 ± 0.00 c ‡ 0.86 ± 0.22 0.72 ± 0.18 0.82 ± 0.23 0.2
1.00 ± 0.00 c †‡ 3.32 ± 5.42 2.65 ± 1.98 4.11 ± 3.66 2.0
1.00 ± 0.00 c ‡† ‡ 2.90 ± 2.39 3.17 ± 1.71 3.67 ± 3.71 1.0
1.00 ± 0.00 c 5.31 ± 5.83
‡
SR Modulus 4.99 ± 5.73 4.66 ± 5.52 0.5
1.00 ± 0.00 5.67 ± 7.69 8.72 ± 16.1 20.8 ± 53.2 0.3
1.00 ± 0.00 c ‡x 7.80 ± 6.69 14.6 ± 18.6 11.1 ± 20.1 0.2
Pmeviaormaliyogray (MMG iniSS, midSS, (inamiddlel and
after signal segments,nd 2. 1.s, 0.5 nalysis windAs). †—value iffe from cl (x),
p < 0.05. ‡, *—valuical dree froms (c, #), p <eaer frekewtrum;
zero-crossings—the ns the biphas signal seline; SR—spec
hese symbols must be interpreted by looking at a spe-
ci
ints differences between iniSS and the three
other segments, whereas MPF does not differentiate
ost hoc results of an ± standard detion of nzed mechanomph) values duringfinSS and aftSSitil, , fina
respectively) a0 s,0 s, 0.3 s, and 0.2 s aow lengths (WLhas statistical drenceontro
quency; μ3—ses have statist
umber of time
iffe nc
ic
respective control
crossed the ba
0.05. MPF—m
tral ratio.
n powness of the spec
Each pair of symbols (‡, c) represents data that were
different in a comparison between two signal segments.
able to detect such changes. In almost all WLAs, zero-
crossing po
T
fic parameter and two segments (e.g. MPF modulus at
iniSS and aftSS). During the sustained submaximal
torque exercise performed in this study, for the modulus
of MPF, SR and zero-crossing, 1.0 s WLAs could detect
differences between MMG data obtained with fresh and
fatigued muscle. With 2.0 s WLA, SR and µ3 were un-
iniSS from midSS. Concerning zero-crossings and MPF,
the use of 2.0, 1.0 or 0.5 s WLAs informed the same dif-
ferences. µ3 modulus and SR modulus raised the same
differences between iniSS and aftSS with 2.0, 1.0 and 0.5
s WLAs. Although monitoring signals with each of these
parameters will investigate the same phenomena (spec-

G. N. NOGUEIRA-NETO ET AL. 183
trum compression towards the lower frequency region),
the use of triaxial MMG modulus response indicates that
they can present different results.
3.3. Electromyography
Figure 3 shows the normalized EMG RMS (mean ±
standard deviation) of all WLAs and segments. The val-
ues of iniSS, midSS and finSS presented statistical dif-
ference with aftSS for 2.0 s WLA. The aftSS presented
d 1.0 s WLAs. Table 3 sum-
viation of all parameters that
0.75 in
and EMG, respectively. This work
rifying whether similar parameters
except with 2.0 s
d statistical differences between segments as
sh
ross (time domain, but related to the
do
s.
-
EMG and MMG techniques used dif-
ysis algorithms (Table 6) and different
difference between 2.0 s an
marizes mean ± standard de
showed statistical differences between segments.
3.4. Correlations
Correlation data show which parameters presented prop-
ortional variations along the test. Table 4 and Table 5
inform Pearson’s correlation coefficients of all parame-
ters that had at least one strong correlation (r >
black font) for MMG
was interested in ve
would also show linear correlation. Therefore, the focus
was set on correlations of amplitude parameters (RMS,
AbsMean, AbsInt, peak-to-peak, and zero-crossing) and
spectral parameters (MPF, µ3 and SR).
Table 4 shows for MMG signal that AbsInt had a
strong positive correlation with AbsMean. Interestingly,
likewise RMS, none of these parameters showed signifi-
cant statistical differences between signal segments as
indicated in Table 2. MPF showed strong correlation
with SR, zero crossing and peak count
WLA.
Similarly, peak to peak showed a strong correlation
with number of zero-crossings. Nevertheless, zero-cross
presente
own in Table 2.
Table 5 shows that EMG RMS had strong correlation
with AbsInt. MPF (frequency domain) had a strong cor-
relation with zero-c
minant frequency), except with 2.0 s WLA. MPF also
showed a strong correlation with SR and 1.0 s WLA.
Both these findings are in accordance with Table 3.
The parameters described in the Methods that are not
present in Table 4 and Table 5 did not show statistical
differences or strong correlation with other parameter
4. Discussion
Studies conducted in the last decades that analyzed mu
scle behavior with
ferent signal anal
muscle contractions [15,28,29]. Kaplanis et al. conducted
a study with many EMG parameters monitored con-
comitantly [30]. In addition to EMG, this work incorpo-
rated triaxial MMG analysis and aimed to contribute in
two ways. First one was to observe the responses of
variable-size WLAs of MMG and EMG parameters si-
multaneously during submaximal fatiguing contractions.
The second goal was to use triaxial MMG sensors and to
investigate which information would result from the
combination of each axis acceleration data. This charac-
teristic can be useful since muscles have a nonuniform
distribution of fiber types [31] and geometry can influ-
ence muscle response [32]. Individual axes could present
interesting results; however, our focus was in response of
Figure 3. One-way analysis of variance and Fisher’s least squares difference post hoc results of electromyography (EMG)
normalized root mean square (RMS) values during iniSS, midSS, finSS and aftSS (initial, middle, final and after signal
segments, respectively) and their different analysis window lengths (WLAs)—*Significant difference between segments (p <
0.05)—†Significant difference between 2.0 s and 1.0 s WLA (p < 0.05).

G. N. NOGUEIRA-NETO ET AL. 184
Table 3. One-way analysis of variance and Fisher’s least squares difference.
Analysis iniSS midSS finSS aftSS WLA (s)
1.00 ± 0.00 1.05 ± 0.16 ‡0.94 ± 0.27 0.78 ± 0.39 c 2.0
1.00 ± 0.30 1.0
1.00 ± 0.98 0.96 ±0.88
‡# ‡*
00 1.01 ± 0.15 0.97 ± 0.30 0.96 ± 0.
0.00 ± 0.18 0.29 ± 0.30 0.5
1.00 ± 0.00
AbsInt
1.00 ± 0.00
0.96 ± 0.18 0.96 ± 0.31 0.84 ± 0.3 0.3
0.93 ± 0.22 0.90 ± 0.33 0.85 ± 0.37 0.2
1.00 ± 0.00 c * ‡ * 0.90 ± 0.07 0.77 ± 0.08 0.75 ± 0.08 2.0
1.00 ± 0.00 c 0.91 ± 0.09 #‡*
#‡*
MPF
‡ *
0.77 ± 0.14 0.75 ± 0.14 1.0
1.00 ± 0.00 c ‡ 0.87 ± 0.12 0.72 ± 0.17 0.76 ± 0.21 0.5
1.00 ± 0.00 c ‡‡ 0.88 ± 0.15 0.78 ± 0.29 0.79 ± 0.24 0.3
1.00 ± 0.00 c ‡‡ 0.85 ± 0.12 0.78 ± 0.25 0.72 ± 0.21 0.2
1.00 ± 0.00 2.80 ± 4.96 4.40 ± 12.5 13.6 ± 28.9 2.0
1.00 ± 0.00 c 1.08 ± 0.30 #*
μ3
‡‡
1.52 ± 0.73 1.44 ± 0.49 1.0
1.00 ± 0.00 c ‡ 1.12 ± 0.36 1.22 ± 0.46 1.66 ± 1.11 0.5
1.00 ± 0.00 1.14 ± 0.47 1.31 ± 0.70 1.52 ± 1.12 0.3
1.00 ± 0.00 1.19 ± 0.47 1.37 ± 0.81 1.57 ± 1.24 0.2
1.00 ± 0.00 0.87 ± 0.11 0.80 ± 0.21 0.89 ± 0.38 2.0
1.00 ± 0.00 c 0.87 ± 0.12 0.81 ± 0.21 ‡
‡
‡
Zero-cross
‡ 0.77 ± 0.23 1.0
1.00 ± 0.00 c ‡ 0.87 ± 0.13 0.78 ± 0.20 0.78 ± 0.25 0.5
1.00 ± 0.00 c ‡ 0.89 ± 0.13 0.77 ± 0.26 0.80 ± 0.29 0.3
1.00 ± 0.00 c ‡ 0.93 ± 0.15 0.80 ± 0.30 0.82 ± 0.26 0.2
1.00 ± 0.00 c ‡‡ 2.10 ± 1.60 3.60 ± 3.10 3.50 ± 1.65 2.0
1.00 ± 0.00 c 1.50 ± 0.83 #*
‡
SR
‡
‡‡ *
3.05 ± 2.39 3.60 ± 2.07 1.0
1.00 ± 0.00 c ‡ 2.02 ± 2.03 5.75 ± 7.35 6.08 ± 7.98 0.5
1.00 ± 0.00 ‡ c 1.39 ± 0.83 4.57 ± 4.83 3.48 ± 3.01 0.3
1.00 ± 0.00 ‡ c 2.15 ± 2.04 4.28 ±3.59 8.12 ± 6.77 0.2
Post hlts of meviatofrmalizraphy (EMG) va midSS, finStiaile, fina after
signal sents, respe.0 s, s, 5 s, 0.3alysindow le ‡,lues hiff fromive
controls (c, #), p < 0.tegraE signa powquencss of the spectr—thber os the
biphasic signal crosse SR—l ratio.
