Watanabe et al. European Review of Aging and Physical Activity (2018) 15:3
https://doi.org/10.1186/s11556-018-0192-z

RESEARCH ARTICLE

Open Access

Relationships between muscle strength andmulti-channel surface EMG parameters ineighty-eight elderly
Kohei Watanabe1*, Motoki Kouzaki2, Madoka Ogawa3,4, Hiroshi Akima3,5 and Toshio Moritani6,7

Abstract
Background: Since age-related muscle strength loss cannot be explained solely by muscle atrophy, other
determinants would also contribute to muscle strength in elderly. The present study aimed to clarify contribution ofneuromuscular activation pattern to muscle strength in elderly group. From 88 elderlies (age: 61~ 83 years), multichannel surface electromyography (EMG) of the vastus lateralis muscle was recorded with two-dimensional 64
electrodes during isometric submaximal ramp-up knee extension to assess neuromuscular activation pattern.
Correlation analysis and stepwise regression analysis were performed between muscle strength and the parameters
for signal amplitude and spatial distribution pattern, i.e., root mean square (RMS), correlation coefficient, andmodified entropy of multi-channel surface EMG.
Results: There was a significant correlation between muscle strength and RMS (r = 0.361, p = 0.001) in the elderly.
Muscle thickness (r = 0.519, p < 0.001), RMS (r = 0.288, p = 0.001), and normalized RMS (r = 0.177, p = 0.047) wereselected as major determinants of muscle strength in stepwise regression analysis (r = 0.664 in the selected model).
Conclusion: These results suggest that inter-individual difference in muscle strength in elderly can be partly
explained by surface EMG amplitude. We concluded that neuromuscular activation pattern is also major
determinants of muscle strength on elderly in addition to indicator of muscle volume.
Keywords: Aging, High-density surface electromyography, Quadriceps femoris muscles

BackgroundAge-related decline in muscle strength is generally
greater than decline in muscle mass with aging [29].
This means that while morphological factors are major
predictors of muscle strength in human including elderly[31, 42, 43], other causes also contribute to age-related
decline in muscle strength. In many possible contributors, neuromuscular function would be one of the important factors since voluntary muscle contraction isregulated by neuromuscular activation system. Similarly
to age-related morphological changes, modifications
with aging in neuromuscular activation pattern havebeen reported in previous studies, i.e., decrease in motor
unit firing rate [10, 20, 34, 38]. However, little is known* Correspondence: wkohei@lets.chukyo-u.ac.jp
1
Laboratory of Neuromuscular Biomechanics, School of International LiberalStudies, Chukyo University, Yagotohonmachi, Showa-ku, Nagoya 466-8666,
Japan
Full list of author information is available at the end of the article

about relationship between decline in muscle strength andmodification in neuromuscular activation pattern for elderly whereas many studies have demonstrated that agerelated decline in muscle strength is strongly associatedwith morphological changes in aged muscle [31, 42, 43].
This suggests that contribution of alteration in neuromuscular activation pattern to decline in muscle strength withaging is not well understood. It can be partly explained bymethodological limitation to measure and quantify neuromuscular function.
Surface electromyography (EMG) has been widelyused for measuring and/or quantification of neuromuscular function since this method can non-invasively detect motor unit action potentials which is a trigger ofimportant physiological process in muscle contraction[2, 7, 15]. On the other hand, this methodology cannot
investigate detailed motor unit activation properties,
such as firing rate or recruitment threshold of individual
motor units, since the detected signals are the result of© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Watanabe et al. European Review of Aging and Physical Activity (2018) 15:3

summation of action potentials from multiple motor
units under the electrodes [13, 15]. Intramuscular EMG
is a limited tool for direct detecting of individual motor
unit action potentials [9, 25], but is not useful for various types of subjects such as elderly because of invasiveness. Instead of these methodologies, some studies
including our studies recently apply multi-channel surface EMG technique to estimate motor unit recruitment
pattern from spatial distribution pattern of action potential within a muscle [11, 14, 17, 18, 39–41]. From aspatial inhomogeneity in the location of different types ofmuscle fibers and a clustering of muscle fibers innervatedby one motor unit in limited territory [3, 4, 22, 23, 35], alteration in spatial distribution pattern of surface EMG
within a muscle would be available to estimate motor unit
recruitment pattern. Holtermann et al. [17] and Farina etal. [14] demonstrated that changes in spatial distribution
pattern of surface EMG within a muscle with an increaseof contraction level and fatigue for estimating motor unit
recruitment patterns [14, 17]. Our previous study demonstrated that lesser change in spatial distribution pattern ofsurface EMG within vastus lateralis (VL) muscle with anincrease in exerted force for elderly comparing with young[39]. This phenomenon is seemed to be fitted with agerelated morphological changes in skeletal muscle, such as
decrease in number of motor unit, increase in innervation
ratio, or remodeling of motor units from fast to slow types[8, 21–23, 33, 35]. However, relationship between muscle
strength and/or age-related muscle weakness and neuromuscular functions estimated from spatial distribution pattern of surface EMG are not investigated because of small
sample size and limited range of muscle strength for elderly subjects.
We aimed to clarify contribution of neuromuscular activation pattern to muscle strength in elderly group by
using multi-channel surface EMG variables. In thepresent study, spatial distribution pattern of surface
EMG within the VL muscle were used to determine thedegree of association with muscle strength and werecompared among subject group of elderly with different
muscle strength and young subjects. Power et al. [33]showed that elderly with lifelong high-intensity physicalactivity could mitigate age-related loss of motor units.
Other studies demonstrated changes in motor unit activation properties such as EMG amplitude or firing
rate following resistance training even in aged muscle[19, 30]. These studies suggest motor unit remodeling
and/or modification of motor unit activation properties with aging could be varied among individuals anddepend on amount of lifelong physical activity. Fromage-related alteration in spatial distribution pattern ofsurface EMG could be partly explained by motor unit
remodeling and modification of motor unit activation
properties with aging, we hypothesized that motor

