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Real-Time Gait Phase Estimation for Robotic Hip Exoskeleton Control During Multimodal Locomotion | IEEE Journals & Magazine | IEEE Xplore

Real-Time Gait Phase Estimation for Robotic Hip Exoskeleton Control During Multimodal Locomotion


Abstract:

We developed and validated a gait phase estimator for real-time control of a robotic hip exoskeleton during multimodal locomotion. Gait phase describes the fraction of ti...Show More

Abstract:

We developed and validated a gait phase estimator for real-time control of a robotic hip exoskeleton during multimodal locomotion. Gait phase describes the fraction of time passed since the previous gait event, such as heel strike, and is a promising framework for appropriately applying exoskeleton assistance during cyclic tasks. A conventional method utilizes a mechanical sensor to detect a gait event and uses the time since the last gait event to linearly interpolate the current gait phase. While this approach may work well for constant treadmill walking, it shows poor performance when translated to overground situations where the user may change walking speed and locomotion modes dynamically. To tackle these challenges, we utilized a convolutional neural network-based gait phase estimator that can adapt to different locomotion mode settings to modulate the exoskeleton assistance. Our resulting model accurately predicted the gait phase during multimodal locomotion without any additional information about the user's locomotion mode, with a gait phase estimation RMSE of 5.04 ± 0.79%, significantly outperforming the literature standard (p <; 0.05). Our study highlights the promise of translating exoskeleton technology to more realistic settings where the user can naturally and seamlessly navigate through different terrain settings.
Published in: IEEE Robotics and Automation Letters ( Volume: 6, Issue: 2, April 2021)
Page(s): 3491 - 3497
Date of Publication: 26 February 2021

ISSN Information:

PubMed ID: 34616899

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