Integrated Optical Fiber Force Myography Sensor as Pervasive Predictor of Hand Postures. (March 2020)

• Record Type: Journal Article
• Authors: Wu, Yu Tzu; Gomes, Matheus K; da Silva, Willian HA; Lazari, Pedro M; Fujiwara, Eric
• Is Part Of: Biomedical engineering and computational biology. Volume 11(2020)
• Publication Date: 2020-03
• Subjects: Force myography -- optical fiber sensor -- user interface -- gesture recognition -- integrated sensor; Biomedical engineering -- Periodicals; Computational biology -- Periodicals; Biomedical Engineering; Computational Biology; Biomedical engineering; Computational biology; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 610.2805
• DOI: 10.1177/1179597220912825
• Languages: English
• ISSNs: 1179-5972

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 ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer.