Agradecimentos: This research was funded by Sao Paulo Research Foundation (FAPESP), Grant Number 2017/25666-2, by FAPESP CEPID Brainn, Grant Number 2013/07559-3, by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Grant Number 403418/2021-6, and by Coordenacao de Pessoal de Nivel Superior, Finance Code 001 Ver menos

Abstract: Force myography (FMG) detects hand gestures based on muscular contractions, featuring as an alternative to surface electromyography. However, typical FMG systems rely on spatially-distributed arrays of force-sensing resistors to resolve ambiguities. The aim of this proof-of-concept study is to develop a method for identifying hand poses from the static and dynamic components of FMG waveforms based on a compact, single-channel optical fiber sensor. As the user performs a gesture, a micro-bending transducer positioned on the belly of the forearm muscles registers the dynamic optical signals resulting from the exerted forces. A Raspberry Pi 3 minicomputer performs data acquisition and processing. Then, convolutional neural networks correlate the FMG waveforms with the target postures, yielding a classification accuracy of (93.98 ± 1.54)% for eight postures, based on the interrogation of a single fiber transducer Ver menos