Implementation of machine learning for classifying prosthesis type through conventional gait analysis

Robert Lemoyne, Timothy Mastroianni, Anthony Hessel, Kiisa C Nishikawa

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

10 Scopus citations

Abstract

Current forecasts imply a significant increase in the quantity of lower limb amputations. Synergizing the capabilities of a conventional gait analysis system and machine learning facilitates the capacity to classify disparate types of transtibial prostheses. Automated classification of prosthesis type may eventually advance rehabilitative acuity for selecting an appropriate prosthesis for a given aspect of the rehabilitation process. The presented research utilized a force plate as a conventional gait analysis device to acquire a feature set for two types of prosthesis: passive Solid Ankle Cushioned Heel (SACH) and the iWalk BiOM powered prosthesis. The feature set consists of both temporal and kinetic data with respect to the force plate signal during stance. Intuitively a passive prosthesis and powered prosthesis generate distinctively different force plate recordings. A support vector machine, which is type of machine learning application, achieves 100% classification between a passive prosthesis and powered prosthesis regarding the feature set derived from force plate recordings.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages202-205
Number of pages4
Volume2015-November
ISBN (Print)9781424492718
DOIs
StatePublished - Nov 4 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015Aug 29 2015

Other

Other37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
CountryItaly
CityMilan
Period8/25/158/29/15

Keywords

  • Force Plate
  • Gait Analysis
  • Machine Learning
  • Powered Prosthesis
  • Support Vector Machine

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Lemoyne, R., Mastroianni, T., Hessel, A., & Nishikawa, K. C. (2015). Implementation of machine learning for classifying prosthesis type through conventional gait analysis. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Vol. 2015-November, pp. 202-205). [7318335] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7318335