e use of 1.0 s
e fatigue in isometric muscle
the volunteer could keep 70% MVC and his muscle was
oc resu
gme
an ± standard deion noed electromyoglues during iniSS,S and aftSS (inil, m ddl and
ctively) and 2
05. iEMG—in
1.0
l of
0.
MG
s, and 0.2 s an
l; MPF—mean
is w
er fre
ngths (WLAs).
y; μ3—skewne
*—vaave statistical d
um; Zero-cross
eren
e num
ce respect
f time
d the baseline;spectra
MMG modulus parameters.
Inspecting the literature related to MMG end EMG
analysis, it is clear for a preference in th
erated in the beginning of the test was determined for a
fresh muscle. FinSS is the last signal segment in which
WLA. The analysis of muscl
contractions requires that EMG signals be wide-sense
stationary for segments ranging from 0.5 s to 2.0 s WLA
at low contraction levels [33], and 0.5 s to 1.5 s WLA for
50% - 80% MVC [34]. Notwithstanding, there are stud-
ies using WLAs ranging from 0.1 s to 2.0 s.
The volunteers performed sustained 70% MVC as long
as they could. Table 1 shows data similarity from iniSS
to finSS and no significant difference was observed. This
implies that volunteers maintained torque statistically
around 70% MVC between iniSS and finSS. In addition,
the visual feedback system allowed maintenance of
torque level along the 70% reference line. Although vol-
unteers were instructed to maintain a constant torque,
brief MU-generated fluctuations in the torque signal can
be explained by the recruitment and derecruitment of
MU that are the main mechanisms for generating force
between 40% and 80% MVC [35]. The 70% MVC gen-
fatigued. In this moment, the torque was the maximum
that the participant could generate. We assumed that
muscle contractile properties suffered changes during
finSS when compared to the muscle condition in iniSS.
This assumption is supported by the literature [36] and
by statistical differences between the segments in Table
2 for MMG and in Table 3 for EMG.
Some papers used three contractions in order to det-
ermine MVC, but in this study volunteers performed only
one contraction to quantify the maximum torque. There-
fore it is possible that small errors in MVC determination
can have occurred. Although care was taken so as to
guarantee that volunteers did not perform prohibited
movements during the test and the determined 70%
MVC level was successfully sustained from iniSS to
finSS, small muscle length changes can have occurred in
between. The BB is a biarticular muscle, and the results
can have been affected due to small displacements of the

G. N. NOGUEIRA-NETO ET AL. 185
Table 4. Pearson’s correlation betw mechanomyography parameters.
Parameters WLA AbsInt AbsMean MPF Peak to Peak Zero-cross SR
een
2.0 1.000
1.0 1.000
0.
AbsInt
1.
5 1.000
0.3 1.000
0.2 1.000
2.0 0.999 000
1.0 0.999 1.
1.
1.
AbsMean
1.
000
0.5 0.999 000
0.3 0.999 000
0.2 0.999 1.000
2.0 0.159 0.159 000
1.0 −0.556 −0.556 1.
1.
1.
MPF
1.
000
0.5 −0.602 −0.602 000
0.3 −0.610 −0.611 000
0.2 −0.592 −0.591 1.000
2.0 −0.522 −0.522 0.384 000
1.0 −0.677 −0.677 0.925 1.
1.
1.
Peak to Peak
1.
000
0.5 −0.614 −0.614 0.759 000
0.3 −0.523 −0.525 0.893 000
0.2 −0.524 −0.523 0.869 1.000
2.0 −0.539 −0.539 0.476 0.942 000
1.0 −0.736 −0.736 0.866 0.920 1.
1.
1.
Zero-cross
1.
