Introdução a Eletromiografia

Prévia do material em texto

Prof. Taian Vieira 
 taian.vieira@polito.it 
Escola de Educação Física e Desportos, Universidade Federal do Rio de Janeiro, Brasil 
Laboratorio di Ingegneria del Sistema Neuromuscolare, Politecnico di Torino, Italia 
Introdução à 
Eletromiografia de Superfície 
Curso de Pós Graduação em Biomecânica – Módulo em Eletromiografia 
Rio de Janeiro, 20 de Abril de 2013 
Apresentação do módulo 
20/04/13 – Eletromiografia: introdução e aplicações 
– Geração, propagação e extinção de potenciais de ação 
– Representação dos potenciais de ação na superfície cutânea 
11/05/13 – Eletromiografia de alta densidade 
– Detecção de potenciais de ação com vetores mono- e bi-
dimensionais de eletrodos 
– Representação dos eletromiogramas (EMGs) em imagens 
– Relação entre a anatomia muscular e os EMGs 
25/05/13 – Descritores eletromiográficos e avaliação 
– Efeito de fatores anatômicos e fisiológicos nos descritores de 
amplitude e frequência dos EMGs 
– Possível aula de demonstração (EMG audio-feedback) 
– Avaliação 
Anatomy of the motor neuron 
Figura retirada e adaptada do site abaixo em 16/03/2012 
http://faculty.weber.edu/lfowler/images/Intro/Homework/The%20Neuron%20and%20the%20Action%20Potential.htm 
The excitable cell (neurons or muscle fibers) 
nucleus 
Membrane, 
permeable to water and to 
all ions, with different 
permeability protoplasm 
Sodium active pump 
(pumps Na+ ions outside 
the cell) 
Potassium active pump 
(pumps K+ ions inside the 
cell) 
Electric analogy: 
Active pump: current generators 
Permeability: electric conductance 
Gradient of concentration: gradient of potential 
(electric field) that balance the gradient of 
concentration) 
Major ions: Na+, K+, Cl- 
The membrane potential stabilizes at 
a stead value close to 70 mV, with – 
inside and + outside. 
Na+ 
K+ Na+ 
K+ 
E Na+ E K+ 
Gradient of 
concentration 
Generators of equivalent tension (compensates for 
the concentration gradient) 
Conductance equivalent to Na+, 
K+, Cl- permeability pumps 
+ outside 
- inside 
Flow due to the 
gradient of 
concentration 
Flow due to the 
electric field 
membrane 
Cl- 
Cl
- 
E Cl- 
LISiN, Torino 
Generation of action potentials 
the current tripole 
0 3 6 time (ms) 
Excitation threshold 
0 
- 70 
Transmembrane voltage 
gNa 
gK 
Na+ and K+ conductivities 
(qualitative patterns) 
Outside 
Membrane 
Inside 
+ 
+ - 
- 
- 
+ 
Current lines across the membrane 
 out in out 
Na+ influx 
 + + + - - - - + + + + + + + + + + 
muscle fiber 
- - - - + + + - - - - - - - - - - - - - 
- - - - + + + - - - - - - - - - - - - -- 
Su
b
th
re
sh
o
ld
 
