The Effects of Torque Magnitude and Stiffness in Arm Guidance Through Joint Torque Feedback

Abstract

Joint torque feedback is a new and intuitive way of delivering kinesthetic feedback to a person or guiding them during motion tasks via wearable devices. In this study, we performed three experiments to understand how the elbow joint responds to guidance via small torques ( $ < \!1$ Nm). We first applied open-loop torque pulses to the elbow, and determined the magnitude and delay of the resulting arm motion. Second, we provided pulses of a desired position trajectory in combination with a torque proportional to the error between the joint’s angle and the target angle. We compared the effects of different ratios between the error and applied torque, which is the torque stiffness. Finally, we investigated step inputs from one angle to another in conjunction with different torque stiffnesses. We found that open-loop extensional torques caused large elbow movements, independent of the torque magnitude or duration, while flexional torques caused arm motion proportional to both the magnitude and duration. With position pulses, the highest gain of 0.095 Nm/deg resulted in mean position errors of less than 10 degrees, while the lowest gain of 0.012 Nm/deg resulted in mean position errors of nearly 20 degrees. The higher gains caused the arm to move faster and required higher torques, likely due to masking. The arm had a bandwidth of close to 2 Hz, and step inputs resulted in larger mean errors during flexional motion (15.3 degrees for flexion vs. 9.0 degrees for extension).

Keywords

• Haptic feedback
• motion training
• physical human-robot interaction
• virtual wall
• joint torque feedback