| Autor: |
Matheus K. Gomes, Willian H. A. da Silva, Antonio Ribas Neto, Julio Fajardo, Eric Rohmer, Eric Fujiwara |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Automation, Vol 3, Iss 4, Pp 622-632 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2673-4052 |
| DOI: |
10.3390/automation3040031 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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