ex
ci
ta
ti
o
n
 
 + + + - - - - + + + + + + + + + + 
Resting voltage 
gNa = sodium conductance 
gK = potassium conductance 
1 2 3 4 
α 
α 
β γ 
t
o 
Local fiber depolarization 
LISiN, Torino 
Tripole 
generation 
Tripole 
separation 
Tripole 
propagation 
_ 
+ _ 
+ 
_ 
+ 
+ _ 
+ _ 
_ 
+ _ 
+ 
_ 
+ 
+ _ 
+ _ 
_ 
+ 
_ 
+ 
_ 
+ 
_ 
+ 
_ 
+ 
+ 
_ 
_ 
+ 
_ 
+ 
_ 
+ _ 
+ 
_ 
+ 
+ _ 
+ _ 
_ 
+ _ 
+ 
_ 
+ 
+ _ 
+ _ 
t1 
t2 
t3 
Axonal branch 
LISiN, Torino 
Tripole propagation along the muscle fibre 
_ 
_ 
+ 
+ 
_ 
+ 
_ 
+ 
+ _ 
+ 
_ 
a) 
b) 
c) 
d) 
_ 
+ 
+ _ 
+ 
_ 
+ 
+ _ 
+ _ 
_ 
t1 
t2 
e) 
f) 
_ 
+ 
+ 
_ 
+ 
+ 
_ 
t3 
g) 
h) 
_ 
+ 
+ 
_ 
+ 
+ 
_ 
t4 
_ 
_ 
D1 
D1 D2 
D2 
D2 
D2 
end of fiber 
Tripole extinction: the end of fibre effect 
LISiN, Torino 
Axon 
 motoneuron 
Schwann cells and 
Ranvier nodes 
0 
- 70 
action potential 
(90-100 mVpp) 
1 ms or 4 mm 
muscle fibers 
4 m/s = 4 mm/ms 4 m/s = 4 mm/ms 
The Motor Unit (MU) 
(electrical activity) 
inputs from 
other neurons 
One muscle: 10-1000 MU One MU: 50-1000 fibers of the same type (I or II) 
LISiN, Torino 
Time 
+ - 
0 
100 
Temporal representation of surface action potentials: 
a single active motor unit 
10 pps 
Voltage 
Axon of 
MU 1 
Fiber of MU 1 
Fiber of MU 2 Axon of 
MU 2 
In
n
e
rv
at
io
n
 
zo
n
e
 1
 
Neuromuscular junction 
(end-plate) Muscle-tendon 
junction 
Muscle-tendon 
junction 
Territory 
of MU 2 
Territory 
of MU 1 
Muscle Architecture of a motor unit 
Territory 
Innervation Zone (IZ) 
Muscle-tendon 
In
n
e
rv
at
io
n
 
zo
n
e
 2
 
LISiN, Torino 
Fiber of MU 1 
Fiber of MU 2 
skin 
Differential amplifier 
electrodes 
Surface contributions from fibers of 
 MU 1 MU 2 
MUAP 1 
10 ms 
“Interference” EMG signals (several MUs). 
MUAP 2 
-200 
0 
200 
V 
0.0 0.5 0.4 0.3 0.2 0.1 s 
LISiN, Torino 
Fibers of MU 1 Fibers of MU 2 
skin 
Needle, surface and thin wire EMG 
Detection volume of a coaxial 
needle (needle of Adrian): 
spherical portion with ~1 mm 
radius. Shifts small as 1 mm 
change chiefly the signal 
detected. 
Although the needle might be inserted at 
different depths, it “sees” a few fibers of a few 
motor units within a volume of 1-2 mm3. Surface 
electrodes “see” motor units in several cm3, but 
not at several depths. 
Thin insulated wires 
with exposed tips 
surface electrodes 
Detection volumes 
L
IS
iN
, T
o
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n
o
 
