Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection from impaired subjects. Additionally, EMG signals exhibit significant variations across different conditions, sessions, and subjects, making it hard for classifiers to generalize. Traditional approaches require a large labeled dataset from the new condition, session, or subject to train intent classifiers; however, this data collection process is burdensome and time-consuming. In this paper, we propose ChatEMG, an autoregressive generative model that can generate synthetic EMG signals conditioned on prompts (i.e., a given sequence of EMG signals). ChatEMG enables us to collect only a small dataset from the new condition, session, or subject and expand it with synthetic samples conditioned on prompts from this new context. ChatEMG leverages a vast repository of previous data via generative training while still remaining context-specific via prompting. Our experiments show that these synthetic samples are classifier-agnostic and can improve intent inferral accuracy for different types of classifiers. We demonstrate that our complete approach can be integrated into a single patient session, including the use of the classifier for functional orthosis-assisted tasks. To the best of our knowledge, this is the first time an intent classifier trained partially on synthetic data has been deployed for functional control of an orthosis by a stroke survivor. Videos and additional information can be found at https://jxu.ai/chatemg.

翻译：针对中风患者的手部矫形器进行意图推断具有挑战性，主要原因在于从功能受损的受试者处收集数据十分困难。此外，肌电（EMG）信号在不同条件、不同会话以及不同受试者之间表现出显著差异，这使得分类器难以实现泛化。传统方法需要从新的条件、会话或受试者处收集大量标注数据来训练意图分类器；然而，这种数据收集过程既繁琐又耗时。本文提出ChatEMG，一种自回归生成模型，能够根据提示（即给定的EMG信号序列）生成合成EMG信号。ChatEMG使我们仅需从新的条件、会话或受试者处收集少量数据，并利用来自该新上下文的提示条件生成合成样本以扩展数据集。ChatEMG通过生成式训练利用了大量历史数据存储库，同时通过提示机制保持上下文特异性。我们的实验表明，这些合成样本与分类器无关，能够提高不同类型分类器的意图推断准确率。我们证明了完整的方案可以集成到单次患者会话中，包括将分类器用于功能性矫形器辅助任务。据我们所知，这是首次将部分基于合成数据训练的意图分类器部署用于中风幸存者对矫形器进行功能性控制。视频及更多信息请访问 https://jxu.ai/chatemg。