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Hybrid Deep Neural Networks for Sparse Surface EMG-Based Hand Gesture Recognition

The paper proposes a novel multiple-model (hybrid) deep learning architecture for the task of hand gesture recognition based on multi-channel surface Electromyography (sEMG) signals. By capitalizing on the recent success of multiple-modeling approaches, the proposed architecture integrates two deep learning models simultaneously and in a parallel fashion. The designed architecture is different from the majority of existing data-driven models for the task of hand gesture recognition, which are commonly developed based on a stand-alone deep model and can hardly provide robustness across different scenarios. More specifically, the proposed hybrid solution consists of two parallel paths, i.e., one Long-Short Term Memory (LSTM) model and one Convolutional Neural Networks (CNN) path, followed by a fully connected multilayer fusion network acting as the fusion centre combining the outputs of the two paths to perform the classification task. The LSTM path is utilized to extract temporal features while simultaneously the CNN path is used to extract spatial features. The proposed hybrid architecture is evaluated based on the NinaPro DB2 dataset. Our comprehensive set of experiments and comparisons with state-of-the-art approaches obtained over the same dataset shows that the proposed architecture significantly outperforms its counterparts and achieves exceptionally high classification performance of 98.01.