TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge

• URL: http://arxiv.org/abs/2512.15729v1
• Date: Fri, 05 Dec 2025 17:36:57 GMT
• Title: TinyMyo: a Tiny Foundation Model for Flexible EMG Signal Processing at the Edge
• Abstract summary: We present TinyMyo, a lightweight foundation model for surface electromyography (EMG)<n>The model is pre-trained in a self-supervised manner on publicly available datasets and achieves high reconstruction fidelity with only 3.6M parameters.<n>We demonstrate generalization across hand gesture classification, hand kinematic regression, speech production and recognition, with performance comparable to or surpassing the state of the art (SoA)<n>We report, to the best of our knowledge, the first deployment of an EMG FM on an ultra-low-power microcontroller (GAP9), achieving an average power envelope of 36.45mW.
• Score: 14.115504534589896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface electromyography (EMG) is a non-invasive sensing modality used in several domains, including biomechanics, rehabilitation, prosthetic control, and emerging human-machine interaction paradigms. Despite decades of use, significant challenges remain in achieving robust generalization across subjects, recording systems, and acquisition protocols. To tackle these challenges, foundation models (FMs) are gaining traction when targeting end-to-end applications based on EMG signals. Yet, existing EMG FMs remain limited to single downstream tasks and lack deployability on embedded platforms. In this work, we present TinyMyo, a lightweight FM based on a Transformer encoder architecture. The model is pre-trained in a self-supervised manner on publicly available datasets and achieves high reconstruction fidelity with only 3.6M parameters. With minimal task-specific head adaptations, the same backbone is used to tackle multiple downstream tasks, leveraging datasets acquired from diverse sensing locations and hardware platforms. We demonstrate generalization across hand gesture classification, hand kinematic regression, speech production and 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 ($89.4\pm0.16\%$), UCI-EMG ($97.56\pm0.32\%$), and EPN-612 ($96.74\pm0.09\%$) datasets. We report, to the best of our knowledge, the first deployment of an EMG FM on an ultra-low-power microcontroller (GAP9), achieving an average power envelope of 36.45mW. By open-sourcing the pre-trained and the downstream task architectures (https://github.com/pulp-bio/BioFoundation), we aim to provide a flexible resource that can accelerate future research and serve as a common foundation for the EMG community.

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