Intelligent Wearable Photonic Sensing System for Remote Healthcare Monitoring Using Stretchable Elastomer Optical Fiber

被引:4
|
作者
Zha, Bingjie [1 ]
Wang, Zhuo [1 ]
Ma, Lin [2 ]
Chen, Jun [1 ]
Wang, Heng
Li, Xiaoli [1 ]
Kumar, Santosh [3 ]
Min, Rui [1 ]
机构
[1] Beijing Normal Univ, Ctr Cognit & Neuroergon, State Key Lab Cognit Neurosci & Learning, Zhuhai 519087, Peoples R China
[2] Shenyang Aerosp Univ, Coll Sci, Shenyang 110136, Peoples R China
[3] K L Univ, Dept Elect & Commun Engn, Guntur 522302, India
来源
IEEE INTERNET OF THINGS JOURNAL | 2024年 / 11卷 / 10期
关键词
Artificial intelligence; human motion monitoring; polymer materials; smart healthcare; wearable sensor; SENSORS; STRAIN; INTERNET; THINGS;
D O I
10.1109/JIOT.2024.3356574
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Medical Internet of Things technology can effectively enable remote physiological data collection, monitoring, and transmission by integrating flexible sensors, wireless communication, and the human body in a wearable and embedded manner. Herein, a stretchable elastomer optical fiber has been developed and sandwiched with two performance of polymethyl methacrylate optical fibers to form a fully flexible polymer optical fiber sensor. The optical fiber integrated with the system mainly consists of a microcomputer, a light-emitting diode (LED) driver, a LED light source, a photodiode, and a Bluetooth module. One intelligent wearable photonic sensing system has been developed based on the Beer-Lambert law of the stretchable elastomer optical fiber for remote healthcare monitoring. Benefiting from the use of elastomer polymer materials, the sensing system features a maximum strain of more than 250%, a high-tensile strain of up to 100%, and a durability of >500 tests. Also, based on the advantage of elastomer optical fiber, the sensing part can be flexibly pasted on the skin surface as a wearable device for real-time monitoring of multiple physiological parameters. In this study, we successfully realized the monitoring of breathing pattern, heart rate, pulse, facial micro-activity, and joint activity, and the recognition of articulatory activity and knee joint activity using a 1-D convolutional neural network, with an accuracy of more than 90% for each activity recognition. Such merits demonstrate its potential as a medical toolkit and indicate promise for remote healthcare monitoring.
引用
收藏
页码:17317 / 17329
页数:13
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