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Adaptation of a Haptic Robot in a 3T fMRI
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Human-exoskeleton interaction portrait.

Mohammad Shushtari1, Julia Foellmer2, Arash Arami3,4

  • 1Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.

Journal of Neuroengineering and Rehabilitation
|September 5, 2024
PubMed
Summary

This study introduces the interaction portrait (IP) to analyze human-robot co-adaptation in lower limb exoskeletons. IP reveals distinct strategies for hybrid torque controllers (HTC) and adaptive model-based torque controllers (AMTC), improving user-exoskeleton coordination.

Keywords:
ControlExoskeletonPhysical Interaction

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Area of Science:

  • Robotics
  • Biomechanics
  • Human-Computer Interaction

Background:

  • Optimizing user experience and robot performance in human-robot physical interaction is critical.
  • Assessing user adaptation and co-adaptation in wearable robots, like lower limb exoskeletons, requires advanced analytical methods.
  • Traditional analyses of muscle activity and interaction torque alone may not fully capture complex human-robot co-adaptation dynamics.

Purpose of the Study:

  • To introduce a novel method, the interaction portrait (IP), for evaluating human-robot interaction and co-adaptation in lower limb exoskeletons.
  • To compare the co-adaptation strategies elicited by a hybrid torque controller (HTC) and a novel adaptive model-based torque controller (AMTC) against a time-based controller (TBC).
  • To demonstrate the potential of IP analysis in comparing and designing controllers for optimized human-robot interaction in wearable robots.

Main Methods:

  • Muscle activity and interaction torque were analyzed as a two-dimensional random variable.
  • The interaction portrait (IP) was introduced, visualizing this variable's distribution in polar coordinates.
  • IP was applied to compare HTC, AMTC, and TBC during treadmill walking at varying speeds, assessing normalized oxygen uptake.

Main Results:

  • Both HTC and AMTC significantly reduced users' normalized oxygen uptake compared to TBC, indicating improved user-exoskeleton coordination.
  • IP analysis identified two distinct co-adaptation strategies: HTC promoted yielding control to the exoskeleton, while AMTC encouraged user engagement with reduced interaction torque.
  • IP phase evolution provided insights into individual user interaction strategy formation.

Conclusions:

  • The interaction portrait (IP) offers a novel approach to understanding and quantifying human-robot co-adaptation in lower limb exoskeletons.
  • Different controllers elicit distinct co-adaptation strategies, with AMTC showing promise for rehabilitation and gait training applications due to enhanced user engagement.
  • IP analysis has the potential to significantly advance the design and optimization of controllers for wearable robots.