Scholarlink

<p>This study designed a 7-degree of freedom (7-DOF) upper limb rehabilitation robot with modular mechanical structure and multi-modal control strategies to meet the full-cycle rehabilitation needs of stroke patients. Through ergonomic adaptation design, it achieves 100% body type coverage. By integrating fuzzy PID, improved sparrow search algorithm optimized support vector machine (SSO-LSSVM), and adaptive admittance control, a "passive assistance-active guidance-compliant collaboration" full-stage rehabilitation system is constructed. Experiments show that the end position error of the system is less than 1.5&#xa0;mm, the accuracy rate of motion intention recognition reaches 95.2%, and the human–machine interaction force response time is controlled within 50&#xa0;ms. The workspace covers 92% of the daily activity range for adults, including elderly stroke patients, validated by simulating geriatric upper-limb kinematics. It supports &gt; 90% of daily tasks across all age groups. This study provides an intelligent and personalized rehabilitation solution for stroke patients and takes a key step towards the clinical popularization of rehabilitation robot technology.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Design and optimization of 7-DOF upper limb rehabilitation robot based on multimodal control strategy

  • 期刊论文

  • 发表日期：

    2025年10月21日

查看全文
  • Xinyi Gao,
  • Jianmin Zhang

摘要

This study designed a 7-degree of freedom (7-DOF) upper limb rehabilitation robot with modular mechanical structure and multi-modal control strategies to meet the full-cycle rehabilitation needs of stroke patients. Through ergonomic adaptation design, it achieves 100% body type coverage. By integrating fuzzy PID, improved sparrow search algorithm optimized support vector machine (SSO-LSSVM), and adaptive admittance control, a "passive assistance-active guidance-compliant collaboration" full-stage rehabilitation system is constructed. Experiments show that the end position error of the system is less than 1.5 mm, the accuracy rate of motion intention recognition reaches 95.2%, and the human–machine interaction force response time is controlled within 50 ms. The workspace covers 92% of the daily activity range for adults, including elderly stroke patients, validated by simulating geriatric upper-limb kinematics. It supports > 90% of daily tasks across all age groups. This study provides an intelligent and personalized rehabilitation solution for stroke patients and takes a key step towards the clinical popularization of rehabilitation robot technology.