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Optical fiber angle sensors for the PrHand prosthesis: Development and application in grasp types recognition with machine learning

De Arco, Laura; Pontes, Maria José; Vieira Segatto, Marcelo Eduardo Vieira; Monteiro, Maxwell E; Cifuentes, Carlos A; Díaz, Camilo A R

Optical fiber angle sensors for the PrHand prosthesis: Development and application in grasp types recognition with machine learning Thumbnail


Authors

Laura De Arco

Maria José Pontes

Marcelo Eduardo Vieira Vieira Segatto

Maxwell E Monteiro

Carlos A Cifuentes

Camilo A R Díaz



Abstract

This work presents the instrumentation of the PrHand upper-limb prosthesis with optical fiber sensors to measure the angle of the proximal interphalangeal joint. The angle sensors are based on bending-induced loss and are fabricated with polymer optical fiber (POF). The finger angle information is used in a k-Nearest Neighbor (k-NN) machine learning algorithm for grasp recognition. Four kinds of grasp are evaluated: hook grip, spherical grip, tripod pinch, and cylindrical grip, with three objects each. As mentioned in the algorithm validation, it is essential to note: The average accuracy was 92.81 %.

Presentation Conference Type Conference Paper (published)
Conference Name 2022 IEEE Latin America Electron Devices Conference, LAEDC 2022
Start Date Jul 4, 2022
End Date Jul 6, 2022
Acceptance Date Jun 7, 2022
Publication Date Oct 10, 2022
Deposit Date Jul 14, 2022
Publicly Available Date Jul 14, 2022
Publisher Institute of Electrical and Electronics Engineers (IEEE)
DOI https://doi.org/10.1109/LAEDC54796.2022.9908232
Keywords Angle sensor; k-NN; Machine learning; upper- limb prosthesis
Public URL https://uwe-repository.worktribe.com/output/9691368
Publisher URL https://ieeexplore.ieee.org/document/9908232
Related Public URLs https://ieeexplore.ieee.org/xpl/conhome/1831384/all-proceedings

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Copyright Statement
This is the author’s accepted manuscript. The final published version is available here: URL
“© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.








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