Abstract
Electromyography (EMG) signals vary substantially across individuals, body regions, recording sessions, and sensing hardware, limiting the generalization of models for assistive devices and human-computer interaction. Existing time-series foundation models are also computationally expensive for real-time wearable deployment and often fail to capture EMG-specific time-frequency characteristics. We present LiteEMG-FM, an efficient hybrid CNN-Transformer foundation model for practical EMG sensing. Pretrained on 16 diverse upper- and lower-limb EMG datasets, LiteEMG-FM learns representations that generalize across users and datasets. For resource-constrained deployment, we implement a hierarchical wake-up architecture in which a lightweight, always-on 1D-CNN filters rest and non-target activity and activates LiteEMG-FM only for valid gestures. We evaluate full inference offloading, split inference, and full on-device processing, characterizing their trade-offs in latency, power consumption, and memory footprint. Across diverse evaluation settings, LiteEMG-FM outperforms state-of-the-art time-series foundation models and supervised baselines, particularly under zero-calibration cross-participant and data-scarce conditions. These results demonstrate that LiteEMG-FM is an effective, efficient, and deployable foundation model for EMG applications.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Wu, T., Wu, X., Radmehr, A., Yu, J., Wu, Y., Nguyen, P., & Liu, J. (2026). LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing. https://omanscience.com/en/articles/liteemg-fm-an-efficient-and-deployable-foundation-model-for-robust-emg-sensing
MLA 9
Wu, Tianhao, et al. "LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing." https://omanscience.com/en/articles/liteemg-fm-an-efficient-and-deployable-foundation-model-for-robust-emg-sensing.
Chicago (author–date)
Wu, Tianhao, Xu Wu, Amirmohammad Radmehr, Jiawei Yu, Yi Wu, Phuc Nguyen, and Jian Liu. 2026. "LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing." https://omanscience.com/en/articles/liteemg-fm-an-efficient-and-deployable-foundation-model-for-robust-emg-sensing.
Harvard
Wu, T., Wu, X., Radmehr, A., Yu, J., Wu, Y., Nguyen, P. and Liu, J. (2026) 'LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing', Available at: https://omanscience.com/en/articles/liteemg-fm-an-efficient-and-deployable-foundation-model-for-robust-emg-sensing.
Vancouver
Wu T, Wu X, Radmehr A, Yu J, Wu Y, Nguyen P, et al. LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing. https://omanscience.com/en/articles/liteemg-fm-an-efficient-and-deployable-foundation-model-for-robust-emg-sensing
IEEE
T. Wu, X. Wu, A. Radmehr, J. Yu, Y. Wu, P. Nguyen, and J. Liu, "LiteEMG-FM: An Efficient and Deployable Foundation Model for Robust EMG Sensing," https://omanscience.com/en/articles/liteemg-fm-an-efficient-and-deployable-foundation-model-for-robust-emg-sensing.