Traditional Sensors Research Articles

Ultrafast humidity sensor and transient humidity detections in high dynamic environments

Limited by the adsorption and diffusion rate of water molecules, traditional humidity sensors, such as those based on polymer electrolytes, porous ceramics, and metal oxides, typically have long response times, which hinder their application in monitoring transient humidity changes. Here we present an ultrafast humidity sensor with a millisecond-level response. The sensor is prepared by assembling monolayer graphene oxide quantum dots on silica microspheres using a simple electrostatic self-assembly technique. Benefiting from the joint action of the micro spheres and the ultrathin humidity-sensitive film, it displays the fastest response time (2.76 ms) and recovery time (12.4 ms) among electronic humidity sensors. With the ultrafast response of the sensor, we revealed the correlation between humidity changes in speech airflow and speech activities, demonstrated the noise immunity of humidity speech activity detection, confirmed the humidity shock caused by explosions, realized ultrahigh frequency respiratory monitoring, and verified the effect of humidity-triggering in the non-invasive ventilator. This ultrafast humidity sensor has broad application prospects in monitoring transient humidity changes.

Flexible Tactile Sensors with Self-Assembled Cilia Based on Magnetoelectric Composites.

Traditional tactile sensors are single-function, and it is difficult to meet the needs of applications in complex environments. This paper describes the development and applications of flexible tactile sensors with cilia based on magnetoelectric composites made of neodymium iron boron (NdFeB) microparticles with a silver (Ag) nanoshell in polydimethylsiloxane (PDMS). These sensors adopt the inherent magnetism of NdFeB microparticles and the excellent conductivity of the Ag coating. Self-assembly of the composites of NdFeB@Ag/PDMS under the combined effect of intrinsic magnetism and external magnetic field yields cilia that are sensitive to forces with a high conductivity of 36479.33 S/m. The resulting sensors can measure forces in the range of 0.02-0.05 N and recognize surrounding magnetic fields whose magnitudes are larger than 10 mT. These sensors have been used to perform texture recognition, Braille recognition, and object recognition to yield accuracies of 98%, 100%, and 94.58%, respectively. This research based on NdFeB@Ag magnetoelectric microparticles presents a convenient approach to construct tactile sensors with randomly distributed surficial microstructures, leading to the prevalence of low-cost but highly sensitive tactile sensors for humanoid, human machine interface, and healthcare.

Highly sensitive, stretchable, and transparent multidirectional wearable strain sensors based on patterned vertical graphene array

Abstract Since traditional uniaxial strain sensors constructed from isotropic materials mainly focus on identifying strain amplitude without the information of strain direction, multidimensional strain sensors are critical for capturing complex movements, such as human motions. Here, we propose a highly sensitive, stretchable, and transparent multidirectional strain sensor based on patterned vertical graphene array (PVGA) synthesized by plasma-enhanced chemical vapor deposition (PECVD) followed by selective etching and transferring. The obtained PVGA/Polydimethylsiloxane (PDMS) strain sensor exhibits outstanding anisotropic strain sensing performance, including high gauge factor (GF=85.8 at 10% strain), excellent linearity (R2 = 0.99), and outstanding selectivity (|▽|=4.17). Cross-plied PVGA /PDMS strain sensors are also fabricated to demonstrate the application of detecting joint movements of the human body, as well as monitoring wrist motion for controlling a fruit-slicer game in the virtual environment. The sensor’s remarkable multi-dimensional sensing performance confirms their significant potential for diverse applications, notably in wearable electronics, such as personal health sensing, artificial intelligence, human-computer interaction, and soft robotics.

Shape‐Reconfigurable Crack‐Based Strain Sensor with Ultrahigh and Tunable Sensitivity

AbstractIn the field of wearable electronics and human–machine interfaces, there is a growing need for highly sensitive and adaptable sensors capable of detecting a wide range of stimuli with high precision. Traditional sensors often lack the versatility to adjust their sensitivity for different applications. Inspired by the mechanosensory system of spiders, a shape‐reconfigurable crack‐based sensor with ultrahigh and tunable strain sensitivity based on the precise control of nanocrack formation on a shape memory polymer substrate is demonstrated. This design incorporates a line‐patterned substrate composed of a thermoplastic polyurethane (TPU) matrix and thermo‐responsive shape memory polymer, poly(lactic acid) (PLA), to form parallel nanocracks in a thin platinum film. This design achieves an ultrahigh gauge factor of 2.7 × 109 at 2% strain, significantly surpassing conventional sensors. The shape memory property of the TPU/PLA substrate enables tunable strain sensitivity according to the desired strain range, eliminating the need for multiple sensors. The sensor demonstrates exceptional capabilities in detecting subtle strains (as low as 0.025%), monitoring biological signals, and sensing acoustic waves (100–20 000 Hz) with a response time of 0.025 ms. This work represents a significant advancement toward strain sensors with both ultrahigh and tunable sensitivity.

