集成光纤肌力传感器作为手部姿势的普遍预测器。

IF 2.3 Q3 ENGINEERING, BIOMEDICAL
Biomedical Engineering and Computational Biology Pub Date : 2020-03-24 eCollection Date: 2020-01-01 DOI:10.1177/1179597220912825
Yu Tzu Wu, Matheus K Gomes, Willian Ha da Silva, Pedro M Lazari, Eric Fujiwara
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引用次数: 10

摘要

力肌图(FMG)在生物医学应用中是传统肌电图的一个有吸引力的替代方案,主要是由于其更简单的信号模式和对电干扰的免疫。然而,大多数FMG传感器将数据发送到计算机进行进一步处理,这降低了用户的移动性,从而降低了实际应用的机会。在这个意义上,本工作提出用更小的便携式组件改造典型的光纤FMG传感器。此外,所有数据采集和处理例程都迁移到Raspberry Pi 3 Model B微处理器上,确保了使用的舒适性和可移植性。该传感器采用2个隐层和1个竞争输出层的前馈人工神经网络,成功实现了2个输入通道和9个姿态分类,平均精度和准确度分别为~99.5%和~99.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

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.

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