Skip to main content

Table 2 Major memory usage comparison between the new retraining method and the previous retraining method

From: A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition

  New fast retraining Previous retraining
AB subjects (772 windows for each class, 7 classes, 6 channels, 4 features per channel) \( {\tilde{\mu}}_g \):(6 × 4) × 4 bytes = 96 bytes; Total size of the feature matrix:
\( \tilde{\varSigma} \) : (6 × 4) × (6 × 4) × 4 bytes = 2304 bytes; (6 4) × 772 × 7 × 4 bytes = 518784 bytes
Total: 96 × 7 + 2304 = 2976 bytes = 2.9 Kbytes = 506.6 Kbytes
TR1 subject (772 windows for each class, 5 classes, 6 channels, 4 features per channel) \( {\tilde{\mu}}_g \):(6 × 4) × 4 bytes = 96 bytes; Total size of the feature matrix:
\( \tilde{\varSigma} \) : (6 × 4) × (6 × 4) × 4 bytes =2304 bytes; (6 × 4) × 772 × 5 × 4 bytes = 370560 bytes
Total: 96 × 5 + 2304 = 2784 bytes = 2.7 Kbytes = 361.9 Kbytes