Wearable Healthcare and Continuous Vital Sign Monitoring with IoT Integration

Technical and accessibility issues in hospitals often prevent patients from receiving optimal mental and physical health care,which is essential for independent living,especially as societies age and chronic diseases like diabetes and cardiovascular disease become more common.Recent advances in the Internet of Things(IoT)-enabled wearable devices offer potential solutions for remote health monitoring and everyday activity recognition,gaining significant attention in personalized healthcare.This paper comprehensively reviews wearable healthcare technology integrated with the IoT for continuous vital sign monitoring.Relevant papers were extracted and analyzed using a systematic numerical review method,covering various aspects such as sports monitoring,disease detection,patient monitoring,and medical diagnosis.The review highlights the transformative impact of IoTenabled wearable devices in healthcare,facilitating real-time monitoring of vital signs,including blood pressure,temperature,oxygen levels,and heart rate.Results from the reviewed papers demonstrate high accuracy and efficiency in predicting health conditions,improving sports performance,enhancing patient care,and diagnosing diseases.The integration of IoT in wearable healthcare devices enables remote patient monitoring,personalized care,and efficient data transmission,ultimately transcending traditional boundaries of healthcare and leading to better patient outcomes.

Microarrow sensor array with enhanced skin adhesion for transdermal continuous monitoring of glucose and reactive oxygen species

Conventional blood sampling for glucose detection is prone to cause pain and fails to continuously record glucose fluctuations in vivo.Continuous glucose monitoring based on implantable electrodes could induce pain and potential tissue inflammation,and the presence of reactive oxygen species(ROS)due to inflammationmay affect glucose detection.Microneedle technology is less invasive,yet microneedle adhesion with skin tissue is limited.In this work,we developed a microarrow sensor array(MASA),which provided enhanced skin surface adhesion and enabled simultaneous detection of glucose and H_(2)O_(2)(representative of ROS)in interstitial fluid in vivo.The microarrows fabricated via laser micromachining were modified with functional coating and integrated into a patch of a three-dimensional(3D)microneedle array.Due to the arrow tip mechanically interlocking with the tissue,the microarrow array could better adhere to the skin surface after penetration into skin.The MASA was demonstrated to provide continuous in vivo monitoring of glucose and H_(2)O_(2) concentrations,with the detection of H_(2)O_(2) providing a valuable reference for assessing the inflammation state.Finally,the MASA was integrated into a monitoring system using custom circuitry.This work provides a promising tool for the stable and reliable monitoring of blood glucose in diabetic patients.

Short-term displacement prediction for newly established monitoring slopes based on transfer learning

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program,an unprecedented disaster mitigation program in China,where lots of newly established monitoring slopes lack sufficient historical deformation data,making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards.A slope displacement prediction method based on transfer learning is therefore proposed.Initially,the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data,thus enabling rapid and efficient predictions for these slopes.Subsequently,as time goes on and monitoring data accumulates,fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy,enabling continuous optimization of prediction results.A case study indicates that,after being trained on a multi-slope integrated dataset,the TCN-Transformer model can efficiently serve as a pretrained model for displacement prediction at newly established monitoring slopes.The three-day average RMSE is significantly reduced by 34.6%compared to models trained only on individual slope data,and it also successfully predicts the majority of deformation peaks.The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%,demonstrating a considerable predictive accuracy.In conclusion,taking advantage of transfer learning,the proposed slope displacement prediction method effectively utilizes the available data,which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.

PIMS:An Efficient Process Integrity Monitoring System Based on Blockchain and Trusted Computing in Cloud-Native Context

