Wearable Health Monitoring Research Articles

Modeling and Simulation of Circuit-Level Nonidealities for an Analog Computing Design Approach With Application to EEG Feature Extraction

This article presents a design approach for the modeling and simulation of ultralow power (ULP) analog computing machine learning (ML) circuits for seizure detection using electroencephalography (EEG) signals in wearable health monitoring applications. In this article, we describe a new analog system modeling and simulation technique to associate power consumption, noise, linearity, and other critical performance parameters of analog circuits with the classification accuracy of a given ML network, which allows to realize a power and performance optimized analog ML hardware implementation based on diverse application-specific needs. We carried out circuit simulations to obtain nonidealities, which are then mathematically modeled for an accurate mapping. We have modeled noise, nonlinearity, resolution, and process variations such that the model can accurately obtain the classification accuracy of the analog computing-based seizure detection system. Noise has been modeled as an input-referred white noise that can be directly added at the input. Device process and temperature variations were modeled as random fluctuations in circuit parameters, such as gain and cut-off frequency. Nonlinearity was mathematically modeled as a power series. The combined system level model was then simulated for classification accuracy assessments. The design approach helps to optimize power and area during the development of tailored analog circuits for ML networks with the ability to potentially trade power and performance goals while still ensuring the required classification accuracy. The simulation technique also enables to determine target specifications for each circuit block in the analog computing hardware. This is achieved by developing the ML hardware model, and investigating the effect of circuit nonidealities on classification accuracy. Simulation of an analog computing EEG seizure detection block shows a classification accuracy of 91%. The proposed modeling approach will significantly reduce design time and complexity of large analog computing systems. Two feature extraction approaches are also compared for an analog computing architecture.

Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities.

Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.

Textile Knitted Stretch Sensors for Wearable Health Monitoring: Design and Performance Evaluation

The advancement of smart textiles has led to significant interest in developing wearable textile sensors (WTS) and offering new modalities to sense vital signs and activity monitoring in daily life settings. For this, textile fabrication methods such as knitting, weaving, embroidery, and braiding offer promising pathways toward unobtrusive and seamless sensing for WTS applications. Specifically, the knitted sensor has a unique intermeshing loop structure which is currently used to monitor repetitive body movements such as breathing (microscale motion) and walking (macroscale motion). However, the practical sensing application of knit structure demands a comprehensive study of knit structures as a sensor. In this work, we present a detailed performance evaluation of six knitted sensors and sensing variation caused by design, sensor size, stretching percentages % (10, 15, 20, 25), cyclic stretching (1000), and external factors such as sweat (salt-fog test). We also present regulated respiration (inhale-exhale) testing data from 15 healthy human participants; the testing protocol includes three respiration rates; slow (10 breaths/min), normal (15 breaths/min), and fast (30 breaths/min). The test carried out with statistical analysis includes the breathing time and breathing rate variability. These testing results offer an empirically derived guideline for future WTS research, present aggregated information to understand the sensor behavior when it experiences a different range of motion, and highlight the constraints of the silver-based conductive yarn when exposed to the real environment.

Carboxyl Graphene Oxide Functionalized Cotton Textile with Superwettable Patterns for Humidity Sensing

In this study, a carboxyl-modified graphene oxide (GO–COOH) was prepared and deposited into flexible superwettable patterned cotton (SPC) for humidity sensing. The morphology of the as-prepared electronic-SPC (ESPC) was characterized by field emission scanning electron microscopy (FESEM). The carboxylation process was investigated by comparing the chemical properties of GO–COOH and the unmodified GO via X-ray diffraction, X-ray photoelectron spectroscopy, Fourier transform infrared, and Raman. The as-prepared ESPC was then applied for humidity sensing for the first time, whereby the GO–COOH impregnated into the superwettable patterns of SPC played an important role. The as-prepared ESPC processed a high sensitivity for humidity sensing with a quite short response time (less than 1 s at ΔRH of 60%) and the output current increased with increasing relative humidity (ΔRH). The output current was affected by the thickness of the GO–COOH layer, which could be controlled by varying the effective area (diameter of the dots) as well as the loading amount (concentration of GO–COOH). The as-prepared ESPC showed high stability and durability whereby the output current did not show a significant change after overnight washing. Meanwhile, the high flexibility of ESPC was confirmed by the bending test, and results showed that it could maintain an output current of 25 nA even after 2000 bending cycles. The high performance noncontact ESPC was further applied in human breathing sensing test and the results showed that the ESPC could be a promising material for wearable health monitoring.