de=50 mm 
4 
6 
7 
5 
1 
3 
2 
de=10 mm 
de=10 mm 
de=10 mm 
de=20 mm 
de=20 mm 
de=30 mm 
1 
15 
Differential signals acquired with adjacent electrodes (de=10mm) 
8 
6 4 2 
7 5 3 1 
1 
15 
8 
a) 
b) 
10 mm 
Depolarized 
zone 
Diff. signals acquired with various de and in diverse locations 
1 mV 50 ms 
de=40 mm 
Low quality 
signals 
High quality signal 
LISiN, Torino 
Subcutaneous tissue 
Vm(x) 
x 
CV 
Skin 
Depolarized 
Zone 
- 70 mV 
x 
CV CV 
Action potentials travelling towards the tendons 
 0 mV 
Potential distribution on the skin 
V(t) 
t 
What if different electrodes are positioned over different skin regions? 
Muscle-tendon 
junctions 
Axon 
branch 
CV 
NMJ 
Example of a single 
differential EMG signals 
detected from the knee 
and ankle extensor and 
flexor muscles of a healthy 
subject. 
Rectus Femoris (RF), Vastus 
Medialis (VM), Semitendinous 
(ST), Tibialis Anterior (TA), 
Peroneus Longus (PL), 
Gastrocnemius Medialis (GM) 
and Soleus (SO). Additionally, 
the basographic signal (BA) is 
displayed; this signal was 
acquired with a couple of 
plantar interrupters 
positioned at the talus and at 
the first metatarsal head. 
What can we possibly do with a couple of electrodes? 
Time (s) 
Heel strikes 
LISiN, Torino 
How can we quantify some 
features related to muscle 
activation from EMGs? 
Traditional descriptors associated to 
EMGs amplitude and spectrum are 
typically considered 
Average of the rectified value (ARV) 
Root Mean Square (RMS) value 
Median Frequency (MDF) 
Mean Frequency (MNF) 
time In
st
an
ta
n
e
o
u
s 
am
p
lit
u
d
e 
In
st
an
ta
n
e
o
u
s 
re
ct
if
ie
d
 v
al
u
e
 
time 
Average of the rectified value (ARV) 
“Epoch” used to compute the average 
rectified value 
Parameters of a sinusoid: the Average Rectified Value 
Period T 
Peak value 
The concept of ARV applies to any signal, not only to sinusoids. 
time 
time 
V(t) 
V2(t) 
Mean Squared 
value. 
The square root of the Mean Squared value is called “Root Mean Square value” (RMS). 
The concept of RMS applies to any signal, not only to sinusoids. 
Parameters of a sinusoid: the Root Mean Squared Value 
(Square root of the mean of the squared value) 
In
st
an
ta
n
eo
u
s 
am
p
lit
u
d
e
 
In
st
an
ta
n
eo
u
s 
sq
u
ar
e
d
 v
al
u
e
 
“Epoch” used to compute the mean squared 
value 
The concept of envelope detection 
R
e
ct
if
ie
d
 s
ig
n
al
 (
m
V
) 
Av. Rect. Value = 0.138 mV 
Low-pass filtered (smoothed) 
rectified value (envelope) 
Rectified value 
Sq
u
ar
e
d
 s
ig
n
al
 
Mean Square Value = 0.0268 mV2 
Low-pass filtered (smoothed) 
squaredvalue (envelope) 
Squared 
value 
0 50 100 150 200 250 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0 50 100 150 200 250 
00163mV0.0268RMS 
Time (ms) 
LISiN, Torino 
What if the sinusoid is non-stationary? 
(deterministic example) 
Epoch 1 : T1 Epoch 2: T2 Epoch 3 : T3 
Epoch 4 = Epoch 1 + Epoch 2 + Epoch 3 
S(t) 
frequency 
p
o
w
er
 
frequency frequency 
Epoch 1 Epoch 2 Epoch 3 
frequency 
p
o
w
er
 
Epoch 4 Long epochs do NOT GIVE WRONG results but do 
not allow to see changes taking place during the 
epoch. THIS MAY OR MAY NOT BE IMPORTANT. 
LISiN, Torino 
0 100 200 300 400 
0.0 
0.5 
1.0 
a ) 
1 
2 
3 
4 
N
o
rm
al
iz
e
d
 s
p
e
ct
ra
 
Frequency (Hz) 
0 0.25 0.50 
-0.1 
0.0 
0.1 b ) 
t (s) 0 0.25 0.50 
c ) 
EMG whose spectrum is shown as n.4 EMG whose spectrum is shown as n.1 
t (s) 
Beginning of contraction End of contraction 
Representation of a signal 
spectrum across time. 
“Quasi” stationary signal. 
Epoch (b) 
spectrum 1 
Epoch (c) 
spectrum 4 
EMG recorded during isometric, constant force 
contraction. 
Estimates of mean and median (MNF and MDF) 
frequencies of the surface EMG spectrum 
MDF: divides the spectrum in two regions of equal power 
MNF: represents the baricenter or the “first order 
 moment” 
0 200 400 Hz 
For an asymmetric spectrum with a 
tail on the right 
MNF > MDF. 
 
