Application of a multilayer perceptron neural network for classifying software platforms of a powered prosthesis through a force plate

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

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

11 Scopus citations

Abstract

The amalgamation of conventional gait analysis devices, such as a force plate, with a machine learning platform facilitates the capability to classify between two disparate software platforms for the same bionic powered prosthesis. The BiOM powered prosthesis is applied with its standard software platform that incorporates a finite state machine control architecture and a biomimetic software platform that uniquely accounts for the muscle modeling history dependence known as the winding filament hypothesis. The feature set is derived from a series of kinetic and temporal parameters derived from the force plate recordings. The multilayer perceptron neural network achieves 91% classification between the software platforms for the BiOM powered prosthesis conventional finite state machine control architecture and biomimetic software platform based on the force plate derived feature set.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages402-405
Number of pages4
ISBN (Electronic)9781509002870
DOIs
StatePublished - Mar 2 2016
EventIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States
Duration: Dec 9 2015Dec 11 2015

Publication series

NameProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

Other

OtherIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Country/TerritoryUnited States
CityMiami
Period12/9/1512/11/15

Keywords

  • BiOM powered prosthesis
  • Gait analysis
  • Machine learning
  • Multilayer perceptron
  • Neural network
  • Powered prosthesis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

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