FedEMG: Achieving Generalization, Personalization, and Resource Efficiency in EMG-based Upper-Limb Rehabilitation through Federated Prototype Learning

Upper extremity amputation, often necessitated by traumatic injuries, significantly impacts an individual's well-being. This paper addresses the critical challenges of deploying deep learning for real-time electromyography-based gesture recognition in prosthetic control: generalization across users and time, the personalization-generalization trade-off, and computational constraints. We propose Federated Electromyography (FedEMG), a novel Federated Prototype Learning (FPL) framework that leverages a prototype-based approach for efficient knowledge transfer and a unique adaptive personalization mechanism. Unlike existing Federated Learning (FL) methods, FedEMG balances global knowledge with user-specific adaptations, achieving high accuracy and personalization without sacrificing generalization. Furthermore, FedEMG utilizes a lightweight gesture detector in combination with an efficient neural network architecture optimized for resource-constrained devices, enabling real-time performance. Extensive evaluations on public and neural-prosthetic interface datasets demonstrate FedEMG's superior accuracy in intra- and inter-subject gesture recognition under various non-IID cases, while also highlighting its efficient resource utilization. FedEMG thus advances the field of upper-limb rehabilitation through improved and accessible prosthetic control.