For a symmetric, EMG spectrum MDF 
= MNF. 
 
If the asymmetry increases to the 
right MNF / MDF increases. 
 
In most cases, MDF reduces more 
than MNF. 
harmonics 
p
o
w
e
r 
Identification of muscle 
anatomical and physiological 
parameters 
When sampled with a grid of electrodes, EMGs might reveal 
distinct aspects of the neuromuscular system 
Some examples on the application 
of surface electromyography 
Potencial de ação propagando no axônio Geração do MUAP na junção neuromuscular Propagação do MUAP ao longo das fibras Extinção do MUAP no tendão Fim do MUAP 
Neurônio 
motor 
Tendão 
Tendão 
Músculo 
Vetor de 
eletrodos 
Tempo 
[ms] 
sEMG plot 
1 
col. 
2 
5 
3 
1 
1 
4 
Sp
ac
e
 (
y)
 
col. 1 2 3 4 5 time 
Space (x) time samples 
For each time sample a picture of the instantaneous potential 
distribution is obtained. Interpolation provides a finer map. Plotting 
of these maps in sequence provides a “movie” of the time evolution 
of the potential distribution generated by each MU. 
SD generation of a MU potential map (2D MU signature) 
LISiN, Torino 
 
• each frame corresponds to one sample 
(sampling interval = 0.6 ms) 
2D and 3D maps of a single 
MUAP in time 
LISiN, Torino 
Myoelectric 
manifestation of fatigue 
Earlier than decrease in muscle mechanical work, 
alterations in the amplitude and spectrum are observed 
Some examples on the application 
of surface electromyography 
EMG signals detected during a constant force contraction 
(biceps brachii muscle, 15%MVC) 
50 ms 
0 100 200 300 400 500 600 s 
0 
 5 
10 
 15 
20 
Torque 
 (% MVC) 
0 300 600 s 
50 
100 
150 
MNF (Hz) 
0 300 600 s 
3.0 
3.5 
4.0 
4.5 
CV (m/s) 
0 300 600 s 
0 
50 
100 
150 
ARV (V) 
15% MVC 10% MVC 5% MVC LISiN, Torino 
Abductor Pollicis 
• ischemic condition 
(to accelerate fatigue and simulate higher 
contraction levels) 
• isometric contractions 
• force ramps: 
– from 0 % MVC to 10 % MVC and back 
to 0 % MVC in 12 seconds 
– 28 ramp repetitions 
– 20 MUs identified, 
– in each force ramp, MUAPs were 
estimated by spike triggered 
averaging technique after 
decomposition. 
Load 
Cell 
Cuff Electr. 
array 
Extensible 
Arm 
Let us look at four different MUs during the 
same contraction. 
LISiN, Torino 
Myoelectric manifestations of 
fatigue are associated to motor 
unit type 
0 13 0 13 
Time [ms] 
Subject A, MU number 8 
0 13 0 13 
-230 
0 
230 
-230 
0 
230 
-230 
0 
230 
-230 
0 
230 
-230 
0 
230 
-230 
0 
230 
-230 
0 
230 
-230 
0 
230 
-230 
0 
230 
-230 
0 
230 
-230 
0 
230 
-230 
0 
230 
0 13 
1st ramp 
28th ramp 
M
U
A
P
 a
m
p
lit
u
d
e
 [
μ
V
] 
Decrease of CV 
Col. 1 2 3 4 5 
R
o
w
 
