Deep Generative Replay-based Class-incremental Continual Learning in sEMG-based Pattern Recognition.
Journal:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
PMID:
40039191
Abstract
Developments in neural networks and sensing technologies have increased focus on modules for surface electromyogram (sEMG)-based pattern recognition. Incremental updating of parameters based on pre-trained networks can flexibly respond to user requirements and enhance user-centered interfaces. However, updating the parameters of a pre-trained network with new class data can easily lead to catastrophic forgetting. While mitigating the phenomenon by straightforwardly replaying historical data, this approach necessitates significant memory resources, a constraint that proves often impractical in real-world applications where access to historical data is limited. To avoid this limitation and incrementally add new classes to the pre-trained network, we proposed a deep generative replay-based continual learning (CL) framework. The performance was evaluated using a public sEMG dataset under two-class-incremental learning scenario until four tasks. As a result, the proposed framework performed better than other conventional CL methods except for experience replay, which simply reuses historical data.