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.

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.

多功能热电织物用于可穿戴无线传感系统

Flexible thermoelectric materials play an important role in smart wearables,such as wearable power generation,self-powered sensing,and personal thermal management.However,with the rapid development of Internet of Things(IoT)and artificial intelligence(AI),higher standards for comfort,multifunctionality,and sustainable operation of wearable electronics have been proposed,and it remains challenging to meet all the requirements of currently reported thermoelectric devices.Herein,we present a multifunctional,wearable,and wireless sensing system based on a thermoelectric knitted fabric with over 600 mm·s^(-1)air permeability and a stretchability of 120%.The device coupled with a wireless transmission system realizes self-powered monitoring of human respiration through an mobile phone application(APP).Furthermore,an integrated thermoelectric system was designed to combine photothermal conversion and passive radiative cooling,enabling the characteristics of being powered by solar-driven in-plane temperature differences and monitoring outdoor sunlight intensity through the APP.Additionally,we decoupled the complex signals of resistance and thermal voltage during deformation under solar irradiation based on the anisotropy of the knitted fabrics to enable the device to monitor and optimize the outdoor physical activity of the athlete via the APP.This novel thermoelectric fabricbased wearable and wireless sensing platform has promising applications in next-generation smart textiles.

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.

Smart epidermal electrophysiological electrodes:Materials,structures,and algorithms

Epidermal electrophysiological monitoring has garnered significant attention for its potential in medical diagnosis and healthcare,particularly in continuous signal recording.However,simultaneously satisfying skin compliance,mechanical properties,environmental adaptation,and biocompatibility to avoid signal attenuation and motion artifacts is challenging,and accurate physiological feature extraction necessitates effective signal-processing algorithms.This review presents the latest advancements in smart electrodes for epidermal electrophysiological monitoring,focusing on materials,structures,and algorithms.First,smart materials incorporating self-adhesion,self-healing,and self-sensing functions offer promising solutions for long-term monitoring.Second,smart meso-structures,together with micro/nanostructures endowed the electrodes with self-adaption and multifunctionality.Third,intelligent algorithms give smart electrodes a“soul,”facilitating faster and more-accurate identification of required information via automatic processing of collected electrical signals.Finally,the existing challenges and future opportunities for developing smart electrodes are discussed.Recognized as a crucial direction for next-generation epidermal electrodes,intelligence holds the potential for extensive,effective,and transformative applications in the future.

RELATIONSHIP BETWEEN PLASMA OPTIC SIGNAL AND PENETRATION DEPTH FOR PARTIAL-PENETRATION LASER WELDING

Through sampling and analyzing of plasma optic signals of 400-600 nm emitted from partial-penetration laser welding processes, how the penetration depth is related to the welding parameter and the plasma optic signal is studied, Under the experimental conditions, the plasma optic signal has good response to variety of the weld penetration, and the signal＇s RMS value increases with the penetration in a quadratic curve mode. The inherent relation between the plasma optic signal and the penetration depth is also analyzed. It is also found that, between the two common parameters of laser power and welding speed, laser power has more influence on penetration while welding speed has more influence on weld width. The research results provide theoretic and practical bases for penetration real-time monitoring or predicting in partial-penetration laser welding,

Web Control System for Transcorneal Electric Stimulation Devices

The electrical stimulation systems dedicated to generating unconventional waveforms have been shown to have a positive effect in the treatment of channelopathies, for example, in open-angle glaucoma. However, these signals can be distorted due to different external circumstances, which could lead to counterproductive effects in treatments such as increased intraocular pressure IOP or other effects that are unknown due to poor electrical signaling. In the present work, a web control system capable of communicating with transcorneal electrical stimulation equipment is proposed for the remote control of treatments applied to patients suffering from various ocular channelopathies. As the first phase of this system, it will only focus on treating patients with open-angle glaucoma since this disease is characterized by an increase in IOP and can be immediately measured by an ophthalmologist.

Study of Synthesis Identification in Cutting Process with Fuzzy Neural Network

With the development of industrial production modernization, FMS and CIMS will become more and more popularized. For its control system is increasingly modeled, intellectualized and automatized, in order to raise the reliability and stability in the manufacturing process, the comprehensive monitoring and diagnosis aimed at cutting tool wear and chatter become more and more important and get rapid development. The paper tried to discuss of the intellectual status identification method based on acoustics-vibra characteristics of machining process, and propose that the working conditions may be taken as a core, complex fuzzy inference neural network model based on artificial neural network theory, and by using various kinds of modernized signal processing method to abstract enough characteristics parameters which will reflect overall processing status from machining acoustics-vibra signal as information source, to identify different working condition, and provide guarantee for automation and intelligence in machining process. The complex network is composed of NNw and NNs, Each of them is composed of BP model network, NNw is weight network at rule condition, NNs is decision-making network of each status. Y out is final inference result which is to take subordinate degree as weight from NNw, to weight reflecting result from NNs and obtain status inference of monitoring system. In the process of machining, the acoustics-vibor signal were gotten by the acoustimeter and the acceleration piezoelectricity detector, the date is analysed by the signal processing software in time and frequency domain, then form multi feature parameter vector of criterion pattern samples for the different stage of cutting chatter and acoustics-vibra multi feature parameter vector. The vector can give a accurate and comprehensive description for the cutting process, and have the characteristic which are speediness of time domain and veracity of frequency domain. The research works have been practically applied in identification of tool wear, cutting chatter, experiment results showed that it is practicable to identify the cutting chatter based on fuzzy neural network, and the new method based on fuzzy neural network can be applied to other state identification in machining process.

