A Novel Physiologically-Inspired Method for Myoelectric Prosthesis Control Using Pattern Classification

Strahinja Dosen*, Gauravkumar K. Patel, Claudio Castellini, Janne M. Hahne, Dario Farina

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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The contemporary myoelectric prostheses are advanced mechatronic systems, but human-machine interfacing for robust control of these devices is still an open challenge. We present a novel method for the recognition of user intention based on pattern classification which is inspired by the natural coordination of multiple muscles during hand and wrist motions. The coordinated muscle activation produces a characteristic distribution of the amplitude features of the electromyography signals, and the novel method establishes the class boundaries to capture this natural distribution. The method has been tested in healthy subjects operating a prosthesis during a challenging functional task (bottle grasping, turning and releasing). The novel approach outperformed the commonly used benchmark (linear discriminant analysis), while using shorter training and fewer features. Further developments can, therefore, lead to a method that is suitable for practical implementation and allows robust and efficient control.

Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on NeuroRehabilitation : ICNR2018
EditorsLorenzo Masia, Silvestro Micera, Metin Akay, José L. Pons
Number of pages5
PublisherSpringer Publishing Company
Publication date1 Jan 2019
ISBN (Print)978-3-030-01844-3
ISBN (Electronic)978-3-030-01845-0
Publication statusPublished - 1 Jan 2019
EventInternational Conference on Neurorehabilitation - Pisa, Italy
Duration: 16 Oct 201820 Oct 2018


ConferenceInternational Conference on Neurorehabilitation
Internet address
SeriesBiosystems and Biorobotics


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