Page 2 of 10

unit recruitment pattern which is estimated frommulti-channel surface EMG variables could shed somelight upon inter-individual difference in muscle
strength in elderly.

Methods
Subjects

A total of 111 subjects including 88 elderlies (age: 61~
83 years) and 23 young (age: 19~ 26 years) participatedin this study. The subjects living in a nursing home were
included in the elderly group. The young subjects weremale university students and not participated in any
competitive sports events. Twenty-seven females were
included in elderly group.
The subjects gave written informed consent for the
study after receiving a detailed explanation of the purposes, potential benefits, and risks associated with participation. All procedures used in this study wereapproved by the Committee for Human Experimentation
at the Graduate School of Human and Environmental
Studies, Kyoto University and the Research Ethics
Committee of Chukyo University, and were in accordance with the Declaration of Helsinki.
Experimental design

To familiarize themselves with the motor tasks used in
the present study, all subjects came to the laboratory >1 week before the experimental day. The subjects performed maximal voluntary contraction (MVC) and submaximal incremental ramp contraction during unilateralisometric knee extension. External force at the distal
portion of the shank that fixed in a custom-made dynamometer (Takei Scientific Instruments Co., Ltd., Niigata,Japan) with a force transducer (LU-100KSE; Kyowa
Electronic Instruments, Tokyo, Japan) was measured
during isometric knee extension. During knee extension,both hip and knee joint angles were flexed at 90° (180°corresponds to full extension). Since European consensus on definition and diagnosis used this knee joint angle[6], the present study selected this knee joint angle.
The MVC trial consists of a gradual increase in knee
extension force from baseline to maximum in 2–3 s anda plateau phase at maximal contraction for 2–3 s. The
timing of the task was based on a verbal count given at
1-s intervals with vigorous encouragement from the investigators. After three submaximal trials at approximately 50, 70, and 90% of MVC as warming up, thesubjects performed two MVC trials with ≥ 2 min rest in
between. MVC force was mean value of 1 s, when thehighest force was produced, during a plateau phase.
MVC torque was calculated as the product of MVC
force and the distance between the estimated knee joint
center and center of force transducer at the distal portion of the shank. To normalize difference in body type

Watanabe et al. European Review of Aging and Physical Activity (2018) 15:3

among the subjects, MVC torque relative to body mass
(MVC/BM) was also calculated and used for further
analysis.
After MVC trials, the subjects performed submaximal
ramp contractions from 0 to 70% MVC in 35 s (rate offorce increase: 5% MVC/s) (Fig. 1). Target and performed forces were shown to the subjects on a monitor.
Two submaximal contractions were performed with ≥2 min rest in between. Out of two trials, the one trial
with the smaller error between the targeted and performed forces was selected with visual inspection andused for analysis.
Multi-channel surface EMG

Surface EMG signal was recorded from the VL muscle
with a semi-disposable adhesive grid of 64 electrodes(ELSCH064R3S, OT Bioelectronica, Torino, Italy). The
two-dimensional electrode grid is comprised of 13 rows
and 5 columns of electrodes with 1 missing electrode in
the upper left corner. Each electrode is 1-mm diameterand inter electrode distance was 8-mm inter-electrodedistance in both directions (Fig. 1). Before attaching theelectrode, the skin was cleaned with alcohol. The line
between the head of the great trochanter and inferior
lateral edge of the patella which was defined and used as
reference line for positioning the electrode grid. The
center of electrode grid was positioned at middle pointof the reference line and columns of electrodes alignedwith the reference line. The position of the missing electrode was located proximally. To assure proper electrodecontact with the skin, conductive gel was inserted into

Page 3 of 10

the cavities of the grid electrode. The grid electrode wasconnected to the amplifier through 4 connectors whichwere fixed to the skin by elastic tape. A reference electrode (C-150, Nihon Kohden, Tokyo, Japan) was placedat the iliac crest.
Fifty-nine bipolar surface EMG signals along the columns were made from 64 electrodes. Root mean square(RMS) values were calculated from rom bipolar signal
sampled over 1 s at 5% increment from 20 to 65% of
MVC ramp contraction. Sampled signals were overlappedby 0.5 s between neighboring contraction levels, since performed ramp rate was 10% of the MVC force / 1 s.
We used correlation coefficients and modified entropy
to compare spatial distribution pattern and to quantify aheterogeneity for RMS values of multi-channel surface
EMG within a grid, respectively. Correlation coefficientswere calculated from the 59 pairs of RMS values at thesame regions between 20% of MVC and those of allother torque levels to compare the spatial EMG potential distribution pattern as used in our previous studies[40]. Decrease of correlation coefficient indicates change
in spatial EMG potential distribution pattern. Modified
entropy was calculated as done by Farina in a previous
work [14] and our previous studies [39–41], modified
entropy was defined as entropy of the signal power, that
is.
Ε¼−