MoS_(2) nanoflowers and PEDOT:PSS nanocomposite enabling wearable dual-mode pressure sensors

A versatile sensing platform employing inorganic MoS_(2) nanoflowers 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 MoS_(2)/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.

An overview of healthcare monitoring by flexible electronics

Flexible electronics integrated with stretchable/bendable structures and various microsensors that monitor the temperature,pressure, sweat, bioelectricity, body hydration, etc., have a wide range of applications in the human healthcare sector. The science underlying this technology draws from many research areas, such as information technology, materials science, and structural mechanics, to efficiently and accurately monitor technology for various signals. In this paper, we make a classification and comb to the designs, materials, structures and functions of numerous flexible electronics for signal monitoring in the human healthcare sector. Some perspectives in this field are discussed in the concluding remarks.

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.

Stress-deconcentrated ultrasensitive strain sensor with hydrogen-bonding-tuned fracture resilience for robust biomechanical monitoring

Recently,rapid advances in flexible strain sensors have broadened their application scenario in monitoring of various mechanophysiological signals.Among various strain sensors,the crack-based strain sensors have drawn increasing attention in monitoring subtle mechanical deformation due to their high sensitivity.However,early generation and rapid propagation of cracks in the conductive sensing layer result in a narrow working range,limiting their application in monitoring large biomechanical signals.Herein,we developed a stress-deconcentrated ultrasensitive strain(SDUS)sensor with ultrahigh sensitivity(gauge factor up to2.3×10^(6))and a wide working range(0%-50%)via incorporating notch-insensitive elastic substrate and microcrack-tunable conductive layer.Furthermore,the highly elastic amine-based polymer-modified polydimethylsiloxane substrate without obvious hysteresis endows our SDUS sensor with a rapid response time(2.33 ms)to external stimuli.The accurate detection of the radial pulse,joint motion,and vocal cord vibration proves the capability of SDUS sensor for healthcare monitoring and human-machine communications.

A novel satellite-equipped receiver for autonomous monitoring of GNSS navigation signal quality

Global navigation satellite system(GNSS) comes with potential unavoidable application risks such as the sudden distortion or failure of navigation signals because its satellites are generally operated until failure. In order to solve the problems associated with these risks, receiver autonomous integrity monitoring(RAIM) and ground-based signal quality monitoring stations are widely used. Although these technologies can protect the user from the risks, they are expensive and have limited region coverage. Autonomous monitoring of satellite signal quality is an effective method to eliminate these shortcomings of the RAIM and ground-based signal quality monitoring stations; thus, a new navigation signal quality monitoring receiver which can be equipped on the satellite platform of GNSS is proposed in this paper. Because this satellite-equipped receiver is tightly coupled with navigation payload, the system architecture and its preliminary design procedure are first introduced. In theory, code-tracking loop is able to provide accurate time delay estimation of received signals. However, because of the nonlinear characteristics of the navigation payload, the traditional code-tracking loop introduces errors. To eliminate these errors, the dummy massive parallel correlators(DMPC) technique is proposed. This technique can reconstruct the cross correlation function of a navigation signal with a high code phase resolution. Combining the DMPC and direct radio frequency(RF) sampling technology, the satellite-equipped receiver can calibrate the differential code bias(DCB) accurately. In the meantime, the abnormities and failures of navigation signal can also be monitored. Finally, the accuracy of DCB calibration and the performance of fault monitoring have been verified by practical test data and numerical simulation data, respectively. The results show that the accuracy of DCB calibration is less than 0.1 ns and the novel satellite-equipped receiver can monitor the signal quality effectively.

Dispersion-Induced Waveform Distortion Detection in 42.7 Gbps CS-RZ Signals by Optical Time Domain Level Monitoring

We propose a technique for chromatic dispersion monitoring based on optical time domain level monitoring. Experimental and simulation results show that the technique is effective for the monitoring of dispersion in 42.7-Gbps CS-RZ signals for dynamic dispersion compensation.

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.

Stretchable human-machine interface based on skin-conformal sEMG electrodes with self-similar geometry

Current stretchable surface electrodes have attracted increasing attention owing to their potential applications in biological signal monitoring, wearable human-machine interfaces（HMIs） and the Internet of Things. The paper proposed a stretchable HMI based on a surface electromyography（sEMG） electrode with a self-similar serpentine configuration. The sEMG electrode was transfer-printed onto the skin surface conformally to monitor biological signals, followed by signal classification and controlling of a mobile robot. Such electrodes can bear rather large deformation（such as 〉30%） under an appropriate areal coverage. The sEMG electrodes have been used to record electrophysiological signals from different parts of the body with sharp curvature, such as the index finger,back of the neck and face, and they exhibit great potential for HMI in the fields of robotics and healthcare. The electrodes placed onto the two wrists would generate two different signals with the fist clenched and loosened. It is classified to four kinds of signals with a combination of the gestures from the two wrists, that is, four control modes. Experiments demonstrated that the electrodes were successfully used as an HMI to control the motion of a mobile robot remotely.