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EMG ACQUISITION AND MYOELECTRIC CONTROL STRATEGIES
MICHAEL TWARDOWSKI,
RBE PHD STUDENT
De Luca CJ, The Use of Surface Electromyography in Biomechanics,
J. Applied Biomechanics, 13: 135-163, 1997
“Electromyography is too easy to use
and
consequently too easy to abuse”
What is EMG?
[s]370 372 374 376 378 380
[Volts]
-0.002
-0.001
0
0.001
0.002
muscle fibers
Electro – Electric
Myo – Muscle
Graphy – Graph
“Electromyography (EMG) is the
study of muscle function through
the enquiry of the electrical signal
the muscles emanate”
Muscles Alive
Basmajian & De Luca, 1985
4copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
The surface EMG (sEMG) signal can be used in the following applications
Obtain parameters from individual or groups of muscles
• Force Activation Time (ON – OFF)
• Fatigue
Compare behavior of different muscles
• Relative amount of contribution
• Co-activation
• Pattern identification (tasks)
Why Use the sEMG Signal?
Revealing and harnessing
the neural control of
movement
How the Brain Controls Muscles…
Neural Control
Nervous System
The spinal cord is the major pathway of the nervous system.
Motor Unit
Motorneuron: neuron in
the spinal cord that
connects to muscles.
Motor Unit:
Motorneuron, its axon
and all of the muscle
fibers that in connects to.
It is the basic functional
unit of the
neuromuscular system.
Motorneuron Communication
Neuron Action Potential:
• change in electrical
potential
• produced by flow of
molecules in/out of cell
• propagates in one
direction along cell
• Means of neural
communication
Motor Unit Mechanical Output
De Luca CJ & Erim Z. Common drive of motor units in regulation of muscle force. Trends in Neuroscience, 17: 299-305,
1994.
Twitch Tetanic ForceMU firings
Spinal Cord
Motor neurons
Synapse
Muscle fibers
copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
Drive
to MUs
De Luca CJ and Erim Z. Common drive of motor units in regulation of muscle force. Trends in Neuroscience, 17: 299-305, 1994.
Noise 1
Motor Unit Action
Potential
MU 1
EMG Signal
MU 2
MU N
Noise 2
Noise NFiring MUAPT
+
+
+
+
Motor Unit Control and EMG Signal
sEMG Signal Acquisition
EMG Signal
• System subtracts two signals:
(m1+n) – (m2+n) noise is removed
• Resultant EMG signal:
(m1-m2) is amplified
Recording
copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
• The quality of the sEMG signal should be the first concern of any tests performed to collect sEMG signals.
• The quality of the EMG signal depends on:• Sensor characteristics
• Sensor Location
• Electrode-skin interface
• Noise contamination
• Cross-talk from other muscles
Signal Quality
copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
Sensor Characteristics
• Small inter-electrode spacing• sensors for small muscles
• selective recording from individual muscles
• reducing cross-talk
• Differential amplification• removing ambient noise (50, 60 Hz, Radio frequencies)
• Fixed spacing of electrodes for consistent signal properties• Amplitude and frequency of signal is a function of the distance
between the electrodes
contoured skin-
electrode interfacedEMG System
for MU studies
Trigno™
EMG + ACC
Ag/AgCl
Electrodes
High Density EMG
copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
StancePhase
SwingPhase
SwingPhase
SwingPhase
StancePhase
Sensor Characteristics
10mm DD bar
• Crosstalk reduced at 10 mm spacing• Crosstalk substantially reduced for 10 mm Double Differential
Double
Differential
10mm
Crosstalk
muscle
22mm -
+
-
+
-+
-+ -
+
Single
Differential
Single
Differential
10mm
De Luca CJ. The use of surface electromyography in biomechanics. Journal of Applied Biomechanics, 13: 135-163, 1997.
Innervation
Zone Proper
sensor location
• Increases the signal
• Increases the signal
to noise ratio
• Reduces cross-talk
Sensor Location:
Easiest, cheapest, most effective way of obtaining a better
signal to noise ratio
copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
• Contoured surface maintains proper electrical contact with the skin during movement
Electrode Skin Interface
copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
Baseline noise + EMG signal
Noise Contamination of the EMG Signal
50 100 150 200 250 300 350 400 450 500
Frequency
0.2
0.8
0.6
1.0
0.4Po
wer
20
0
Baseline noise = 1.2 uV RMS sEMG signal (Delsys sensors)
copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
• Physiological Noise• EKG, EOG, respiratory signals, etc.