Merkel cell-inspired skin-like hybrid hydrogels for wearable health monitoring

High-performance strain/pressure sensors are receiving much attention in soft robotics, motion detection and healthcare. Inspired by the sensing function of Merkel cells, bionic skin-like strain/pressure sensors with great elasticity (1500 %), low hysteresis (7.1 %), low pressure detection limit (0.508 Pa), high sensitivity (14.117 kPa−1) and excellent cycling performance (4000 cycles) were designed, benefiting from the excellent skin-like properties of polyacrylamide/gelatin/MXene hybrid hydrogels with rich porous dual networks and micro-cylinder arrays patterned on the surface of hydrogel. Particularly, the hydrogel sensors exhibited resistance–capacitance dual response signals, and the basic understanding of this phenomenon was proposed through a simplified circuit model and finite element method analysis. Furthermore, to mimic the regionalization of Merkel cells in the skin, a multiplexed sensing array was implemented and then successfully applied in the field of wireless wearable health monitoring, providing a promising strategy for the development of smart personalized healthcare.

Highly Efficient and Stable Self-Powered Mixed Tin-Lead Perovskite Photodetector Used in Remote Wearable Health Monitoring Technology.

Realization of remote wearable health monitoring (RWHM) technology for the flexible photodiodes is highly desirable in remote-sensing healthcare systems used in space stations, oceans, and forecasting warning, which demands high external quantum efficiency (EQE) and detectivity in NIR region. Traditional inorganic photodetectors (PDs) are mechanically rigid and expensive while the widely reported solution-processed mixed tin-lead (MSP) perovskite photodetectors (PPDs) exhibit a trade-off between EQE and detectivity in the NIR region. Herein, a novel functional passivating antioxidant (FPA) strategy has been introduced for the first time to simultaneously improve crystallization, restrain Sn2+ oxidization, and reduce defects in MSP perovskite films by multiple interactions between thiophene-2-carbohydrazide (TAH) molecules and cations/anions in MSP perovskite. The resultant solution-processed rigid mixed Sn-Pb PPD simultaneously achieves high EQE (75.4% at 840 nm), detectivity (1.8 × 1012 Jones at 840 nm), ultrafast response time (trise /tfall = 94 ns/97 ns), and improved stability. This work also highlights the demonstration of the first flexible photodiode using MSP perovskite and FPA strategy with remarkably high EQE (75% at 840 nm), and operational stability. Most importantly, the RWHM is implemented for the first time in the PIN MSP perovskite photodiodes to remotely monitor the heart rate of humans at rest and after-run conditions.

A Step Forward for Smart Clothes─Fabric-Based Microfluidic Sensors for Wearable Health Monitoring.

We report the first demonstration of fabric-based microfluidics for wearable sensing. A new technology to develop microfluidics on fabrics, as a part of an undergarment, is described here. Compared to conventional microfluidics from polydimethylsiloxane, fabric-based microfluidics are simple to make, robust, and suitable for efficient sweat delivery. Specifically, acrylonitrile butadiene styrene (ABS) films with precut microfluidic patterns were infused through fabrics to form hydrophobic areas in a specially controlled sandwich structure. Experimental tests and simulations confirmed the sweat delivery efficiency of the microfluidics. Electrodes were screen-printed onto the fabric-based microfluidic. A novel wearable potentiometer based on Arduino was also developed as the transducer and signal readouts, which was low-cost, standardized, open-source, and capable of wireless data transfer. We applied the sensor system as a standalone or as a module of a T-shirt to quantify [Ca2+] in a wearer's sweat, with physiological and accurate results generated. Overall, this work represents a critical step in turning regular undergarments into biochemically smart platforms for health monitoring, which will broadly benefit human healthcare.

Integration of the Health Monitoring System with IoT Application in Sports Technology: A Review

Nowadays, monitoring health systems in robust technology has been extensively applied in the sports field. Even though massive utilization of wearable device technologies aims to quantify athlete performance, inconsistent performance still exists between training sessions and competition. The rigorous discussion about the latest research in monitoring technological systems will help trainers obtain accurate data about athlete performance. This paper focuses on the athlete monitoring system in terms of psychological and physiological parameters and applications in individual sports based on Internet of Things (IoT) Technology. The study incorporates three factors: the parameters that affect athlete performance, multiple device sensors in sports health monitoring, and IoT technology’s application for athletes. Based on analysis and observation, efficient sports health monitoring can effectively enhance athlete performance in physiological and psychological conditions. An IoT system encompasses four main aspects: sensing, networking, data processing and application layer. These aspects provide real-time information on the athlete’s body condition during training and games. Therefore, this monitoring system greatly assists coaches in designing practical training and activities for athletes. It is highlighted that wearable health monitoring systems by IoT technology will be further built based on athlete requirements.

Antimicrobial MXene-based conductive alginate hydrogels as flexible electronics

Flexible electronics have received considerable attention for their potential applications in wearable human health monitoring. Recently, conductive hydrogels have shown great potential in the application of flexible electronic devices. However, the restricted space of the 2D plane restricts the performance of MXene sensors, and conventional conductive hydrogels lack antibacterial properties, limiting their deep application in biomedical-related products. To overcome these limitations, we proposed an antibacterial and conductive MXene-SA-TSA-AM hydrogel through the radical polymerization of acrylamide in this work. Specifically, the coupling between sodium alginate (SA) and dimethyloctadecyl-(3-trimethoxysilylpropyl) ammonium chloride (TSA) brought about excellent antibacterial properties. MXene-SA-TSA-AM Hydrogels showed compelling swelling ratios (36–38 times), great mechanical properties (up to 1780 %), excellent self-healing ability (<10 min), and reproducible adhesion. During the pressure sensor applications, MXene-SA-TSA-AM hydrogels were demonstrated to accurately and rapidly monitor human physiological activities of finger bend and pulse with a rapid response (<0.2 s) and high stability (up to 5000 cycles). Furthermore, the MXene-SA-TSA-AM hydrogel exhibited excellent antibacterial properties, which can successfully inhibit Staphylococcus aureus and Escherichia coli with efficiency up to 99.6 % and 99.3 %, respectively. Finally, we anticipated that this novel antibacterial and conductive MXene-SA-TSA-AM hydrogel would further promote the deeper application of flexible electronics in biomedical-related products.

Ambilateral convergent directional freeze casting meta-structured foams with a negative Poisson’s ratio for high-performance piezoresistive sensors

Flexible piezoresistive sensors show broad application value in wearable electronic skin, health monitoring and motion detection. The practical range of conventional piezoresistive sensors, however, is often severely limited by their sensitivity, measuring span, mechanical properties, and lateral expansion caused by positive Poisson's ratio (PPR) effects. Herein we introduce a generally applicable methodology, ambilateral convergent directional freeze casting technique, for the fabrication of anisotropic porous foams with a negative Poisson's ratio (NPR). Different from traditional freeze casting technique, this new design methodology enables precise control of pore types and pore orientations, thereby endowing artificial materials with special mechanical properties. Under uniaxial compression, synthetic foams exhibit macroscopic lateral shrinkage, with a Poisson's ratio below – 4.2% at the strain of 25%. These conductive meta-structured foams are used to construct piezoresistive sensors. Compared with traditional structures, metastructures with NPR effects significantly increase the number of wall-to-wall contacts and the formation of electrical paths, thereby enhancing the sensitivity and sensing reliability of sensors. In addition, the NPR effect markedly enhances the mechanical elasticity and compression resistance of sensors, enabling it to have excellent signal stability even in the face of reiterative compressions. These meta-structured sensors also exhibited smart resistivity repair behavior in the face of overload damage. Integrated sensors can be used for monitoring of pressure distribution and athlete's gait. Lateral contractions caused by NPR effects can avoid the contact of adjacent sensors and narrow the gap between two sensors, thus improving sensing stability and pressure distribution resolution of distributed sensors.

Eliminate the hardware: Mobile terminals-oriented food recognition and weight estimation system

Food recognition and weight estimation based on image methods have always been hotspots in the field of computer vision and medical nutrition, and have good application prospects in digital nutrition therapy and health detection. With the development of deep learning technology, image-based recognition technology has also rapidly extended to various fields, such as agricultural pests, disease identification, tumor marker recognition, wound severity judgment, road wear recognition, and food safety detection. This article proposes a non-wearable food recognition and weight estimation system (nWFWS) to identify the food type and food weight in the target recognition area via smartphones, so to assist clinical patients and physicians in monitoring diet-related health conditions. In addition, the system is mainly designed for mobile terminals; it can be installed on a mobile phone with an Android system or an iOS system. This can lower the cost and burden of additional wearable health monitoring equipment while also greatly simplifying the automatic estimation of food intake via mobile phone photography and image collection. Based on the system's ability to accurately identify 1,455 food pictures with an accuracy rate of 89.60%, we used a deep convolutional neural network and visual-inertial system to collect image pixels, and 612 high-resolution food images with different traits after systematic training, to obtain a preliminary relationship model between the area of food pixels and the measured weight was obtained, and the weight of untested food images was successfully determined. There was a high correlation between the predicted and actual values. In a word, this system is feasible and relatively accurate for one automated dietary monitoring and nutritional assessment.

