4 papers
Generative versus Discriminative Approaches for Class-Incremental Learning of EMG Signals: Effectiveness of Scale Mixture Modeling
Seitaro Yoneda, Suguru Kanoga, Akira Furui
In electromyogram (EMG)-based motion recognition, it is impractical to predefine all motions that may be required during deployment, necessitating class-incremental learning that s…
Inter-Subject Variance Transfer Learning for EMG Pattern Classification Based on Bayesian Inference
Seitaro Yoneda, Akira Furui
In electromyogram (EMG)-based motion recognition, a subject-specific classifier is typically trained with sufficient labeled data. However, this process demands extensive data coll…
Recognition of Unseen Combined Motions via Convex Combination-based EMG Pattern Synthesis for Myoelectric Control
Itsuki Yazawa, Seitaro Yoneda, Akira Furui
Electromyogram (EMG) signals recorded from the skin surface enable intuitive control of assistive devices such as prosthetic limbs. However, in EMG-based motion recognition, collec…
Bayesian Approach for Adaptive EMG Pattern Classification Via Semi-Supervised Sequential Learning
Seitaro Yoneda, Akira Furui
Intuitive human-machine interfaces may be developed using pattern classification to estimate executed human motions from electromyogram (EMG) signals generated during muscle contra…