• Ambient Noise• Power line radiation (50, 60 Hz)
• Cable motion artifact
• Baseline Noise• Electro-chemical noise (skin-electrode interface)• Thermal noise (property of semi-conductors)
• Movement Artifact noise• Movement of electrode with respect to the skin (induced
by force transients or movement of the skin)• This is the most obstreperous noise
Noise Contamination
copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
• Reduce Baseline noise by preparing the skin • Clean skin with alcohol
• Remove hair
• Peel off top dead layer of skin with hypoallergenic tape
• Use state-of-the-art active sensors
Baseline Noise
copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
• Induced by force transmission through the muscle and skin
• Caused by relative movement of sensor with respect to skin• Poor electrical contact between electrode and skin• The electrolyte material between electrode and skin
• More dominant in low level EMG signals
• Contaminates EMG signal• Frequency components superimpose on those of EMG signal
• How to Reduce Movement Artifacts• Skin Preparation• Not using electrolyte gel• Filtering
Movement Artifact
copyright © 2007 Delsys Inc. ISBN: 978-0-
9798644-0-7
Baseline noise + EMG signal + Artifact
50 100 150 200 250 300 350 400 450 500
Frequency
0.2
0.8
0.6
1.0
0.4
0
Po
wer
-2.5
-1.5
-0.5
0.5
1.5
2.5
0.2 0.4 0.6
Time (s)
Accele
rati
on
(g
)
Movement Artifact
Noise Contamination of EMG Signal
20
copyright © 2007 Delsys Inc. ISBN: 978-0-9798644-0-7
EMG signal with Baseline noise and Artifact reduced
0.2
0.8
0.6
1.0
0.4
0
Po
wer
FILTER 20- 450 Hz
Filtering the sEMG Signal
50 100 150 200 250 300 350 400 450 50020
Frequency
On/Off and Proportional Control of Prostheses
AMPLITUDE ESTIMATION
Sliding RMS (20 ms) Sliding RMS (250 ms)
Commercially available control schemes require unintuitive, unnatural sequences of
contractions to control multiple DOF.
Pattern Recognition Control
PATTERN RECOGNITION
Li, G., & Kuiken, T. A. (2009). EMG pattern recognition control of multifunctional prostheses by transradial amputees.
FEATURE EXTRACTION
Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert Systems with Applications.
• Amplitude Estimates (Signal Energy)
• Root Mean Square
• Mean Amplitude Value
• Signal Complexity
• Waveform Length
• Slope Sign Changes
• Zero Crossings
• Frequency
• Willison Amplitude
• Prediction Model
• Autoregressive coefficients
• Time Dependencies
• Mean Absolute Value Slope
NON LINEARITY sEMG VS. FORCE
Investigations have shown that the amplitude of the sEMG
signal is nonlinearly related to the actual force of the
contracting muscle.
Lawrence, J. H., & De Luca, C. J. (1983). Myoelectric signal versus force relationship in different human muscles. Journal of Applied Physiology .
Force
RM
S (
MV
C %
)
0 20 40 60 80 0 20 40 60 800 20 40 60 80
Implanted Electrodes
Traditional Intramuscular Recording Techniques
INVASIVE EMG
Motor unit action potentials (MUAPs) can
be recorded with the use of needle EMG.
The tip of the needle is the recording
electrode.
Another method is fine wire EMG. The needle
has a thin wire in it with a hook at the end.
Once inserted, the needle is removed and
only the wire remains in the muscle.
IMES
Implantable Myoelectric Sensors
• Control of 1 DOF for every 2 sensors
IMPLANTABLE DRAWBACKS
Implantation and maintenance of invasive neural interface:
• disturb the integrity of the residual nerves or muscles
• pose great health risks
• present substantially greater health costs
Intense fear and mutilation anxiety associated with surgery
THE INCEPTION
OF A LEGACYEMG Decomposition
First conceived and implemented by Carlo J. De Luca in 1982.
By 2000s developed non-invasive surface dEMG.
[Insert 20s video clip of decomposing EMG signal]
36
. . . . . . . . .
