Generative versus Discriminative Approaches for Class-Incremental Learning of EMG Signals: Effectiveness of Scale Mixture Modeling

Abstract

In electromyogram (EMG)-based motion recognition, it is impractical to predefine all motions that may be required during deployment, necessitating class-incremental learning that sequentially adds new motion classes. The primary challenges in class-incremental learning are catastrophic forgetting, where previously acquired knowledge is overwritten when learning new classes, and the memory cost of retaining past data to counteract it. In particular, for EMG-based motion recognition intended for edge devices with limited computational resources, it is essential to suppress catastrophic forgetting and maintain low memory cost. In this paper, we conducted a comparative evaluation of eight class-incremental learning methods spanning generative and discriminative approaches, including both deep and non-deep learning methods, for EMG signal classification. Using four datasets, we evaluated each method in terms of classification accuracy, backward transfer, and memory cost. The results demonstrated that deep learning-based methods suffered significant accuracy degradation from catastrophic forgetting as the number of tasks increased, whereas generative models maintained stable accuracy with low memory cost. Among generative models, the scale mixture classification model (SMCM), which captures EMG signal variability, achieved the most favorable accuracy-memory trade-off while effectively suppressing catastrophic forgetting across all datasets.