Classification of Prehensile EMG Patterns With Simplified Fuzzy ARTMAP Networks Marko Vuskovic and...
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Transcript of Classification of Prehensile EMG Patterns With Simplified Fuzzy ARTMAP Networks Marko Vuskovic and...
Classification of Prehensile EMG Patterns With Simplified Fuzzy ARTMAP Networks
Marko Vuskovic and Sijiang Du
Department of Computer Science
San Diego State UniversitySan Diego, CA 92182-7720
IJCNN'02Honolulu, HawaiiMay 12-17, 2002
1. Multifunctional Prosthetic Hand Control
2. Classification of Prehensile Patterns
3. New ART Networks
4. Experimental Results
5. Conclusion
Multifunctional (Prosthetic) Hand
Multifunctional Prosthetic Hand Control
Classification of Prehensile Patterns
Schlesinger Classification of Grasp Types
Classification of Prehensile Patterns (cont.)Raw EMGs and Features
Clustering of Features(2D projection after Fisher-Rao Transformation)
Clustering of Features (cont.)(90% Confidence Ellipses)
ART Networks(Unsupervised clustering)
Carpenter and G rossberg, 1987
ARTMAP Networks(Supervised clustering of b inary data)
Carpenter and G rossberg, 1991
Fuzzy ARTMAP Networks(Supervised clustering of analog data)
Carpenter, G rossberg et a l., 1992
Sim plified Fuzzy ARTMAP NetworksKasuba, 1993
Baraldi and A lpaydin, 1998
This paper
Simplified Fuzzy ARTMAP
1 2 1 2ˆ ˆ ˆ ˆ ˆ ˆ, ,... ,1 ,1 ,...,1 ,d dx ff ff ffæ ö÷ç ÷ç ÷ç ÷çè ø= - - -
( )/ ,j j jt x w wa= Ù +
/ ,j jm x w x= Ù
( ): 1 .j j jw w x wb b= - + Ù
Input pattern:
ˆ .ff f=Features (normalized):
Activation function:
Matching function:
Update function:
Match: jm r>
SFAM Based on Euclidian Distance
x f=Input pattern:
Activation function:
Matching function:
Update function:
( ) ( )T
j j jt x w x w= - -
/ max( , )T Tj j j jm t x x w w=
( ): 1j jw w xb b= - +
Match:
jm
jm
SFAM Based on Mahalanobis Distance
x f=Input pattern:
Activation function:
Matching function:
Update functions:
j jm t=
( ): 1j jw w xb b= - +
( ) ( )1T
j j j jt x w S x w-= - -
( ) ( )( )( )2: 1 1T
j j j jS S x w x wb b b= - + - - -
Match:2 ( , )jm d p
Experimental Results
Four categories (cylindrical, spherical, lateral and tip grasp)
Classical SFAM
EuclidianActivation Function
MahalanobisActivation Function
Average classification hit rate
85.7 % 86.53 % 94.6 %
Avr. number of output nodes
9.4 30.1 5.2
Avr. learning time (per pattern)
27.9 ms 13.3 ms 9.1 ms
Avr. classification time (per pattern)
24.7 ms 12.9 ms 4.8 ms
Measured on 233 MHz Pentium II machine using Matlab
Experimental Results (cont.)
Six categories (cylindrical and spherical grasps are split into large and small apertures)
Classical SFAM
EuclidianActivation Function
MahalanobisActivation Function
Average classification hit rate
61.1 % 60.1 % 77.6 %
Avr. number of output nodes
24.3 53.7 7.0
Avr. learning time (per pattern)
61.9 ms 22.2 ms 10.6 ms
Avr. classification time (per pattern)
61.0 ms 21.7 ms 5.9 ms
Circle-in-the-square test(1000 samples, 3 epochs = 13.8 )
Carpenter, 1992:
Hit rate: 95%
Output nodes: 27
This paper:
Avr. hit rate: 95.7%
(min 92.6%/max 98.7%)
Avr. output nodes: 13.1
(min 10/max 16)
Averaged over 100 experiments
Circle-in-the-square test(1000 samples, 3 epochs, = 30)
Circle-in-the-square test(1000 samples, 3 epochs, = 8.5)
Conclusion
• Mahalanobis based SFAM applied to EMG: 8 to 16 % higher hit rate 2 to 3 times less output nodes 5 to 10 times faster classification 3 to 6 times faster training
• Circle-in-the-square test: 2 times less output nodes at equal hit rate
• Future work:consider more complex features (like STFT)improve algorithms