Personalized Health Monitoring Systems: Integrating Wearable and AI

The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearable technologies and AI on healthcare, highlighting the development and theoretical application of the Integrated Personal Health Monitoring System (IPHMS). By integrating data from various wearable devices, such as smartphones, Apple Watches, and Oura Rings, the IPHMS framework aims to revolutionize personal health monitoring through real-time alerts, comprehensive tracking, and personalized insights. Despite its potential, the practical implementation faces challenges, including data privacy, system interoperability, and scalability. The evolution of healthcare technology from traditional methods to AI-enhanced wearables underscores a significant advancement towards personalized care, necessitating further research and innovation to address existing limitations and fully realize the benefits of such integrated health monitoring systems.

Design of IoT-based Volatile Organic Compounds Monitoring System

Volatile organic compounds(VOC)gas detection devices based on semiconductor sensors have become a common method due to their low cost,simple principle,and small size.However,with the continuous development of materials science,various new materials have been applied in the fabrication of gas sensors,but these new materials have more stringent requirements for operating temperature,which cannot be met by existing sensor modules on the market.Therefore,this paper proposes a temperature-adjustable sensor module and designs an environmental monitoring system based on the STM32F103RET6 microprocessor.This system primarily utilizes multiple semiconductor gas sensors to monitor and record the concentrations of various harmful gases in different environments.It can also monitor real-time temperature,humidity,and latitude and longitude in the current environment,and upload the data to the Internet of Things via 4G communication.This system has the advantages of small size,portability,and low cost.Experimental results show that the sensor module can achieve precise control of operating temperature to a certain extent,with an average temperature error of approximately 3%.The monitoring system demonstrates a certain level of accuracy in detecting target gases and can promptly upload the data to a cloud platform for storage and processing.A comparison with professional testing equipment shows that the sensitivity curves of each sensor exhibit similarity.This study provides engineering and technical references for the application of VOC gas sensors.

Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.

An IoT-Based Aquaculture Monitoring System Using Firebase

Indonesia is a producer in the fisheries sector,with production reaching 14.8 million tons in 2022.The production potential of the fisheries sector can be optimally optimized through aquaculture management.One of the most important issues in aquaculture management is how to efficiently control the fish pond water conditions.IoT technology can be applied to support a fish pond aquaculture monitoring system,especially for catfish species(Siluriformes),in real-time and remotely.One of the technologies that can provide this convenience is the IoT.The problem of this study is how to integrate IoT devices with Firebase’s cloud data system to provide reliable and precise data,which makes it easy for fish cultivators to monitor fishpond conditions in real time and remotely.The IoT aquaculture fishpond monitoring use 3 parameters:(1)water temperature;(2)pHwater level;and(3)turbidity level of pond water.IoT devices use temperature sensors,pH sensors,and turbidity sensors,which are integrated with a microcontroller and Wi-Fi module.Data from sensor readings are sent to the Firebase cloud via theWi-Fi module so that it can be accessed in real time by end users with an Androidbased mobile app.The findings are(1)the IoT-based aquaculture monitoring system device has a low error rate in measuring temprature,pH,and turbidity with a percentage of 1.75%,1.94% and 9.78%,respectively.Overall,the total average error of the three components is 4.49%;(2)in cost analysis,IoT-based has a cost-effectiveness of 94.21% compared to labor costs.An IoT-based aquaculture monitoring system using Firebase can be effectively used as a technology for monitoring fish pond conditions in real-time and remotely for fish cultivators that contribute to providing an IoT-based aquaculture monitoring system that produces valid data,is precise,is easy to implement,and is a low-cost system.

Glucose metabolism profile recorded by flash glucose monitoring system in patients with hypopituitarism during prednisone replacement

BACKGROUND Commonly used glucocorticoids replacement regimens in patients with hypopituitarism have difficulty mimicking physiological cortisol rhythms and are usually accompanied by risks of over-treatment,with adverse effects on glucose metabolism.Disorders associated with glucose metabolism are established risk factors of cardiovascular events,one of the life-threatening ramifications.AIM To investigate the glycometabolism profile in patients with hypopituitarism receiving prednisone(Pred)replacement,and to clarify the impacts of different Pred doses on glycometabolism and consequent adverse cardiovascular outcomes.METHODS Twenty patients with hypopituitarism receiving Pred replacement[patient group(PG)]and 20 normal controls(NCs)were recruited.A flash glucose monitoring system was used to record continuous glucose levels during the day,which provided information on glucose-target-rate,glucose variability(GV),period glucose level,and hypoglycemia occurrence at certain periods.Islet β-cell function was also assessed.Based on the administered Pred dose per day,the PG was then regrouped into Pred>5 mg/d and Pred≤5 mg/d subgroups.Comparative analysis was carried out between the PG and NCs.RESULTS Significantly altered glucose metabolism profiles were identified in the PG.This includes significant reductions in glucose-target-rate and nocturnal glucose level,along with elevations in GV,hypoglycemia occurrence and postprandial glucose level,when compared with those in NCs.Subgroup analysis indicated more significant glucose metabolism impairment in the Pred>5 mg/d group,including significantly decreased glucose-target-rate and nocturnal glucose level,along with increased GV,hypoglycemia occurrence,and postprandial glucose level.With regard to islet β-cell function,PG showed significant difference in homeostasis model assessment(HOMA)-β compared with that of NCs;a notable difference in HOMA-βwas identified in Pred>5 mg/d group when compared with those of NCs;as for Pred≤5 mg/d group,significant differences were found in HOMA-β,and fasting glucose/insulin ratio when compared with NCs.CONCLUSION Our results demonstrated that Pred replacement disrupted glycometabolic homeostasis in patients with hypopituitarism.A Pred dose of>5 mg/d seemed to cause more adverse effects on glycometabolism than a dose of≤5 mg/d.Comprehensive and accurate evaluation is necessary to consider a suitable Pred replacement regimen,wherein,flash glucose monitoring system is a kind of promising and reliable assessment device.The present data allows us to thoroughly examine our modern treatment standards,especially in difficult cases such as hormonal replacement mimicking delicate natural cycles,in conditions such as diabetes mellitus that are rapidly growing in worldwide prevalence.