Ultrasensitive iontronic pressure sensor based on microstructure ionogel dielectric layer for wearable electronics

Flexible pressure sensors show great promise for applications in such fields as electronic skin, healthcare, and intelligent robotics. Traditional capacitive pressure sensors, however, face the problem of low sensitivity, which limits their wider application. In this paper, a flexible capacitive pressure sensor with microstructured ionization layer is fabricated by a sandwich-type process, with a low-cost and simple process of inverted molding with sandpapers being used to form a thermoplastic polyurethane elastomer ionic film with double-sided microstructure as the dielectric layer of the sensor, with silver nanowires as electrodes. The operating mechanism of this iontronic pressure sensor is analyzed using a graphical method, and the sensor is tested on a pressure platform. The test results show that the sensor has ultrahigh pressure sensitivities of 3.744 and 1.689 kPa−1 at low (0–20 kPa) and high (20–800 kPa) pressures, respectively, as well as a rapid response time (100 ms), and it exhibits good stability and repeatability. The sensor can be used for sensitive monitoring of activities such as finger bending, and for facial expression (smile, frown) recognition, as well as speech recognition.

Analysis of Uneven Settlement of Long-Span Bridge Foundations Based on SBAS-InSAR

Bridge foundation settlement monitoring is crucial for infrastructure safety management, as uneven settlement can lead to stress redistribution, structural damage, and potentially catastrophic collapse. While traditional contact sensors provide reliable measurements, their deployment is labor-intensive and costly, especially for long-span bridges. Current remote sensing methods have not been thoroughly evaluated for their capability to detect and analyze complex foundation settlement patterns in challenging environments with multiple influencing factors. Here, we applied Small Baseline Subsets Synthetic Aperture Radar Interferometry (SBAS-InSAR) technology to monitor foundation settlement of a long-span bridge. Our analysis revealed distinct deformation patterns: uplift in the north bank approach bridge foundation and the left-side main bridge foundation (maximum rate: 36.97 mm/year), concurrent with subsidence in the right-side main bridge foundation and south bank approach bridge foundation (maximum rate: 35.59 mm/year). We then investigated the relationship between these settlement patterns and various environmental factors, including geological conditions, Sediment Transport Index (STI), Topographic Wetness Index (TWI), precipitation, and temperature. The observed settlement patterns were attributed to the combined effects of stratigraphic heterogeneity, dynamic hydrological conditions, and seasonal climate variations. These findings demonstrate that SBAS-InSAR technology can effectively capture complex bridge foundation deformation processes, offering a cost-effective alternative to traditional monitoring methods. This advancement in bridge monitoring technology could enable more widespread and frequent assessment of bridge foundation stability, ultimately improving infrastructure safety management.

Flexible Site-Specific Labeling-Mediated Self-Assembly Sensor Based on Quantum Dots and LUMinescent AntiBody Sensor for Duplexed Detection of Antibodies.

Over recent years, the LUMinescent AntiBody Sensor (LUMABS) system, utilizing bioluminescence resonance energy transfer (BRET), has emerged as a highly effective method for antibody detection. This system incorporates NanoLuc (Nluc) as the donor and fluorescent protein (FP) as the acceptor. However, the limited Stokes shift of FP poses a challenge, as it leads to significant spectral cross-talk between the excitation and emission spectra. This issue complicates the implementation of multiplexed detection. To address this challenge, we present an innovative enhancement to the LUMABS sensor with quantum dots (QDs) as the acceptor instead of FP. The use of QDs offers several advantages over those of traditional FP-based sensors. The biotin-avidin system facilitates the flexible interchangeability of QDs, allowing for a more convenient multicolor sensor construct. The new QD-LUMABS system overcomes the limitations of spectral cross-talk and provides better spectral separation. This breakthrough enables the successful implementation of multiplexed detection for multiple targets simultaneously. Results demonstrated that the wavelength-tunable QD-LUMABS sensors achieved picomolar-level detection limits for antibodies and that this sensor-construction strategy was generally applicable among various epitopes and their antibodies. Furthermore, this sensor displayed excellent duplexing capabilities. These features underscore its potential for future clinical disease diagnosis applications.