With the advantages of lightweight and high resource utilization,cloud-native technology with containers as the core is gradually becoming themainstreamtechnical architecture for information infrastructure.However,malware attacks such as Doki and Symbiote threaten the container runtime’s security.Malware initiates various types of runtime anomalies based on process form(e.g.,modifying the process of a container,and opening the external ports).Fortunately,dynamic monitoring mechanisms have proven to be a feasible solution for verifying the trusted state of containers at runtime.Nevertheless,the current routine dynamic monitoring mechanisms for baseline data protection are still based on strong security assumptions.As a result,the existing dynamicmonitoringmechanismis still not practical enough.To ensure the trustworthiness of the baseline value data and,simultaneously,to achieve the integrity verification of the monitored process,we combine blockchain and trusted computing to propose a process integrity monitoring system named IPMS.Firstly,the hardware TPM 2.0 module is applied to construct a trusted security foundation for the integrity of the process code segment due to its tamper-proof feature.Then,design a new format for storing measurement logs,easily distinguishing files with the same name in different containers from log information.Meanwhile,the baseline value data is stored on the blockchain to avoidmalicious damage.Finally,trusted computing technology is used to perform fine-grained integrity measurement and remote attestation of processes in a container,detect abnormal containers in time and control them.We have implemented a prototype system and performed extensive simulation experiments to test and analyze the functionality and performance of the PIMS.Experimental results show that PIMS can accurately and efficiently detect tampered processes with only 3.57% performance loss to the container.

The integrated monitoring system for running parameters of key mining equipment based on condition monitoring technology

An integrated monitoring system for running parameters of key mining equipmenton the basis of condition monitoring technology and modern communication networktechnology was developed.The system consists of a client computer with functions ofsignal acquisition and processing, and a host computer in the central control room.Thesignal acquisition module of the client computer can collect the running parameters fromvarious monitoring terminals in real-time.The DSP high-speed data processing system ofthe main control module can quickly achieve the numerical calculation for the collectedsignal.The signal modulation and signal demodulation are completed by the frequencyshift keying circuit and phase-locked loop frequency circuit, respectively.Finally, the signalis sent to the host computer for logic estimation and diagnostic analysis using the networkcommunication technology, which is helpful for technicians and managers to control therunning state of equipment.

Modern Monitoring Intraocular Pressure Sensing Devices Based on Application Specific Integrated Circuits

Glaucoma is a neurodegenerative condition that is the leading cause of irreversible blindness worldwide. Elevated intraocular pressure (IOP) is the main risk factor for the development and progression of the disease. Methods to lower IOP remain the first line treatments for the condition. Current methods of IOP measurement do not permit temporary noninvasive monitoring 24-hour IOP on a periodic basis. Ongoing research will in time provide a means of developing a device that will enable continuous or temporary monitoring of IOP. At present a device suitable for clinical use is not yet available.This review contains a description of different devices currently in development for measuring IOP: soft contact lens, LC resonant circuits and on-chip sensing devices. All of them use application-specific integrated circuits (ASICS) to process the measured signals and send them to recording devices. Soft contact lens devices are based on an embedded strain gauge, LC circuits vary their resonance frequency depending on the intraocular pressure (IOP) and, finally, on-chip sensing devices include an integrated microelectromechanical sensor (MEMS). MEMS are capacitors whose capacity varies with IOP. These devices allow for an accurate IOP measurement (up to +/– 0.2 mm Hg) with high sampling rates (up to 1 sample/min) and storing 1 week of raw data. All of them operate in an autonomous way and even some of them are energetically independent.

Design and Application of Integrated Monitoring Model for Provincial Meteorological Observation Data Transmission

With the development of meteorological services, there are more and more types of real-time observation data, and the timeliness requirements are getting higher and higher. The monitoring methods of existing meteorological observation data transmission can no longer meet the needs. This paper proposes a new monitoring model, namely the “integrated monitoring model” for provincial meteorological observation data transmission. The model can complete the whole network monitoring of meteorological observation data transmission process. Based on this model, the integrated monitoring system for meteorological observation data transmission in Guangdong Province is developed. The system uses Java as the programming language, and integrates J2EE, Hibernate, Quartz, Snmp4j and Slf4j frameworks, and uses Oracle database as the data storage carrier, following the MVC specification and agile development concept. The system development uses four key technologies, including simple network management protocol, network connectivity detection technology, remote host management technology and thread pool technology. The integrated monitoring system has been put into business application. As a highlight of Guangdong’s meteorological modernization, it has played an active role in many major meteorological services.

Research on Network-based Integrated Condition Monitoring Unit for Rotating Machinery

In this paper, a network-based monitoring unit for condition monitoring andfault diagnosis of rotating machinery is designed and implemented. With the technology of DSP(Digital signal processing) , TCP/IP, and simultaneous acquisition, a mechanism of multi-process andinter-process communication, the integrating problem of signal acquisition, the data dynamicmanagement and network-based configuration in the embedded condition monitoring system is solved. Itoffers the input function of monitoring information for network-based condition monitoring and afault diagnosis system.

