Advances in Wireless,Batteryless,Implantable Electronics for Real‑Time,Continuous Physiological Monitoring

This review summarizes recent progress in developing wireless,batteryless,fully implantable biomedical devices for real-time continuous physiological signal monitoring,focusing on advancing human health care.Design considerations,such as biological constraints,energy sourcing,and wireless communication,are discussed in achieving the desired performance of the devices and enhanced interface with human tissues.In addition,we review the recent achievements in materials used for developing implantable systems,emphasizing their importance in achieving multi-functionalities,biocompatibility,and hemocompatibility.The wireless,batteryless devices offer minimally invasive device insertion to the body,enabling portable health monitoring and advanced disease diagnosis.Lastly,we summarize the most recent practical applications of advanced implantable devices for human health care,highlighting their potential for immediate commercialization and clinical uses.

RFID Positioning and Physiological Signals for Remote Medical Care

The safety of patients and the quality of medical care provided to them are vital for their wellbeing.This study establishes a set of RFID(Radio Fre-quency Identification)-based systems of patient care based on physiological sig-nals in the pursuit of a remote medical care system.The RFID-based positioning system allows medical staff to continuously observe the patient's health and location.The staff can thus respond to medical emergencies in time and appropriately care for the patient.When the COVID-19 pandemic broke out,the proposed system was used to provide timely information on the location and body temperature of patients who had been screened for the disease.The results of experiments and comparative analyses show that the proposed system is superior to competing systems in use.The use of remote monitoring technology makes user interface easier to provide high-quality medical services to remote areas with sparse populations,and enables better care of the elderly and patients with mobility issues.It can be found from the experiments of this research that the accuracy of the position sensor and the ability of package delivery are the best among the other related studies.The presentation of the graphical interface is also the most cordial among human-computer interaction and the operation is simple and clear.

Breathable Electronic Skins for Daily Physiological Signal Monitoring

With the aging of society and the increase in people’s concern for personal health,long-term physiological signal monitoring in daily life is in demand.In recent years,electronic skin(e-skin)for daily health monitoring applications has achieved rapid development due to its advantages in high-quality physiological signals monitoring and suitability for system integrations.Among them,the breathable e-skin has developed rapidly in recent years because it adapts to the long-term and high-comfort wear requirements of monitoring physiological signals in daily life.In this review,the recent achievements of breathable e-skins for daily physiological monitoring are systematically introduced and discussed.By dividing them into breathable e-skin electrodes,breathable e-skin sensors,and breathable e-skin systems,we sort out their design ideas,manufacturing processes,performances,and applications and show their advantages in long-term physiological signal monitoring in daily life.In addition,the development directions and challenges of the breathable e-skin are discussed and prospected.

Physiological signal processing in heart rate variability measurement:A focus on spectral analysis

Human physiological(biological)systems function in such a way that their complexity requires mathematical analysis.The functioning of the brain,heart and other parts are so complex to be easily comprehended.Under conditions of rest or work,the temporal distances of successive heartbeats are subject to fluctuations,thereby forming the basis of Heart Rate Variability(HRV).In normal conditions,the human is persistently exposed to highly changing and dynamic situational demands.With these demands in mind,HRV can,therefore,be considered as the human organism’s ability to cope with and adapt to continuous situational requirements,both physiologically and emotionally.Fast Fourier Transform(FFT)is used in various physiological signal processing,such as heart rate variability.FFT allows a spectral analysis of HRV and is great help in HRV analysis and interpretation.

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.

Self-Assembled Porous-Reinforcement Microstructure-Based Flexible Triboelectric Patch for Remote Healthcare

Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However,wearable sensors with low power consumption and high sensitivity to both weak physiological signals and large mechanical stimuli remain challenges.Here, a flexible triboelectric patch(FTEP) based on porous-reinforcement microstructures for remote health monitoring has been reported. The porousreinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge. The mechanical properties of the FTEP can be regulated by the concentrations of silicone rubber dilution. For pressure sensing, its sensitivity can be effectively improved fivefold compared to the device with a solid dielectric layer, reaching 5.93 kPa^(-1) under a pressure range of 0–5 kPa. In addition, the FTEP has a wide detection range up to 50 kPa with a sensitivity of 0.21 kPa^(-1). The porous microstructure makes the FTEP ultra-sensitive to external pressure, and the reinforcements endow the device with a greater deformation limit in a wide detection range. Finally, a novel concept of the wearable Internet of Healthcare(Io H) system for real-time physiological signal monitoring has been proposed, which could provide real-time physiological information for ambulatory personalized healthcare monitoring.

