An Eigen Based Feature on Time-Frequency Representation of EMG
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An Eigen Based Feature on Time-Frequency Representation of EMG
Direk Sueaseenak1,3, Theerasak Chanwimalueang2, Manas Sangworasil1, Chuchart Pintavirooj1
1Department of Electronics, Faculty of Engineering,King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand
2Biomedical Engineering Programme, Faculty of Engineering, Srinakharinwirot University, Nakhon-Nayok, Thailand
3Faculty of Medicine, Srinakharinwirot University, Nakhon-Nayok, Thailand
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Direk Sueaseenak, Theerasak Chanwimalueang, Manas Sangworasil, Chuchart Pintavirooj, “An Eigen Based Feature on Time-Frequency Representation of EMG ” IEEE-RIVF 2009, Danang University of Technology, VietNam, July 13-17, 2009
Publication & Present
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Introduction to our research1
Goal and objective of research2
SEMG Acquisition System3
Outline
SEMG BSS4
Feature Extraction5
Conclusion6
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Biomedical,Image,Signal and System ( Biosis LAB )
Assoc.Prof.Dr.Chuchart Pintavirooj Assoc.Prof.Dr.Manas Sangworasil
MemberM.Eng 10 คนPh.D 6 คน
I S
Mini CT
Image reconstruction
Face & fingerprintrecognition
UCT
EMG Analysis and Recognition
Infant Incubator
EEG and BCI
ECG monitor
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EMG Control Prosthesis Research TeamFaculty of Engineering (KMITL)
Faculty of Engineering(SWU)
Faculty of Medicine (SWU)
Direk Sueaseenak (SWU+KMITL)
Chuchart Pintavirooj
Manas Sangworasil
Niyom Laoopugsin
Theerasak Chanwimalueang
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Multi-channel EMG Pattern Classification(M.Eng Thesis)
4x4 EMG Sensor 16 channel EMG 16 ch Raw EMG 16 ch FFT EMG
∑ Area from 16 channel
Spline InterpolationEMG Pattern
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Hand Close
Wrist extension
Wrist flexion
Wrist pronation
Hand open
Wrist supination
Radial flexion Ulnar flexion
Hand Movement and EMG Pattern
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Muscular contraction % Accuracy
1.Wrist extension 100%
2.Wrist flexion 33%
3.Wrist pronation 53.3%
4.Hand closed 86.7%
5.Radial flexion 93.3%
6.Ulnar flexion 93.3%
7.Wrist supination 93.3%
8.Hand open 100%
Classification Result
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Disadvantage System complexity
Impossible in real application
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Goal of Research (Ph.D research)
Portable EMG Signal Acquisition and Pre-
processing
EMG Feature Extraction
EMG Classification
Minimum :EMG Measurement Channel
Maximum :Accuracy Rate of EMG Classification
No complexity for Real Application
Mechanical Control
Feedback Control
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EMG Surface Electrode EMG Acquisition System FAST ICA Separation
Time-Frequency Analysis Eigen based Feature Extraction
Feature 1 Feature 2 STFT ICA 1 STFT ICA 2
Object of Research
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Surface EMG Acquisition And Measurement
System
Direk Sueaseenak, Theerasak Chanwimalueang, Manas Sangworasil, Chuchart Pintavirooj, “PSOC-BASED MULTICHANNEL ELECTROMYOGRAM
ACQUISITION SYSTEM WITH APPLICATION IN MUSCULAR FATIGUE ASSESSMENT” Proceedings of ThaiBME2007, vol.1, pp. 110-114,2007.
Publication
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Surface EMG Acquisition System
SurfaceElectrode
Instrumentation Amplifier
PSOC MCU (PGA,ADC,UART)
EMG Recorder
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Channel 1 Flexor carpi radialis
Channel 2 Flexor carpi ulnaris
SWAROMED Al/AgCl Electrode
Surface EMG Placement
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SEMG Signal
Channel 1 Channel 2
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SEMG Blind Source Separation“Independent Component
Analysis”
Direk Sueaseenak, Theerasak Chanwimalueang, Manas Sangworasil, Chuchart Pintavirooj, “An Investigation of Robustness in Independent Component
Analysis EMG” Proceedings of ECTI-CON2009, vol.2, pp. 1102-1105,2009.
Publication
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Blind Source Separation : Cocktail Party Problem
The mathematical mode l of CPP :X1(t)=A11S1+A12S2
X2(t)=A21S1+A22S2
x = As
s = Wx (1)
(2)
(3)
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SEMG Blind Source Separation
ICACh1
Ch2
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The mathematical mode l of CPP :X1(t)=A11S1+A12S2
X2(t)=A21S1+A22S2
x = As
s = Wx (1)
(2)
(3)
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“Nongaussian is Independent”: Central Limit Theorem
x = As s = Wx www.bmekmitl.org
X1(t)=A11S1+A12S2
X2(t)=A21S1+A22S2
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Measures of Nongaussianity
By kurtosis
Subgaussian
Supergaussian
• Subgaussian kurtosis < 0
• Superguassian kurtosis > 0
• Gaussian kurtosis = 0
(4)
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Initialize W (Set the weight vector to random values) Newton 's method (until convergence)
Normalization
G(u)=u3(5)
Process of ICA
s = Wx (6)
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SEMG BSS Result Channel 1 Channel 2
ICA 1 ICA 2
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SEMG Time-Frequency Analysis
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Short-Time Fourier Transform
(7)
Source: http://www.clecom.co.uk www.bmekmitl.org
NnkjN
n
etnWnxktSTFT /21
0
)()(),(
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STFT Result
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Eigen based Feature Extraction
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Concept of Moment
dxdyyxpyxnmM nm ),(),(
dxdyyxpyxnmU ny
mx ),()()(),(
(8)
(9)
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Concept of Moment(cont.)
J
j
K
k
nj
mk kjfyxnmM
1 1
),()()(),(
J
j
K
k
njj
mkk kjfyyxxnmU
1 1
),()()(),(
0010 /mmx 0001 /mmy
Where
(10)
(11)
(12)
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)2,0()1,1(
)1,1()0,2(
uu
uuU
Concept of Moment(cont.)
UEET
EMG Features =
21 /
(13)
(14)
(15)
2
1
0
0
,2221
1211
ee
eeE
(16)
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Eigen Feature Extraction Result
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ICA-applied EMG
without ICA-applied EMG
AVG SD AVG SD
Wrist flexion 2.2743 0.2379 1.9630 0.4652
Relaxation 1.5695 0.3214 1.530 0.4718
Quantitative measurement of robustness of ICA application
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Conclusions We used a multi-channel electromyogram acquisition
system from previous work to acquire two channel surface electrodes on forearm muscles. and performed a
blind signal separation by using an independent component analysis (ICA) technique.
We purposed the novel features extraction for the EMG contraction classification. Our features are based on Eigen-vector approach. The time-frequency analysis is applied on the time-frequency magnitude spectrum of
the Independent component analysis EMG. The ratio between the two Eigen values are the novel features.
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Simple EMG Robotic Control Experiment
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Acknowledgment
Office of the Higher Education Commission
Faculty of MedicineSWU
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