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An Eigen Based Feature on Time- Frequency Representation of EMG Direk Sueaseenak 1,3, Theerasak Chanwimalueang 2, Manas Sangworasil 1, Chuchart Pintavirooj.

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Presentation on theme: "An Eigen Based Feature on Time- Frequency Representation of EMG Direk Sueaseenak 1,3, Theerasak Chanwimalueang 2, Manas Sangworasil 1, Chuchart Pintavirooj."— Presentation transcript:

1 An Eigen Based Feature on Time- Frequency Representation of EMG Direk Sueaseenak 1,3, Theerasak Chanwimalueang 2, Manas Sangworasil 1, Chuchart Pintavirooj 1 1 Department of Electronics, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand 2 Biomedical Engineering Programme, Faculty of Engineering, Srinakharinwirot University, Nakhon-Nayok, Thailand 3 Faculty of Medicine, Srinakharinwirot University, Nakhon-Nayok, Thailand

2 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

3 Introduction to our research 1 Goal and objective of research 2 SEMG Acquisition System 3 Outline SEMG BSS 4 Feature Extraction 5 Conclusion 6

4 Company Logo Biomedical,Image,Signal and System ( Biosis LAB ) Assoc.Prof.Dr.Chuchart PintaviroojAssoc.Prof.Dr.Manas Sangworasil Member M.Eng 10 คน Ph.D 6 คน IS Mini CT Image reconstruction Face & fingerprint recognition UCT EMG Analysis and Recognition Infant Incubator EEG and BCI ECG monitor

5 EMG Control Prosthesis Research Team Faculty of Engineering (KMITL) Faculty of Engineering(SWU) Faculty of Medicine (SWU) Direk Sueaseenak (SWU+KMITL) Chuchart Pintavirooj Manas Sangworasil Niyom Laoopugsin Theerasak Chanwimalueang

6 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 Interpolation EMG Pattern

7 Hand Close Wrist extension Wrist flexion Wrist pronation Hand open Wrist supination Radial flexion Ulnar flexion Hand Movement and EMG Pattern

8 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

9 Disadvantage  System complexity  Impossible in real application

10 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

11 EMG Surface Electrode EMG Acquisition System FAST ICA Separation Time-Frequency Analysis Eigen based Feature Extraction Feature 1Feature 2 STFT ICA 1 STFT ICA 2 Object of Research

12 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 ,2007. Publication

13 Surface EMG Acquisition System Surface Electrode Instrumentation Amplifier PSOC MCU (PGA,ADC,UART) EMG Recorder

14 Channel 1 Flexor carpi radialis Channel 2 Flexor carpi ulnaris SWAROMED Al/AgCl Electrode Surface EMG Placement

15 SEMG Signal Channel 1 Channel 2

16 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 ,2009. Publication

17 Blind Source Separation : Cocktail Party Problem The mathematical model of CPP : X 1 (t)=A 11 S 1 +A 12 S 2 X 2 (t)=A 21 S 1 +A 22 S 2 x = As s = Wx (1) (2) (3)

18 SEMG Blind Source Separation ICA Ch1 Ch2 The mathematical model of CPP : X 1 (t)=A 11 S 1 +A 12 S 2 X 2 (t)=A 21 S 1 +A 22 S 2 x = As s = Wx (1) (2) (3)

19 “Nongaussian is Independent”: Central Limit Theorem x = As s = Wx X 1 (t)=A 11 S 1 +A 12 S 2 X 2 (t)=A 21 S 1 +A 22 S 2

20 Measures of Nongaussianity  By kurtosis Subgaussian Supergaussian Subgaussian kurtosis < 0 Superguassian kurtosis > 0 Gaussian kurtosis = 0 (4)

21  Initialize W (Set the weight vector to random values)  Newton 's method (until convergence)  Normalization G(u)=u 3 (5) Process of ICA s = Wx (6)

22 SEMG BSS Result Channel 1 Channel 2 ICA 1 ICA 2

23 SEMG Time-Frequency Analysis

24 Short-Time Fourier Transform (7) Source:

25 STFT Result

26 Eigen based Feature Extraction

27 Concept of Moment (8) (9)

28 Concept of Moment (cont.) Where (10) (11) (12)

29 Concept of Moment (cont.) EMG Features = (13) (14) (15) (16)

30 Eigen Feature Extraction Result

31 ICA-applied EMG without ICA- applied EMG AVGSDAVGSD Wrist flexion Relaxation Quantitative measurement of robustness of ICA application

32 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.

33 Simple EMG Robotic Control Experiment

34

35 Acknowledgment Office of the Higher Education Commission Faculty of Medicine SWU

36 Company Logo

37 LOGO


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