Integrated Optical Fiber Force Myography Sensor as Pervasive Predictor of Hand Postures

Author(s)

Wu, Yu Tzu ^[1] ; Gomes, Matheus K. ^[1] ; da Silva, Willian H. A. ^[1] ; Lazari, Pedro M. ^[1] ; Fujiwara, Eric ^[1]

Affiliation

^[1] Univ Estadual Campinas, Sch Mech Engn, Lab Photon Mat & Devices, BR-13083860 Campinas, SP - Brazil

• Document type: Journal article
• Source: BIOMEDICAL ENGINEERING AND COMPUTATIONAL BIOLOGY; v. 11, MAR 2020.
• Web of Science Citations: 0

Abstract

Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of similar to 99.5% and similar to 99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer. (AU)

• FAPESP's process: 17/25666-2 - Development of an optical fiber force myography sensor for applications in human-robot interfaces
• Grantee: Eric Fujiwara
• Support Opportunities: Regular Research Grants