ATOPIC DERMATITIS IMPACT PATIENT LIVES AS MEASURED WITH WEARABLE DIGITAL TECHNOLOGY

<h3>Introduction</h3> Atopic Dermatitis(AD) impacts patient quality of life (QoL), daily activity and requires self-management. Wearable digital health monitor(WDHM) applications help patients manage chronic diseases. This study investigated patient experience with a wrist WDHM application to measure Itch and scratch while sleeping along with quality of life (QoL), satisfaction with the app and technology. <h3>Methods</h3> This exempt study enrolled participants between Dec 2020 – April 2021. After consent, the WDHM was shipped, the participant was instructed to download the app software and use the WDHM for 7 nights. Also, participants completed daily QoL surveys and an end of study satisfaction survey. <h3>Results</h3> Ninety participants used the WDHD. The maximum length of itch/scratch in one night was 32 minutes. Among the participants, 90% felt their skin was itchy, sore, painful, and stinging, 75% felt embarrassed or self-conscious because of their skin, 65% of clothes worn was influenced by their skin, and 35% had sexual difficulties because of their skin. While 89% found the information collected was useful, only 53% agreed the information collected would allow them to better discuss AD with their physician, yet 79% agreed AD had a moderate-large effect on their life. <h3>Conclusion</h3> The use of WDHM for atopic dermatitis itch and scratch while sleeping is useful to the user. The percentage of people who agree AD impacts their life on a moderate to large scale, only about one-third of them will discuss the status of their AD with their health care practitioner.

A Rainbow Structural Color by Stretchable Photonic Crystal for Saccharide Identification.

Photonic crystals (PCs) with fascinating structural color nanomaterials present effectively spontaneous emission modulation and selectively optical signal amplification. Stretchability or elasticity could enable the feasible tunability for structural colors. Aimed at the regulation of structural colors, we endeavored to achieve the PC nanomatrix evolution and optical property during stretching. In this work, a rainbow structural color by stretchable PCs was exploited to provide abundant optical information for multianalyte recognition. The finite element analysis proved the electric field distribution in the PC matrix, which completely matched with the phenomenon of the measured PC spectra. By simply employing analysis of the multistate PC during stretching, the mono PC matrix chip can differentially enhance fluorescence signals in broad spectral regions, resulting in diverse sensing information for high-efficiency multianalysis. The stretchable PC chip can facilely discriminate 14 similar structured saccharides with a minimum concentration of 10-7 M using only one fluorescence complex. Furthermore, saccharides in different concentrations, mixtures, and real samples (beverages and sweets) also can be successfully distinguished. The exploration on fluorescent stretch dependence behavior of the photonic crystal contributes the biomatching optical platform for wearable devices, dynamic environment, clinical, or health monitoring auxiliary.

