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<p>Recent demographic shifts, including global population aging and rising disability prevalence, have significantly increased demand for robot-assisted rehabilitation technologies. These systems enable repetitive motor training to enhance neuromuscular recovery and functional mobility. This study presents an assist-as-needed (AAN) control framework featuring a performance assessment index derived from dynamic similarity metrics between actual and reference velocity trajectories, along with adaptive impedance modulation based on real-time patient performance evaluation. A higher index indicates greater patient ability, prompting the robot to apply resistance to augment task difficulty and training efficacy. Conversely, a low index suggests an unexpected situation, prompting the robot to become fully compliant to prevent injury. In intermediate scenarios, the robot either provides assistance to facilitate task completion or withholds assistance to encourage greater patient initiative. Additionally, a Lyapunov function is proposed to evaluate the stability of the AAN strategy, which confirms the bounded-input-bounded-output stability of the closed-loop system. Experimental outcomes demonstrate the efficacy of the proposed framework in dynamically adjusting assistance levels to meet patient needs during robot-assisted therapy sessions.</p>

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Developing an assist-as-needed control strategy for rehabilitation through ideal performance comparison

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • Yongxiang Zou,
  • Long Cheng,
  • Yu Zhang,
  • Haoyu Zhang,
  • Lijun Han,
  • Yifan Wang,
  • Zeyu Liu

摘要

Recent demographic shifts, including global population aging and rising disability prevalence, have significantly increased demand for robot-assisted rehabilitation technologies. These systems enable repetitive motor training to enhance neuromuscular recovery and functional mobility. This study presents an assist-as-needed (AAN) control framework featuring a performance assessment index derived from dynamic similarity metrics between actual and reference velocity trajectories, along with adaptive impedance modulation based on real-time patient performance evaluation. A higher index indicates greater patient ability, prompting the robot to apply resistance to augment task difficulty and training efficacy. Conversely, a low index suggests an unexpected situation, prompting the robot to become fully compliant to prevent injury. In intermediate scenarios, the robot either provides assistance to facilitate task completion or withholds assistance to encourage greater patient initiative. Additionally, a Lyapunov function is proposed to evaluate the stability of the AAN strategy, which confirms the bounded-input-bounded-output stability of the closed-loop system. Experimental outcomes demonstrate the efficacy of the proposed framework in dynamically adjusting assistance levels to meet patient needs during robot-assisted therapy sessions.