Delaying Ambulation Mode Transition Decisions Improves Accuracy of a Flexible Control System for Powered Knee-Ankle Prosthesis

Ann M. Simon*, Kimberly A. Ingraham, John A. Spanias, Aaron J. Young, Suzanne B. Finucane, Elizabeth G. Halsne, Levi J. Hargrove

*Corresponding author for this work

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

Powered lower limb prostheses can assist users in a variety of ambulation modes by providing knee and/or ankle joint power. This study's goal was to develop a flexible control system to allow users to perform a variety of tasks in a natural, accurate, and reliable way. Six transfemoral amputees used a powered knee-ankle prosthesis to ascend/descend a ramp, climb a 3-A nd 4-step staircase, perform walking and standing transitions to and from the staircase, and ambulate at various speeds. A mode-specific classification architecture was developed to allow seamless transitions at four discrete gait events. Prosthesis mode transitions (i.e., the prosthesis' mechanical response) were delayed by 90 ms. Overall, users were not affected by this small delay. Offline classification results demonstrate significantly reduced error rates with the delayed system compared to the non-delayed system (p < 0.001). The average error rate for all heel contact decisions was 1.65% [0.99%] for the non-delayed system and 0.43% [0.23%] for the delayed system. The average error rate for all toe off decisions was 0.47% [0.16%] for the non-delayed system and 0.13% [0.05%] for the delayed system. The results are encouraging and provide another step towards a clinically viable intent recognition system for a powered knee-ankle prosthesis.

Original languageEnglish (US)
Article number7574344
Pages (from-to)1164-1171
Number of pages8
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume25
Issue number8
DOIs
StatePublished - Aug 2017

Keywords

  • Above-knee amputation
  • intent recognition
  • robotic leg control
  • signal processing

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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