DNN-Assisted activity classification using fiber interferometer sensor

doi:10.1117/12.2601294

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 瞬态光学技术国家重点实验室

	DNN-Assisted activity classification using fiber interferometer sensor
	Zhu, Guohao 9; Xu, Wei 7,8; Yu, Cheung Chuen 6; Sun, Wenye 5; Dong, Bo 4; Yu, Changyuan 2,3; Zhao, Wei 1,7,8; Wang, Yishan 1,7,8
	2021
会议日期	2021-10-10
会议地点	Nantong, China
关键词	MZI Activity Monitoring DNN CNN LSTM FNN
卷号	11894
DOI	10.1117/12.2601294
英文摘要	Deep Neural Network (DNN) assisted activity monitoring algorithms are investigated, aiming to discriminate three activity states, including presence without movement, nobody in bed, and presence with movement. The signal is collected from a fiber-based Mach-Zehnder Interferometer (MZI) sensor, which is placed under a 20-cm thick mattress. When people are lying on the mattress, cardiopulmonary activities will lead to the change of the phase difference of the MZI optical fiber sensor. In this paper, three kinds of DNNs are developed to investigate the classification performance, including feedforward neural network (FNN), convolutional neural network (CNN), and long short-Term memory network (LSTM). The accuracy of FNN, CNN and LSTM is 95.14%, 99.01%, and 99.37% within one second, respectively. Moreover, LSTM has low time and space complexity and better performance. The algorithms constructed can obtain high accuracy and robustness with low computational overhead and storage consumption and have broad application prospects. What's more, the MZI optical fiber sensor has many advantages such as low cost and anti-electromagnetic interference, which means that the system can be popular in medical treatment and households. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
产权排序	2
会议录	Optoelectronic Devices and Integration X
会议录出版者	SPIE
语种	英语
ISSN号	0277786X;1996756X
ISBN号	9781510646377
内容类型	会议论文
源URL	[http://ir.opt.ac.cn/handle/181661/95632]
专题	西安光学精密机械研究所_瞬态光学技术国家重点实验室
作者单位	1.Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan; 030006, China 2.Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong; 3.Hong Kong Polytechnic University Shenzhen Research Institute, Guangdong, Shenzhen, China; 4.College of New Materials and New Energy, Shenzhen Technology University, Shenzhen; 518118, China; 5.Second Affiliated Hospital of Soochow University, Suzhou; 215000, China; 6.Haina-Intelligent Photonic System Research Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, Jiaxing; 341006, China; 7.University of Chinese Academy of Sciences, Beijing; 100049, China; 8.State Key Laboratory of Transient Optics and Photonics, Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; 9.Department of Electrical and Computer Engineering, National University of Singapore, 117583, Singapore;
推荐引用方式 GB/T 7714	Zhu, Guohao,Xu, Wei,Yu, Cheung Chuen,et al. DNN-Assisted activity classification using fiber interferometer sensor[C]. 见:. Nantong, China. 2021-10-10.