Digital Processor to Monitor the Muscular Energy Drop in Surface Electromyograms

1. Digital Processor to Monitor
the Muscular Energy Drop
in Surface Electromyograms
Fábio Ferreira

2. Contents
 Abstract
 Introduction
 Objectives
 Surface Electromyogram (sEMG)
 Neuromuscular Electric Stimulation (NMES)
 Muscular Contraction Parameters
 NMES Control System
 Digital Processing Unit (DPU)
 Conclusions

3. Abstract
This research aims to develop support instrumentation for
motor rehabilitation treatments of medullar injured individuals.
The objective is to monitor the muscular energy decreasing as a
function of the exercise time, in order to avoid the total muscular
structure fatigue, mainly when under effects of Neural-Muscular
Electrical Stimulation (NMES).
It was carried out an initial study over the electric activity of
clinically normal muscular groups under voluntary contraction,
and of paralyzed muscular groups under electrical stimulation
effects. Both types of surface electromyogram signals (sEMG)
have been subjected to a specific mathematical processing to
extract the spectral parameters that best quantify the myoelectric
manifestation of fatigue.

4. Abstract
Based on the selected parameters to monitor the muscular
energy decreasing, it was proposed a NMES Control System
which main unit (the Digital Processing Unit) was specified,
developed, simulated and validated using Matlab DSP toolset
and Hardware Description Language.

5. Introduction
A normal neuromuscular system is able to evaluate the
quantity of metabolic energy necessary to generate force and
movement in a finely controlled manner. This control is only
possible due to the capabilities of the central neuro-system to
activate each individual motor unit using a multiplexed space-
time distribution.
The activation of the motor units is done according to a
“firing rate” that varies depending on the requested resultant
force. As much strength is requested from the muscular
structure, as bigger will be the frequency of the activation
impulses (time) and the quantity of activated motor units
(space).

6. Introduction
When the capability to use brain processing to activate
muscular groups is lost due to medullar injuries, it is possible to
subject the motor units to the surface NMES. Beyond other clinic
aspects, the NMES contributes to:
●
Muscular reeducation
●
Osteoporosis and Atrophy prevention
●
Reduction of Spasticities, Contractures and Edemas
●
Increasing of blood circulation
●
Increasing in muscular strength and resistance
●
Improvement of the cardiopulmonary capacity
●
Avoiding implant organic rejection due to usage of
surface electrodes

7. Introduction
Despite all benefits that NMES treatments can bring to the
patient, it may subject the muscular group to the phenomenon of
Fatigue. This is due to the fact that NMES activates a large amount of
motor units at the same time and to their maximum strength.
Any muscular structure is subjected to the myoelectric
manifestation of fatigue when exposed to recurrent persistent intense
static or dynamic effort.
By following the NMES motor rehabilitation treatment of medullar
injured patients, it was clearly observed that the muscular groups
reduce the response strength or become totally irresponsive as a
function of the exercise time and stimulus waveform. By changing
amplitude, frequency and active regions of the stimulus waveform, it is
possible to prolongate the exercise session, depending on the user
physiology.

8. Introduction
Muscle Exhaustion and Maximum Fatigue
For NMES, the Maximum Fatigue occurs when a muscular group
becomes temporarily irresponsive to any stimulus waveform. The
Muscular Exhaustion is the point of failure of the muscle group strength
for a specific waveform. Normally the muscular group that reaches
exhaustion, but max fatigue, can still respond to a different stimulus
waveform.
Muscle Fatigue is the point of failure of the muscular energy. For
motor rehabilitation purposes, the fatigue is identified as the instant
when the muscular group is no longer able to sustain a pre-determined
force. It is of common knowledge that the fatigue is a continuous
phenomenon that starts manifesting since the very beginning of the
contraction and can be somehow monitored since then.

9. Introduction
The clinically normal individual, when having a specific
muscular group subjected to maximum stress condition, will
perceive the critical energy reduction long before any visual or
mechanical manifestation. This is only possible due to
sensibility (pain) in the affected region.
In most cases, the medullar injured individual does not
have the sensibility in the stimulated region and therefore will
not be able to perceive the energy reduction during the NMES
exercises.

10. Objectives
 Analyse the myoelectric manifestation of the muscular
fatigue phenomenon
 Develop support instrumentation for motor
rehabilitation treatments of medullar injured individuals
 Monitor the progressive reduction of the muscular
energy through Muscular Contraction Parameters
 Propose a NMES Control System
 Develop the Digital Processing Unit using Hardware
Description techniques
 Validate scientific and technologic parts with Matlab
Digital Signal Processing toolbox.

11. Surface Electromyogram
Monitoring the Muscular Exhaustion to prevent Fatigue is
probably the most promising clinic application of the Surface
Electromyogram. The Electromyogram is the most commonly
used way to represent the summation of contributions of all
motor units.
Relatively simple to obtain (sampling and storage), it
measures the potential generated in the conductor volume
involving the fibers of each motor unit, supplying information
related to the anatomy and physiology of the muscular groups:
length, depth, width, fiber orientation of the enervation zones.

