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Adaptive Gait Generation Using Dynamic Movement Primitives with Multimodal Sensors for Assistive Lower-limb Exoskeletons
  • Lingzhou Yu,
  • Juan Carlos Arceo,
  • Shaoping Bai
Lingzhou Yu

Corresponding Author:[email protected]

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Juan Carlos Arceo
Shaoping Bai

Abstract

For assisting human lower-limb movement, most present exoskeletons provide predefined gaits which are not natural and adaptive to different walking conditions. This study presents a novel method for generating adaptive gait for lower-limb exoskeletons using multimodal sensor bands. The multimodal sensors can capture the users' limb movements and force myography information. Through a feed-forward neural network, an adjustable gait can be generated and given to a dynamic movement primitives (DMP) model for real-time adjustments to walking trajectories. The experimental validation and metabolic evaluations confirm the effectiveness of this strategy, demonstrating enhanced walking assistance compared to standard gait models.
21 May 2024Submitted to TechRxiv
30 May 2024Published in TechRxiv