发表机构
University of Georgia; University of Oklahoma; University of Massachusetts Amherst（佐治亚大学; 俄克拉荷马大学; 马萨诸塞大学阿默斯特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对肌电信号跨个体、设备泛化难及现有基础模型计算开销大的问题，提出高效混合CNN-Transformer基础模型LiteEMG-FM，预训练于16个数据集，采用分层唤醒架构，在多种部署模式下优于现有模型，实现高效可部署的鲁棒肌电传感。
AI 中文摘要
肌电（EMG）信号在不同个体、身体部位、记录会话和传感硬件之间差异显著，限制了用于辅助设备和人机交互的模型的泛化能力。现有的时间序列基础模型对于实时可穿戴部署而言计算成本过高，且往往无法捕捉肌电特有的时频特征。我们提出了LiteEMG-FM，一种用于实际肌电传感的高效混合CNN-Transformer基础模型。LiteEMG-FM在16个多样化的上肢和下肢肌电数据集上进行了预训练，学习到的表征能够跨用户和数据集泛化。针对资源受限的部署场景，我们实现了一种分层唤醒架构，其中轻量级、常开的1D-CNN用于过滤静息和非目标活动，仅在有效手势时激活LiteEMG-FM。我们评估了全推理卸载、分割推理和全设备端处理，并刻画了它们在延迟、功耗和内存占用方面的权衡。在多样化的评估设置中，LiteEMG-FM优于最先进的时间序列基础模型和监督基线，特别是在零校准跨受试者和数据稀缺条件下。这些结果表明，LiteEMG-FM是一种有效、高效且可部署的肌电应用基础模型。
英文摘要
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.
