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A walk in the wild helps to tailor robotic leg exoskeletons

An innovative approach to controlling wearable robots takes the optimization process out of the laboratory. The method uses a data-driven model to infer a user’s energy consumption and to personalize the assistance it provides.
  1. Carlos Rodriguez-Guerrero

    1. Carlos Rodriguez-Guerrero is in the Department of Mechanical Engineering, KU Leuven, 3001 Heverlee Leuven, Belgium.

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Wearable robots can improve a person’s quality of life by alleviating physical disabilities or augmenting their existing capabilities (or both). But differences between humans require these robots to adapt their behaviour to each user’s needs in a personalized way. The optimization methods typically used to achieve this rely on lengthy and cumbersome tests performed under laboratory conditions that don’t encourage natural movement in a user. Now, writing in Nature, Slade et al.1 report a data-driven approach to controlling wearable robots that can be carried out rapidly and under real-world conditions, bringing the promise of assistive technologies a step closer to widespread adoption.

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Nature 610, 260-261 (2022)

doi: https://doi.org/10.1038/d41586-022-03175-9

Updates & Corrections

  • Correction 08 December 2022: The original version of this article said that Slade and colleagues’ raw data were not publicly available. In fact, they were made available at go.nature.com/3ev4f3h when they were published in ref. 6. The source code was not included at the time of publication, but is available now (see go.nature.com/3ev4f3w).

References

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Competing Interests

The author declares no competing interests.

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