Publications by authors named "R W Gregg"

Partial-assist ankle exoskeletons have been limited by inherent trade-offs between favorable characteristics including high torque capacity, high control bandwidth, back-drivability, compliance, and low mass. Emerging quasi-direct drive actuators have a rigid transmission with a low gear ratio, enabling inherent backdrivability and compliance with accurate torque and position control. Our existing modular, backdrivable exoskeleton system () uses quasi-direct drive actuators at the hip and/or knee to deliver high assistive torques alongside low dynamic backdrive torques, enabling natural interaction with users with remnant voluntary motion.

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Article Synopsis
  • Various lower-limb exoskeletons can assist movement for both able-bodied individuals and those with mild to moderate gait disorders, but a universal control system for all activities doesn’t exist.
  • The paper introduces a new modular control framework that optimizes joint torques for any exoskeleton configuration in real-time during daily activities like walking, ascending stairs, or transitioning from sitting to standing.
  • The study tested this framework on eight able-bodied users with different joint setups, finding that unilateral configurations significantly reduced muscle activation during tasks, while bilateral setups showed minimal effects likely due to weight and design limitations.
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Although powered prosthetic legs have enabled more biomimetic joint kinematics during steady-state activities like walking and stair climbing, transitions between these activities are usually handled by discretely switching controllers without considering biomimicry or the distinct role of the leading leg. This study introduces two data-driven, phase-based kinematic control approaches for seamless inter-leg transitions (i.e.

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Emerging task-agnostic control methods offer a promising avenue for versatile assistance in powered exoskeletons without explicit task detection, but typically come with a performance trade-off for specific tasks and/or users. One such approach employs data-driven optimization of an energy shaping controller to provide naturalistic assistance across essential daily tasks with passivity/stability guarantees. This study introduces a novel control method that merges energy shaping with a machine learning-based classifier to deliver optimal support accommodating diverse individual tasks and users.

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Research in powered prosthesis control has explored the use of impedance-based control algorithms due to their biomimetic capabilities and intuitive structure. Modern impedance controllers feature parameters that smoothly vary over gait phase and task according to a data-driven model. However, these recent efforts only use continuous impedance control during stance and instead utilize discrete transition logic to switch to kinematic control during swing, necessitating two separate models for the different parts of the stride.

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