Power-free knee rehabilitation robot for home-based isokinetic training.

Journal: Nature communications
PMID: 40057479

Abstract

Robot-assisted isokinetic training has been widely adopted for knee rehabilitation. However, existing rehabilitation facilities are often heavy, bulky, and extremely energy-consuming, which limits the rehabilitation opportunities only at designated hospitals. In this study, we introduce a highly integrated and lightweight (52 kg) knee rehabilitation robot that can provide home-based isokinetic training without external power. By integrating a motor, torque/angle sensors, control circuit, and energy regeneration circuit into a single driver module, our robot can provide power-free isokinetic training by recycling mechanical work from the trainee. Ten postsurgical subjects were involved in an interventional randomized trial (ChiCTR2300076715, Part I) and the cross-sectional area of trained legs (experimental group) was significantly higher than that of untrained legs (control group). The primary outcomes, muscle growth (quadriceps: 5.93%, hamstrings: 10.27%) and strength improvements (quadriceps: 70%, hamstrings: 84%), achieved with our robots surpass those of existing commercial rehabilitation devices. These findings indicate that our robot presents a viable option for home-based knee rehabilitation, significantly enhancing the accessibility of effective treatment.

Authors

  • Yanggang Feng
  • Haoyang Wu
    Department of Chemical Engineering, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA whgreen@mit.edu kfjensen@mit.edu.
  • Jiaxin Ren
    School of Mechanical Engineering and Automation, Beihang University, Beijing, China.
  • Wuxiang Zhang
    School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China zhangwuxiang@buaa.edu.cn.
  • Xiu Jia
    CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, China.
  • Xiuhua Liu
    Intelligent Science & Technology Academy Limited of CASIC, Beijing, China.
  • Xingyu Hu
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China. Electronic address: huxingyu@hit.edu.cn.
  • Haoxiang Jing
    School of Mechanical Engineering and Automation, Beihang University, Beijing, China.
  • Yuebing Li
    School of Mechanical Engineering and Automation, Beihang University, Beijing, China.
  • Yuhang Zhao
    Human Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan 410083, China.
  • Ziyan Wang
    University of Michigan, Ann Arbor.
  • Xuzhou Lang
    School of Mechanical Engineering and Automation, Beihang University, Beijing, China.
  • Junjia Xu
    School of Mechanical Engineering and Automation, Beihang University, Beijing, China.
  • Yixin Shao
    School of Mechanical Engineering and Automation, Beihang University, Beijing, China.
  • Qi Su
    School of Foreign Languages, Peking University, Beijing, China; MOE Key Laboratory of Computational Linguistics, School of EECS, Peking University, Beijing, China. Electronic address: sukia@pku.edu.cn.
  • Yuanmingfei Zhang
    Peking University Third Hospital, Beijing, China.
  • Mouwang Zhou
    Peking University Third Hospital, Beijing, China.
  • Ke Liu
    State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China.
  • Yong Nie
    Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China. Electronic address: nieyong1983@wchscu.cn.
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Fuzhen Yuan
    Peking University Third Hospital, Beijing, China.
  • Liu Wang
    CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, Anhui, China. liuwang@mit.edu.
  • Xilun Ding
    School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.

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