MFCC CNN-LSTM model on TENG-based sensor glove data achieves 93.33% accuracy across 11 sign classes, outperforming random forest by 23 percentage points.
Comparison of parametric representations for monosyllabic word recognition,
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Development of ML model for triboelectric nanogenerator based sign language detection system
MFCC CNN-LSTM model on TENG-based sensor glove data achieves 93.33% accuracy across 11 sign classes, outperforming random forest by 23 percentage points.