1
 
 2
 
 3
 
 
4
 
 5
 
 6
 
 
7
 
 8
 
 9
 
 1
0
 
1
1
 
 1
2
 
LISiN, Torino 
0 13 
0 
280 
Time [ms] 
Subject A, MU = 8 (row 2, column 3) 
1st ramp 
28th ramp 
M
U
A
P
 a
m
p
lit
u
d
e
 [
μ
V
] 
26 
Decrease of CV 
1st ramp 
28th ramp 
fatigue resistant MU 
LISiN, Torino 
0 13 0 13 
Time [ms] 
Subject A, MU number 2 
0 13 0 13 
-190 
0 
190 
-190 
0 
190 
-190 
0 
190 
-190 
0 
190 
-190 
0 
190 
-190 
0 
190 
-190 
0 
190 
-190 
0 
190 
-190 
0 
190 
-190 
0 
190 
-190 
0 
190 
-190 
0 
190 
M
U
A
P
 a
m
p
li
tu
d
e
 [
μ
V
] 
0 13 
1st ramp 
28th ramp 
Note: Strong changes in amplitude 
with fatigue. 
Col. 1 2 3 4 5 
R
o
w
 
1
 
 
2
 
 3
 
 4
 
 
5
 
 6
 
 7
 
 
8
 
 9
 
 1
0
 
1
1
 
 1
2
 
LISiN, Torino 
0 13 
-230 
0 
230 
M
U
A
P
 a
m
p
lit
u
d
e
 [
μ
V
] 
Time [ms] 
Subject A, MU = 2 (row 2, column 3) 
1st ramp 
28th ramp 
1st ramp 
28th ramp 
26 
Note: Strong changes in 
amplitude with fatigue 
fatigable MU 
Strong decrease of 
conduction velocity 
LISiN, Torino 
Four motor units from Abd. Pollicis showing very different behaviors during 28 12 s long 
ramp-up ramp-down isometric contractions in ischemic conditions. 
first 
last 
first 
last 
first 
last 
first 
last 
-280 
fatigable MU 
fatigable MU 
fatigue resistant MU 
fatigue resistant MU 
LISiN, Torino 
Decomposition of surface EMGs into the 
constituent motor unit action potentials 
Some examples on the application 
of surface electromyography 
Why should we do it ? 
Because it provides a window on the CNS control srategies 
Because it allows us to study individual motor units (rather than the 
whole muscle) and their electrophysiological properties 
+ - + - + - + - 
+ - + - + - + - 
+ - + - + - + - 
Possible approaches to identify sources 
Space of MU discharge patterns / 
 innervation pulse trains 
 
statistical properties of MU 
discharge patterns 
Space of MUAPs / MUAP trains 
 
 
morphological differences of 
MUAPs 
+ single or multichannel EMG 
 
- sensitive to MUAP 
superimpositions 
+ not sensitive to MUAP 
superimpositions 
- multichannel EMG (large arrays 
of electrodes) 
Template matching Blind Source Separation 
vs. vs. 
C
o
l 1
 
1 2 3 4 5 
..
. 
..
. ..
. 
MU 2 
MU N 
MU 1 
MU innervation pulse trains (discharge rates) obtained from surface EMG 
Brain and 
Spinal cord 
N
e
rv
e
 w
it
h
 
h
u
n
d
re
d
s 
o
f 
m
o
to
n
e
u
ro
n
s 
EMG 
detection 
array over 
biceps 
m
o
to
r 
u
n
it
 
fi
n
ge
rp
ri
n
ts
 
U
n
sc
ra
m
b
lin
g 
p
ro
ce
ss
 
C
o
l 5
 
Col 2, 3, 4 
0.5 s 
Algorithm for decomposition of the EMG signals into the MU signatures 
MU 1 MU 2 MU N 
1 2 4 5 col 
LISiN, Torino 
Ergonomy 
Contraction types: 
 
