Scholarlink

<p>This review explores the potential of incorporating Machine Learning (ML) and Artificial Intelligence (AI) into the control systems of Lower Limb Rehabilitation Exoskeletons (LLREs), with a focus on its capacity to advance the field of rehabilitation robotics. It examines applications in optimizing personalized control supporting trajectory adaptation and key metrics in rehabilitation and discusses the challenges and limitations of traditional ML control methods supervised, unsupervised, and Reinforcement Learning (RL) in LLRE control. We analyze the specific aspects of traditional ML techniques in controlling the rehabilitation exoskeleton. Finally, the results of scientific solutions and developments in the implementation of gait parameters for personalized control that support the trajectory adaptation of the rehabilitation process are studied. Clinical applications and case studies directly describe the shortcomings and advantages of the field.</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

Enhancing Lower Limb Exoskeleton Control in Rehabilitation Through Traditional Machine Learning Techniques: A Review

  • 期刊论文

  • 发表日期：

    2026年1月28日

查看全文
  • Javlonbek Rakhmatillaev,
  • Nodirbek Kimsanboev,
  • Umidjon Takabaev,
  • Vytautas Bučinskas,
  • Zafar Juraev

摘要

This review explores the potential of incorporating Machine Learning (ML) and Artificial Intelligence (AI) into the control systems of Lower Limb Rehabilitation Exoskeletons (LLREs), with a focus on its capacity to advance the field of rehabilitation robotics. It examines applications in optimizing personalized control supporting trajectory adaptation and key metrics in rehabilitation and discusses the challenges and limitations of traditional ML control methods supervised, unsupervised, and Reinforcement Learning (RL) in LLRE control. We analyze the specific aspects of traditional ML techniques in controlling the rehabilitation exoskeleton. Finally, the results of scientific solutions and developments in the implementation of gait parameters for personalized control that support the trajectory adaptation of the rehabilitation process are studied. Clinical applications and case studies directly describe the shortcomings and advantages of the field.