Self-Healing Flexible Fiber Optic Sensors for Safe Underwater Monitoring.

The advancement of underwater monitoring technologies has been significantly hampered by the limitations of traditional electrical sensors, particularly in the presence of electromagnetic interference and safety concerns in aquatic environments. Fiber optic sensors are therefore nowadays widely applied to underwater monitoring devices. However, silicon- and polymer-based optical fibers often face challenges, such as rigidity, susceptibility to environmental stress, and limited operational flexibility. Here, we propose an ingenious flexible step-index fiber construction strategy for the preparation of core-cladding poly(polymerizable deep eutectic solvent (PDES)) optical fiber (CPOF) by in situ light curing of the functional PDES monomer in a commercial silicone tube. The liquid-free poly(PDES) fiber core not only possesses high transparency (>90%), excellent flexibility, and wide temperature range tolerance (from -27 to 156 °C), but the supramolecular network of it also provides self-adhesion and optical self-healing, which ensures the bonding stability of the core-cladding interface as well as the lifetime of the optical device. On the other hand, the hydrophobic fiber cladding allows CPOF to stably transmit optical signals under water, and the application potential of CPOF for underwater sensing devices was verified by underwater motion monitoring.

Optical and Electrical Dual-Mode Detection of a Carcinogenic Substance Based on Synergy of Liquid Crystals and Ionic Liquids.

Visual, sensitive, and selective detection of carcinogenic substances is highly desired in portable health protection and practical medicine production. However, achieving this goal presents significant challenges with the traditional single-mode sensors reported so far, as they have limited sensing mechanisms and provide only a single output signal. Here, we report an effective optical and electrical dual-mode sensor for the visual, sensitive, and selective detection of N-nitrosodiethylamine (NDEA), a typical volatile carcinogenic substance, leveraging the synergy of ionic liquid-doped liquid crystals (IL-LC). The optical mode derived from LCs provides the sensor with a visual identification recognizable by the naked eye, while the electrical mode derived from ILs offers a quantitative detection capability. It is noteworthy that the synergistic effect of the IL and LC enhances the performance of both optical and electrical modes. Unique sensing mechanisms derived from the interaction between NDEA and IL-LC endow the sensor with excellent selectivity. As a proof of concept, a portable kit based on a dual-mode sensor has been developed for the real-time and on-site analysis of N-nitrosamine impurities in pharmaceuticals. This work provides valuable insights and a theoretical foundation for developing portable multimode chemical sensors.

Highly sensitivity and wide-range flexible humidity sensor based on LiCl/cellulose nanofiber membrane by one-step electrospinning

Cellulose is considered an ideal material for flexible electronic humidity sensors due to its green renewable nature, rich surface groups and strong hydrophilicity. However, the traditional cellulose-based humidity sensor cannot simultaneously have wide detection range, high sensitivity and fast response time, which seriously hinder their development and application. Here, an ultrathin Lithium chloride (LiCl)/cellulose nanofiber membrane as moisture sensitive materials was prepared by one-step electrospinning and used to assemble a high-performance humidity sensor. N, N-Dimethylacetamide/Lithium chloride (DMAc/LiCl) solvent system was used to dissolve the cellulose, and DMAc volatilized into the air while LiCl remained in the cellulose nanofibers during the electrospinning process. LiCl as a highly moisture-sensitive inorganic salt has strong hydrophilicity and enhances the moisture sensing detection limit of cellulose fiber (especially under low humidity conditions), thus giving the cellulose moisture sensor excellent sensitivity (up to 4191 %) and a wide detection range (5–98 % RH). The nanometer size of cellulose fibers, the extremely low thickness and large pores of cellulose membranes accelerate the mass exchange of moisture between the air and the membrane, thus giving cellulose humidity sensor a fast response/recovery time (99/110 s) and low hysteresis (2.9 %). Moreover, the performances of humidity sensors didn’t degrade even after being subjected to long-term application (>30 days), high/low temperatures (73.6/0.1 ℃) and thousands of bending cycles. The proposed LiCl/cellulose nanofiber humidity sensor brings innovative solutions for the design of cellulose based sensor with great potential for use in non-contact humidity detection, respiratory monitoring and sleep apnea detection applications.