A Method of Multimodal Emotion Recognition in Video Learning Based on Knowledge Enhancement

With the popularity of online learning and due to the significant influence of emotion on the learning effect,more and more researches focus on emotion recognition in online learning.Most of the current research uses the comments of the learning platform or the learner’s expression for emotion recognition.The research data on other modalities are scarce.Most of the studies also ignore the impact of instructional videos on learners and the guidance of knowledge on data.Because of the need for other modal research data,we construct a synchronous multimodal data set for analyzing learners’emotional states in online learning scenarios.The data set recorded the eye movement data and photoplethysmography(PPG)signals of 68 subjects and the instructional video they watched.For the problem of ignoring the instructional videos on learners and ignoring the knowledge,a multimodal emotion recognition method in video learning based on knowledge enhancement is proposed.This method uses the knowledge-based features extracted from instructional videos,such as brightness,hue,saturation,the videos’clickthrough rate,and emotion generation time,to guide the emotion recognition process of physiological signals.This method uses Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM)networks to extract deeper emotional representation and spatiotemporal information from shallow features.The model uses multi-head attention(MHA)mechanism to obtain critical information in the extracted deep features.Then,Temporal Convolutional Network(TCN)is used to learn the information in the deep features and knowledge-based features.Knowledge-based features are used to supplement and enhance the deep features of physiological signals.Finally,the fully connected layer is used for emotion recognition,and the recognition accuracy reaches 97.51%.Compared with two recent researches,the accuracy improved by 8.57%and 2.11%,respectively.On the four public data sets,our proposed method also achieves better results compared with the two recent researches.The experiment results show that the proposed multimodal emotion recognition method based on knowledge enhancement has good performance and robustness.

Exploring the Application of Vibration Analysis Technology in the Medical Field

In the field of engine maintenance and assurance, the technology of unit condition detection through vibration analysis is relatively mature. More and more patents and technical products have been released, proving the practical value of the technology in mechanical vibration from the application level. In medical science, signals such as heart sounds and pulses are also vibration signals in nature, in order to expand the application of the technology and explore the value of the technology in medical applications. In order to extend the application of the technology and to explore the value of the technology in medical applications, the wavelet analysis technology was used to program the Labview2022 software to implement the corresponding analysis program for the analysis of the collected physiological signals. Finally, the wavelet transform-based analysis of the physiological signals was successfully implemented. It is demonstrated that the design concept can be achieved by applying this technique, which makes it valuable in the field of physiological signal detection and analysis.

Developing a Physiological Signal-Based, Mean Threshold and Decision-Level Fusion Algorithm (PMD) for Emotion Recognition

With the development of computers,artificial intelligence,and cognitive science,engagement in deep communication between humans and computers has become increasingly important.Therefore,affective computing is a current hot research topic.Thus,this study constructs a Physiological signal-based,Mean-threshold,and Decision-level fusion algorithm(PMD)to identify human emotional states.First,we select key features from electroencephalogram and peripheral physiological signals,and use the mean-value method to obtain the classification threshold of each participant and distinguish individual differences.Then,we employ Gaussian Naive Bayes(GNB),Linear Regression(LR),Support Vector Machine(SVM),and other classification methods to perform emotion recognition.Finally,we improve the classification accuracy by developing an ensemble model.The experimental results reveal that physiological signals are more suitable for emotion recognition than classical facial and speech signals.Our proposed mean-threshold method can solve the problem of individual differences to a certain extent,and the ensemble learning model we developed significantly outperforms other classification models,such as GNB and LR.