Intelligent Protection and Monitoring System of Lightning Disaster

Based on Internet technology,modern wireless communication technology and new sensor technology,an intelligent protection and monitoring system of lightning disaster was established to collect lightning current(lightning intensity,frequency and time),induced lightning current and leakage current of surge protector(SPD),earthing resistance,working voltage,temperature and humidity in the lightning environment and other data in real time.Through the online analysis of visual data and automatic alarm beyond the preset value of cloud platform,it can realize the intelligent online monitoring and management of lightning protection devices,providing scientific and reliable lightning protection technical support for lightning protection and disaster reduction,and ensuring the smooth development and efficient management of lightning protection safety work.

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.

Suitability and Sustainability of Rainwater QualityMonitoring System in Cistern for Domestic Use

Rainwater harvesting(RWH)systems have been the source of domestic water for many years and still becoming essential in many communities of developing countries.However,due to various reasons,there are several sources of contamination in the rainwater cistern systems.Dissolved chemicals from the roofing,storage,and conveyance materials,together with the suspended particulate matter from the airborne,are examples of water contamination.In this work,the water quality monitoring system has been designed and implemented.Chemical and physical parameters of water samples were collected from three locations using a data acquisition(DAQ)system and rainwater quality was analyzed using Water Pollution Index(WPI).Results obtained from three locations have been presented.

A Smart Home Energy Monitoring System Based on Internet of Things and Inter Planetary File System for Secure Data Sharing

Energy demand will continue to rise as a result of predicted population growth. In this work, a user-friendly home energy monitoring system based on IoT is described, which is capable of collecting, analyzing, and displaying data. Users register their sensors and devices on the monitoring platform. PostgreSQL and Elasticsearch databases are used to store the resulting measurements. In a smart home, the wireless sensor ACS712 was used to monitor the flow of electricity (current and voltage) for a household device. The user can share data about electricity consumption and costs with a third party via the private IPFS (InterPlanetary File System) network. A third party can download all the energy consumption data for a device or many devices from the platform for 1 day, 3 months, 6 months, and 1 year. The studies on the development of energy-efficient technology for home devices benefit greatly from the gathered data. For security in the system, it is preferred to run Keyrock Idm, Wilma Pep Proxy, and Orion Context Broker in HTTPS mode, and MQTTS is used to retrieve sensor data. The experimental results showed that the energy monitoring system accurately records voltage, current, active power, and the total amount of power used and offers low-cost solutions to the users using household devices in a day.

Construction and Practice of Teaching Staff and Teaching Quality Monitoring System in University Affiliated Hospitals Under the Background of Education Informatization

Objective:To build a set of teaching staff construction and teaching quality monitoring system suitable for clinical practice teaching in affiliated hospitals of medical colleges,achieve continuous improvement of clinical education and teaching level,and ensure the quality of medical education talent cultivation.Methods:A modern clinical practice teaching quality monitoring system is constructed based on organizational structure construction,teaching staff system construction,quality control system construction,and information platform construction,combined with external audit and evaluation.Results:The hospital has established a Faculty Development and Teaching Evaluation Office specifically responsible for the cultivation of clinical teachers and the evaluation and supervision of teaching quality.A relatively complete teacher construction and teaching quality monitoring system has also been established for clinical practice teaching,thus achieving integration with the school’s quality control system in terms of management mechanism.At the same time,a set of teaching quality control mode based on the“Internet+”platform has been created by means of informatization.At present,this mode has won three national computer software copyrights and two second prizes for school-level teaching achievements.Conclusion:Through five years of practice,an“Internet+”teaching quality evaluation and monitoring system with the characteristics of teaching hospitals affiliated to local medical colleges has been established.In order to further standardize the training system of clinical teachers in affiliated hospitals,achieve self-monitoring and self-improvement in terms of teaching quality,and ensure the continuous improvement of clinical teaching quality,we will continue to promote the development of clinical teachers with quality and excellence,enrich the main team of quality monitoring,guide the transformation of the education mode from being“centered on teachers”to being“centered on students,”and realize the integration of internal and external quality monitoring systems.