Fully printed non-contact touch sensors based on GCN/PDMS composites: enabling over-the-bottom detection, 3D recognition, and wireless transmission

ABSTRACT The rapid advancement in intelligent bionics has elevated electronic skin to a pivotal component in bionic robots, enabling swift responses to diverse external stimuli. Combining wearable touch sensors with IoT technology lays the groundwork for achieving the versatile functionality of electronic skin. However, most current touch sensors rely on capacitive layer deformations induced by pressure, leading to changes in capacitance values. Unfortunately, sensors of this kind often face limitations in practical applications due to their uniform sensing capabilities. This study presents a novel approach by incorporating graphitic carbon nitride (GCN) into polydimethylsiloxane (PDMS) at a low concentration. Surprisingly, this blend of materials with higher dielectric constants yields composite films with lower dielectric constants, contrary to expectations. Unlike traditional capacitive sensors, our non-contact touch sensors exploit electric field interference between the object and the sensor’s edge, with enhanced effects from the low dielectric constant GCN/PDMS film. Consequently, we have fabricated touch sensor grids using an array configuration of dispensing printing techniques, facilitating fast response and ultra-low-limit contact detection with finger-to-device distances ranging from 5 to 100 mm. These sensors exhibit excellent resolution in recognizing 3D object shapes and accurately detecting positional motion. Moreover, they enable real-time monitoring of array data with signal transmission over a 4G network. In summary, our proposed approach for fabricating low dielectric constant thin films, as employed in non-contact touch sensors, opens new avenues for advancing electronic skin technology.

Magnetic Induction Phase Difference for Cerebral Hemorrhage Detection.

Magnetic induction phase shift is a promising technology for the detection of cerebral hemorrhage, owing to its nonradioactive, noninvasive, and real-time detection properties. To enhance the detection sensitivity and linearity, a zero-flow sensor was proposed. The uniform primary magnetic field and its counteraction were achieved. Phase-change responses to solutions of varying conductivities and rabbits with cerebral hemorrhage were investigated and compared with traditional sensors. The sensitivities in detecting solutions with different conductivities were 1.84, 1.39, and 1.22 times higher than those for a low-pass birdcage coil, planar gradiometer, and Bx-sensor, respectively. The results for rabbits with cerebral hemorrhage showed that the sensitivities increased by 1.17, 1.67, and 6.3 times compared with a low-pass birdcage coil, symmetric cancelation-type sensor, and single co-axial coil, respectively. This sensor could accurately detect three stages in the pathological process. Blood loss of 1 mL meant that the compensatory mechanism of cerebrospinal fluid began to fail, and 1.4 mL of blood loss meant that the compensatory mechanism failed completely. The adjusted R-squared value of the first-order linear fit was above 0.98 in both physical and animal experiments, indicating that high detection linearity was achieved. The proposed sensor provides a more accurate method for cerebral hemorrhage detection and facilitates the practical application of magnetic induction phase shift in pre-hospital and bedside real-time detection.

Design and Application of Agricultural Integrated Management System Based on TLINK

With the continuous advancement of science and technology, the era of the Internet of Everything has arrived. Agricultural management is becoming more and more intelligent and refined. Agricultural Internet of Things technology is vital in data collection, transmission, user management, etc. To achieve a highly integrated and low-cost smart agriculture with data display. This paper designs an intelligent agricultural management system based on low-cost ESP8266 WIFI Internet of Things technology, aiming to transform traditional agricultural management into a smart, remote, and data-user collaborative mode. The system integrates sensor nodes such as temperature, humidity, light, and carbon dioxide to replace traditional industrial sensors for real-time agricultural environmental data collection, which can realise the automation of irrigation, ventilation, temperature control, lighting, etc. It can start the ecological control system through local voice control. WIFI data transmission, data management through the TLINK cloud platform, PC or mobile phone APP, online remote data viewing and control at any time for agricultural management. The experimental results show that the system has good stability, reliability, high integration, strong environmental adaptability, accurate data, and less packet loss. It dramatically reduces the cost of intelligent agricultural management and construction difficulty, bringing users higher economic benefits.

Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors

Trains used in urban mass passenger transit often have side entrance doors through which passengers can rapidly enter and exit the train. These doors are typically electrically powered and automated. Many incidents have occurred in which a passenger is trapped and injured while passing through the doors as they are closing. Existing solutions rely on sensitive-edge sensors and current signal peak detection in the time domain to detect door obstructions. However, these methods have notable limitations: sensors are expensive, and sensor failure can result in safety risks, while time-domain signal analysis is prone to noise, potentially leading to false peak detection. The proposed efficient and cost-effective method enhances safety by implementing the torque control of a DC motor which limits the door closing force to prevent potential injuries. In addition, it reduces reliance on traditional edge sensors, which are prone to failure and may result in undetected obstructions. By using a robust time–frequency domain approach, the system ensures more accurate detection, minimizing potential injury risks. An obstruction of the door causes a corresponding change in the motor current. These changes can be detected by using the discrete wavelet transform to decompose the current signal. The norm and peak of the current are used as obstacle detection features, and appropriate threshold values are obtained from a simulation. The simulation results were validated through an experiment. The proposed novel system effectively detects forces between 100 and 200 N (indicating the presence of an object) within 0.3 s and complies with EN14752 safety standard. It can also differentiate between soft and hard objects trapped in train doors.

Polymer-Dispersed Liquid Crystal Gas Sensor for Acetone Detection Using Correlated Laser Speckles

Acetone is a widely used volatile organic compound in various industries, and several gas sensors have been developed for its detection and real-time monitoring. This study reported a novel method for determining the acetone vapor concentration based on correlated laser speckles using polymer-dispersed liquid crystals (PDLCs). Here, PDLC films comprising a mixture of the thermotropic nematic liquid crystal (LC) and ultraviolet-curable polymers were fabricated using different LC mass ratios and ultraviolet curing conditions. The laser beam was transmitted through the PDLC film to generate scattered light and speckles. When the PDLC film was exposed to the acetone vapor, the acetone molecules diffused into the PDLC film and interacted with the LC molecules, modifying the orientation of the LC molecules and the equivalent refractive index of the LC droplets. This in turn decreased the correlation coefficient of the speckle images. The experimental results indicated that the PDLC gas sensor was selectively sensitive to different concentrations of the acetone vapor, ranging from 1 800 ppm to 3 200 ppm. In comparison with traditional LC gas sensors that use a polarizing microscope to detect the change in brightness of the modulated light field, the proposed method is simpler, less expensive, and more robust under external disturbances such as vibrations.

Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning

BackgroundAiming at the problem that traditional transfer methods are prone to lose data information in the overall domain-level transfer, and it is difficult to achieve the perfect match between source and target domains, thus reducing the accuracy of the soft sensor model.MethodsThis paper proposes a soft sensor modeling method based on the transfer modeling framework of substructure domain. Firstly, the Gaussian mixture model clustering algorithm is used to extract local information, cluster the source and target domains into multiple substructure domains, and adaptively weight the substructure domains according to the distances between the sub-source domains and sub-target domains. Secondly, the optimal subspace domain adaptation method integrating multiple metrics is used to obtain the optimal projection matrices Ws and Wt that are coupled with each other, and the data of source and target domains are projected to the corresponding subspace to perform spatial alignment, so as to reduce the discrepancy between the sample data of different working conditions. Finally, based on the source and target domain data after substructure domain adaptation, the least squares support vector machine algorithm is used to establish the prediction model.ResultsTaking Pichia pastoris fermentation to produce inulinase as an example, the simulation results verify that the root mean square error of the proposed soft sensor model in predicting Pichia pastoris concentration and inulinase concentration is reduced by 48.7% and 54.9%, respectively.ConclusionThe proposed soft sensor modeling method can accurately predict Pichia pastoris concentration and inulinase concentration online under different working conditions, and has higher prediction accuracy than the traditional soft sensor modeling method.

Luminescent Tactile Sensor System for Robots: Enhancing Human-Computer Interaction in Complex Dark Environments.