Highly sensitive and stretchable piezoelectric strain sensor enabled wearable devices for real-time monitoring of respiratory and heartbeat simultaneously

The World Health Organization has declared COVID-19 a pandemic.The demand for devices or systems to diagnose and track COVID-19 infections noninvasively not only in hospitals but also in home settings has led to increased interest in consumer-grade wearables.A common symptom of COVID-19 is dyspnea,which may manifest as an increase in respiratory and heart rates.In this paper,a novel piezoelectric strain sensor is presented for real-time monitoring of respiratory and heartbeat signals.A highly sensitive and stretchable piezoelectric strain sensor is fabricated using a piezoelectric film with a serpentine layout.The thickness of the patterned PVDF flexible piezoelectric strain sensor is only 168μm,and the voltage sensitivity reaches 0.97 mV/με.The effective modulus is 13.5 MPa,which allows the device to fit to the skin and detect the small strain exhibited by the human body.Chest vibrations are captured by the piezoelectric sensor,which produces an electrical output voltage signal conformally mapped with respiratory–cardiac activities.The separate heart activity and respiratory signals are extracted from the mixed respiratory–cardiac signal by an empirical mode decomposition data processing algorithm.By detecting vital signals such as respiratory and heart rates,the proposed device can aid early diagnosis and monitoring of respiratory diseases such as COVID-19.

Home Monitoring of Pets Based on AIoT

With technological and social development in recent decades,people have begun pursuing more comfortable lives that frequently feature household pets that are treated like members of the family.On average,one out of every three households has a pet.This has also led to the creation and growth of many businesses in the pet industry.A few companies have developed a system that allows busy office workers to remotely care for pets at home based on the Internet of Things and an intelligent adjustment function.As owners of two dogs,the authors of this study observed their pets’living habits and recorded environmental conditions that appear suitable for them.These data were then used to develop an automatic control system to care for pets.The observational data on the pets’habits and environment were written in a program in Arduino by using the ESP8266 Wi-Fi module.The module and booster module control is the switch and setting of various household appliances.According to the loop setting of the program,the system does not need to manually switch or adjust the electrical settings of the environment.Instead,the pet's living environment is assessed by using various sensors.The use of Arduino programs helps develop a system that can automatically adjust the environment to one that is most suitable for the pet's comfort.

MoS2 nanoflowers and PEDOT:PSS nanocomposite enabling wearable dual-mode pressure sensors

A versatile sensing platform employing inorganic MoS2nanoflowers and organic poly(3,4-ethylene dioxythiophene):poly(styrene sulfonate)(PEDOT:PSS)has been investigated to develop the resistive and capacitive force-sensitive devices.The microstructure of the sensing layer heightens the sensitivity and response time of the dual-mode pressure sensors by augmenting electron pathways and inner stress in response to mechanical stimuli.Consequently,the capacitive and resistive sensors exhibit sensitivities of 0.37 and 0.12 kPa^(-1),respectively,while demonstrating a remarkable response time of approximately 100 ms.Furthermore,it is noteworthy that the PEDOT:PSS layer exhibits excellent adhesion to polydimethylsiloxane(PDMS)substrates,which contributes to the development of highly robust force-sensitive sensors capable of enduring more than 10000loading/unloading cycles.The combination of MoS2/PEDOT:PSS layers in these dual-mode sensors has shown promising results in detecting human joint movements and subtle physiological signals.Notably,the sensors have achieved a remarkable precision rate of 98%in identifying target objects.These outcomes underscore the significant potential of these sensors for integration into applications such as electronic skin and human-machine interaction.

Porous fiber paper and 3D patterned electrodes composed high-sensitivity flexible piezoresistive sensor for physiological signal monitoring

The research on flexible pressure sensors has drawn widespread attention in recent years,especially in the fields of health care and intelligent robots.In practical applications,the sensitivity of sensors directly affects the precision and integrity of weak pressure signals.Here,a pressure sensor with high sensitivity and a wide measurement range composed of porous fiber paper and 3D patterned electrodes is proposed.Multi-walled carbon nanotubes with excellent conductivity were evenly sprayed on the fiber paper to form the natural spatial conducting networks,while the copper-deposited polydimethylsiloxane films with micropyramids array were used as electrodes and flexible substrates.Increased conducting paths between electrodes and fibers can be obtained when high-density micro-pyramids fall into the porous structures of the fiber paper under external pressure,thereby promoting the pressure sensor to show an ultra-high sensitivity of 17.65 kPa^(-1)in the pressure range of 0–2 kPa,16 times that of the device without patterned electrodes.Besides,the sensor retains a high sensitivity of 2.06 kPa^(-1)in an ultra-wide measurement range of 150 kPa.Moreover,the sensor can detect various physiological signals,including pulse and voice,while attached to the human skin.This work provides a novel strategy to significantly improve the sensitivity and measurement range of flexible pressure sensors,as well as demonstrates attractive applications in physiological signal monitoring.

