Optimization of HD-sEMG-Based Cross-Day Hand Gesture Classification by Optimal Feature Extraction and Data Augmentation

Human–machine interaction requires accurate recognition of human intentions (e.g., via hand gestures). Here, we assessed the cross-day robustness of widely used hand gesture classification techniques applied to high-density surface electromyogram (HD-sEMG) signals (256 channels). Our evaluation covered techniques in each stage of the classification framework: first, 50 temporal-spectral-spatial domain features, second, 15 feature optimization techniques, and third, seven classifiers. Moreover, although HD-sEMG provides sufficient neuromuscular information, some of the channels may present low signal-to-noise ratio and should therefore be treated as outliers. Accordingly, we performed our evaluation with, first, all outlier channels retained, and second, removal of the features corresponding to poor-quality channels and substitution with interpolated values from neighbor channels. The impact of sliding window and data augmentation was also investigated. We examined the results on a 35-gesture classification task using HD-sEMG acquired from 20 subjects on two sessions in separate days. The results showed that interpolation of features from outlier channels significantly improved the performance in most cases. Use of a sliding window and of data augmentation contributed to a higher classification accuracy. For the classification of 11 selected gestures of common daily use, the support vector machine classifier achieved the highest classification accuracy of 91.9% in a cross-day validation protocol using an optimal combination of 13 features (each extracted from sliding windows), feature optimization by linear discriminant analysis, and data augmentation. Our work can serve as a technique-screening tool on cross-day applications of human–machine interactions.