Soft Electronics for Health Monitoring Assisted by Machine Learning

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.

Evaluation of yields and quality parameters of oils from Cornus wilsoniana fruit extracted by subcritical n-butane extraction and conventional methods

Cornus wilsoniana fruit oil is a very important woody oil and is the main raw material of biodiesel.In this study,the oil yield,physicochemical properties,fatty acid composition,rheological properties,thermal stability,and Fourier transform infrared(FTIR)spectra of C.wilsoniana fruit oil obtained by subcritical n-butane extraction(SBE)and conventional methods such as pressing extraction(PE)and Soxhlet extraction(SE)were determined to study the influence of different extraction methods on the quality and yield of C.wilsoniana fruit oil.The oil yield of SBE(19.47%)was higher than that of PE(9.93%)but slightly lower than that of SE(21.08%).All of the extracted oils exhibited similar physicochemical properties,and the SBE oil was richer in polyunsaturated fatty acids(PUFA)than that of the PE oil,with an approximate 1:2 ratio of total saturated fatty acids against unsaturated fatty acids.The results of rheological behavior and thermal stability showed that all extracted oils had Newtonian flow characteristics,wherein the SBE oil exhibited lower viscosity and higher thermal stability.Furthermore,scanning electron microscopy(SEM)images of the surface topography indicated that different oil extraction methods will affect the residual oil content of the C.wilsoniana fruit powder.Compared with PE,the pores on the surface of the C.wilsoniana fruit powder after oil extraction were clearly visible,indicating that the driving force of SBE for oil extraction is stronger than that of PE.Based on the above results,it is implied that SBE is the best of the three methods for extracting C.wilsoniana fruit oil and can be potentially applied to extract other edible oils.

Micromesh reinforced strain sensor with high stretchability and stability for full-range and periodic human motions monitoring

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.

Microcavity assisted graphene pressure sensor for single-vessel local blood pressure monitoring

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.

Tweezing and manipulating micro- and nanoparticles by optical nonlinear endoscopy

The precise control and manipulation of micro-and nanoparticles using an optical endoscope are potentially important in biomedical studies,bedside diagnosis and treatment in an aquatic internal organ environment,but they have not yet been achieved.Here,for the first time,we demonstrate optical nonlinear endoscopic tweezers(ONETs)for directly controlling and manipulating aquatic micro-and nanobeads as well as gold nanorods.It is found that two-photon absorption can enhance the trapping force on fluorescent nanobeads by up to four orders of magnitude compared with dielectric nanobeads of the same size.More importantly,two-photon excitation leads to a plasmon-mediated optothermal attracting force on nanorods,which can extend far beyond the focal spot.This new phenomenon facilitates a snowball effect that allows the fast uploading of nanorods to a targeted cell followed by thermal treatment within 1 min.As two-photon absorption allows an operation wavelength at the center of the transmission window of human tissue,our work demonstrates that ONET is potentially an unprecedented tool for precisely specifying the location and dosage of drug particles and for rapidly uploading metallic nanoparticles to individual cancer cells for treatment.