将信号翻译为语言以用于基于表面肌电信号的活动识别
Translating Signals to Languages for sEMG-Based Activity Recognition
把连续时序信号（sEMG）通过语言导向映射转成离散 token 序列，再交给冻结/微调的 LLM 做识别，这套「信号→语言 token→LLM 推理」的范式可直接迁移到视觉时序信号（骨架动作、眼动、IMU、事件相机流）的动作识别上。
原文依据 we design a language-oriented mapping mechanism that converts continuous sEMG sequences into "sEMG language"
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于表面肌电信号的活动识别近年来受到越来越多的研究关注。为构建准确的基于表面肌电信号的活动识别器，已有大量方法被提出。一些研究侧重于设计更大、表达能力更强的模型架构以增强对表面肌电信号的表征能力，另一些研究则旨在通过大规模预训练丰富模型的先验，从而提升识别性能。近来，大语言模型在自然语言处理中展现出显著的泛化与推理能力，其从大量关于动作的语言描述中习得的隐式知识，为解读表面肌电信号并推断活动意图开辟了新的可能。受此启发，我们提出 LLM-sEMG，一个将大语言模型用作表面肌电活动识别器的新框架。在该框架内，我们设计了一种面向语言的映射机制，将连续的表面肌电序列转换为「肌电语言」，并整合若干策略以进一步促进信号到语言的映射过程。大量实验表明，所提出的框架利用大语言模型实现了高精度的基于表面肌电信号的活动识别。
英文原文摘要 1277 字符
Surface electromyography (sEMG) signal-based activity recognition has attracted increasing research attention in recent years. To develop accurate sEMG signal-based activity recognizers, numerous approaches have been proposed. Some studies focus on designing larger and more expressive model architectures to enhance the representational capacity of sEMG signals, while others aim to enrich model priors through large-scale pretraining, thereby improving recognition performance. Recently, large language models (LLMs) have shown remarkable generalization and reasoning capabilities in natural language processing, whose implicit knowledge, learned from extensive linguistic descriptions of actions, opens new possibilities for interpreting sEMG signals and inferring activity intentions. Motivated by this, we propose LLM-sEMG, a novel framework that leverages LLMs as sEMG activity recognizers. Within this framework, we design a language-oriented mapping mechanism that converts continuous sEMG sequences into "sEMG language," integrating several strategies to further facilitate the signal-to-language mapping process. Extensive experiments demonstrate that the proposed framework achieves highly accurate sEMG signal-based activity recognition using large language models.