Isometric or quasi isometric contractions, at constant force 
Isometric or quasi isometric contractions, at variable or intermittent force 
Non isometric (dynamic) contractions 
Some examples on the application 
of surface electromyography 
UPPER TRAPEZIUS ACTIVITY DURING OCCUPATIONAL JOBS: 
4 cm 
10.4 
cm 
64 electrodes 
(13 x 5) -1 
51 SD channels 
(13 x 4) -1 
+ 
- SINGLE DIFFERENTIAL 
IZ IZ 
Propagation directions 
LISiN, Torino 
3 CONDITIONS: 
Wrist support (low chair) 
Forearm support (normal chair) 
No support (high chair) 
2 ACTIVITIES: 
30 seconds Keyboardtyping 
30 seconds Mouse using 
X 
1 - WRIST SUPPORT 
2 - FOREARM SUPPORT 
3 - NO SUPPORT 
KEYBOARD TYPING (30 seconds, mean ARV maps): 
1 
2 
3 
Max = 
24 μv 
Max = 
10 μv 
Max = 
16 μv 
1 
Max = 
24 μv 
Max = 
11 μv 
Max = 
24 μv 
2 
3 
0 μV 
10 μV 
20 μV 
4 cm 
30 μV 
10.4 cm 
LEFT RIGHT 
LI
Si
N
, T
o
ri
n
o
 
1 - WRIST SUPPORT 
2 - FOREARM SUPPORT 
3 - NO SUPPORT 
MOUSE USING (30 seconds, mean ARV maps): 
1 
2 
Max = 
6 μv 
Max = 
2 μv 
Max = 
9 μv 
Max = 
34 μv 
Max = 
9 μv 
0 μV 
10 μV 
20 μV 
30 μV 3 
1 
2 
3 
4 cm 
Max = 
33 μv 
10.4 cm 
LEFT RIGHT 
LI
Si
N
, T
o
ri
n
o
 
MNF 
spatial average 
ARV 
spatial average 
T.A., Upper trap. 50% MVC sustained to endurace, LSD 
Hz 
Hz 
μV 
μV 
a) 
c) 
e) 
g) 
b) 
d) 
f) 
h) 
IZ IZ 
90 
90 
LISiN, Torino 
Monitoring muscle activity for the optimization of industrial 
workstations (Project CyberManS) 
L
IS
iN
, T
o
ri
n
o
 
A
p
p
lic
at
io
n
s 
in
 in
d
u
st
ri
al
 e
rg
o
n
o
m
ic
s 
Subject experienced in welding. 
Burst activation of muscles is evident. 
Subject not experienced in welding. 
Sustained activation of several muscles during the 
welding cycle is evident. 
norm.val= 0.7 mV norm.val= 0.6 mV 
norm.val= 0.25 mV 
norm.val= 0.75 mV 
norm.val= 0.55 mV 
norm.val= 0.6 mV 
norm.val= 0.6 mV 
norm.val= 0.5 mV 
norm.val= 0.65 mV 
norm.val= 0.85 mV 
norm.val= 0.5 mV 
norm.val= 0.3 mV 
norm.val= 0.75 mV 
norm.val= 0.55 mV 
norm.val= 0.65 mV norm.val= 0.6 mV 
norm.val= 0.55 mV norm.val= 0.35 mV 
 norm.val= 0.7 mV norm.val= 0.65 mV 
norm.val= 0.45 mV norm.val= 0.45 mV 
norm.val= 0.2 mV norm.val= 0.25 mV 
left right left right 
A
n
te
ri
o
r 
D
e
lt
o
id
 
P
o
s
te
ri
o
r 
D
e
lt
o
id
 
B
ic
e
p
s
 B
ra
c
h
ii
 
S
. 
H
e
a
d
 
L.
 H
ea
d
 
 
U
p
p
e
r 
T
ra
p
e
z
iu
s
 
L
a
te
ra
l 
D
e
lt
o
id
 
Sixty seconds of signals acquired during simulated welding. Two subjects, SD signals, normalized with respect to 
the peak to peak value. The channels selected for the CV estimation are in red. 
Two examples of signal acquisition during welding LISiN, Torino 
Control of prosthesis 
From EMGs, individuals’ intention might be codded and 
then translated into action of an artificial limb 
Some examples on the application 
of surface electromyography 
Co-contraction 
No action or lock 
Hand Close 
H
a
n
d
 O
p
en
 
