LLM-Enabled Incremental Learning Framework for Hand Exoskeleton Control

作者：Wenyuan Chen, Guangyong Li, Mingwei Li, Wenxue Wang, Peng Li, Xiujuan Xue, Xin-Gang Zhao, Lian-Qing Liu · 发表于：IEEE Transactions on Automation Science and Engineering · 年份：2025 · DOI：10.1109/tase.2024.3382679 · 被引用次数：36 · 研究领域：Computer Science

It remains a formidable challenge to accurately recognize motion intentions of patients thus to control hand exoskeletons according to their volition. Current methods primarily focus on recognition of limited patient’s motion intentions, with the purpose of controlling preconfigured gestures of a hand exoskeleton for grasping objects. These methods exhibit a marked shortfall when encountering scenarios that are unexpected or not designed in advance, such as non-preprogrammed hand movements and object manipulation tasks. To tackle this issue, large language model (LLM) and speech recognition technology are employed in this study to allow the patient to control a hand exoskeleton at will. In particular, two LLMs are tailored to formulate codes of either generating non-preprogrammed gestures or dealing with unencountered objects. Additionally, an incremental learning framework is proposed to enable patients to perform both predefined and non-predefined operation tasks by integrating a natural language parser with the two LLM-based learners. The natural language parser can directly control the hand exoskeleton to perform predefined operations tasks from prestored command set, while the LLM-based learners can incrementally expand the control command set so as to enhance adaptability of the hand exoskeleton to complex activities over daily use. This study is a pioneering work in the field of hand exoskeletons, which will revolutionize the way to control hand exoskeletons. Furthermo...