X59
i¼1

pðiÞ2 log2 pðiÞ2

where p(i) is the square of the RMS value of channel i
divided by the sum of the squares of all the 59 RMS

Fig. 1 Electrode, experimental setup, and motor task used in the present study and representative spatial distribution of root mean square ofmulti-channel surface electromyography in vastus lateralis muscle shown as color map for two elderlies with different muscle strength. MVC: maximal voluntary contraction

Watanabe et al. European Review of Aging and Physical Activity (2018) 15:3

values, at the given force level. Therefore p(i)2
represents the normalized power of each channel. It
is E = 0 when all the p(i) are zero except one and ismaximal and equal to log259 = 5.884 when the p(i)values are identical and equal to 1/59 (all channels
have the same energy). Decrease in modified entropy
mean that increase of heterogeneity in spatial EMG
potential distribution within an electrode grid.
At the center of electrode location, longitudinalultrasonographic images (SSD-900, ALOKA, Tokyo,
Japan, or Fazone CB, Fuji-film, Tokyo, Japan) weretaken to measure the thickness of the subcutaneous
tissue and VL muscle before attaching the electrodegrid. On NIH ImageJ software, the vertical line of theimage was defined on the center of horizontal axis
for measurements. Distance between the intersectionsof that vertical line and deeper and superficial aponeurosis of the VL muscle were measured as muscle
thickness of VL. Thickness of subcutaneous tissues
was distance between the intersections of that verticalline and superficial aponeurosis of the VL muscle andskin surface.
Statistics

All data are provided as the mean and standard deviation. To test relationship between muscle strengthand neuromuscular function, spearman’s rank correlation coefficient was calculated for MVC with multichannel surface EMG variables and others for theelderly and young groups, respectively. We also performed a stepwise regression analysis for MVC in theelderly group. Nine independent variables, i.e., age,muscle thickness of VL, thickness of subcutaneous
tissues, and six surface EMG variables, were entered
the stepwise regression if they represented a significant contribution to the explained variance corresponding to an alpha level of p < 0.05. Six EMG
variables were modified entropy, correlation coefficient value, mean RMS across all 59 channels, standard deviation of RMS across all 59 channels, mean
RMS across all 59 channels normalized by those at
20% of MVC (normalized RMS), and standard deviation of mean RMS across all 59 channels normalized
by those at 20% of MVC (normalized standard deviation) and these variables calculated at 65% of MVC
were used for correlation analysis.
To compare neuromuscular function among the elderly with different muscle strength, 88 elderly subjects
were divided into three groups based on MVC using cumulative frequency distribution, i.e., (Weak) < 33.3%,(Mid) 33.4~ 66.6%, and (Strong) > 66.7%, and the weak
and strong groups were used for further analysis. Age,height, body mass, MVC, MVC/BM, muscle thickness of
VL, and thickness of cutaneous tissues, and modified

Page 4 of 10

entropy, correlation coefficient value, and mean RMS
across all 59 channels at from 20 to 65% of MVC werecompared between the weak and strong strength groups
by using Mann-Whitney test. EMG variables for the elderly groups with weak and strong strength were compared with young subjects by using Mann-Whitney test.
We also compared modified entropy, correlation coefficient value, and mean RMS across all 59 channels at
from 20 to 65% of MVC between the elderly and younggroups which are matched for MVC. MVC/BM, bodymass, muscle thickness of VL, and thickness of cutaneous tissues were also matched between these groups.
The level of significance was set at p < 0.05. Statisticalanalysis was performed using SPSS (version 15.0, SPSS,Tokyo, Japan) and MATLAB (R2009b, MathWorks GK,
Tokyo, Japan).

Results
Representative multi-channel surface EMG from 20% to
65% of MVC for two subjects with high and low muscle
strength were shown as two-dimensional map of color
scaled RMS (Fig. 1). With increase in exerted force,
RMS of each channel non-uniformly increased within anelectrode grid in both subjects and higher RMS wereseen in subject 79 than subject 34. While increase in
RMS is represented at limited area (distal portions) in
subject 34, increase in RMS were demonstrated in larger
area and spatial distribution pattern of RMS markedly
changed in subject 79. This was reflected as lower correlation coefficient value at 50~ 65% of MVC in subject
79. Also, area of similar RMS seemed to be larger insubject 79 at all contraction levels comparing with subject 34, reflecting higher modified entropy at each force
level in subject 79.
Correlation analysis

There were significant correlations with MVC in muscle
thickness of VL (p < 0.001) (Fig. 2 left), RMS (p = 0.001)(Fig. 2 right), and normalized RMS (p = 0.003) in theelderly group (Table 1).
Significant correlations with MVC were found in
modified entropy (p = 0.004) in the young group(Table 2).
In stepwise regression analysis for the elderly group,
muscle thickness of VL, RMS, and normalized RMS
were selected from ten independent variables influencing
MVC (Table 3). The final regression equation was:
MVC = 34.619 x muscle thickness of VL + 349.554 x
RMS + 8.921 x normalized RMS.
The correlation coefficient R, R2 and adjusted R2 forthis model was 0.664, 0.441, and 0.419, respectively.
Also, there were no multicollinearity (Variance Inflation
factor < 10.0, for all) among the selected variables,
meaning that the selected variables are independent.

