Rehabilitation robotics continues to confront substantial challenges, particularly in achieving smooth, safe, and intuitive human-robot interactions for upper limb motor training. Many current systems depend on complex mechanical designs, direct physical contact, and multiple sensors, which not only elevate costs but also reduce accessibility. Additionally, delivering seamless weight compensation and precise motion tracking remains a highly complex undertaking. To overcome these obstacles, we have developed a novel magnetic-based actuation mechanism for end-effector robotic rehabilitation. This innovative approach enables smooth, non-contact force transmission, significantly enhancing patient safety and comfort during upper limb training. To ensure consistent performance, we integrated an Extended Kalman Filter (EKF) alongside a controller for real-time position tracking, allowing the system to maintain high accuracy or recover even in the event of sensor malfunction or failure. In a user study with 12 participants, 75% rated the system highly for its smoothness, while 66.7% commended its safety and effective weight compensation. The EKF demonstrated precise tracking performance, with root mean square error (RMSE) values remaining within acceptable limits (under 2 cm). By combining magnetic actuation with advanced closed-loop control algorithms, this system marks a significant advancement in the field of upper limb rehabilitation robotics.

翻译：康复机器人领域持续面临重大挑战，尤其是在实现用于上肢运动训练的平滑、安全且直观的人机交互方面。当前许多系统依赖于复杂的机械设计、直接物理接触以及多个传感器，这不仅增加了成本，还降低了可及性。此外，提供无缝的重量补偿和精确的运动跟踪仍然是一项高度复杂的任务。为了克服这些障碍，我们开发了一种用于末端执行器式机器人康复的新型磁驱动机制。这种创新方法实现了平滑的非接触式力传递，显著提升了患者在上肢训练期间的安全性和舒适度。为确保性能稳定，我们集成了扩展卡尔曼滤波器（EKF）与一个用于实时位置跟踪的控制器，使系统即使在传感器故障或失效的情况下也能保持高精度或恢复。在一项有12名参与者参与的用户研究中，75%的参与者高度评价了系统的平滑性，而66.7%的参与者称赞了其安全性和有效的重量补偿。EKF展示了精确的跟踪性能，均方根误差（RMSE）值保持在可接受的限度内（低于2厘米）。通过将磁驱动与先进的闭环控制算法相结合，该系统标志着上肢康复机器人领域的一项重大进展。