Short-term warning and integrity monitoring algorithm for coal mine shaft safety

A new short-term warning and integrity monitoring algorithm was proposed for coal mine shaft safety. The Kalman filter (KF) model was used to extract real global positioning system (GPS) kinematic deformation information. The short-term warning model was built by using the two-side cumulative sum (CUSUM) test, which further improves the warning system reliability. Availability (the minimum warning deformation, MWD), false alarm rate (the average run length, ARL), missed rate (the warning delay, WD) and the relationships among them were analyzed and the method choosing warning parameters is given. A test of a deformation simulation platform shows that the warning algorithm can be effectively used for steep deformation warning. A field experiment of the Malan mine shaft in Shanxi coal area illustrates that the proposed algorithm can detect small dynamic changes and the corresponding occurring time. At given warning thresholds (MWD is 15 mm and ARL is 1000),the detected deformations of two consecutive days’ deformation sequences with the algorithm occur at the 705th epoch (705 s) and the 517th epoch (517 s), respectively.

NEW ALGORITHM FOR RECEIVER AUTONOMOUS INTEGRITY MONITORING

A new integrity metric for navigation systems is proposed based on the measurement domain. Proba-hilistic optimization design offers tools for fault detection by considering the required navigation performance （RNP） parameter and the uncertainty noise. The choice of the proper performance parameter provided the single-valued mapping with the missed detection probability estimates the probability of failure. The desirable characteristics of the residual sensitivity matrix are exploited to increase the efficiency for identifying erroneous observations. The algorithm can be used to support the performance specification and the efficient calculation of the integrity monitoring process. The simulation for non-precision approach （NPA） validates both the viability and the effectiveness of the proposed algorithm.

Integrated Monitor System of Water and Fertilizer of Greenhouse Intelligent Irrigatio

The integration of water and fertilizer is a comprehensive technology combined irrigation and fertilizer, which has outstanding advantages of saving fertilizer, saving water, saving labor, protecting environment, high yield and high efficiency. Currently, most of the water and fertilizer integrated irrigation and fertilization and irrigation operation in the production-based greenhouse is achieved relying on artificial experience, which is hard to achieve timely, scientific and intelligent irrigation. In this study, the application of STM32 embedded system realized the real-time collection of the data from the humidity sensors buried in top, middle and low depth of soil, and water and fertilizer integrated irrigation work was completed in the greenhouse through automatic control according to the predetermined fertilization and irrigation strategies for different crops. Moreover, the system had remote monitoring function, which used the global system for mobile （GSM） module to provide users with remote short message services, and therefore, the users could not only achieve the remote intelligent monitoring on the irrigation, light, ventilation of the greenhouse through short messages, but also could start and stop the remote control system operation, so as to realize the automatic management of the greenhouse environment, achieving the purpose of remote fertilization and water-saving irrigation.

Early faults prediction of running state of electromechanical systems and reconfigurable integration of series safety monitoring systems

Fault prediction technology of running state of electromechanical systems is one of the key technologies that ensure safe and reliable operation of electromechanical equipment in health state. For multiple types of modern, high-end and key electromechanical equipment, this paper will describe the early faults prediction method for multi-type electromechanical systems, which is favorable for predicting early faults of complex electromechanical systems in non-stationary, nonlinear, variable working conditions and long-time running state; the paper shall introduce the reconfigurable integration technology of series safety monitoring systems based on which the integrated development platform of series safety monitoring systems is built. This platform can adapt to integrated R&D of series safety monitoring systems characterized by high technology, multiple species and low volume. With the help of this platform, series safety monitoring systems were developed, and the Remote Network Security Monitoring Center for Facility Groups was built. Experimental research and engineering applications show that: this new fault prediction method has realized the development trend features extraction of typical electromechanical systems, multi-information fusion, intelligent information decision-making and so on, improving the processing accuracy, relevance and applicability of information; new reconfigurable integration technologies have improved the integration level and R&D efficiency of series safety monitoring systems as well as expanded the scope of application; the series safety monitoring systems developed based on reconfigurable integration platform has already played an important role in many aspects including ensuring safety operation of equipment, stabilizing product quality, optimizing running state, saving energy consumption, reducing environmental pollution, improving working conditions, carrying out scientific maintenance, advancing equipment utilization, saving maintenance charge and enhancing the level of information management.

