Abstract:
Effectively integrating the time-space-frequency information of multi-modal signals from armband sensor, including surface electromyogram (sEMG) and accelerometer data, is critical for accurate gesture recognition. Existing approaches often neglect the abundant spatial relationships inherent in multi-channel sEMG signals obtained via armband sensors and face challenges in harnessing the correlations across multiple …