An Ultra-Sensitive Flexible Resistive Sensor with Double Strain Layer and Crack Inspired by the Physical Structure of Human Epidermis: Design, Fabrication, and Cuffless Blood Pressure Monitoring Application

被引:18
|
作者
Li, Junliang [1 ]
Liu, Ping [1 ]
Hu, Qiusheng [1 ]
Tong, Wei [1 ]
Sun, Yifan [1 ]
Feng, Han [1 ,2 ]
Wu, Shunge [1 ]
Hu, Ruohai [1 ]
Liu, Caixia [1 ]
Wang, Yubing [1 ]
Tian, Helei [1 ]
Bu, Yi [1 ]
Zhang, Yugang [1 ]
Ma, Yuanming [1 ]
Teng, Fei
Liu, Jian [1 ]
Guo, Xinxin [2 ]
Yang, Austin [3 ]
Song, Aiguo [4 ]
Yang, Xiaoming [5 ]
Huang, Ying [1 ]
机构
[1] Hefei Univ Technol, Sch Microelect, Hefei 230009, Peoples R China
[2] State Grid Chuzhou Power Supply Co, Chuzhou 239000, Peoples R China
[3] Pomona Coll, Dept Mol Biol, Pomona, CA 91711 USA
[4] Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Peoples R China
[5] Zhejiang Ouren New Mat Co Ltd, Jiaxing 314103, Peoples R China
关键词
crack; cuffless blood pressure monitoring; flexible resistive sensors; neural networks; Poly(3,4-ethylenedioxythiophene) :poly(styrenesulfonate); NEURAL-NETWORK MODEL; PEDOTPSS;
D O I
10.1002/admt.202201466
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The cuffless blood pressure (BP) monitoring device has attracted much attention because of its comfortable and real-time monitoring. Inspired by the physical structure of the human epidermis, an ultra-sensitive flexible resistive sensor with a double strain layer and crack structure is prepared by screen printing in this study. The strain material of the sensor is mainly prepared by blending Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) with waterborne polyurethane and reduced graphene oxide. The sensor exhibits a high gauge factor (GF, approximate to 1682), fast response (approximate to 48 ms), and long-term stability (> 2000 cycles) at low strain (approximate to 0-5%). The sensor is placed on the human radial artery and can accurately measure the pulse wave. The features of the pulse wave are automatically extracted using a convolutional neural network. Then the features are corrected using a long short-term memory neural network, and the current BP value is predicted using a fully connected network layer. The BP result meets the A-level standards of the Association for the Advancement of Medical Instrumentation and the British Hypertension Society. This study provides an efficient device and method for continuous and non-invasive BP monitoring.
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页数:15
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