GNSS receiver autonomous integrity monitoring(RAIM)algorithm based on robust estimation

Integrity is significant for safety-of-life applications. Receiver autonomous integrity monitoring（RAIM） has been developed to provide integrity service for civil aviation. At first,the conventional RAIM algorithm is only suitable for single fault detection, single GNSS constellation. However, multiple satellite failure should be considered when more than one satellite navigation system are adopted. To detect and exclude multi-fault, most current algorithms perform an iteration procedure considering all possible fault model which lead to heavy computation burden. An alternative RAIM is presented in this paper based on multiple satellite constellations（for example, GPS and Bei Dou（BDS） etc.） and robust estimation for multi-fault detection and exclusion, which can not only detect multi-failures,but also control the influences of near failure observation. Besides, the RAIM algorithm based on robust estimation is more efficient than the current RAIM algorithm for multiple constellation and multiple faults. Finally, the algorithm is tested by GPS/Bei Dou data.

Integrity Monitoring in Navigation Systems: Fault Detection and Exclusion RAIM Algorithm Implementation

The use of GPS is becoming increasingly popular for real-time navigation systems. To ensure that satellite failures are detected and excluded at the receiver is of high importance for the integrity of the satellite navigation system. The focus of this paper is to implement a fault detection and exclusion algorithm in a software GPS receiver in order to provide timely warnings to the user when it is not advisable to use the GPS system for navigation. The GPS system currently provides some basic integrity information to users via the navigation message, but it is not timely enough for safety-critical applications. RAIM is a means of providing integrity with the capability of detecting when a satellite failure or a measurement error has occurred. It is the simplest and most cost effective technique for integrity monitoring. After applying the iterative fault detection and the exclusion algorithm, a significant improvement in positioning accuracy is achieved.

Theory and Application of System Integration for Real-Time Monitoring of Core Rock-Fill Dam Filling Construction Quality

The theory and method of system integration for the real-time monitoring of core rock-fill dam filling con- struction quality are studied in this paper. First, the importance analysis of system integration factors is carried out with the analytic hierarchy process. Then, according to the analysis result of integration factors, the conceptual model of system integration is built based on function integration, index integration, technology integration and information integration, the index structure of core rock-fill dam filling construction quality control is constructed and the method of function integration and technology integration is studied. The mathematical model of process monitoring is built according to monitoring objective, process and indexes. Research results have been applied in Nuozhadu core rock-fill dam construction management, realizing system integration through building appropriate monitoring work flow and comprehensive information platform of digital dam.

Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.

Fuzzy Integral Based Information Fusion for Water Quality Monitoring Using Remote Sensing Data

To improve the monitoring precision of lake chlorophyll a (Chl-a), this paper presents a fusion method based on Choquet Fuzzy Integral (CFI) to estimate the Chl-a concentration. A group of BPNN models are designed. The output of multiple BPNN model is fused by the CFI. Meanwhile, to resolve the over-fitting problem caused by a small number of training sets, we design an algorithm that fully considers neighbor sampling information. A classification experiment of the Chl-a concentration of the Taihu Lake is conducted. The result shows that, the proposed approach is superior to the classification using a single neural network classifier, and the CFI fusion method has higher identification accuracy.

Probing multi-physical process and deformation mechanism of a largescale landslide using integrated dual-source monitoring

