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Classification of Prehensile EMG Patterns With Simplified Fuzzy ARTMAP Networks

Classification of Prehensile EMG Patterns With Simplified Fuzzy ARTMAP Networks Marko Vuskovic and Sijiang Du Department of Computer Science San Diego State University San Diego, CA 92182-7720 IJCNN'02 Honolulu, Hawaii May 12-17, 2002. Multifunctional Prosthetic Hand Control

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Classification of Prehensile EMG Patterns With Simplified Fuzzy ARTMAP Networks

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  1. Classification of Prehensile EMG Patterns With Simplified Fuzzy ARTMAP Networks Marko Vuskovic and Sijiang Du Department of Computer Science San Diego State University San Diego, CA 92182-7720 IJCNN'02 Honolulu, Hawaii May 12-17, 2002

  2. Multifunctional Prosthetic Hand Control • Classification of Prehensile Patterns • New ART Networks • Experimental Results • Conclusion

  3. Multifunctional (Prosthetic) Hand

  4. Multifunctional Prosthetic Hand Control

  5. Classification of Prehensile PatternsSchlesinger Classification of Grasp Types

  6. Classification of Prehensile Patterns (cont.)Raw EMGs and Features

  7. Clustering of Features(2D projection after Fisher-Rao Transformation)

  8. Clustering of Features (cont.)(90% Confidence Ellipses)

  9. Simplified Fuzzy ARTMAP Features (normalized): Input pattern: Activation function: Matching function: Match: Update function:

  10. SFAM Based on Euclidian Distance Input pattern: Activation function: Matching function: Match: Update function:

  11. SFAM Based on Mahalanobis Distance Input pattern: Activation function: Matching function: Match: Update functions:

  12. Classical SFAM Euclidian Activation Function Mahalanobis Activation 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 Experimental Results Four categories (cylindrical, spherical, lateral and tip grasp) Measured on 233 MHz Pentium II machine using Matlab

  13. Classical SFAM Euclidian Activation Function Mahalanobis Activation 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 Experimental Results (cont.)Six categories (cylindrical and spherical grasps are split into large and small apertures)

  14. Circle-in-the-square test(1000 samples, 3 epochs, r = 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

  15. Circle-in-the-square test(1000 samples, 3 epochs, r = 30)

  16. Circle-in-the-square test(1000 samples, 3 epochs, r = 8.5)

  17. 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

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