Off 
FLEXOR ACTIVITY 
EX
TE
N
SO
R
 A
C
TI
V
IT
Y 
S1 
S2 
Two channel control of a hand prosthesis 
(near wrist amputation) 
Wrist 
Extension 
Wrist 
Extension 
Fingers 
Extension 
Thumb 
Extension 
What if EMGs from the forearm muscles could 
be collected with a grid of electrodes? 
middle little index thumb ring 
Isometric effort of each finger appears locally in the surface EMGs! 
Cramps 
Some examples on the application 
of surface electromyography 
Cramps can be elicited by electrical stimulation and 
their time evolution can be monitored by 2D arrays 
 
Their central or peripheral nature is still 
controversial 
Surface EMG detection: 
bidimensional electrode array 
(30 contacts, 5 mm apart) 
Stimulation 
electrode (size 
10x10 mm) 
Large electrode (size 50x80 mm) to close the 
stimulation current loop 
Skin 
thermistor 
Eliciting and monitoring cramps in the abductor hallucis 
L
IS
iN
, T
o
ri
n
o
 
Minetto M., Botter A. Elicitability of muscle cramps 
in different leg and foot muscles, Muscle & Nerve, 
2009;40:535-544 
Medial 
5mm 
D
is
ta
l 
0 
20 
40 
60 
A
R
V
 (

V
) 
1st epoch (1s - 2s) 2nd epoch (2s - 3s) 3rd epoch (3s - 4s) 
4th epoch (4s - 5s) 5th epoch (5s - 6s) 6th epoch (6s - 7s) 7th epoch (7s - 8s) 8th epoch (8s - 9s) 
Example of spatio-temporal 
development of a cramp of the abductor 
hallucis muscle, elicited by a stimulation 
frequency of 22 Hz 
Surface EMG average rectified value (ARV) 
distribution after stimulation stops 
L
IS
iN
, T
o
ri
n
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Obstetrics 
Some examples on the application 
of surface electromyography 
Tissue lesions (spontaneous tears or episiotomy) 
seems positively strongly associated to appearance 
of frecal incontinence 
 
Can EMG prevent denervation of external anal 
sphincter muscle induced by episiotomy? 
Episiotomy 
The prevalence of incontinence in 
unselected samples ranges between 
2% and 12%. In postpartal women, these 
numbers increase to 5% and 15%. 
 
Odds ratios of incontinence 
 
Vaginal delivery Odds ratio (95%CI) versus 
nulliparous 2.59 (1.58-4.28) 
 
Instrumented delivery 
versus nulliparous 3.37 (1.93-5.91) 
 
No clear information is available 
about the possible role of 
episiotomy in EAS denervation 
and consequent incontinence. 
LISiN, Torino 
EAS 
External anal sphincter (EAS) muscle of females (perineal view) 
gluteus 
Episiotomy 
(2-4 cm) 
Anorectal musculature 
Frontal Section 
Pelvic floor anatomy – External anal sphincter muscle 
Possible sphincter damage due to episiotomy 
V 
Surgical 
incision 
A 
Low 
risk 
V 
A 
Medium 
risk 
P V 
A 
High 
risk 
P 
P 
Innervation 
V = vaginal opening 
P = perineal wall 
A = anal opening 
V 
A Very 
high 
risk 
P 
All sphincters are different ! 
Enck P. et al. Innervation zone of the external anal sphincter in heathy males and females 
Digestion, 2004;69:123-130. 
L
IS
iN
, T
o
ri
n
o
 
Probes and 
instrumentation 
EMG multichannel amplifier 
Multi-array reusable or 
disposable probes. 
16 to 128 ch amplifiers. 
Six publications in 
international journals. 
500 women under 
investigation in 10 European 
centers. 
 
