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Brain-machine interaction and assist effect evaluation of a single-degree-of-freedom sit-to-stand transfer robot

Published online by Cambridge University Press:  11 July 2025

Chengyu Hou ,
Qian Chen ,
Peng Chen Open the ORCID record for Peng Chen [Opens in a new window] ,
Xiangyun Li  and
Kang Li
Chengyu Hou
Affiliation:
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
Qian Chen
Affiliation:
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
Peng Chen*
Affiliation:
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
Xiangyun Li
Affiliation:
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
Kang Li
Affiliation:
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China West China PITEC Co., Ltd., Chengdu, China
*
Corresponding author: Peng Chen; Email: chenpeng@swjtu.edu.cn
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Abstract

In response to the prevailing trend of an aging society and the increasing requirements of rehabilitation, this paper presents an approach involving brain-machine interaction (BMI) for a single-degree-of-freedom (1-DOF) sit-to-stand transfer robot. Based on a 1-DOF rehabilitation robot, three experiment paradigms involving motor imagery (MI), action observation of motor imagery (AO-MI) and motor execution are designed using both electroencephalography (EEG) and electromyography (EMG). To enhance motion intention recognition accuracy, a Gumbel-ResNet-KANs decoding model is established. The Gumbel-ResNet-KANs model integrates the Gumbel-Softmax method with the ResNet-KANs network module and demonstrates strong decoding capability, as demonstrated by comparative tests in this paper. To validate the effect of robotic assistance, EEG and EMG coherence are analyzed to assess the impact of robotic assistance on rehabilitation from a neuromuscular perspective in both assisted and unassisted conditions. We assessed the effect of robotics on rehabilitation from an emotional perspective by analyzing the difference between the differential entropy of the right and left brain. The proposed study also reveals that the movement-related cortical potentials in AO-MI are beneficial for promoting the performance of BMI in sit-to-stand training, which provides a possible approach for the development of new types of robots for lower limb rehabilitation.

Keywords

rehabilitation robotbrain-machine interactionelectroencephalographyGumbel-ResNet-KANsevaluation of assistive effectiveness

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Type
Research Article
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Robotica , First View , pp. 1 - 30
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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