Objective: Surface electromyography (EMG) is a non-invasive sensing modality widely used in biomechanics, rehabilitation, prosthetic control, and human-machine interfaces. Despite decades of use, achieving robust generalization across subjects, recording systems, and acquisition protocols remains challenging. While foundation models (FMs) are gaining traction for EMG, existing approaches remain limited to single downstream tasks and lack deployability on embedded platforms. This work addresses these limitations. Methods: We present TinyMyo, a lightweight FM based on a Transformer encoder architecture. The model is pre-trained in a self-supervised manner using masked reconstruction on publicly available datasets. With only 3.6M parameters, TinyMyo is designed to support multiple downstream tasks through minimal task-specific head adaptations. Results: We demonstrate generalization across hand gesture classification, hand kinematic regression, speech production and speech recognition, with performance comparable to or surpassing the state of the art (SoA), and model size below 5M parameters. We achieve SoA results compared to previous FM-based works on the NinaPro DB5 (89.4%), UCI-EMG (97.56%), and EPN-612 (96.74%) datasets. We demonstrate the first-time deployment of an EMG FM on an ultra-low power microcontroller (GAP9), with an inference time of 0.785 s, energy of 44.91 mJ and power envelope of 57.18 mW. Conclusion: TinyMyo demonstrates that compact, self-supervised EMG FM can guarantee strong generalization across multiple downstream tasks while remaining compatible with low-power edge devices. Significance: TinyMyo is the first EMG FM for ultra-low power edge devices, enabling scalable and energy-efficient sensing for motor intent decoding, neuromuscular assessment, and biosignal driven human-machine interaction.

翻译：目的：表面肌电图（EMG）是一种广泛应用于生物力学、康复、假肢控制和人机交互的非侵入式传感技术。尽管已有数十年的应用历史，但在不同受试者、记录系统和采集协议之间实现稳健的泛化仍然具有挑战性。虽然基础模型（FMs）在肌电信号处理中日益受到关注，但现有方法仍局限于单一的下游任务，并且缺乏在嵌入式平台上的部署能力。本研究旨在解决这些局限性。方法：我们提出了TinyMyo，一种基于Transformer编码器架构的轻量级基础模型。该模型利用公开数据集，通过掩码重建进行自监督预训练。TinyMyo仅包含360万个参数，旨在通过最小化的任务特定头部适配来支持多种下游任务。结果：我们展示了模型在手势分类、手部运动学回归、语音生成及语音识别任务上的泛化能力，其性能达到或超越了当前最优水平（SoA），同时模型参数量低于500万。在NinaPro DB5（89.4%）、UCI-EMG（97.56%）和EPN-612（96.74%）数据集上，与先前基于基础模型的工作相比，我们取得了最优结果。我们首次实现了肌电基础模型在超低功耗微控制器（GAP9）上的部署，推理时间为0.785秒，能耗为44.91毫焦，功率范围为57.18毫瓦。结论：TinyMyo表明，紧凑的自监督肌电基础模型能够在保持与低功耗边缘设备兼容的同时，保证在多种下游任务上的强大泛化能力。意义：TinyMyo是首个面向超低功耗边缘设备的肌电基础模型，为运动意图解码、神经肌肉评估以及生物信号驱动的人机交互提供了可扩展且高能效的传感解决方案。