Flexible bioelectronics for physiological signals sensing and disease treatment

Flexible bioelectronics,including wearable and implantable electronics,have revolutionized the way of human-machine interaction due to the fact that they can provide natural and seamless interactions with humans and keep stable and durable at strained states.As sensor elements or biomimetic actuators,flexible bioelectronics can dynamically sense and monitor physiological signals,reveal real-time physical health information and provide timely precise stimulations or treatments.Thus,the flexible bioelectronics are playing increasingly important roles in human-health monitoring and disease treatment,which will significantly change the future of healthcare as well as our relationships with electronics.This review summarizes recent major progress in the development of flexible substrates or encapsulation materials,sensors,circuits and energy-autonomous powers toward digital healthcare monitoring,emphasizing its role in biomedical applications in vivo and problems in practical applications.A future perspective into the challenges and opportunities in emerging flexible bioelectronics designs for the next-generation healthcare monitoring systems is also presented.

Nacre-inspired MXene-based film for highly sensitive piezoresistive sensing over a broad sensing range

As the main component of wearable electronic equipment,flexible pressure sensors have attracted wide attention due to their excellent sensitivity and their promise with respect to applications in health monitoring,electronic skin,and human-computer interactions.However,it remains a significant challenge to achieve epidermal sensing over a wide sensing range,with short response/recovery time and featuring seamless conformability to the skin simultaneously.This is critical since the capture of minute electrophysiological signals is important for health care applications.In this paper,we report the preparation of a nacre-like MXene/sodium carboxymethyl cellulose(CMC)nanocomposite film with a“brick-and-mortar”interior structure using a vacuum-induced self-assembly strategy.The synergistic behavior of the MXene“brick”and flexible CMC“mortar”contributes to attenuating interlamellar self-stacking and creates numerous variable conductive pathways on the sensing film.This resulted in a high sensitivity over a broad pressure range(i.e.,0.03-22.37 kPa:162.13 kPa^(-1);22.37-135.71 kPa:127.88 kPa^(-1);135.71-286.49 kPa:100.58 kPa^(-1)).This sensor also has a low detection limit(0.85 Pa),short response/recovery time(8.58 ms/34.34 ms),and good stability(2000 cycles).Furthermore,we deployed pressure sensors to distinguish among tiny particles,various physiological signals of the human body,space arrays,robot motion monitoring,and other related applications to demonstrate their feasibility for a variety of health and motion monitoring use cases.

Use of multimodal physiological signals to explore pilots’cognitive behaviour during flight strike task performance

This study explored the use of multi-physiological signals and simultaneously recorded high-density electroencephalography(EEG),electrocardiogram(ECG),and eye movements to better understand pilots’cognitive behaviour during flight simulator manoeuvres.Multimodal physiological signals were collected from 12 experienced pilots with international aviation qualifications under the wide-angle and impressive vision simulation.The data collection spanned two flight strike missions,each with three mission intensities,resulting in a data set of EEG,ECG,and eye movement signals from six subtasks.The multimodal data were analysed using signal processing methods.The results indicated that,when the flight missions were performed,the pilots’physiological characteristics exhibited rhythmic changes in the power spectrum ofθwaves in the EEG,r-MSSD in the ECG,and average gaze duration.Furthermore,the pilots’physiological signals were more sensitive during the target mission than during the empty target mission.The results also showed correlations between different physiological characteristics.We showed that specific multimodal features are useful for advancing neuroscience research into pilots’cognitive behaviour and processes related to brain activity,psychological rhythms,and eye movement.

