Design Of Health Monitoring System Research Articles

Retraction Note: Design of a real-time health monitoring and prediction system for table tennis players based on optical detection sensor networks

Retraction Note: Design of a real-time health monitoring and prediction system for table tennis players based on optical detection sensor networks

Design of Health and Elderly Care Intelligent Monitoring System Based on IoT Wireless Sensing and Data Mining

Design of Health and Elderly Care Intelligent Monitoring System Based on IoT Wireless Sensing and Data Mining

Data Processing of Bridge IoT Monitoring Based on Task Scheduling of Cloud Service Listening Signal

For the long-term continuous monitoring of bridge-related indicators, it is necessary to arrange relatively perfect acquisition equipment on the bridge, which can feedback various information parameters of the bridge. However, there are many parameters to feedback the bridge information, which leads to the complex and overstaffed structure of the monitoring system. Furthermore, the huge amount of data collected and the complex calculation process also increase the difficulty of the operation of the monitoring system. In this regard, we should choose more scientific and reasonable indicators, lightweight data structure, stable data transmission, and analysis programs to improve the accuracy of continuous monitoring. To establish a stable and efficient bridge monitoring system, we use the distance coefficient-effective independent algorithm to optimize. Then, we calculate the relevant information of the strain environment with the help of a neural network model, strengthen the training of deep learning through the YOLOv5s model, and improve the task scheduling strategy of attention concentration. Through that, we solve the problem of embedded systems with relatively low computing power. Different weights are assigned to each fused feature map, and the nodes at the highest level and the lowest level are deleted so that a concise and efficient lightweight network model is constructed. Multiple iterations are performed to achieve deeper feature fusion. Therefore, the complexity of the model is effectively reduced, and the monitoring performance can be effectively improved. Finally, through the experimental analysis, it is proved that compared with the traditional fusion model, the number of parameters of the improved fusion network structure in bridge health monitoring is reduced by 7.37%. The detection speed is increased by 18.2%. The amount of computation is reduced by 42.92%, and the average detection accuracy is required to reach 95.33%. It is verified that the proposed method can effectively improve the accuracy and risk control ability of the detection data by learning from the samples with small labels. It also has great practical significance and market value for the design and optimization of the bridge health monitoring system, which is suitable for the monitoring data of large-scale construction projects.

Recent advances and innovations in the design and fabrication of wearable flexible biosensors and human health monitoring systems based on conjugated polymers

Recent advances and innovations in the design and fabrication of wearable flexible biosensors and human health monitoring systems based on conjugated polymers

Risk and Value Informed Structural Health Monitoring System Design for the Miter Gates

The ultimate goal of a Structural Health Monitoring and Prognostics system (SHM/DP) is to interrogate the data gathered in situ to infer the state of the structure (diagnostics), evaluate the system's evolution over time (prognos-tics), and leverage the diagnostic and prognostic results to facilitate decision-making. As a compelling business proposition, SHM/DP is expected to deliv-er economic returns through data-informed lifecycle management decisions, surpassing the estimated costs encompassing the design, initial deployment operations, and maintenance of the SHM/DP system. Therefore, it is prudent to approach a system design problem with a cost-benefit analysis perspective, aiming to achieve a design that maximizes the Value of Information (VoI) ac-quired by an information-acquisition system, evaluated in a pre-posterior sense. In this paper, our focus is on a sensor optimization framework that aims to maximize the VoI over the lifecycle of a miter gate structure. We em-ploy the proposed Bayesian sensor optimization framework to assess the ef-fectiveness of various Value of Information (VoI) metrics in quantifying the overall benefits of an SHM/DP system. Additionally, we explore the impact of the decision maker's risk profile and the adopted decision policies on the system design.

An Intelligent Non-Invasive Blood Pressure Monitoring System Based on a Novel Polyvinylidene Fluoride Piezoelectric Thin Film.