000
0.5 −0.686 −0.686 0.808 0.874 000
0.3 −0.648 −0.648 0.798 0.680 000
0.2 −0.522 −0.520 0.859 0.880 1.000
2.0 0.210 0.210 −0.535 −0.596 −0.561 000
1.0 0.757 0.757 −0.798 −0.835 −0.760 1.
1.
1.
SR
000
0.5 0.680 0.680 −0.677 −0.644 −0.696 000
0.3 0.486 0.486 −0.489 −0.420 −0.387 000
0.2 0.405 0.405 −0.636 −0.550 −0.597 1.000
Data were split by analysis w length (WLA). AbsInt—abtegral; Absute mespectral ratio; ean powercy;
RMS—rooan square; zeronumbeo-crossings igment; peanumber of the segment. Aome paramow
strong coron (r < 0.75, p ), they desent signifiical diffen in Ta
shoulder joint.
Table 2 indicates that MPF showed differences bet-
f spectral energy leakage due to the nature of the dis-
rithm [37]. The smaller the number of
sa
she specttent to therequencon
means that muscles are becoming fatigued [11].
ad more statistical differences be-
tween segments than the largest and the smallest WLAs.
re
indowsolute inMean—absolan; SR—MPF—m frequen
t me
relati
-cross—
< 0.05
r of zer
id not pr
n the se
cant statist
k count—
rence as show
peaks in
ble 2.
lthough seters sh
ween the analyzed signal segments. Depending on the
WLA, MPF parameters should suffer the negative effect
Table 3 presented EMG data and showed that WLAs
of intermediate sizes h
o
crete FFT algo
mples provided to the FFT the greater the resolution
loss in the spectrum that will affect the precision of MPF.
The increasing in μ3 values for MMG signals repress-
ented a spectrum compression to the left that can be con-
firmed by the decreasing values of MPF. The compres-
Since the 70% MVC level limits the length of WLAs to
1.5 s [34], differences found with 2.0 s WLA must be
ion of tral con lower fy regi
jected. The WLA that showed more statistical differ-
ences was 1.0 s WLA. However, care must be taken
when choosing WLA and force contraction level due to
non-stationarity problems. During muscle fatiguing con-

G. N. NOGUEIRA-NETO ET AL. 186
Table 5. Pearson’s correlation betwee
PF
n electromyography parameters.
Parameters WLA RMS AbsInt M μ3 Peak-to-Peak Zero-cross SR
2.0 1.000
1.0 1.000
0.5 1.000
0.3
RMS
0.2 1.000
1.000
2.0 0.985 1.000
1.0 0.958 1.
1.
1.
AbsInt
1.
000
0.5 0.967 000
0.3 0.965 000
0.2 0.988 000
2.0 0.247 0.276 1.000
1.0 −0.104 −0.107 1.
− −000
−000
1.
− 1.
000
0.5 0.028 0.031 1.
0.3 0.
0.2
074
0.068
0.033 1.
0.061
MPF
000
2.0 −0.028 −0.064 0.937000
1.0 −0.520 −0.436 −0.542 1.
−000
−000
1.
1.
000
0.5 −0.542
−
−0.579 0.498 1.
0.3
0.2
0.600
−0.524
−0.569
−0.529
0.608 1.
−0.629
μ3
000
2.0 0.677 0.660 0.358 0.091 000
1.0 0.377 0.336 0.351 −0.843 1.
−000
−000
Peak-to-Peak
1.
− 1.
000
0.5 0.300 0.253 0.529 0.385 1.
0.3
0.2
0.497 0.
0.445
474 0.
0.446
449
0.451
0.339 1.
−0.534 000
2.0 −0.433 −0.463 0.369 −0.470 0.337000
1.0 −0.201 −0.252 0.853 −0.302 0.196 1.
1.
1.
Zero-cross
1.
−0.2 1.
000
0.5 −0.064 −0.074 0.951 −0.506 0.390 000
0.3 0.061 −0.005 0.951 −0.630 0.383 000
0.2 0.001 0.000 0.893 −0.553 0.360 000
2.0 −0.277 −0.318 −0.723 0.811 −0.200 30000
1.0 0.072 0.083 −0.892 0.469 −0.302 −0.780 1.
1.
1.
SR
1.
000
0.5 −0.112 −0.131 −0.797 0.588 −0.314 −0.791 000
0.3 −0.073 0.007 −0.801 0.463 −0.318 −0.766 000
0.2 −0.348 −0.343 −0.637 0.711 −0.216 −0.624 000
Data were split analysis window length (WLA). SR—speAbsInt—il of rectifiel; Rt meaner
frequency; zero-cross—nr of zero-cand μ3—s of the
tractiere are aC reduce to thf
myofibrils to produce force [ a reduin the
muscle contractioncity [2we bhe 1.0
dicate more differences. The EMG interelectrode dis-
tweens.
