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Transcript of ACCAS2011 Paper
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7/31/2019 ACCAS2011 Paper
1/2
The 7th Asian Conference on Computer Aided Surgery (ACCAS 2011)
Improving of sEMG Signal Feature Combination for Gait
Phase Detection
Jirapong Manit Prakarnkiat Youngkong
King Mongkut's University of Technology, Thonburi King Mongkut's University of Technology, [email protected] [email protected]
1. IntroductionSurface electromyography (sEMG) signal is widely
used in many studies as the intelligent prosthetic control
input. While using the raw sEMG signal is difficult toclassify subject movements or activities, various type of
domains extracted from raw signal such as time domain,
frequency domain and time-scale domain were introducedto make the sEMG pattern classification more reliable [1,
2]. But the only one domain feature able to show limited
signal information [3]. {Explain some more} In this study,we present the combination of multiple domain feature setfor improving gait phase recognition performance which is
used in the variable damper above-knee prostheses [].{Explain some more}
2. MethodsA. Data Acquisition
The data was collected with sampling rate of 2000 Hzfrom three muscles; Rectus femoris, Vastus medialis and
Biceps femoris, on both legs. ZeroWire electrodes were
attached on a single leg as shown in figure 1. Five healthy
males subjects between 22 to 34 years old were instructedto walk in a comfortable pace on a 10 meters length hardfloor repeatedly 8 times. Three force plates were placed on
the floor, and used to identify the interval of swing phaseand stance phase. All collected data was labeled its class
number manually; 0 for stance phase and 1 for swingphase.
Figure 1. sEMG electrode location : (1) Rectus femoris(2) Vastus medialis and (3) Biceps femoris
B. Feature Extraction
Two kinds of feature domain, time and frequencydomain, were applied to raw sEMG signal for creating
single and multiple domain feature combination. Time
domain features include Mean Absolute Value (MAV),Variance (VAR), Waveform Length (WL), log-Detector
(logDet) and Willison Amplitude (wAmp). And, frequencydomain features consist of Mean Frequency (MNF) and
Median Frequency (MDF). Data from each channel wasfirst segmented into a 256-sample window, extracted to
seven features from the window data. Next, the windowwas shifted by 64 samples until reaching the end of data.
The combinations of two different domain features were
made by selectively choosing one feature from timedomain and another one from frequency domain.
Therefore, a total number of features for testing is fifteen.
C. Dimensional Reduction
This section is very important for sEMG pattern
classification to obtain an optimal computational time.Hence, the dimensional reduction technique helps making
the input data size smaller. Principle component analysis
(PCA) was proved to be as a good dimensional reductiontechnique for applying with sEMG signal data [ ]. It is a
simple linear projection technique that is fast and easy toimplement. So, it was implemented in this study. At the
end of the part, the dimension of the input feature wasreduced from three to two.
2.4 Pattern Classification
Support Vector Machine (SVM) had been shown to bethe best classifier. Fast computational time and ability to
handle with non-linearity of SVM are advantages whichare suitable for sEMG signal classification [ ]. Radial
basis function (RBF) was used as the kernel function inSVM in this study. The feature with reduced dimension
was divided into two sets, equally. The first set was usedfor training the classifier model and the second set was
used for testing. Output of the classifier is the numberthat associates to the gait phase.
3. ResultsAccuracy is used to show the performance, e.g. the
best feature set shall give the highest accuracy. Table 1 to
4 display accuracies in percent of gait phase detection foreach feature. The average values of accuracy in the time
domain features and in the combination between timedomain and frequency domain are 89.97% and 96.15%,
respectively. The result emphasizes that the combinationbetween two domains feature give higher detection
accuracy. give detection accuracy higher than a single
time domain feature.
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7/31/2019 ACCAS2011 Paper
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The 7th Asian Conference on Computer Aided Surgery (ACCAS 2011)
Table 1 Detection accuracy of time domain features.
Feature MAV VAR WL wAmp logDet
% Acc
Table 2 Detection accuracy of frequency domain
features.
Feature MDF MND
% Acc
Table 3 Detection accuracy of combination betweentime domain features and Median Frequency.
Feature MAV+
MDF
VAR+
MDF
WL+
MDF
wAmp+
MDF
logDet
+MDF
% Acc
Table 4 Detection accuracy of combination betweentime domain features and Mean Frequency.
Feature MAV+
MNF
VAR+
MNF
WL+
MNF
wAmp+
MNF
logDet
+MNF
% Acc
4. Conclusions
As shown in the result, the combinations between time
domain feature and frequency domain feature improve the
accuracy of gait phase classification and the bestcombination feature is {wait'n for the result} which shallbe applied in the variable damper above-knee prostheses.
AcknowledgementsThis research was financially supported by National
Science and Technology Development Agency (NSTDA),
the Higher Education Research Promotion and NationalResearch University Project of Thailand, Office of the
Higher Education Commission and King MongkutsUniversity of Technology Thonburi, Thailand, and
medically supported by Sirindhorn National Medical
Rehabilitation Centre (SNMRC).
References
[1] Dennis T, He H, Tood A K. Journal of Nuero-
Engineering and Rehabilitation, 2010, 7:21[2] Zeeshan O K, Zhen G X, Carlo Menon. BioMedical
Engineering Online, 2010, 9:41[3] Mahammadreza A O, Housheng H. Biomedical
Signal Processing and Control 2,- 2007: 275-294[4]
[5][6]
[7]
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