Using bilateral lower limb kinematic and myoelectric signals to predict locomotor activities: A pilot study

Blair H. Hu*, Elliott J. Rouse, Levi J. Hargrove

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Active lower limb exoskeletons can provide assistance to the lower extremities and may drastically improve the walking abilities of millions of individuals with gait impairments. However, most currently available control systems for these devices cannot predict the user's intended movements and have yet to enable walking with seamless transitions. Recent developments in intent recognition for active lower limb prostheses have demonstrated that using kinematic and kinetic signals from the device and myoelectric signals from the user can provide an intuitive control interface for seamlessly transitioning between different locomotor activities. In this work, we determined the baseline performance of intent recognition systems using neuromechanical signals presumably accessible for controlling active lower limb exoskeletons. We collected bilateral lower limb joint kinematics and muscle activity from three able-bodied subjects while they walked on level ground, ramps, and stairs in order to train an intent recognition system. We found that both combining kinematic and myoelectric signals and including signals from the contralateral leg significantly improved intent recognition performance. We achieved an average offline prediction error rate of 1.4 ± 0.90% using bilateral kinematic and myoelectric signals, demonstrating the promising potential of translating prosthesis-based intent recognition as an alternative control strategy for active lower limb exoskeletons.

Original languageEnglish (US)
Title of host publication8th International IEEE EMBS Conference on Neural Engineering, NER 2017
PublisherIEEE Computer Society
Pages98-101
Number of pages4
ISBN (Electronic)9781538619162
DOIs
StatePublished - Aug 10 2017
Event8th International IEEE EMBS Conference on Neural Engineering, NER 2017 - Shanghai, China
Duration: May 25 2017May 28 2017

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other8th International IEEE EMBS Conference on Neural Engineering, NER 2017
Country/TerritoryChina
CityShanghai
Period5/25/175/28/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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