The mf accelr-based MMG senff-
ts the y of md paraand eve
to operate at the highest allowable sensibility. There-
fore, their low weight and small dimensions theoretically
ctral ratio;
skewnes
ntegra
spectrum.
d EMG signaMS—roo square; MPF—mean pow
umberossings;
on th MVtion due inability o be
1] andction
velo] so that elieve t ec
s WLA was the one that better represented EMG pattern
variations during the fatiguing protocol, because it could
mass can cause serious distortions [38]. The sensors were
set
in
tance was a limitation in this study, because the Surface
Electromyography for the Non-Invasive Assessment of
Muscles (SENIAM) project does not recommend using
distances above 20 mm. Therefore, unstable recordings
with a high level of non-stationarity can have occurred
and can be the cause that no differences were observed
favored the acquisition of very small vibrations, confer-
ring more reliability to the MMG acquisition system. On
the other hand, it can be argued whether there would be
practical benefits in using these sensors because they
have high sensibility and, thus, unwanted movements
could eventually introduce spurious noise. Indeed, abrupt
WLA
ass oerometesors a
qualitonitoremeters xcessi

G. N. NOGUEIRA-NETO ET AL. 187
Ref: reference; Sign: signal
Table 6. Investigated analysis window lengths and parameters in the recent literature.
nt; Int: integral; Abs. mean: absolute mean; Var: variance. ; RMS: root mean square; MPF: mean power frequency; MC2: second spectral moment; μ3: third spectral mome

G. N. NOGUEIRA-NETO ET AL. 188
movement artifacts add unwanted noise to the signals
and this can be observed even in monoaxial accelerome-
ter-based sensors. Triaxial accelerometers register this
abrupt noise in all axes; therefore it can be identified and
processed.
The contractions measured in this investigation were
under voluntary control. So, some noise has been ac-
cepted due to the activity of other muscles involved in
the task in addition to BB.
This study investigated many MMG and EMG par-
ameters simultaneously during fatiguing isometric con-
tractions and can help determine useful parameters for
neuroprosthesis control strategies. For example, Table 3
shows that MPF presented more differences than SR and
µ3. Zero-crossing analyses identified differences be-
tween iniSS and midSS, except for 0.2 s WLA. In neuro-
prosthesis control it is important to preview forthcoming
losses in torque performance. Zero-crossing and MPF
could be used for FES control strategies. In a sustained
70% MVC isometric contraction of the BB, first differ-
ences could be observed with zero-crossing. Then, when
MPF started showing differences between the computed
parameters and iniSS, it could be appropriate to start a
new FES profile for avoiding most severe effects caused
by muscle fatigue in the muscle performance. Apparently,
the larger WLAs presented more differences than the
smaller ones giving the impression that they are better for
monitoring muscle physiological conditions along time
or for FES control. However, large WLAs could raise
usability problems for real time strategies because deci-
sion making based on events that have occurred more
than 1 s before can be impractical or catastrophic. Be-
cause of the serious implications, it is recommended that
future studies address these problems.
The influence of variations in WLA has been studied
for EMG and MMG using temporal and spectral para-
meters. Regarding MMG, the 1.0 s WLA had the best
tradeoff between WLA and the identification of varia-
tions in muscle condition along time. Observing more
than one parameter simultaneously can be useful for
monitoring muscle fatigue, because they can indicate
variations in muscle condition that begin to be observed
in different segments and following this response pattern
can help detect the installation of severe muscle fatigue.
In spite of MPF, µ3 and SR be related in meaning, MPF
and zero-crossing were able to identify more consistent
differences between iniSS and the other segments, in
spite of variations in WLA. Therefore, they were consid-
ered more useful for monitoring changes in muscle con-
dition.
Concerning EMG, the conclusion is that for the aftSS
the 1.0 s and 2.0 s WLAs have normalized RMS differ-
ence. There was strong correlation between different
domain parameters (MPF and zero-crossing).
A future step is the investigation of the same para-
meters and WLAs with different muscles. Because of the
amount of data, however, future studies investigating
different levels of contraction will concentrate only on
few parameters and WLAs.
5. Acknowledgements
G. N. N. N., E. M. S., E. K. and P. N. would like to thank
CNPq and CAPES for the financial support and grants
received. Authors would also like to thank FINEP, SETI,
and Fundação Araucária.
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