STM32-based Health Monitoring System for Infants and Toddlers

In order to allow the guardians to monitor the physiological parameters of the infant more intuitively and to be able to respond to sudden irregularities in the pulse rate,abnormal blood oxygen,high or low body temperature and other conditions,and to facilitate communication with the medical staff or to request assistance in treatment,an STM32 microcontroller-based infant health monitoring system is designed.The digital signal acquisition module for pulse,blood oxygen and body temperature acquire the raw data,and the microcontroller performs algorithmic processing to display the physiological parameters such as pulse,blood oxygen and body temperature of the infant,and configures the threshold alarms for the physiological parameters by means of a keypad module.Finally,the test results are compared and tested against the standard physiological parameters of infants and children to verify that the system meets the requirements of medical precision and accuracy.

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.

Optimal edge-cloud collaboration based strategies for minimizing valid latency of railway environment monitoring system

Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.

Real-time moving object detection for video monitoring systems

Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.

Development of an automatic monitoring system for rice light-trap pests based on machine vision

Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.

Warehouse Environment Parameter Monitoring System and Sensor Error Correction Model Based on PSO-BP

The warehouse environment parameter monitoring system is designed to avoid the networking and high cost of traditional monitoring system.A sensor error correction model which combines particle swarm optimization(PSO)with back propagation(BP)neural network algorithm is established to reduce nonlinear characteristics and improve test accuracy of the system.Simulation and experiments indicate that the PSO-BP neural network algorithm has advantages of fast convergence rate and high diagnostic accuracy.The monitoring system can provide higher measurement precision,lower power consume,stable network data communication and fault diagnoses function.The system has been applied to monitoring environment parameter of warehouse,special vehicles and ships,etc.

Astronomical Site Monitoring System at Lijiang Observatory

We installed two sets of Astronomical Site Monitoring Systems(ASMSs)at Lijiang Observatory(GMG),for the running of the 2.4-meter Lijiang optical telescope(LJT)and the 1.6-meter Multi-channel Photometric Survey Telescope(Mephisto).The Mephisto is under construction.The ASMS has been running on robotic mode since 2017.The core instruments:Cloud Sensor,All-Sky Camera and AutonomousDIMM that are developed by our group,together with the commercial Meteorological Station and Sky Quality Meter,are combined into the astronomical optical site monitoring system.The new Cloud Sensor's Cloud-Clear Relationship is presented for the first time,which is used to calculate the All-Sky cloud cover.We designed the Autonomous-DIMM located on a tower,with the same height as LJT.The seeing data have been observed for a full year.ASMS's data for the year 2019 are also analysed in detail,which are valuable to observers.

Multi-dimensional database design and implementation of dam safety monitoring system

To improve the effectiveness of dam safety monitoring database systems, the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mode. The optimal data model was confirmed by identifying data objects, defining relations and reviewing entities. The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely. On this basis, a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established, for which factual tables and dimensional tables have been designed. Finally, based on service design and user interface design, the dam safety monitoring system has been developed with Delphi as the development tool. This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design. It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.

Fatigue Performance Analysis and Evaluation for Steel Box Girder Based on Structural Health Monitoring System

Taizhou Yangtze River Bridge as a long-span suspension bridge,the finite element model(FEM)of it is established using the ANSYS Software.The beam4 element is used to simulate the main beam to establish the“spine beam”model of the Taizhou Yangtze River Bridge.The calculated low-order vibration mode frequency of the FEM is in good agreement with the completion test results.The model can simulate the overall dynamic response of the bridge.Based on the vehicle load survey,the Monte Carlo method is applied to simulate the traffic load flow.Then the overall dynamic response analysis of FEM is car-ried out.Taking the bending moment of the main beam as the control index,the fatigue sensitive section in the steel box girder of FEM is analyzed.Based on the strain time history data of steel box girder recorded by the structural health mon-itoring system(SHM),the true stress response of steel box girder under vehicle load is extracted.Taking the cumulative fatigue damage increment as the evalua-tion index,the fati gue performance evaluation of the steel box girders is con-ducted based on the collected health monitoring data.The fatigue effect of the beam section near the steel tower,especially the first section of the middle tower,is the key section of the fatigue analysis by health morning system,which is con-sistent with the calculation results of FEM.

DISTRIBUTED MONITORING SYSTEM RELIABILITY ESTIMATION WITH CONSIDERATION OF STATISTICAL UNCERTAINTY

Taking into account the whole system structure and the component reliability estimation uncertainty, a system reliability estimation method based on probability and statistical theory for distributed monitoring systems is presented. The variance and confidence intervals of the system reliability estimation are obtained by expressing system reliability as a linear sum of products of higher order moments of component reliability estimates when the number of component or system survivals obeys binomial distribution. The eigenfunction of binomial distribution is used to determine the moments of component reliability estimates, and a symbolic matrix which can facilitate the search of explicit system reliability estimates is proposed. Furthermore, a case of application is used to illustrate the procedure, and with the help of this example, various issues such as the applicability of this estimation model, and measures to improve system reliability of monitoring systems are discussed.