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.

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.

Research on Smart Energy Monitoring and Management System Based on Digital Twin Technology

Smart energy monitoring and management system lays a foundation for the application and development of smart energy. However, in recent years, the work efficiency of smart energy development enterprises has generally been low, and there is an urgent need to improve the application efficiency, resilience and sustainability of smart energy monitoring and management system. Digital twin technology provides a data-centric solution to improve smart energy monitoring and management system, bringing an opportunity to transform passive infrastructure assets into data-centric systems. This paper expounds on the concept and key technologies of digital twin, and designs a smart energy monitoring and management system based on digital twin technology, which has dual significance for promoting the development of smart energy field and promoting the application of digital twin.

Intelligent Examination Monitoring and Maintenance for the Safety of Operation of Flexible Manufacturing Systems

Based on the system of electric power supply for flexible manufacturing systems (FMS), a study has been carried out on the intelligent safety examination, monitoring and maintenance of its running environment. On the basis of the specific feature of the power supply network of an FMS, real time monitoring system of the power supply network and the fault diagnostic expert system for the power equipment have been designed. This system can diagnose not only definite fault phenomena, but also fuzzy, uncertain fault phenomena as well. Fault diagnostic knowledge base for the power equipment has been founded hierarchy architecture model and the method of fault tree analysis. Feasibility of this system has been proved by computer simulation.

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.

Applying Neural Networks for Tire Pressure Monitoring Systems

A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire.A quarter-car model was developed with MATLAB and Simulink to generate simulated accelerometer output data.Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks(RNN-LSTM)and a convolutional neural network(CNN)developed in Python with Tensorflow.Bayesian Optimization via SigOpt was used to optimize training and model parameters.The predictive accuracy and training speed of the two models with various parameters are compared.Finally,future work and improvements are discussed.

Monitored Rehab systems下肢综合训练在膝关节功能康复中的应用

目的：膝关节创伤后功能康复的常用治疗方案为理疗、运动治疗结合关节松动术手法治疗．本文探讨将Monitored Re-hab systems训练增加到运动治疗方案中，观察其对膝关节功能康复的影响。方法：选择2010年1月～2012年12月南京医科大学第一附属医院康复医学科诊治的膝关节创伤后功能障碍患者60例，随机分为2组，对照组和治疗组各30例。对照组进行理疗、运动治疗及关节松动术手法治疗，治疗时间为lh／d。10天为1个疗程，共治疗3个疗程。治疗组增加下肢Monitored Rehab Systems（MRS）训练，每次治疗时间为30分钟，1次／d。下肢MRS治疗包括：（1）等长训练；（2）向心训练；（3）离心训练；（4）本体感觉训练。评估：应用HSS全膝关节评定系统，基本分是100分。对照量表对60例患者治疗前后的膝关节活动范围及功能进行评估。结果：60例患者，分为2组，全部进入结果分析，无脱落。2组患者治疗3个疗程后膝关节ROM及功能评分变化：（1）对照组治疗3个疗程后，膝关节活动范围及功能评分均提高，与本组治疗前比较差异有统计学意义（P〈0．05）；（2）治疗组治疗3个疗程后膝关节活动范围及功能评分均提高，与本组治疗前比较差异有统计学意义（P〈0．05）；与对照组治疗后比较差异有统计学意义（P〈0．05）。结论：下肢Monitored Rehab systems治疗对增大膝关节活动范围及改善膝关节运动功能有显著效用。

针刺联合Monitored Rehab Systems下肢智能运动训练系统对卒中偏瘫患者平衡及步行功能的影响

脑卒中是常见脑血管疾病,具有发病率高、致残率高等特点[1-2]。随着社会进步,医疗水平提升,脑卒中患者存活率增加,但大约有80%脑卒中患者会伴随运动功能障碍,其中以偏瘫为主,伴随步行能力下降、平衡协调功能障碍、运动能力下降等,严重影响患者日常生活,增加患者家庭及社会负担[3-4]。Monitored Rehab Systems为闭链训练方式,可训练患者肢体控制、肌肉协同及关节匹配度,常用于下肢功能训练。针刺是中医外治疗法。本研究旨在观察观察针刺联合Monitored Rehab Systems下肢智能运动训练系统对卒中偏瘫患者平衡及步行功能影响,报道如下。

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.

