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Table 4: Classification hit rates in % (C/S)
New approach Englehart
WT WPT
Sig.
length
/chnls
CO4 SY5 CO4 SY5
DWT
C4
WPT
S5
200/1 75.0 76.7 79.2 79.2 60.8 62.5
200/4 90.0 94.2 94.2 95.0 86.7 88.3
400/1 80.8 80.0 80.8 77.5 60.8 59.2
400/4 99.2 96. 7 98.3 97.5 88.3 93.3
Table 5: Classification hit rates in % (L/P)
New approach Englehart
WT WPT
Sig.
length
/chnls
CO4 SY5 CO4 SY5
WT WPT
200/1 73.3 81.7 81.7 83.3 56.7 53.3
200/4 91.7 96.7 90.0 98.3 80.0 93.3
400/1 96.7 95.0 93.3 91.7 56.7 63.3
400/4 99.9 99.9 99.9 99.9 88.3 95.0
11 CONCLUSIONS
A new approach of wavelet-based feature extraction
from temporal signals has been proposed. The ap-
proach extends the Englehart's discrete wavelet trans-
form and wavelet packet transform by subjecting the
two-scale, three-sequence wavelet coefficients to
temporal moment computation. This has helped re-
duce significantly the dimensionality of the resulting
feature vectors without loosing the essential informa-
tion in the original patterns. It was found experimen-
tally that first five raw moments represent a good
compromise. The new methods are applied to pre-
hensile EMG signals of various lengths and various
amounts of input signals (surface EMG channels)
and compared to the best approaches of Englehart, on
the same set of signals. For the comparison are used
two quantitative measures: Hotelling statistic and
classification hit rates. The classifier applied to the
extracted features was linear support vector machine,
which has exceptionally good performance in case of
large feature spaces and fewer training samples. The
results have shown superior performance of the new
approach. A brief complexity analysis also shows
that the new approach is more efficient time wise.
Although the methodology was demonstrated on
EMG signals, we believe the methodology can
equally successfully be applied to other temporal
signals.
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