In order to achieve interaction and collaboration with humans, robots need to have the ability for tactile perception of simulating human. Traditional methods use electrically connected sensors with complex arrays, leading to intricate wiring, high manufacturing costs, and demanding current environments. A flexible sensor with simple structure, easy preparation process, and low cost based on triboluminescence effect is proposed in this paper, which avoids the complex array and wiring of traditional sensors. The study discusses the relationship between luminescent intensity and factors such as luminescent particle content, luminescent layer thickness, encapsulation layer thickness, and friction layer thickness. It also analyzes the mechanism of luminescence. A micro charge-coupled device is configured for the luminescent unit to collect optical information and is integrated with the robot's manipulator for testing. A simple sensing system is constructed to demonstrate environmental perception, acquiring, and feeding back shape and size information of contact objects in the dark. The system successfully identifies and judges target objects in complex dark environments, offering insights for applications such as unmanned assembly lines. It overcomes the challenge of intricate electrical connections, paving new avenues for intelligent object recognition research in human-computer interaction.

Development of a Scaled Down Test-Rig for Wheel–Rail Contact Thermal Experiments Using Optical Sensors

Abstract Rail wheel contact measurement is crucial for several reasons. First and foremost, it directly affects the safety of passengers, crew, and the general public. Accurate measurements help identify irregularities in wheel-rail contact, such as wheel defects, wear, or track anomalies, which can lead to derailments or accidents if left unaddressed. An inventive technique for measuring the temperatures at rail wheel contact at various speeds is presented in this research. The novel approach uses a 1:5 scaled-down test rig model of a wheel and rail with Fiber Bragg Grating (FBG) sensor to combine the experimental and finite element analysis simulation to determine the rail wheel contact temperature. By employing the various data acquisition and data analysis techniques rail wheel contact temperature at different speeds ranging between 10kmph to 40kmph was determined 1538.735 nm to 1538.831nm with centre wavelength of 1538.438 nm. The results illustrate the possibilities of the downsized test rig with experimental observations at varying speeds by examining the benefits of FBG sensors over traditional sensors. The experimental results are used to determine the equivalent wavelength shift. Fibre Bragg Grating (FBG) sensor design and simulation are done with the Grating MOD optical tool. For this temperature range and Bragg's wavelength of 1538.438 nm, the sensitivity of Fiber Bragg Grating is observed to be 13.6pm/°c.

Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO2 Surface Response to Pulsed Temperature Profile.

The need for odor measurement and pollution source identification in various sectors (aeronautic, automobile, healthcare…) has increased in the last decade. Multisensor modules, such as electronic noses, seem to be a promising and inexpensive alternative to traditional sensors that were only sensitive to one gas at a time. However, the selectivity, the non-repetitiveness of their manufacture, and their drift remain major obstacles to the use of electronic noses. In this first work, we show how the mathematical modeling of the sensor response can be used to find new selectivity characteristics, different from those classically used in the literature. We identified new specific characteristics that have no physical meaning that can be used to find criteria for the presence of formaldehyde and nitrogen dioxyde alone or in a mixture. We discuss the limitations of the methodology presented and suggest avenues for improvement, with more precise modeling techniques involving symbolic regression.

The Application of FMCW Radar Technology in Human Heartbeat Respiration Monitoring

In recent years, FMCW radar technology has become a research hotspot in the field of non-contact monitoring of vital signs. Compared with traditional contact sensors, FMCW radar-based monitoring devices can detect vital signals such as respiration and heartbeat without direct contact, providing a more convenient detection experience, and are expected to become the leading vital sign monitoring technology. This technology has a wide range of applications in anti-terrorism detection, medical monitoring, disaster rescue and autonomous driving. FMCW radar has the advantages of non-contact, long-range detection capability, anti-jamming capability, multi-parameter monitoring, and real-time performance, which makes it widely used in the fields of healthcare, security and intelligent transportation, providing a better guarantee for people's lives and health. This study explores the application of FM wave radar technology in human heartbeat respiration detection and finds that its signal processing process is complex, involving steps such as false alarm judgment, signal processing and frequency extraction, which requires precise signal processing and algorithm design to obtain accurate life information. Current technical challenges include optimising signal processing algorithms, miniaturising devices and reducing costs. Future research will focus on improving the accuracy and reliability of monitoring, expanding the application scenarios, and promoting the commercialisation and diffusion of the technology.