The proposed model captures more comprehensive information about temporal energy, shape, and frequency characteristics of the EMG signal through feature fusion, enabling it to effectively learn short EMG bursts.
Sign language recognition plays a crucial role in enabling inclusive human–computer interaction, yet accurate recognition of complex gestures remains challenging due to the variability in motion dynamics and muscle activations. In this study, we propose a deep learning-based dual-arm neural network for recognizing Indi...
Advances in artificial intelligence have made hand gesture recognition an important human–computer interaction modality. Graph convolutional networks (GCNs) are widely used for skeleton-based hand gesture recognition, yet their performance can be limited by weak semantic topology modeling, underused feature channels, a...
BACKGROUND EEG provides direct neural measurements with high temporal resolution for emotion recognition. However, many spectrogram-based approaches process channels independently or integrate channel information only at later stages. NEW METHOD We propose a stacked spectrogram representation that vertically concaten...
It has been proved that the proposed model gives better results in terms of the various performance metrices.
A novel action recognition method, named MICA-Net, which combines data from multiple sensors to improve the efficiency of the HAR model, and a new compact version of a wrist-worn sensor device with Wi-Fi connectivity to an edge device, enhancing usability in human-machine interaction applications.
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