Multi-attribute wearable pressure sensor based on multilayered modulation with high constant sensitivity over a wide range

Flexible pressure sensors capable of monitoring diverse physiological signals and body movements have garnered tremendous attention in wearable electronic devices.Thereinto,high constant sensitivity over a wide pressure range combined with breathability,biocompatibility,biodegradability is pivotal for manufacturing of reliable pressure sensors in practical sensing applications.In this work,inspired by the multilayered structure of skin epidermis,we propose and demonstrate a multi-attribute wearable piezoresistive pressure sensor consisting of multilayered gradient conductive poly(ε-caprolactone)nanofiber membranes composites.In response to externally applied pressure,a layer-by-layer current path is activated inside the multilayered membranes composites,leading to the most salient sensing performance of high constant sensitivity of 33.955 kPa^(−1) within the pressure range of 0–80 kPa.The proposed pressure sensor also exhibits a fast response–relaxation time,a low detection limit,excellent stability,which can be successfully used to measure human physiological signals.Lastly,an integrated sensor array system that can locate objects’positions is constructed and applied to simulate sitting posture monitoring.These results indicate that the proposed pressure sensor holds great potential in health monitoring and wearable electronic devices.

A sleep staging model for the sleep environment control based on machine learning

To date,dynamic sleep environment has been attracted the focus of researchers.Owing to the individual difference on sleep phase and thermal comfort,changes in sleep environment should be occupant-centered,and precise regulation of the environment required current sleep stages.However,few studies connected occupants and the environment through physiological signal-based model of sleep staging.Therefore,this study tried to develop a data driven sleep staging model with higher accuracy through sleep experiments collecting information.Raw database was processed and selected efficiently according to the characteristics of physiological signals.Finally,the sleep staging model with an average accuracy of 93.9%was built,and other mean indicators(precision:82.5%,recall:83.1%,F1 score:82.8%)performed well.The features adopted by model were found to come from different brain regions,and the global brain signals were suggested to play an important role in the construction of sleep staging model.Moreover,the computational processing of physiology signals should consider their characteristics,i.e.,time domain,frequency domain,time-frequency domain and nonlinear characteristics.The model obtained in this study may deliver a credible reference to advance the research on control of sleep environment.

Preparation and application of graphene-based wearable sensors

With the development of digital healthcare technology,the demand for non-invasive monitoring of human health is rapidly increasing.In recent years,the research and application of timely,economical,and easy-to-operate wearable sensing devices have attracted much attention.Among recent studies,graphene has been widely used to improve the sensing performance of wearable sensors due to its advantages in mechanical,electrical,and thermal properties.This review mainly focuses on summarizing graphene and its derivative-based wearable sensors and their latest developments in personal health monitoring.We will first introduce the novel structure and sensing mechanism of different types of graphene sensors.Then,we summarize the latest applications of the graphene wearable sensors in human health monitoring,including human activity,heart rate,pulse,electrophysiological signal,and electronic skin.Finally,the future challenges and prospects of graphene wearable devices will be discussed.

Wearable Alignment‑Free Microfber‑Based Sensor Chip for Precise Vital Signs Monitoring and Cardiovascular Assessment

Continuous pulse wave signals monitoring is the essential basis for clinical cardiovascular diagnosis and treatment.Recent researches show the majority of current electronic pulse sensors usually face challenges in electrical safety concern,poor durability and demanding precision in position alignment.Thus,a highly sensitive,inherently electrical safe,robust and alignment-free device is highly desired.Here,we present a wearable alignment-free microfber-based sensor chip(AFMSC)for precise vital signs monitoring and cardiovascular health assessment.The AFMSC comprises an optical micro/nano fber sensor(MNF)and a fexible soft liquid sac while the MNF sensor is used to perceive the physiological signals and the liquid sac is used to eliminate the misalignment.The real-time and accurate monitoring of the pulse signals was realized by tracking the optical power variation of transmitted light from MNF.Then,the cardiovascular vital signs extracted from radial artery pulse signals were used to evaluate cardiovascular health condition and the results were in accordance with human physiological characteristics.Moreover,the pulse signals from diferent arterial area,the respiration signals from chest and the radial pulse signals before and after exercise were detected and analyzed.The non-invasive,continuous and accurate monitoring of cardiovascular health based on the reported wearable and alignment-free device is promising in both ftness monitoring and medical diagnostics for cardiovascular disease prevention and diagnosis.