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

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 %.

Citation

De Arco, L., Pontes, M. J., Vieira Segatto, M. E. V., Monteiro, M. E., Cifuentes, C. A., & Díaz, C. A. R. (2022). Optical fiber angle sensors for the PrHand prosthesis: Development and application in grasp types recognition with machine learning. . https://doi.org/10.1109/LAEDC54796.2022.9908232

• Conference Name: 2022 IEEE Latin America Electron Devices Conference, LAEDC 2022
• Conference Location: Puebla, Mexico https://attend.ieee.org/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