12. Surface Electromyogram
Sample of Myoelectric Activity of a Volunteer Contraction
20
00
10
50
10
00
50
0
V[V]
0
-0
50
- 00
10
- 50
10
0 10 20 30 40 50 60 70 80 90
t[s]

13. Neuromuscular Electrostimulation
During the volunteer contraction, the motor unit potentials are
asynchronous, while in the NMES contraction, the motor unit
potentials are synchronized with the stimulus impulses, which will be
of great value to evaluate the myoelectric manifestation of fatigue.
In general, there are suggestions that the frequency of the
stimulus signal (f) should be into the range from 16 to 40Hz and the
active period (t) from 100 and 600us, continuous or intermittent.
Stimulus Current Density depends on the Example of NMES stimulation waveform
area of contact of the NMES electrodes
I
∂I = = cte
A

14. Neuromuscular Electrostimulation
Sample of Myoelectric Activity of a Electrostimulated Contraction
3000
2000
1000
V [V ]
0
-1 0 0 0
-2 0 0 0
-3 0 0 0
2 4 .0 5 2 4 .1 2 4 .1 5 2 4 .2 2 4 .2 5 2 4 .3 2 4 .3 5 2 4 .4 2 4 .4 5 2 4 .5
t[s ]

15. Muscular Contraction Parameters
Conduction Velocity (CV)
The Conduction Velocity is a physiologic parameter well
known for being related to the type and diameter of the muscular
fiber, ion concentration, hydrogenionic potential (pH) and firing
rate of the motor unit. It measures the speed that the sEMG
travels through a pre-determined distance.
The Conduction Velocity demands at least 2 sets of
electrodes, which is a mechanical disadvantage. Also, the
Autocorrelation and Cross-Correlation calculus may compromise
the operation in real-time.

16. Muscular Contraction Parameters
The following four parameters demonstrated more
convenient resultant waveforms since they are based on the
direct calculation of the energy of the sEMG.
• RMS: Root Mean Square
• ARV: Average Rectified Value
• MDF: Medium Frequency (spectral parameter)
• MNF: Mean Frequency (spectral parameter)

17. Muscular Contraction Parameters
Root Mean Square (RMS) Average Rectified Value (ARV)
1
T 1
ARV = abs ( s (t ))
= ∫ s(t ) dt
2
s RMS
T 0 N
Medium Frequency of the Mean Frequency of the
Power Spectrum (MDF) Power Spectrum (MNF)
MDF ∞ 1 n
MNF ( n) = ∑ MDF (i )
∫ S ( f )df
0
= ∫ S ( f )df
MDF
n i =1

18. Muscular Contraction Parameters
Volunteer Contraction Electrostimulated Contraction
Comparacao entre Indices Comparacao entre Indices de Fadiga
1.6 1.4
1.3
1.4
1.2
1.2
1.1
Amplitude [V/V]
1 1
0.9
0.8
0.8
0.6 0.7
0.6
0.4
0.5
0 10 20 30 40 50 60 0 10 20 30 40 50 60
Tempo [s] Tempo [s]
♦ : MNF (Mean Frequency)
♦ : MDF (Median Frequency)
♦ : RMS (Root Mean Square)
♦ : ARV (Average Rectified Value)

19. NMES Control System
3
Controle Sinal sEMG
1 Bloco de
Bloco de 2 4 Dinâmica (Contração)
Processamento
Processamento Atuador do Músculo
Digital
Digital (Paciente)
8
9
Conversor 7 Pré 6 Eletrodo 5
Analógico Processamento de sEMG
Digital Analógico (Circuito Sensor)

20. Digital Processing Unit (DPU)
Internal Diagram
Sinais de Saída
Filtro Transformação Somador MDF
Linear de Domínio Cumulativo MNF
Sinais de Entrada
sEMG(12) UCP Comparador fad
Digital
maxfad
start
clk

21. Digital Processing Unit (DPU)
Median Frequency of
Electrostimulated Contraction DPU Input and Output signals
Frequencia Media
1
0.95
clk
Amplitude Normalizada [V/V]
Bloco de fad
0.9 start Processamento
emg(0:11) Digital
0.85 maxfad
0.8
0.75
0.7
0 10 20 30 40 50 60
Tempo [s]

22. Digital Processing Unit (DPU)
The analysis was done by partitioning the sEMG in
intervals of 500ms (quasi-stationary condition). Each interval x(t)
is submitted to Digital Linear Filtering and Domain
Transformation (FFT). The resultant Power Spectrum X'(f) is
then submitted to the Cumulative Sum to obtain the respective
interval MDF and MNF.
Filtro x’(t) Transformação X’(f) Somador
x(t) Linear de Domínio Cumulativo mnf(t)