Watanabe et al. European Review of Aging and Physical Activity (2018) 15:3

Page 5 of 10

Fig. 2 The relationships with maximal voluntary contraction of knee extension joint torque for muscle thickness (left panel) and root meansquare of surface electromyography (right panel) in the vastus lateralis muscle for 88 elderly subjects. MVC: maximal voluntary contraction

Comparisons among different muscle strength in elderly

Elderly with < 91.5 Nm (n = 27) and > 113.9 Nm (n = 28)
of MVC were selected as weak and strong strength
groups. There were significant differences in height,
body mass, MVC, MVC/BM, and muscle thickness of
VL (p < 0.05). No significant differences were found in
RMS, correlation coefficient value, and modified entropy
across all 59 channels at from 20 to 65% of MVC between the elderly groups with weak and strong strength(p > 0.05) (Figs. 3, 4 and 5).

with young group for the weak strength group at 35%,
40%, and 50–65% of MVC and for the strong strength
group at 30% and 40% of MVC (p < 0.05) (Fig. 4).
Ten elderly men (67.6 ± 3.9 ys) and nine young men(21.1 ± 1.1 ys) were selected as the MVC -matched elderly and young groups. Between the MVC-matched elderly and young groups, significant differences were seenin RMS at 20% and 25% of MVC (p < 0.05). There wereno significant differences in modified entropy and correlation coefficient value of surface EMG (p > 0.05).

In comparisons to young group for multi-channel surface EMG parameters, significant differences in RMS
were observed for the weak and strong groups at 45–
65% of MVC (p < 0.05) (Fig. 3). In correlation coefficientwith 20% of MVC, there were significant differences

Discussion
The present study aimed to clarify contribution ofneuromuscular activation pattern to muscle strength inelderly group by using multi-channel surface EMG variables. Our main findings in the present study were that1) maximal muscle strength in elderly was positively

Table 1 Correlation coefficient analysis in elderly (n = 88)

Table 2 Correlation coefficient analysis in young (n = 23)

Comparisons between elderly and young groups

MVC

R

P

Age

− 0.111

0.324

R

P

Age

0.357

0.095

MT

0.524

< 0.001

MT

0.326

0.129

ST

−0.150

ME

0.056

0.182

ST

0.291

0.178

0.622

ME

−0.583

0.004

RMS

0.361

0.001

*

RMS

0.406

0.054

NRMS

0.327

0.003

*

NRMS

−0.0860.697

CRR−0.180

0.108

CRR

0.201

0.359

MVC
*

MVC, maximal voluntary isometric knee extension joint torque; MT, Muscle
thickness of vastus lateralis; ST, subcutaneous tissue thickness; ME, modified
entropy of surface electromyography at 65% of the maximal voluntary
contraction; RMS, root mean square value of surface electromyography for all
channels in electrode grid at 65% of the maximal voluntary contraction; NRMS,
root mean square of surface electromyography for all channels in electrodegrid at 65% of the maximal voluntary contraction normalized by that at 20%of the maximal voluntary contraction; CRR, correlation coefficient value in root
mean square of surface electromyography between 20 and 65% of the
maximal voluntary contraction* indicates significant correlation*

MVC, maximal voluntary isometric knee extension joint torque; MT, Muscle
thickness of vastus lateralis; ST, subcutaneous tissue thickness; ME, modified
entropy of surface electromyography at 65% of the maximal voluntary
contraction; RMS, root mean square value of surface electromyography for all
channels in electrode grid at 65% of the maximal voluntary contraction; NRMS,
root mean square of surface electromyography for all channels in electrodegrid at 65% of the maximal voluntary contraction normalized by that at 20%of the maximal voluntary contraction; CRR, correlation coefficient value in root
mean square of surface electromyography between 20 and 65% of the
maximal voluntary contraction* indicates significant correlation

Watanabe et al. European Review of Aging and Physical Activity (2018) 15:3

Page 6 of 10

Table 3 Stepwise regression analysis for elderly
MVC = 34.619*MT + 349.554*RMS + 8.921*NRMS R = 0.664, R2 = 0.441, Adjusted R2 = 0.419
Dependent variables

