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ORIGINAL RESEARCH article

Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
Volume 12 - 2024 | doi: 10.3389/fbioe.2024.1484265
This article is part of the Research Topic Biomedical Sensing in Assistive Devices View all 5 articles

Sensing Equivalent Kinematics Enables Robot-Assisted Mirror Rehabilitation Training via a Broaden Learning System

Provisionally accepted
Qing Miao Qing Miao 1Xueming Fu Xueming Fu 2*Yi-Feng Chen Yi-Feng Chen 3*
  • 1 Wuhan Polytechnic University, Wuhan, China
  • 2 University of Science and Technology of China, Hefei, Anhui Province, China
  • 3 Southern University of Science and Technology, Shenzhen, Guangdong Province, China

The final, formatted version of the article will be published soon.

    This paper proposes an equivalent kinematics control framework based on the Broaden Learning System model for active robotic mirror rehabilitation, where people's bilateral upper limbs actively perform mirror movements to enhance the impaired limb's participation. The framework accommodates a broaden learning model from sensing multi-kinematic features to adjust the robotic damping coefficient in assisting human participants to complete mirror-symmetry training. Besides, in order to adapt to inter-patients' variability with different disability levels, a challenge-level modification interface is also fused for safer training. This model is verified by additional symmetry indicator such as position trajectory error and force. Experimental results show that the weaker subjects can also maintain mirror movement with the stronger subjects under the help of this model and verify the performance of framework in mirror-symmetry effects and movement smoothness. This leads us to believe that the framework can safely and efficiently assist human participants in completing mirror-symmetry movement.

    Keywords: Robotic mirror rehabilitation, Physical human-robot interaction, equivalent kinematics, Broaden learning system, robotic control

    Received: 21 Aug 2024; Accepted: 29 Oct 2024.

    Copyright: © 2024 Miao, Fu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xueming Fu, University of Science and Technology of China, Hefei, 230026, Anhui Province, China
    Yi-Feng Chen, Southern University of Science and Technology, Shenzhen, 121013, Guangdong Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.