Addressing Multi-Day Generalizability for EMG-Based Gesture Recognition

Myoelectric prostheses, controlled by electromyographic (EMG) signals, suffer from significant inter-day performance degradation due to factors like muscle fatigue and electrode shift. Existing deep learning approaches often require extensive daily retraining, hindering real-world usability. This paper presents a novel transfer learning framework that mitigates this limitation by combining a pre-trained convolutional neural network (CNN) for robust feature extraction with a fine-tuned linear discriminator for efficient daily adaptation. Our methodology demonstrates superior inter-day generalizability and long-term stability compared to state-of-the-art methods on the Kanoga dataset while requiring substantially less daily calibration data.