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Human-in-the-loop optimization of exoskeleton assistance during walking.

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Researchers optimized exoskeleton assistance to reduce the energy cost of walking. This method significantly cut metabolic energy consumption by over 24% in users, improving mobility enhancement device performance.

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Area of Science:

  • Biomechanics
  • Robotics
  • Human-computer interaction

Background:

  • Exoskeletons and active prostheses aim to improve human mobility but face design challenges.
  • Optimizing device parameters based on human performance metrics is crucial for effective designs.

Purpose of the Study:

  • To develop and validate a method for identifying exoskeleton assistance strategies that minimize human energy expenditure during locomotion.
  • To assess the effectiveness of this optimization approach across various conditions and device configurations.

Main Methods:

  • Developed a method to determine optimal torque patterns for ankle exoskeletons to minimize metabolic cost during walking.
  • Tested the optimized assistance with single or bilateral ankle exoskeletons during walking and running.
  • Incorporated individual customization and user learning into the optimization process.

Main Results:

  • Optimized exoskeleton torque reduced metabolic energy consumption by 24.2 ± 7.4% compared to unassisted locomotion.
  • The method proved effective for single/bilateral exoskeletons, varied walking speeds, and running.
  • Individualized generic assistance patterns and user training enhanced performance gains.

Conclusions:

  • Personalized optimization of exoskeleton assistance significantly reduces the metabolic cost of human locomotion.
  • This approach holds promise for developing more effective and user-friendly mobility enhancement devices.
  • Further development of adaptive and user-aware control strategies can unlock greater performance benefits.