50 uV
0
5
10
15
20
25
30
35
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Forc
e (%
MV
C)
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40
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4445
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Time (s)
Mo
tor
Un
it N
um
ber
6 84 504846
Decomposition
Raw sEMG Signal
dEMG
Force Gradation
Recruitment
Threshold,
RT
Firing Rates
Force
Motor Unit Firing TrainMotor Unit Action Potential
0 5 10 15 20 250
10
20
30
40
50
Force (%
MV
C)
Time (s)
5
10
15
20
25M
ean
Fir
ing
Rat
e (p
ps)
50Recruitment Threshold (%MVC)
10 20 30 40
10
5
15
20
25
0
0
y = -0.15x + 22
De Luca CJ, LeFever RS, McCue MP, and Xenakis AP. Behavior of human motor units in different muscles during linearly-varying contractions. Journal of Physiology, 329: 113-128, 1982.De Luca CJ and Erim Z. Common drive of motor units in regulation of muscle force. Trends in Neuroscience, 17: 299-305, 1994
Onion Skin
Fo
rce (
%M
VC
)
Common Drive
25
20
15
10
5
00 5 3510 3015 20 25 40
0
13
26
52
39
Time (s)
Mea
n F
irin
g R
ate
(p
ps)
0-0.1 0.1-0.5 0.5-1 1
-1
1
0
Time
Lag (s)
Cro
ss C
orr
ela
tio
n
30
)()()(
tyxtc
Cross-Correlation Function
UNDERSTANDING MOTOR CONTROL
Motor unit control properties
Correlated control of motor units
Biomechanical benefits of control schemes
dEMGdEMG
Conceptualize new
EMG technology
Development of
EMG product
Advance understanding
of human movement.
Enabling new
research applications
From the NeuroMuscular Research Center
Scientific/Research
Questions
Trigno™ Mini
SensorThe Muscle
Fatigue Monitor
1st commercial
Signal Quality
Monitor
Trigno™ EMG
Wireless Sensor
A Foundation of Innovation in EMG Technology
Supporting today’s innovations
Trigno™ IM and dEMG
1979 1982 1985 1997 2000 2006 2008 2013
Marketed the stand-
alone EMG sensor
Double-Differential,
cross-talk reduction
contoured skin-
electrode interface
dEMG System
for MU studies
Trigno™
EMG + ACC
Trigno™ EMG
+ IMU SensorFirst fixed-spacing
surface sensor
TODAYS UNIVERSITY TALENT SUPPORTS
TOMORROWS INNOVATIONS
Training Researchers for Over 40 years
25 MS Theses, 12 PhD Dissertations, 41 Research Associates
Internship Programs FUTURE EMPLOYEES
Research Fellowship Programs FUTURE EMPLOYEES
CONCEPTS THAT WILL SHAPE OUR FUTURE
Investigating
EMG for
Subvocal
Speech
• AAC – Augmentative and
Alternative Communication
• Does not rely on acoustic
excitation
• Utilizes subvocal (mouthed)
speech
EMG-BASED SUBVOCAL COMMUNICATION
The alternative to voiced speech
Signal
Processing
EMG from multiple muscles
processed concurrently Parameterized data delivered
to back end algorithms Recognition results and error
rates for each utterance
1…2…
3…
4…
nAI
Recognition
Algorithms
Raw
Data
Hypothesis
Testing
SUBVOCAL ALGORITHM ARCHITECTURE
Intelligent Signal Processing + Strategic Machine Learning
- TIMIT standardized
speech data corpus
- Frequency-phoneme
combinations to test
unforeseen words
- State detection of
Start/End of speech
- Separation of speech and
non-speech muscle activity
- EMG features of speech
- LDA Feature reduction
- HMMs based on acoustic
speech processing
- Deep Learning
(experimental)
- Word error rates
- Subject specific
parameter
performance
APPLICATIONS OF SUBVOCAL TECHNOLOGY
Consider the alternatives to voiced speech
Subvocal
prosthesis
for speech
impaired
Subvocal
systems for
stealth
speech
Subvocal
speech in
quiet
environments
Subvocal
speech
translation
Subvocal
speech for
noisy
environments
Tracking
Movement
Disorders
CONCEPTS THAT WILL SHAPE OUR FUTURE
TRACKING THE HEALTH OF HUMAN MOVEMENT
• Consider Parkinson’s Disease,
Essential Tremor, etc.
• Movement disorder changes
throughout the day
• Tracking symptoms is important
for treatment
• But current monitoring methods
are unreliable
SENSOR SYSTEM FOR MONITORING MOVEMENT
Automated real-time detection
TrignoTM EMG + IMU Sensors
- 1 Ch EMG,
- 9 Ch IMU (Acc, Gyro, Mag)
- synchronous sensors
- wireless transmission
SENSOR SYSTEM FOR MONITORING MOVEMENT
Proposed System
Movement Disorder Results
Data Acquisition System Platform-Generated Applications
Data Logger & Sensor
Essential Tremor
Parkinson’s
NormalFreezing
7%
Dyskinesia20%
Tremor33%
Summary Status
Other….
Tablet