It remains a formidable challenge to accurately recognize patients’ motion intentions to flexibly control hand exoskeletons. 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 ad-vance, such as non-preprogrammed hand movements and object manipulation tasks. This paper proposes a large language model (LLM)-enabled incremental learning framework for controlling hand exoskeletons. The framework enables patients to perform both predefined and non-predefined operation tasks with hand exoskeletons through a hand exoskeleton controller and LLM-based learners via voice interaction. Specifically, the framework embeds LLMs as expert modules that are capable of inferring appropriate hand motions and formulating corresponding control commands in accordance with human experiences to fulfill tasks that are unknown to be completed by the hand exoskeleton controller. At the same time, the hand exoskeleton controller incrementally learns to perform these non-predefined tasks by updating the control command set. Therefore, with the frame-work, the hand exoskeleton can incrementally expand its control command set so as to enhance patients’ adaptability to complex activities over daily use until the hand exoskeleton controller no longer needs to consult the LLM-based expert modules. This study is a pioneering work in the field of hand exoskeletons, which will revolutionize the way to control hand exoskeletons by patients.

Hand exoskeletons are wearable devices that can provide outer kinematic coupling with human hands and thus assist movement of human fingers. However, conventional rigid hand exoskeletons are characterized by their bulky and complex structures, which are often incompatible with human finger joints and restrict finger’s natural motion. This paper reports an underactuated base-to-distal hand exoskeleton that provides adaptive grasping assistance. An underactuated 8-bar base-to-distal linkage driven by a cable is used to flex and extend fingers and it applies force only to the distal phalanges of fingers, which not only makes the hand exoskeleton adapt to different sizes of fingers but also allows all phalanges to naturally accommodate the geometry of the objects to be grasped. The kinematic model of the 8-bar linkage is derived in order to generate desired hand ges-tures. A five-finger hand exoskeleton with active flexion/extension (F/E) for all fingers and active abduction/adduction (Ab/Ad) for the thumb is assembled and then tested on a healthy subject and a stroke survivor. Experimental results show that the hand exoskeleton can generate sufficient fingertip force for regular tasks. The hand exoskeleton enables the healthy participant and the stroke survivor to achieve 90% and 52% of their passive range of F/E motions respectively. In addition, the stroke survivor can accomplish various training tasks, such as grasping, pinching and writing, with the assistance of the hand exoskeleton. These results demonstrate that the underactuated base-to-distal hand exoskeleton can be an effective device for rehabilitation training or daily-life assistance for patients with a hemiparetic hand.