Assistive Robotics for Upper Limb Physical Rehabilitation:A Systematic Review and Future Prospects

Physical assistive robotics are oriented to support and improve functional capacities of people.In physical rehabilitation,robots are indeed useful for functional recovery of affected limb.However,there are still open questions related to technological aspects.This work presents a systematic review of upper limb rehabilitation robotics in order to analyze and establish technological challenges and future directions in this area.A bibliometric analysis was performed for the systematic literature review.Literature from the last six years,conducted between August 2020 and May 2021,was reviewed.The methodology for the literature search and a bibliometric analysis of the metadata are presented.After a preliminary search resulted in 820 articles,a total of 66 articles were included.A concurrency network and bibliographic analysis were provided.And an analysis of occurrences,taxonomy,and rehabilitation robotics reported in the literature is presented.This review aims to provide to the scientific community an overview of the state of the art in assistive robotics for upper limb physical rehabilitation.The literature analysis allows access to a gap of unexplored options to define the technological prospects applied to upper limb physical rehabilitation robotics.

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.

Wearable Smart Sensor System for Medical Monitoring with an Assessment of the Level of Blood Loss and Pain Shock Because of Trauma or Injury

Blood loss in peacetime is mainly due to the normal menstrual cycle in women or diseases with surgical intervention. In wartime, blood loss in military personnel is a characteristic sign of a closed or open injury of the body during internal or external bleeding. Access to clinical care for wounded military personnel injured on the battlefield is limited and has long delays compared to patients in peacetime. Most of the deaths of wounded military personnel on the battlefield occur within the first hour after being wounded. The most common causes are delay in providing medical care, loss of time for diagnosis, delay in stabilization of pain shock and large blood loss. Some help in overcoming these problems is provided by the data in the individual capsule, which each soldier of the modern army possesses;however, data in an individual capsule is not sufficient to provide emergency medical care in field and hospital conditions. This paper considers a project for development of a smart real-time monitoring wearable system for blood loss and level of shock stress in wounded persons on the battlefield, which provides medical staff in field and hospital conditions with the necessary information to give timely medical care. Although the hospital will require additional information, the basic information about the victims will already be known before he enters the hospital. It is important to emphasize that the key term in this approach is monitoring. It is tracking, and not a one-time measurement of indicators, that is crucial in a valid definition of bleeding.

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.

Evaluation of Fruit Fly （Diptera： Tephritidae） Monitoring Systems on Mango in Limpopo Province, South Africa

Fruit fly species＇ responses to lures are critically important, especially when a single lure might be recommended for the purpose of trapping multiple fruit fly species in commercial fruit orchards. Fruit industries are facing threats from the recent invasion of the oriental fruit fly Bactrocera dorsalis （Hendel） into novel areas in South Africa. The objective of this study was to test the relative efficiency of 13 different trapping systems for fruit fly species in mango orchards in South Africa. Evaluation of the different monitoring systems was conducted during the 2013-2014 mango season in the Vhembe district municipality of Limpopo, South Africa. Four orchards, where Tommy Atkins was cultivated, were used to compare the efficacy of the trapping systems. Trapping data （N = 48 observation incidences per trapping system） were analyzed using a non-parametric ANOVA. Pronounced variation in species attractiveness across the trapping systems was found. The enriched ginger oil （EGO） PherolureTM captured 33.77% of all the Ceratitis spp., while the Invader-lureTM captured 36.47% of the total number of B. dorsalis trapped. Torula yeast pellets are not recommended for fruit fly trapping due to the relative low trap catch numbers and high non-target catches. These results are important and significant for on-farm monitoring strategies, as well as for invasion monitoring systems currently in place to detect the distribution ofB. dorsalis in South Africa.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

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.