Nanocomposite-Based Electrode Structures for EEG Signal Acquisition

Objective: To fabricate a lightweight, breathable, comfortable, and able to contour to the curvilinear body shape, electrodes built on a flexible substrate are a significant growth in wearable health monitoring. This research aims to create a GNP/FE electrode-based EEG signal acquisition system that is both efficient and inexpensive. Methodology: Three distinct electrode concentrations were developed for EEG signal acquisition, three distinct electrode concentrations (1.5:1.5, 2:1, and 3:0). The high strength-to-weight ratio to form the tribofilm in the fabrication of the electrode will provide good efficiency. The EEG signal is first subjected to a wavelet transform, which serves as a preliminary analysis. The use of biopotential signals in wearable systems as biofeedback or control commands is expected to substantially impact point-of-care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMI), and brain–computer interfaces (BCIs). The graphene oxide (GO), glycerol (GL), and polyvinyl alcohol (PVA) GO/GL/PVA plastic electrodes were measured and compared to that of a commercially available electrode using the biopic equipment. The GO/GL/PVA plastic electrode was able to detect EEG signals satisfactorily after being used for two months, demonstrating good conductivity and lower noise than the commercial electrode. The GO/GL/PVA nanocomposite mixture was put into the electrode mold as soon as it was ready and then rapidly chilled. Results: The quality of an acquired EEG signal could be measured in several ways including by its error percentage, correlation coefficient, and signal-to-noise ratio (SNR). The fabricated electrode yield detection ranged from 0.81 kPa−1 % to 34.90 kPa−1%. The performance was estimated up to the response of 54 ms. Linear heating at the rate of 40 °C per minute was implemented on the sample ranges from 0 °C to 240 °C. During the sample electrode testing in EEG signal analysis, it obtained low impedance with a good quality of signal acquisition when compared to a conventional wet type of electrode. Conclusions: A large database was frequently built from all of the simulated signals in MATLAB code. Through the experiment, all of the required data were collected, checked against all other signals, and proven that they were accurate representations of the intended database. Evidence suggests that graphene nanoplatelets (GNP) hematite (FE2O3) polyvinylidene fluoride (PVDF) GNP/FE2O3@PVDF electrodes with a 3:0 concentration yielded the best outcomes.

Pressure sensor based on wave-structured rGO film for wearable human health monitoring

Pressure sensor based on wave-structured rGO film for wearable human health monitoring

Security vulnerability analysis for an improved anonymous authentication protocol for wearable health monitoring system in an aging society

Security vulnerability analysis for an improved anonymous authentication protocol for wearable health monitoring system in an aging society

(Digital Presentation) Harnessing Wide-Range, Highly Stable Pressure Sensitivity Via PEDOT-Cl Vapor Printed Textiles for Health Monitoring

The advancement of wearable electromechanical sensors to detect biopotentials and body locomotion is critically important in evaluating human performance and improving off-site care applications. Electromechanical pressure sensors are defined as transducers that transform the mechanical deformations caused by applied pressure to a detectable electrical output. Depending on the transduction mechanism, pressure sensors fall within different categories including triboelectric, transistive, capacitive, piezoelectric, and piezoresistive sensors. Piezoresistive pressure sensors, in which the ultimate electrical output is resistance variations, are the most-widely used class due to the simplicity of read-out system in signal acquisition, simple working mechanism that allows a wide variety of materials to be used along with cost-effective fabrication process. However, their practicality is highly restricted by narrow range of detection (RoD), failure to sense both static and dynamic pressures simultaneously, and low stability against aging phenomena such as cycling abrasions, exposure to perspiration and washing. By taking advantage of vapor deposition of a p-doped conjugated polymer, poly(3,4-ethylenedioxythiophene):chloride (PEDOT-Cl), we introduce an ultra-stable pressure sensor that reveals high sensitivity in detecting real-time signals in such a wide range of pressures that has not been reported before (from heart beats to more than body weight). This one-step technique allowed us to tune the conductivity and subsequently sensitivity of the sensor accordingly. We observed that the sensor enjoys a multiscale working mechanism, in which we have the putative decrease in the thickness and therefore reduction in the air trapped in the free volume of the active layer in mili-centmeter scale, the percolation pathways in micro-milimeter scale, and piezoionic effect in nanometer scale which means that the redistribution of ions under the applied mechanical stress leads to the change in resistance. This multiscale sensitivity is the key to its broad RoD along with its ability to simultaneously detect subtle dynamic and static pressure in the presence of a base pressure. We developed two sensors: one with PEDOT-Cl coated cotton fabric and one with PEDOT-Cl coated cotton ball as the active layer. In fact, the ordered structure of fabric and the disordered structure of cotton ball play the role of a lattice for percolation; providing the structure with points of connectivity for charge carriers. As expected, the disordered nature of the cotton ball leads to a higher number of points of connectivity and therefore, lower range of variation and higher precision in data acquisition. While taking advantage of the presence of ions in our sensor, we protected the sensor against all the humidity-induced degradations entangled with ions and other aging processes via vapor deposition of Fluorinated hydrophobic moieties on all the sensor layers. With this protective coating, the sensor shows less than no change in resistance and sensitivity after staying in ~100% humidity for more than a week, and can stand more than 20 laundry cycles without any drop in signal quality. Also, it displays ultrastability with 99.21% over 70,000 bending cycles in ambient conditions, exceeding the durability cycles of sensors reported previously. The broad ability of this sensor was further confirmed by acquiring physiological signals and body motions such as heartbeats, respiration, Joint movements and step. All these properties, along with the low-cost and robust fabrication process, bears the testimony that this sensor will be uniquely placed in wearable health monitoring electronics for both diagnostic and treatment applications.*Figure Caption: a) Schematic illustration of the sensor including the SEM image of the PEDOT-Cl coated layer, b) Physiological and body movements signals acquired via sensor, c) Sensitivity of the sensor over a wide range of pressures Figure 1