The technique is ready for 
clinical testing. 
 
LISiN, Torino 
10s 10s 20s 5s 5s 
Rest Rest 
MVC 
Experimental Set-up 
 
PC 
EMG-USB 
Pat. ref 
Rectal 
Probe 
5 mm 
DRP1x16-05-MC 
C
o
n
tr
ac
ti
o
n
 le
ve
l 
 0 50 100 150 200 250 300 350 400 450 500 
2 
4 
6 
8 
10 
12 
14 
16 
Time (ms) 
Single differential signals 
5
0
0
 
V
 
1 
3 
5 
7 
9 
11 
13 
15 
E1 
E9 
E12 
E13 
E14 E4 
E5 
E6 
E7 E11 
E8 E10 
E3 E15 
E2 E16 
M
U
 #
1
 
MU #2 
 IZ2 
 IZ1 
a1) 
b1) b3) 
M
U
 #
1
 
M
U
 #
2
 
E1 
E2 
E3 
… 
E16 
IZ
1
 
 IZ
2
 
tendon 
-
motoneuron 
electrode 
array 
 
motor unit 
innervation zone 
-moto- 
neuron 
motor unit 
Innervation zone 
Electrode 
array 
tendon 
a2) 
b2) 
 0 50 100 150 200 250 300 350 400 450 500 
2 
4 
6 
8 
10 
12 
14 
Time (ms) 
Single differential 
5
 m
V
 
1 
3 
5 
7 
9 
11 
13 
15 
a3) 
EMG-Biofeedback 
Some examples on the application 
of surface electromyography 
From augmented sensory feedback on the degree 
and timing of activation, subjects are able to learn 
to relax or to timely elicit their muscles 
(neuromuscular re-education). 
Amplitude of EMGs varies proportionaly to force 
Fo
rc
e
 
SmArt by C Cescon LISiN Beees 
Controlling car toys from surface EMGs 
1/10000 Volt 
Amplificação (x 10000) 
A amplitude do sinal amplificado, 
sendo proporcional ao grau de ativação 
muscular, pode ser utilizada para a 
reeducação neuromuscular (jogos, 
sons, etc) 
1 Volt 
Força 
Tempo 20 s 40 s 60 s 
Asymmetrical 
activation of back 
muscles might 
lead to back pain 
Parkin et al. 2010 J Sports Sci 
Asymmetrical 
activation though 
might be crucial for 
boat stabilisation 
No evidence has been spotted 
Boat roll towards 
right side 
Pelvis tilts to the right 
 
Ipsilateral tensile force 
 
Contraleteral compressive force 
 
contraction of muscles in right side 
pushes the boat to left side 
Contraction of back muscles 
(spine manoeuvre) 
Vs. 
Changes in oar height 
(hand manoeuvre) 
 
Which and why predominates? 
1 
2 
3 
4 
5 
6 
7 
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 
Columns of surface electrodes 
R
o
w
s 
0 
200 V 
Drive 1 Drive 2 
Rowing driving phase 
When provided with visualor audio feedback of muscles’ activity, rowers might learn to load equally their 
right and left back muscles in consecutive drives. 
A typical training session for rowing requires about 1000 strokes! 
Wearable Detection System 
(Patent Pending) 
EMG-based 
Visual Feedback 
Distribution of activity in the back muscles during two, 
distinct rowing drives 
Example of how EMG-based feedback might help rowers 
to achieve even activation of lumbar muscles 
EM
G
 A
m
p
lit
u
d
e
 
Hearing our muscles with surface 
EMG: examples in potentially 
promising applications 
• Using audio EMG as a dancing, teaching tool 
 
• Using audio EMG to hear mechanical fatigue 
(and myoelectric manifestations of fatigue??) 
 
• Using audio EMG as a didactic tool 
S A M B A 
Vigorous leg movements 
Trapezius Tibialis anterior 
Dancing 
with 
EMGs