23. Digital Processing Unit (DPU)
Recursive Linear Filtering
ord ord
y n = ∑ ( bk xn −k ) − ∑ ( ak y n− k )
k =0 k =1
Rejeita-Faixa: Chebyshev tipo 1 P s a a a Cno c o
a s -F ix : o v lu a
0
0
Magnitude Response (dB)
-50
agnitude (dB)
-50 -1 0
0
-1 0
5
-100
M
-2 0
0
-150 -2 0
5
0 50 10
0 10
5 20
0 20
5 30
0 30
5 40
0 40
5 50
0
0 50 100 150 200 250 300 350 400 450 500 F qec ( z
re u n y H)
Frequency (Hertz)
0 20
0
Phase (degrees)
-100 0
egrees)
-200 -2 0
0
Phase (d
-300 -4 0
0
-400 -6 0
0
0 50 100 150 200 250 300 350 400 450 500 0 50 10
0 10
5 20
0 20
5 30
0 30
5 40
0 40
5 50
0
F qec ( z
re u n y H)
Frequency (Hertz)

24. Digital Processing Unit (DPU)
Recursive Linear Filtering
20
0
0 30
00
M gnitude (dB)
-0
20
20
00
a
-0
40
10
00
-0
60
0 20
0 40
0 60
0 80
0 10
00 10
20 10
40 10
60
Amplitude
Fe u n y(H)
r qec z
0
50
0
-1 0
00
0
Phase (degrees)
-0
50
-2 0
00
- 00
10
- 50
10 -3 0
00
- 00
20
0 20
0 40
0 60
0 80
0 10
00 10
20 10
40 10
60 5 8
.5 5.6 5 2
.6 5 4
.6 5 6
.6 5 8
.6 5.7 5 2
.7 5 4
.7
Fe u n y(H)
r qec z T m o [s]
e p

25. Digital Processing Unit (DPU)
Fast Fourier Transform
N −1
X n = ∑ x0 (k )e − j 2πnk / N n = 0,1,2,..., N − 1
k =0
Espectro de Potencias
0.45
.
0.4
.
0.35
.
Amplitude Normalizada
0.3
.
0.25
.
0.2
.
0.15
.
0.1
.
0.05
.
0
. . . 2.245 2.25 2.255 2.26
trechos de 1024 pontos x 10
4
x

26. Digital Processing Unit (DPU)
Power Spectrum of each interval
(Quantitative observation of the Spectral Compression)
Interval Frequency

27. Digital Processing Unit (DPU)
Power Spectrum of each interval
(Qualitative observation of the Spectral Compression)
Interval
Frequency

28. Digital Processing Unit (DPU)
Cumulative Sum
1 N −1
y ( n) = ∑ x(n − k )
N − 1 k =0
n = 0,1,2,..., N − 1
1 2
0.9 1.8
0.8 1.6
0.7 1.4
0.6 1.2
ela o ]
Erro R tiv [%
0.5 1
0.4
0.8
0.3
0.6
0.2
0.4
0.1
0.2
0
0
2.025 2.03 2.035 2.04 2.045 2.05 2.055 2.06 2.065 0 0.5 1 1.5 2 2.5 3 3.5
4 P n sd S m Cmla a
o to a o a u u tiv 4
x10
x 10
Maximum Relative Error = 1.94% Medium Relative Error = 0.036%

29. Digital Processing Unit (DPU)
Volunteer Contraction

30. Digital Processing Unit (DPU)
Frequencia Media
1
Volunteer Contraction
0.95
Amplitude Normalizada [V]
0.9
0.85
0.8
0.75
0.7
0 10 20 30 40 50 60 70
Tempo [s]

31. Digital Processing Unit (DPU)
Electrostimulated Contraction

32. Digital Processing Unit (DPU)
Frequencia Media
1
Electrostimulated
0.95
Contraction
Amplitude Normalizada [V/V]
0.9
0.85
0.8
0.75
0.7
0 10 20 30 40 50 60
Tempo [s]

33. Digital Processing Unit (DPU)
Either the output signal 'fad' or 'maxfad' can be used by the
Active Control to automatically apply the most appropriate
NMES waveform.
For clinically normal patients, both signals can be used to
monitor the physical performance of a muscular group in
exercise with constant load.

34. Conclusions
From the results obtained, it was possible to verify the real
applicability of the surface electromyogram to monitor the
myoelectric manifestation of muscular fatigue. This technique
has the main characteristic of being non-invasive, which reduces
organic rejection of the NMES electrodes.
The spectral parameter selected to close the loop and
feedback the DPU, presented more convenient characteristics
than the remaining parameters analyzed in this research, due to
its stabler morphology and for caring out the muscular
exhaustion information since the beginning of the contraction.

35. Conclusions
The process of monitoring the MNF represents a
mechanical advantage: the possibility to use only four
electrodes, two for stimulation and two for feedback to close
the loop of the NMES Control System.
It was verified also that by assuming quasi-stationary
conditions, the analysis of the electric muscular activity from
surface electromyograms maintained a reasonable precision,
convenient to monitor and control the decreasing of the
muscular energy as function of the exercise time.