Development of a Piezoresistive Pressure Sensor Using an in-House Pulsewave Simulator to Ensure Detection of Full Pulse Waveform on Diverse Populations

Flexible and skin mountable wearable health monitoring devices have gained much attention in recent years due to their non-invasive approach to continuous monitoring of weak human physiological signals. In general, ultra-sensitive pressure sensors can be categorized by the operation mechanism into piezoresistive, capacitive, triboelectric, and piezoelectric sensors. Specifically, piezoresistive sensors has drawn much attention because of their fast response, broad detection range, simple structure, and relative simplicity of the signal measuring system. Despite the success of the past efforts in acquiring the full pulse waveform of the human heartbeat, their signal acquisition requires utilization of unwearable measuring equipment. In this study, the central research objective will be to do finite element analysis to optimize the design, and then to fabricate, test and finally verify wearability of the full system (including the signal acquisition portion) on the wrist. A piezoresistive pressure sensor capable of precise monitoring of arterial pulse waveform allows detection of not only the pulse but also possible irregularities in its waveform that can be potentially the harbingers of various medical conditions. Modeling of the sensor’s micro-features showed micro-features with the shape of a pyramid, size of 100 μm, angle of 50-60 degrees and spatial configuration of 1 per 300 μm realizes high sensitivity and signal level (1 mA for pulse waveform detection from the wrist). This helps to eliminate the dependability of the sensor response read-out on complex and unportable measuring devices such as sourcemeters. Furthermore, fabrication of the conductive micro-patterned sensor has been accomplished by the standard micro-fabrication techniques including photolithography and wet etching. Methods such as electrospinning, bipolar exfoliation of graphene and electrostatic spray deposition were used to ensure long term stable conductivity and operation of the device. After the fabrication, this piezoresistive sensor was fully calibrated and characterized by a developed pulse waveform simulator to ensure viability of the system to detect the full pulse waveform from the wrist with various amplitudes of excitation signal (accounting for diverse human population with different pulse strengths). The detailed results will be discussed during the presentation.

Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis.

Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respiratory diseases. In this work, a smart face mask is proposed with an air-permeable and biodegradable self-powered breath sensor as the key component. This smart face mask is easily fabricated, comfortable to use, eco-friendly, and has sensitive and stable output performances in real wearable conditions. To verify the practicability, we use smart face masks to record respiratory signals of patients with chronic respiratory diseases when the patients do not have obvious symptoms. With the assistance of the machine learning algorithm of the bagged decision tree, the accuracy for distinguishing the healthy group and three groups of chronic respiratory diseases (asthma, bronchitis, and chronic obstructive pulmonary disease) is up to 95.5%. These results indicate that the strategy of this work is feasible and may promote the development of wearable health monitoring systems.

Highly adhesive stretchable polymer and highly dynamic stable human electrophysiological monitoring

<p indent="0mm">Highly dynamic and stable human electrophysiological monitoring has broad application prospects in intelligent wearable health monitoring, clinical diagnosis of cardiovascular diseases, treatment of nervous system diseases and intelligent human–computer interaction. As a signal interaction bridge between human and the external environment, on-skin electrodes can be attached to the skin surface to monitor and collect various electrophysiological signals in a non-invasive way, making it an ideal platform for highly dynamic and stable electrophysiological monitoring. From the material point of view, the highly dynamic stability of on-skin electrodes depends on the high adhesion and stretchability of the substrate. Therefore, it is an important research direction in the field of flexible intelligent health monitoring to prepare highly adhesive stretchable polymer and construct highly conformal on-skin electrodes that adhere to human skin surface through rational design of polymer structure, so as to realize highly dynamic and stable human electrophysiological monitoring. In this paper, we summarized the recent research progress in our group on the preparation of highly adhesive stretchable polymer and their applications in electrophysiological monitoring with highly dynamic stability, and discussed the opportunities and challenges of highly adhesive stretchable polymer in the next generation of flexible intelligent health monitoring.