A novel seamless magnetic-based actuating mechanism for end-effector-based robotic rehabilitation platforms

TL;DR

A novel magnetic-based actuation mechanism for end-effector robotic rehabilitation that enables smooth, non-contact force transmission, significantly enhancing patient safety and comfort during upper limb training is developed.

Abstract

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.

Figures (6)

• Figure 1: The designed Graphical User Interface (GUI): a) patient’s view, b) The technician control panel. c) The designed planar robotic platform. d) a preview of running a trial with a participant
• Figure 2: Forces acted upon magnets when going to the right
• Figure 4: Block diagram of the algorithm development with Extended Kalman Filter for the observable system. $x_{1}$, $x_{2}$, $v_{1}$, and $v_{2}$ are the positions and the velocities of the bottom and the top magnet respectively
• Figure 5: Characterization of the dynamic behavior of the system at (a) 10, (b) 20, and (c) 30 RPM speeds (1.46, 3.5, and 4.3 cm/s respectively); and (d) the static trial. During the dynamic trial, the system traveled 60 cm at three different speeds with varying calibration weights placed on the armrest on each run.
• Figure 7: RMSE plots for the bottom (a) and the top magnet (b) from the two EKF estimations for one participant.