Hypertension is a common cause of cardiovascular diseases, closely associated with the high mortality and disability rates of cardiovascular diseases such as stroke and coronary heart disease. Therefore, developing a comfortable and sustainable device for monitoring human pulse signals holds practical significance for the prevention and treatment of hypertension and cardiovascular diseases. PVDF flexible pressure sensors possess the characteristics of high sensitivity, good flexibility, and strong biocompatibility, thereby demonstrating extensive application potential in areas such as health monitoring, wearable devices, and electronic skins. This paper focuses on the development of a modified piezoelectric polymer and its application in an intelligent blood pressure monitoring system, demonstrating its outstanding performance and feasibility through a series of experiments. This research provides innovative material choices for the development of intelligent medical devices and offers beneficial guidance for the design and application of future intelligent health monitoring systems.

Clinker storage silos: Validation of dynamic performance through measurements and finite element simulation

The paper describes the design and application of a structural health monitoring system aimed at analysing the dynamic response of reinforced concrete silos for the storage of clinker. The in-situ dynamic measurements, supported by finite element simulations to assess the structural safety, were prompted by reports from people working in facilities near the silos in question, who complained of strong vibrations. The proposed approach combines the advantages of theoretical and operational modal analysis based on monitoring during normal use of the silos, including their emptying. The measurements were performed automatically and continuously over time, and only significant events, like shocks caused by clinker masses falling off the side walls of the silos, were saved into the remote server as the acceleration-time waveforms. They were registered with 4000 Hz frequency by piezoelectric accelerometers mounted on the bottom of the silos within the support structure. The data management system with predefined post-processing algorithms allow to extract dynamic parameters of the structure, which were then used to validate the spatial finite element model (FEM). What is worth underlying, the analysis took into account different degrees of filling of the silos, as these directly affect the dynamics of the structure. The results described hereafter were the basis for providing expertise on structural safety. The presented case study is an example of how automatic monitoring over a specified period of time can assist engineers in making rational decisions.

Systematic design of health monitoring systems centered on older adults and ADLs

BackgroundOlder adults face unique health challenges as they age, including physical and mental health issues and mood disorders. Negative emotions and social isolation significantly impact mental and physical health. To support older adults and address these challenges, healthcare professionals can use Information and Communication Technologies (ICTs) such as health monitoring systems with multiple sensors. These systems include digital biomarkers and data analytics that can streamline the diagnosis process and help older adults to maintain their independence and quality of life.MethodA design research methodology is followed to define a conceptual model as the main artifact and basis for the systematic design of successful systems centered on older adults monitoring within the health domain.ResultsThe results include a conceptual model focused on older adults' Activities of Daily Living (ADLs) and Health Status, considering various health dimensions, including social, emotional, physical, and cognitive dimensions. We also provide a detailed instantiation of the model in real use cases to validate the usefulness and feasibility of the proposal. In particular, the model has been used to develop two health systems intended to measure the degree of the elders' frailty and dependence with biomarkers and machine learning.ConclusionsThe defined conceptual model can be the basis to develop health monitoring systems with multiple sensors and intelligence based on data analytics. This model offers a holistic approach to caring for and supporting older adults as they age, considering ADLs and various health dimensions. We have performed an experimental and qualitative validation of the proposal in the field of study. The conceptual model has been instantiated in two specific case uses, showing the provided abstraction level and the feasibility of the proposal to build reusable, extensible and adaptable health systems. The proposal can evolve by exploiting other scenarios and contexts.

RETRACTED ARTICLE: Design of a real-time health monitoring and prediction system for table tennis players based on optical detection sensor networks

RETRACTED ARTICLE: Design of a real-time health monitoring and prediction system for table tennis players based on optical detection sensor networks

An Intelligent Health Monitoring System for Buildings Based on IoT Technology

This paper presents the design and development of an intelligent health monitoring system for buildings, utilizing IoT (Internet of Things) technology. In addressing a prevailing issue in current building stress monitoring methods, which often overlooks point cloud data preprocessing, we propose a method that focuses on monitoring vertical stress points in concrete structures and aligning the collected data with finite element analysis models. Subsequently, harnessing the potential of IoT technology, we design a Zigbee-based scheme for collecting environmental data within the building. This scheme incorporates a diverse array of sensors and encompasses the construction of a comprehensive software and hardware system alongside a monitoring platform. The resulting system is adept at real-time monitoring and data collection across various facets of the building, ensuring alignment with actual conditions. It demonstrates significant practical value, offering user-friendly operation, robust stability, and excellent performance in the realm of building monitoring.

