Classifying the intent of novel users during human locomotion using powered lower limb prostheses

Aaron J. Young, Ann M. Simon, Nicholas P. Fey, Levi J. Hargrove

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

24 Scopus citations

Abstract

Intent recognition systems using pattern recognition technology to control powered lower-limb prostheses are promising for seamlessly changing between locomotion modes - such as transitioning from level walking to stair ascent. These transitions can be accomplished by training an algorithm to recognize the patterns of mechanical and/or myoelectric signals an amputee generates during and between different locomotion modes. While low error rates can be achieved with this method, it typically requires a substantial amount of training data to be gathered. To alleviate this burden, this study investigated training a user-independent classifier from a pool of lower limb amputees performing level walking, ramps and stairs on a powered prosthesis and tested generalization of the classifier to a novel subject. The effect of using the amputee's EMG signals in combination with the mechanical sensors on the leg was also evaluated for this user-independent classifier. Generalization was poor to a novel subject - 48% overall recognition rate with EMG and 62% without (mechanical sensors only). However, an important system improvement could be made by including a few level walking trials of the novel subject (only a few minutes of data collection) in the training data, the overall recognition rate improved to 86% with EMG and 83% without.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages311-314
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Publication series

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

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Classifying the intent of novel users during human locomotion using powered lower limb prostheses'. Together they form a unique fingerprint.

  • Cite this

    Young, A. J., Simon, A. M., Fey, N. P., & Hargrove, L. J. (2013). Classifying the intent of novel users during human locomotion using powered lower limb prostheses. In 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 (pp. 311-314). [6695934] (International IEEE/EMBS Conference on Neural Engineering, NER). https://doi.org/10.1109/NER.2013.6695934