Monitoring and simulation of water,heat,and CO_2 fluxes in terrestrial ecosystems based on the APEIS-FLUX system

The Integrated Environmental Monitoring (IEM) project, part of the Asia-Pacific Environmental Innovation Strategy (APEIS) project, developed an integrated environmental monitoring system that can be used to detect, monitor, and assess environmental disasters, degradation, and their impacts in the Asia-Pacific region. The system primarily employs data from the moderate resolution imaging spectrometer (MODIS) sensor on the Earth Observation System-(EOS-) Terra/Aqua satellite, as well as those from ground observations at five sites in different ecological systems in China. From the preliminary data analysis on both annual and daily variations of water, heat and CO2 fluxes, we can confirm that this system basically has been working well. The results show that both latent flux and CO2 flux are much greater in the crop field than those in the grassland and the saline desert, whereas the sensible heat flux shows the opposite trend. Different data products from MODIS have very different correspondence, e.g. MODIS-derived land surface temperature has a close correlation with measured ones, but LAI and NPP are quite different from ground measurements, which suggests that the algorithms used to process MODIS data need to be revised by using the local dataset. We are now using the APEIS-FLUX data to develop an integrated model, which can simulate the regional water, heat, and carbon fluxes. Finally, we are expected to use this model to develop more precise high-order MODIS products in Asia-Pacific region.

Monitoring of Large Size Solar Thermal Systems： Design and Data Analysis

This paper describes how to achieve an efficient design and management of a tele-monitoring system of several solar thermal plants. The system will be able to make an analysis that assures a more efficient management of each plant and of the whole system. In the first part of this study, the features of the monitoring system that allows to monitor the operating parameters and to discover the issues before they actually become dangerous for the plant have been identified. The data collected in the different solar thermal systems realized in Italian jails have been analyzed. The results of these elaborations allowed us both to find out some anomalies of functioning of the plants, and to optimize the management of the whole plant in a more efficient way.

System Reliability Analysis of Redundant Condition Monitoring Systems

The development and application of new reliability models and methods are presented to analyze the system relia- bility of complex condition monitoring systems.The methods include a method analyzing failure modes of a type of redundant con- dition monitoring systems (RCMS) by invoking failure tree model,Markov modeling techniques for analyzing system reliability of RCMS,and methods for estimating Markov model parameters.Furthermore,a computing case is investigated and many conclu- sions upon this case are summarized.Results show that the method proposed here is practical and valuable for designing condition monitoring systems and their maintenance.

Recent Developments in Monitoring of Complex Population Systems

The paper is an update of two earlier review papers concerning the application of the methodology of mathematical systems theory to population ecology, a research line initiated two decades ago. At the beginning the research was concentrated on basic qualitative properties of ecological models, such as observability and controllability. Observability is closely related to the monitoring problem of ecosystems, while controllability concerns both sustainable harvesting of population systems and equilibrium control of such systems, which is a major concern of conservation biology. For population system, observability means that, e.g. from partial observation of the system (observing only certain indicator species), in principle the whole state process can be recovered. Recently, for different ecosystems, the so-called observer systems (or state estimators) have been constructed that enable us to effectively estimate the whole state process from the observation. This technique offers an efficient methodology for monitoring of complex ecosystems (including spatially and stage-structured population systems). In this way, from the observation of a few indicator species the state of the whole complex system can be monitored, in particular certain abiotic effects such as environmental contamination can be identified. In this review, with simple and transparent examples, three topics illustrate the recent developments in monitoring methodology of ecological systems: stock estimation of a fish population with reserve area;and observer construction for two vertically structured population systems (verticum-type systems): a four-level ecological chain and a stage-structured fishery model with reserve area.