Abstract
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.
Abstract (translated)
目标：表面肌电图（EMG）是一种广泛应用于生物力学、康复、假肢控制和人机界面的非侵入式传感模式。尽管经过几十年的应用，实现跨受试者、记录系统及采集协议的良好泛化仍然具有挑战性。虽然基础模型（FMs）在EMG应用中正日益受到重视，但现有方法依然局限于单一下游任务，并且无法部署于嵌入式平台上。这项工作旨在解决这些限制。 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。该模型通过公开可用的数据集上自监督的方式进行预训练，采用掩码重构方式。仅包含3.6M参数的小型模型被设计为能够支持多种下游任务，并且只需要对特定任务头部进行微小调整即可。 结果：我们展示了TinyMyo在手部手势分类、手部运动学回归、语音生成和识别等领域的泛化能力，其性能与现有最佳方法相当或更优，同时模型规模低于5M参数。在NinaPro DB5（89.4%）、UCI-EMG（97.56%）以及EPN-612（96.74%）数据集上相比之前的基于基础模型的工作，我们的结果达到了现有最佳水平。此外，我们首次将一个EMG基础模型部署到了超低功耗微控制器（GAP9）上，在推理时间仅为0.785秒、能耗为44.91 mJ和功率上限为57.18 mW的情况下实现了这一目标。 结论：TinyMyo表明，紧凑的自监督EMG基础模型能够在多种下游任务中保证强大的泛化能力，并且兼容低功耗边缘设备。意义：TinyMyo是首个应用于超低功耗边缘设备的EMG基础模型，这使得运动意图解码、神经肌肉评估及生物信号驱动的人机交互能够实现可扩展和节能的目标感测技术。
URL
https://arxiv.org/abs/2512.15729
