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