Independent variables

Standardized regression coefficient

P

MVC

MT

0.519

< 0.001

RMS

0.288

0.001

NRMS

0.177

0.047

MVC, maximal voluntary isometric knee extension joint torque; MT, Muscle thickness of vastus lateralis; RMS, root mean square of surface electromyography;
NRMS, root mean square of surface electromyography for all channels in electrode grid at 65% of the maximal voluntary contraction normalized by that at 20% of
the maximal voluntary contraction

correlated with surface EMG amplitude (Table 1), 2)
inter-individual difference of muscle strength in elderly
can be explained by muscle thickness and surface EMG
amplitude (Table 3), 3) there were significant differencesin multi-channel surface EMG parameters with youngfor elderly with weak strength at middle to high force
levels and for the elderly with strong strength at middle
force levels (Fig. 4), and 4) absolute muscle strengthmatched elderly and young groups shows similar multichannel surface EMG parameters. Taken together, interindividual difference in muscle strength for elderly isrelated with amplitude of surface EMG and the elderlywith weak and strong strength have different manners in
multi-channel surface EMG parameters. These results
partly support our hypothesis that motor unit recruitment pattern which is estimated from multi-channel surface EMG variables can explain inter-individual
difference in muscle strength in elderly.
Positive correlation between muscle strength and surface
EMG amplitude in the elderly

In the elderly group, muscle thickness, RMS, and normalized RMS were significantly correlated with MVC (p < 0.05)(Table 1) and in stepwise regression analysis muscle thickness, RMS, and normalized RMS were selected as determinants of MVC (Table 3). Also, stepwise regression analysis
shows high standardized regression coefficient in
RMS (0.288, p = 0.001) in addition to MT (0.519, p <0.001) (Table 3). Although significant correlation was
not observed, tendency to correlate between MVC
and RMS was also shown in young (p = 0.054) (Table 2).
These results suggest that the variables related to neuromuscular function are also major determinants of muscle
strength in addition to indicators of muscle volume suchas muscle thickness [16, 31, 42, 43].
Since voluntary muscle contraction is regulated by central nervous system, it would be reasonable to conclude
that neuromuscular function contributes to individuals’
muscle strength. EMG amplitude variables, i.e., RMS andnormalized RMS, mainly reflect number of recruited motor
units and its firing rate during voluntary contraction. Decreases in number of motor unit [24, 33] and in motor unit
firing rate [34] with ageing are well known. These changes
would induce decrease in EMG amplitude in the elderly.
Merletti et al. [26] reported a significantly lower averaged
rectified value of surface EMG of biceps brachii muscle

Fig. 3 Root mean square of multi-channel surface electromyography in vastus lateralis muscle for the elderly groups with different muscle
strength and young. MVC: maximal voluntary contraction, E_Weak: elderly group with weak strength, E_Strong: elderly group with strong
strength, *: significant difference with young groupWatanabe et al. European Review of Aging and Physical Activity (2018) 15:3

Page 7 of 10

Fig. 4 Correlation coefficient with 20% of maximal voluntary contraction of multi-channel surface electromyography in vastus lateralis muscle forthe elderly groups with different muscle strength and young. MVC: maximal voluntary contraction, E_Weak: elderly group with weak strength,
E_Strong: elderly group with strong strength, *: significant difference with young groupduring submaximal isometric contractions in elderly compared with young [26]. On the other hand, age-related decrease in EMG amplitude may not be uniformly manifested
among elderly individuals. Power et al. [33] demonstrated
that the estimated number of motor units in tibialis anterior muscle were significantly lower in elderly than young,but not in master runners [33]. In our recent study thatused a decomposition of motor units action potential frommulti-channel surface EMG, positive correlation between
MVC and motor unit firing rate was demonstrated in the
VL muscle for elderly [38]. Variation in EMG amplitude
among individuals and relationship between EMG amplitude and muscle strength in the elderly may be brought

about by these inter-individual differences in age-related
alterations for anatomy and/or regulation of motor units[24, 33, 34, 38]. However, we should note that surface EMG
is strongly influenced by geometric or non-physiological parameters [12, 27]. When amplitude variables are comparedamong the individuals, differences in the thickness of the
subcutaneous tissue should be considered [28]. To quantify
the effect of inter-individual differences in the subcutaneous
tissue on relationship between EMG amplitude and muscle
strength, we additionally calculated partial correlation coefficient between RMS and MVC adjusted for the thicknessof the subcutaneous tissue in the elderly. Adjusted correlation coefficient (r = 0.291, p = 0.009) was slightly lower

Fig. 5 Modified entropy of multi-channel surface electromyography in vastus lateralis muscle for the elderly groups with different muscle strengthand young. MVC: maximal voluntary contraction, E_Weak: elderly group with weak strength, E_Strong: elderly group with strong strength, *: significant
difference with young groupWatanabe et al. European Review of Aging and Physical Activity (2018) 15:3

than the value before adjustment (r = 0.361, p = 0.001).
While this means that relationship between MVC and
RMS was influenced by the thickness of the subcutaneous
tissue, significance in correlation coefficient value is unchanged. We thus assumed that the thickness of the subcutaneous tissue was not critical effect on our results.
Relationship between muscle strength and estimatedmotor unit recruitment pattern

