Upper Limb Rehabilitation Robot Control based on Large Language Model

Stroke is a life-threatening condition and its survivors often face significant disabilities that reduce their quality of life, making effective rehabilitation systems essential. Automation technology, such as robotics, offers a promising solution to improve rehabilitation quality. However, the implementation of this technology can be challenging for users due to interface complexity. To ensure easier adoption, the user interface of these systems must be both comfortable and easy to use. Large Language Models (LLMs) currently offer a promising solution to this challenge. This study explores the use of an LLM as a control interface for an upper limb rehabilitation robot. By combining high-level task execution based on LLM and low-level forward kinematics, we exploit the intuitiveness of LLM and the accuracy of model-based control to perform various rehabilitation tasks more easily. The findings demonstrate that the LLM-based control interface significantly outperforms traditional keypad-based controls in rehabilitation robot usability tests, achieving a high success rate in the trials.