A training method for locomotion mode prediction using powered lower limb prostheses

Research output: Contribution to journalArticlepeer-review

83 Scopus citations

Abstract

Recently developed lower-limb prostheses are capable of actuating the knee and ankle joints, allowing amputees to perform advanced locomotion modes such as step-over-step stair ascent and walking on sloped surfaces. However, transitions between these locomotion modes and walking are neither automatic nor seamless. This study describes methods for construction and training of a high-level intent recognition system for a lower-limb prosthesis that provides natural transitions between walking, stair ascent, stair descent, ramp ascent, and ramp descent. Using mechanical sensors onboard a powered prosthesis, we collected steady-state and transition data from six transfemoral amputees while the five locomotion modes were performed. An intent recognition system built using only mechanical sensor data was 84.5% accurate using only steady-state training data. Including training data collected while amputees performed seamless transitions between locomotion modes improved the overall accuracy rate to 93.9%. Training using a single analysis window at heel contact and toe off provided higher recognition accuracy than training with multiple analysis windows. This study demonstrates the capability of an intent recognition system to provide automatic, natural, and seamless transitions between five locomotion modes for transfemoral amputees using powered lower limb prostheses.

Original languageEnglish (US)
Article number6650103
Pages (from-to)671-677
Number of pages7
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume22
Issue number3
DOIs
StatePublished - May 2014

Keywords

  • Intent recognition
  • powered lower limb prosthesis
  • robotic leg control
  • transfemoral amputee

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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