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Soft Electronics for Health Monitoring Assisted by Machine Learning 被引量:5
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作者 Yancong Qiao jinan luo +11 位作者 Tianrui Cui Haidong Liu Hao Tang Yingfen Zeng Chang Liu Yuanfang Li Jinming Jian Jingzhi Wu He Tian Yi Yang Tian-Ling Ren Jianhua Zhou 《Nano-Micro Letters》 SCIE EI CAS CSCD 2023年第5期83-168,共86页
Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of ... Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of the important advantages of soft electronics is forming good interface with skin,which can increase the user scale and improve the signal quality.Therefore,it is easy to build the specific dataset,which is important to improve the performance of machine learning algorithm.At the same time,with the assistance of machine learning algorithm,the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis.The soft electronics and machining learning algorithms complement each other very well.It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future.Therefore,in this review,we will give a careful introduction about the new soft material,physiological signal detected by soft devices,and the soft devices assisted by machine learning algorithm.Some soft materials will be discussed such as two-dimensional material,carbon nanotube,nanowire,nanomesh,and hydrogel.Then,soft sensors will be discussed according to the physiological signal types(pulse,respiration,human motion,intraocular pressure,phonation,etc.).After that,the soft electronics assisted by various algorithms will be reviewed,including some classical algorithms and powerful neural network algorithms.Especially,the soft device assisted by neural network will be introduced carefully.Finally,the outlook,challenge,and conclusion of soft system powered by machine learning algorithm will be discussed. 展开更多
关键词 Soft electronics Machine learning algorithm Physiological signal monitoring Soft materials
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Micromesh reinforced strain sensor with high stretchability and stability for full-range and periodic human motions monitoring
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作者 Haidong Liu Chang Liu +11 位作者 jinan luo Hao Tang Yuanfang Li Houfang Liu Jingzhi Wu Fei Han Zhiyuan Liu Jianhe Guo Rongwei Tan Tian-Ling Ren Yancong Qiao Jianhua Zhou 《InfoMat》 SCIE 2024年第4期124-139,共16页
The development of strain sensors with high stretchability and stability is an inevitable requirement for achieving full-range and long-term use of wearable electronic devices.Herein,a resistive micromesh reinforced s... The development of strain sensors with high stretchability and stability is an inevitable requirement for achieving full-range and long-term use of wearable electronic devices.Herein,a resistive micromesh reinforced strain sensor(MRSS)with high stretchability and stability is prepared,consisting of a laser-scribed graphene(LSG)layer and two styrene-block-poly(ethylene-ran-butylene)-block-poly-styrene micromesh layers embedded in Ecoflex.The micromesh reinforced structure endows the MRSS with combined characteris-tics of a high stretchability(120%),excellent stability(with a repetition error of 0.8%after 11000 cycles),and outstanding sensitivity(gauge factor up to 2692 beyond 100%).Impressively,the MRSS can still be used continauously within the working range without damage,even if stretched to 300%.Furthermore,compared with different structure sensors,the mechanism of the MRSS with high stretchability and stability is elucidated.What's more,a multilayer finite element model,based on the layered structure of the LSG and the morphology of the cracks,is proposed to investigate the strain sensing behavior and failure mechanism of the MRSS.Finally,due to the outstanding performance,the MRSS not only performes well in monitoring full-range human motions,but also achieves intelligent recognitions of various respiratory activities and ges-tures assisted by neural network algorithms(the accuracy up to 94.29%and 100%,respectively).This work provides a new approach for designing high-performance resistive strain sensors and shows great potential in full-range and long-term intelligent health management and human-machine interac-tions applications. 展开更多
关键词 flexible strain sensor excellent stretchability and stability layered laser-scribed graphene micromesh reinforced structure multilayer finite element model
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Microcavity assisted graphene pressure sensor for single-vessel local blood pressure monitoring
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作者 jinan luo Jingzhi Wu +15 位作者 Xiaopeng Zheng Haoran Xiong Lin Lin Chang Liu Haidong Liu Hao Tang Houfang Liu Fei Han Zhiyuan Liu Zhikang Deng Chuting Liu Tianrui Cui Bo Li Tian-Ling Ren Jianhua Zhou Yancong Qiao 《Nano Research》 SCIE EI 2024年第11期10058-10068,共11页
Dynamic monitoring of blood pressure (BP) is beneficial to obtain comprehensive cardiovascular information of patients throughout the day. However, the clinical BP measurement method relies on wearing a bulky cuff, wh... Dynamic monitoring of blood pressure (BP) is beneficial to obtain comprehensive cardiovascular information of patients throughout the day. However, the clinical BP measurement method relies on wearing a bulky cuff, which limits the long-term monitoring and control of BP. In this work, a microcavity assisted graphene pressure sensor (MAGPS) for single-vessel local BP monitoring is designed to replace the cuff. The microcavity structure increases the working range of the sensor by gas pressure buffering. Therefore, the MAGPS achieves a wide linear response of 0–1050 kPa and sensitivity of 15.4 kPa^(−1). The large working range and the microcavity structure enable the sensor to fully meet the requirements of BP detection at the radial artery. A database of 228 BP data (60-s data fragment detected by MAGPS) and 11,804 pulse waves from 9 healthy subjects and 5 hypertensive subjects is built. Finally, the BP was detected and analyzed automatically by combining MAGPS and a two-stage convolutional neural network algorithm. For the BP detection method at local radial artery, the first stage algorithm first determines whether the subject has hypertension by the pulse wave. Then, the second stage algorithm can diagnose systolic and diastolic BP with the accuracy of 93.5% and 97.8% within a 10 mmHg error, respectively. This work demonstrates a new BP detection method based on single vessel, which greatly promotes the efficiency of BP detection. 展开更多
关键词 graphene pressure sensor microcavity assisted pressure sensor single-vessel BP monitoring two-stage neural network algorithm
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