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

doi:10.1109/ICMLA.2015.211

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

Biological Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 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 language	English (US)
Title of host publication	Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	402-405
Number of pages	4
ISBN (Electronic)	9781509002870
DOIs	https://doi.org/10.1109/ICMLA.2015.211
State	Published - Mar 2 2016
Event	IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States Duration: Dec 9 2015 → Dec 11 2015

Publication series

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

Other

Other	IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Country/Territory	United States
City	Miami
Period	12/9/15 → 12/11/15

Keywords

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

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/ICMLA.2015.211

Cite this

Lemoyne, R., Mastroianni, T., Hessel, A., & Nishikawa, K. C. (2016). Application of a multilayer perceptron neural network for classifying software platforms of a powered prosthesis through a force plate. In Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 (pp. 402-405). Article 7424345 (Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2015.211

Application of a multilayer perceptron neural network for classifying software platforms of a powered prosthesis through a force plate. / Lemoyne, Robert; Mastroianni, Timothy; Hessel, Anthony et al.
Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 402-405 7424345 (Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lemoyne, R, Mastroianni, T, Hessel, A & Nishikawa, KC 2016, Application of a multilayer perceptron neural network for classifying software platforms of a powered prosthesis through a force plate. in Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015., 7424345, Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, Institute of Electrical and Electronics Engineers Inc., pp. 402-405, IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, Miami, United States, 12/9/15. https://doi.org/10.1109/ICMLA.2015.211

Lemoyne R, Mastroianni T, Hessel A, Nishikawa KC. Application of a multilayer perceptron neural network for classifying software platforms of a powered prosthesis through a force plate. In Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 402-405. 7424345. (Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015). doi: 10.1109/ICMLA.2015.211

Lemoyne, Robert ; Mastroianni, Timothy ; Hessel, Anthony et al. / Application of a multilayer perceptron neural network for classifying software platforms of a powered prosthesis through a force plate. Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 402-405 (Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015).

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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.",

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author = "Robert Lemoyne and Timothy Mastroianni and Anthony Hessel and Nishikawa, {Kiisa C}",

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AB - 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.

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

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