AUTHOR=Xu Jiajun , Huang Kaizhen , Zhang Tianyi , Cao Kai , Ji Aihong , Xu Linsen , Li Youfu 

TITLE=A rehabilitation robot control framework with adaptation of training tasks and robotic assistance

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2023.1244550

DOI=10.3389/fbioe.2023.1244550

ISSN=2296-4185

ABSTRACT=Robot-assisted rehabilitation has exhibited great potential to enhance motor function of physically and neurologically impaired patients. State-of-the-art control strategies usually allow the rehabilitation robot to track the training task trajectory along with the impaired limb, and the robotic motion can be regulated through physical human-robot interaction (pHRI) for comfortable support and appropriate assistance level. However, it is hardly possible, especially for patients with severe motor disabilities, to continuously exert force to guide the robot to complete the prescribed training task. Conversely, reduced task difficulty cannot facilitate stimulating patients’ potential movement capabilities. Moreover, challenging more difficult task with minimal robotic assistance is usually ignored when subjects show improved performance. In this paper, a control framework is proposed to simultaneously adjust both of the training task and robotic assistance according to subjects’ performance, which can be estimated from the users’ electromyography (EMG) signals. Concretely, a trajectory deformation algorithm is developed to generate smooth and compliant task motion with responding to pHRI. An assist-as-needed (ANN) controller along with a feedback gain modification algorithm is designed to promote patients’ active participation according to individual performance variance on completing the training task. The proposed control framework is validated using a lower extremity rehabilitation robot through experiments. The experimental results demonstrate that the control scheme can optimize the robotic assistance to complete the subject-adaptive training task with high efficiency.