In the present study, different manners in multi-channel
surface EMG parameters were observed between the elerlywith weak and strong strength when compared to theyoung subjects (Fig. 4). Significant differences in correlation coefficient values with the young subjects were notedat high force levels in the elderly with weak strength (< 91.
5 Nm) (p < 0.05), but not in the elderly with strong
strength (> 113.9 Nm) (p > 0.05) (Fig. 4). Also, as the results of comparison between the young and elderly under
the condition that MVC were matched, no significant difference was observed in correlation coefficient values at allcontraction levels (p > 0.05). Correlation coefficient value
between RMS from same channels between 20% of MVC
and other force levels was used to assess changes in spatial
distribution of surface EMG within a muscle in the present
study. Holtermann et al. [18] showed that alteration pattern of correlation coefficients during ascending and descending phases during ramp contraction was not thesame [18]. Since a deviation between recruitment andderecruitment of motor units was reported [18], the correlation coefficient of multi-channel SEMG could be reasonably used to estimate recruitment of global motor units
during force production. From the results in the present
study, it can be interpreted that changes in spatial distribution patterns of RMS from 20% to 65% of MVC were lesser
in the elderly with weak muscle strength and were similarin the elderly with strong muscle strength when comparedto the young. Based on our assumption, motor unit recruitment pattern of the young may be different from theelderly, which is corresponds to the conclusion in our previous study [39], but not from the elderly with highmuscle strength. The results of some previous studies
had pointed out that importance of neural factors inmuscle strength for elderly. Previous studies reported
that neural activation is more adaptable for the resistance training in elderly than young [5, 19, 30]. The
results of the present study could support this notion
and suggests that neural factor should be also evaluated for understanding age-related muscle strength
loss in addition to indicator of muscle volume.
In the young subjects, significant correlation with
MVC was noted only in modified entropy of multichannel surface EMG (Table 2). Lower modified entropymeans greater heterogeneity of spatial distribution of
surface EMG amplitude within an electrode grid. Our

Page 8 of 10

previous study demonstrated lower modified entropy at
65% of MVC in the young compared with the elderly[39]. From this result, we estimated that spatial inhomogeneity in location of different types of muscle fibers/
motor units and/or numbers of newly recruited muscle
fibers/motor units are greater in the young. Therefore,
the result of the present study suggest that motor unit
recruitment pattern strongly relates with the muscle
strength in young and the determinants of muscle
strength are not same between the young and elderly.
Limitations

The present study used a specific knee joint angle. Although the knee joint angle of 90° has been widely usedin the related research area [6], the highest knee extension torque is observed at more extended knee joint angles (115°-140°) [36, 37]. We also should note that
surface EMG and muscle thickness were measured only
from the VL muscle in the present study. Knee extension
joint torque is produced by four muscle components ofquadriceps femoris, i.e., the VL, vastus intermedius, vastus medialis, and rectus femoris muscles. Neuromuscular activation pattern are not uniform among the fourmuscle components and their contribution to knee extension torque could be influenced by knee joit angle[32, 37]. Therefore, the results of the present study
would be specific for one knee joint angle and the VL
muscle. Different results may be observed at different
joint angles and in other muscles. Also, the present
study calculated maximal voluntary joint torque for knee
extension from the estimated moment arm length at theresting condition and measured force. Since the soft tissue deforms during loading, the estimated moment arm
dinamically change between the resting and contraction
conditions [1]. Therefore, our calculated joint torquewould not be consistent with the resultant knee joint
moment. This is also the major issues of this study.

Conclusion
The present study aimed to clarify contribution ofneuromuscular activation pattern to muscle strength byusing multi-channel surface EMG variables in elderlygroup. We demonstrated that surface EMG amplitude ispositively correlated with muscle strength. Also, the elderly with weak and strong strength have different manners in the indicators of spatial distribution pattern ofmulti-channel surface EMG. Our findings suggest thatinter-individual difference in muscle strength in elderly
can be partly explained by multi-channel surface EMG
variables. The present study concluded that neuromuscular activation pattern is also major determinants ofmuscle strength on elderly in addition to indicator of
muscle volume.

Watanabe et al. European Review of Aging and Physical Activity (2018) 15:3

Abbreviations
EMG: Electromyography; MVC: Maximal voluntary contraction; SEMG: Surface
electromyography; VL: Vastus lateralis
Acknowledgements
This work was supported by the Japanese Council for Science, Technology andInnovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP
Project ID 14533567 Funding agency: Bio-oriented Technology Research
Advancement Institution, NARO) and Meiji Yasuda Life Foundation of
Health and Welfare.
Funding
This work was supported by the Japanese Council for Science, Technology andInnovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP
Project ID 14533567 Funding agency: Bio-oriented Technology Research
Advancement Institution, NARO) and Meiji Yasuda Life Foundation of
Health and Welfare.
Availability of data and materials
The datasets used and/or analysed during the current study are available
from the corresponding author on reasonable request.
Authors’ contributions
KW corresponding author, conception and design, acquisition, analysis andinterpretation of data, and writing the manuscript; MK conception,interpretation of data, and revising the manuscript; MO acquisition of data
and revising the manuscript; HA interpretation of data and revising themanuscript for important intellectual content, TM conception and design,
and interpretation of data and revising the manuscript for important
intellectual content. All authors given final approval of the version to be
published and agreed to be accountable for all aspects of the work inensuring that questions related to the accuracy or integrity of any part of
the work are appropriately investigated and resolved.
Ethics approval and consent to participate
All procedures used in this study were approved by the Committee for
Human Experimentation at the Graduate School of Human andEnvironmental Studies, Kyoto University and the Research Ethics Committeeof Chukyo University, and were in accordance with the Declaration of
Helsinki.
Our study is approved by Chukyo University showed it in Methods.
Consent for publication
The subjects gave written informed consent for the study after receiving adetailed explanation of the purposes, potential benefits, and risks associatedwith participation.
Competing interests
The authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Author details
1
Laboratory of Neuromuscular Biomechanics, School of International LiberalStudies, Chukyo University, Yagotohonmachi, Showa-ku, Nagoya 466-8666,
Japan. 2Laboratory of Neurophysiology, Graduate School of Human andEnvironmental Studies, Kyoto University, Kyoto, Japan. 3Graduate School of
Education and Human Development, Nagoya University, Nagoya, Japan.
4Japan Society for the Promotion of Science, Tokyo, Japan. 5Research Centerof Health, Physical Fitness & Sports, Nagoya University, Japan raduate Schoolof Education and Human Development, Nagoya University, Nagoya, Japan.
6
Faculty of Sociology, Kyoto Sangyo University, Kyoto, Japan. 7School of
Health and Sports Sciences, Chukyo University, Nagoya, Japan.