The implementation of isolated heterologous monitoring systems for spatially distant borehole deployments often comes with substantial equipment costs,which can limit the effectiveness of geohazard mitigation and georisk management efforts.To address this,we have developed a novel monitoring system that integrates fiber Bragg grating(FBG)and microelectromechanical system(MEMS)techniques to capture soil moisture,temperature,sliding resistance,strain,surface tilt,and deep-seated inclination.This system enables real-time,simultaneous data acquisition and cross-validation analyses,offering a costeffective solution for monitoring critical parameters in geohazard-prone areas.We successfully applied this integrated monitoring system to the Xinpu landslide,an active super-large landslide located in the Three Gorges Reservoir Area(TGRA)of China.The resulting strain profile confirmed the presence of two shallow secondary sliding surfaces at depths of approximately 7 m and 12 m,respectively,in addition to the deep-seated sliding surface at a depth of28 m.The lower secondary sliding surface was activated by extreme precipitation,while the upper one was primarily driven by significant changes in reservoir water levels and secondarily triggered by concentrated rainfalls.Anti-slide piles have remarkably reinforced the upper moving masses but failed to control the lower ones.The gap between the pile heads and the soil amplified the rainwater erosion effect,creating a preferential channel for rainwater infiltration.Multi-physical measurements revealed a mixture of seepage-driven and buoyancy-driven behaviors within the landslide.This study offers an integrated dual-source multi-physical monitoring paradigm that enables collaborative management of multiple crucial boreholes on a large-scale landslide,and contributes to the evaluation and improvement of engineering measures in similar geological settings.

An integrated portable bio-monitoring system based on tough hydrogels for comprehensive detection of physiological activities

Advanced soft ion-conducting hydrogels have been developed rapidly in the integrated portable health monitoring equipment due to their higher sensitivity,sensory traits,tunable conductivity,and stretchability for physiological activities and personal healthcare detection.However,traditional hydrogel conductors are normally susceptible to large deformation and strong mechanical stress,which leads to inferior electro-mechanical stability for real application scenarios.Herein,a strong ionically conductive hydrogel(poly(vinyl alcohol)-boric acid-glycerol/sodium alginate-calcium chloride/electrolyte ions(PBG/SC/EI))was designed by engineering the covalently and ionically crosslinked networks followed by the salting-out effect to further enhance the mechanical strength and ionic conductivity of the hydrogel.Owing to the collective effects of the energy-dissipation mechanism and salting-out effect,the designed PBG/SC/EI with excellent structural integrity and robustness exhibits exceptional mechanical properties(elongation at break for 559.1%and tensile strength of 869.4 kPa)and high ionic conductivity(1.618 S·m^(-1)).As such,the PBG/SC/EI strain sensor features high sensitivity(gauge factor=2.29),which can effectively monitor various kinds of human motions(joint motions,facial micro-expression,faint respiration,and voice recognition).Meanwhile,the hydrogel-based Zn||MnO_(2)battery delivers a high capacity of 267.2 mAh·g^(-1)and a maximal energy density of 356.8 Wh·kg^(-1)associated with good cycle performance of 71.8%capacity retention after 8000 cycles.Additionally,an integrated bio-monitoring system with the sensor and Zn||MnO_(2)battery can accurately identify diverse physiological activities in a real-time and non-invasive way.This work presents a feasible strategy for designing high-performance conductive hydrogels for highly-reliable integrated bio-monitoring systems with excellent practicability.

Development and engineering application of integrated safety monitoring systems for China’s high-speed trains

With the improvement of the running speed of China’s high-speed trains,the demands for running status monitoring and security assurance of High-speed Electric Multiple Units(EMU)have increased significantly.However,the current safety monitoring systems are independent,which is not conducive to the comprehensive monitoring and information sharing of the whole vehicle.The temperature monitoring of running gear is insensitive to early failures.How to develop a train operation safety monitoring system with strong engineering implementation and high integration is a key problem to be solved.For the monitoring of running stationarity,frame stability and running gear health of China’s high-speed trains,an integrated safety monitoring system framework is designed,and the logic and algorithm for the diagnosis of stationarity,stability and health states of rotating parts are constructed.Monitoring software which fused the temperature,high and low frequency vibration data is developed,and the design and installation of the vibration temperature composite sensors are completed.The research results have realized the integration and comprehensive processing of multiple monitoring systems,completed the improvement from single component and vehicle-level safety monitoring to multi-system,train-level and interactive monitoring.In the process of real vehicle application,the developed monitoring system acquires the vehicle operation status data accurately and in real time.The constructed diagnosis algorithm and logic evaluates the vehicle operation status accurately and in a timely manner,and avoids the progression from fault to accident.The research results show that the integrated safety monitoring system can provide technical support for train operation safety.