Page 9 of 10

Received: 16 November 2017 Accepted: 2 April 2018

References
1. Arampatzis A, Karamanidis K, De Monte G, Stafilidis S, Morey-Klapsing G,
Bruggemann GP (2004) Differences between measured and resultant joint
moments during voluntary and artificially elicited isometric knee extension
contractions Clinical biomechanics (Bristol, Avon) 19:277–283 doi:https://doi.
org/10.1016/j.clinbiomech.2003.11.011.
2. Basmajian JV, De Luca CJ (1985) Muscle alive. 5th edn. Williams & Wilkins,
Maryland.
3. Buchthal F, Guld C, Rosenfalck F. Multielectrode study of the territory of a
motor unit. Acta Physiol Scand. 1957;39:83–104.
4. Chanaud CM, Macpherson JM. Functionally complex muscles of the cathindlimb. III. Differential activation within biceps femoris during postural
perturbations. Exp Brain Res. 1991;85:271–80.
5. Christie A, Kamen G. Short-term training adaptations in maximal motor unit
firing rates and afterhyperpolarization duration. Muscle Nerve. 2010;41:651–
60. https://doi.org/10.1002/mus.21539.
6. Cruz-Jentoft AJ, et al. Sarcopenia: European consensus on definition anddiagnosis: report of the European working group on sarcopenia in older
people. Age Ageing. 2010;39:412–23. https://doi.org/10.1093/ageing/afq034.
7. De Luca CJ. The use of surface electromyography in biomechanics. J Appl
Biomech. 1997;13:135–63.
8. Deschenes MR. Effects of aging on muscle fibre type and size. Sports Med.
2004;34:809–24.
9. Duchateau J, Enoka RM. Human motor unit recordings: origins and insight
into the integrated motor system. Brain Res. 2011;1409:42–61.
10. Erim Z, Beg MF, Burke DT, de Luca CJ. Effects of aging on motor-unit
control properties. J Neurophysiol. 1999;82:2081–91.
11. Falla D, Farina D, Graven-Nielsen T. Spatial dependency of trapezius muscle
activity during repetitive shoulder flexion. J Electromyogr Kinesiol. 2007;17:
299–306.
12. Farina D, Cescon C, Merletti R. Influence of anatomical, physical, anddetection-system parameters on surface EMG. Biol Cybern. 2002;86:445–56.
https://doi.org/10.1007/s00422-002-0309-2.
13. Farina D, Holobar A, Merletti R, Enoka RM. Decoding the neural drive tomuscles from the surface electromyogram. Clin Neurophysiol. 2010;121:
1616–23.
14. Farina D, Leclerc F, Arendt-Nielsen L, Buttelli O, Madeleine P. The change inspatial distribution of upper trapezius muscle activity is correlated tocontraction duration. J Electromyogr Kinesiol. 2008;18:16–25.
15. Farina D, Merletti R, Enoka RM. The extraction of neural strategies from thesurface EMG. J Appl Physiol. 2004;96:1486–95.
16. Fukunaga T, Miyatani M, Tachi M, Kouzaki M, Kawakami Y, Kanehisa H.
Muscle volume is a major determinant of joint torque in humans. Acta
Physiol Scand. 2001;172:249–55.
17. Holtermann A, Roeleveld K. EMG amplitude distribution changes over theupper trapezius muscle are similar in sustained and ramp contractions. Acta
Physiol. 2006;186:159–68.
18. Holtermann A, Roeleveld K, Karlsson JS. Inhomogeneities in muscle activation
reveal motor unit recruitment. J Electromyogr Kinesiol. 2005;15:131–7.
19. Kamen G, Knight CA. Training-related adaptations in motor unit
discharge rate in young and older adults. J Gerontol A Biol Sci Med
Sci. 2004;59:1334–8.
20. Kamen G, Sison SV, Du CC, Patten C (1995) Motor unit discharge behavior
in older adults during maximal-effort contractions. J Appl Physiol (1985) 79:
1908–1913.
21. Lang T, Streeper T, Cawthon P, Baldwin K, Taaffe DR, Harris TB. Sarcopenia:
etiology, clinical consequences, intervention, and assessment. OsteoporosInt. 2010;21:543–59.
22. Lexell J, Downham D, Sjostrom M. Distribution of different fibre types in humanskeletal muscles. Fibre type arrangement in m. Vastus lateralis from three groupsof healthy men between 15 and 83 years. J Neurol Sci. 1986;72:211–22.
23. Lexell J, Downham DY. The occurrence of fibre-type grouping in healthy
human muscle: a quantitative study of cross-sections of whole vastus
lateralis from men between 15 and 83 years. Acta Neuropathol. 1991;81:
377–81.
24. Lexell J, Taylor CC, Sjostrom M. What is the cause of the ageing atrophy?
Total number, size and proportion of different fiber types studied in wholeWatanabe et al. European Review of Aging and Physical Activity (2018) 15:3

25.
26.

27.
28.

29.

30.
31.

32.

33.

34.
35.

36.
37.

38.

39.

40.

41.

42.
43.

vastus lateralis muscle from 15- to 83-year-old men. J Neurol Sci. 1988;84:
275–94.
Merletti R, Farina D. Analysis of intramuscular electromyogram signals. Philos
Trans. 2009;367:357–68.
Merletti R, Farina D, Gazzoni M, Schieroni MP. Effect of age on muscle
functions investigated with surface electromyography. Muscle Nerve. 2002;
25:65–76.
Merletti R, Rainoldi A, Farina D. Surface electromyography for noninvasive
characterization of muscle. Exerc Sport Sci Rev. 2001;29:20–5.
Minetto MA, Botter A, Sprager S, Agosti F, Patrizi A, Lanfranco F, Sartorio A.
Feasibility study of detecting surface electromyograms in severely obese
patients. J Electromyogr Kinesiol. 2013;23:285–95. https://doi.org/10.1016/j.
jelekin.2012.09.008.
Mitchell WK, Williams J, Atherton P, Larvin M, Lund J, Narici M. Sarcopenia,
dynapenia, and the impact of advancing age on human skeletal muscle sizeand strength; a quantitative review. Front Physiol. 2012;3:260. https://doi.
org/10.3389/fphys.2012.00260.
Moritani T, deVries HA. Potential for gross muscle hypertrophy in older men.
J Gerontol. 1980;35:672–82.
Overend TJ, Cunningham DA, Kramer JF, Lefcoe MS, Paterson DH. Kneeextensor and knee flexor strength: cross-sectional area ratios in young andelderly men. J Gerontol. 1992;47:M204–10.
Pincivero DM, Salfetnikov Y, Compy RM, Coelho AJ. Angle- and genderspecific quadriceps femoris muscle recruitment and knee extensor torque. J
Biomech. 2004;37:1689–97.
Power GA, Dalton BH, Behm DG, Vandervoort AA, Doherty TJ, Rice CL.
Motor unit number estimates in masters runners: use it or lose it? Med Sci
Sports Exerc. 2010;42:1644–50. https://doi.org/10.1249/MSS.
0b013e3181d6f9e9.
Roos MR, Rice CL, Vandervoort AA. Age-related changes in motor unit
function. Muscle Nerve. 1997;20:679–90.
Sjostrom M, Downham DY, Lexell J. Distribution of different fiber types inhuman skeletal muscles: why is there a difference within a fascicle? Muscle
Nerve. 1986;9:30–6.
Suter E, Herzog W. Extent of muscle inhibition as a function of knee angle. J
Electromyogr Kinesiol. 1997;7:123–30.
Watanabe K, Akima H. Effect of knee joint angle on neuromuscular
activation of the vastus intermedius muscle during isometric contraction.
Scand J Med Sci Sports. 2011;21:e412-e420.
Watanabe K, Holobar A, Kouzaki M, Ogawa M, Akima H, Moritani T. Agerelated changes in motor unit firing pattern of vastus lateralis muscle
during low-moderate contraction. Age (Dordr). 2016;38:48. https://doi.org/
10.1007/s11357-016-9915-0.
Watanabe K, Kouzaki M, Fujibayashi M, Merletti R, Moritani T. Spatial EMG
potential distribution pattern of vastus lateralis muscle during isometric knee
extension in young and elderly men. J Electromyogr Kinesiol. 2012a;22:74–9.
Watanabe K, Kouzaki M, Moritani T. Spatial EMG potential distribution of
biceps brachii muscle during resistance training and detraining. Eur J Appl
Physiol. 2015;115:2661–70. https://doi.org/10.1007/s00421-015-3237-2.
Watanabe K, Miyamoto T, Tanaka Y, Fukuda K, Moritani T. Type 2 diabetes
mellitus patients manifest characteristic spatial EMG potential distribution
pattern during sustained isometric contraction. Diabetes Res Clin Pract.
2012b;97:468–73. https://doi.org/10.1016/j.diabres.2012.03.004.
Young A, Stokes M, Crowe M. Size and strength of the quadriceps musclesof old and young women. Eur J Clin Investig. 1984;14:282–7.
Young A, Stokes M, Crowe M. The size and strength of the quadriceps
muscles of old and young men. Clin Physiol. 1985;5:145–54.

Page 10 of 10