Machine learning enabled robot-assisted virtual health monitoring system design and development

Machine learning enabled robot-assisted virtual health monitoring system design and development

Design of a Blockchain-Based Secure Health Monitoring System Using Decentralized Machine Learning Technique

Design of a Blockchain-Based Secure Health Monitoring System Using Decentralized Machine Learning Technique

Design of Health Monitoring System Based on Internet of Things (IoT): ESP8266 and BLYNK

Health monitoring is important for living creatures. There are many things that are important factors in the health of living creatures, especially humans, including heart rate, body temperature and oxygen saturation. IoT technology is very helpful in the development of health technology so that it becomes possible to monitor patient health in real time and patient emergency information can be accessed easily. This research also aims to design an Android-based heart rate monitoring system using ESP8266 as a microcontroller. The sensor used is Max30102. The research method used is applied research, with experimental research methods. The research stages carried out were literature study, software design, hardware design, system testing, and analysis. The results of the research are the design of a patient health monitoring tool with three measured indicators, namely the patient's heart rate, body temperature and oxygen levels. The system can monitor in real time based on Internet of Things and can be accessed by Android and iOS. From the study results, it was found that the Android-based health monitoring system has an accuracy rate of 97.63% and it can be said that the system can be used for patient measurements. With details, 96.71% is the accuracy level for measuring heart rate, 96.45% accuracy level for measuring oxygen levels in the patient's body, and 99.75% accuracy level for measuring patient temperature.

BIM-based design and setup of structural health monitoring systems

The adoption of a BIM-based approach for the design and setup of a Structural Health Monitoring (SHM) system is an unexplored topic in the scientific literature. This paper presents a methodology designed to optimize the information management of the entire process using Building Information Modeling (BIM), defining the exchange information requirements (EIRs), Process Map, and Levels of Information Need (LoINs).Moreover, to verify the consistency of the information exchanged, this paper employs the information delivery specification (IDS) standard, developing a script that, using LoINs and the BIM model as input, verifies the desired requirements at each stage of the process.The proposed approach is applied to a viaduct on the Naples-Salerno highway, achieving a fully digital and interoperable platform for stakeholders involved in the processes. The results highlight the advantages of the proposed methodology over traditional approaches.

Design of blockchain-enabled secure smart health monitoring system and its testbed implementation

Smart healthcare technology is transforming from the traditional healthcare system in every manner conceivably. Smart healthcare provides several advantages over the existing approaches. However, it suffers from healthcare data security and privacy issues. As the Internet attackers may get access to sensitive healthcare data through the use of various types of cyber attacks. In this paper, an architecture of a blockchain-enabled secure smart health monitoring system has been presented (in short, it is called as BSSHM). BSSHM consists of various health data monitoring sensors, i.e., temperature, heartbeat, etc., which monitor the real time health data of the different patients. The healthcare data of the patients can be transmitted to the connected health servers in a secure way, where this data can be stored securely for its various uses. The formal security verification of the proposed BSSHM is also done through the widely-accepted Scyther tool. It has been proved that BSSHM is able to defend various potential attacks.

Model Updating of Cultural Heritage Buildings Through Swarm Intelligence Algorithms

ABSTRACT Masonry buildings constitute a great Italian historical and cultural heritage, but they were also severely damaged by earthquakes over the centuries. Therefore, to assess their structural and seismic performance, it is crucial to gain a refined and trustworthy numerical model adopting model updating techniques, sometimes based on artificial intelligence algorithms. This article deals with the development and the updating of a finite element model of an historical church that account for the presence of both the seismic damage and securing systems. The model updating is performed adopting the particle swarm optimization algorithm and is based on the comparison between numerical and experimental modal parameters, the latter achieved by an extensive dynamic test campaign. The obtained calibrated numerical model has been adopted to support the restoration work design, as well as the design of a structural health monitoring system that has been permanently installed on the church.

Retracted: Design and Analysis of Adolescent Physical Health Monitoring System under the Background of Internet of Things and 5G.

[This retracts the article DOI: 10.1155/2021/5208976.].

Integration of sensors in sandwich composites for the detection of structural damage

A decisive factor in achieving the climate targets of the German government is the reduction of CO2 through the gradual electrification of transport. In battery-electric vehicles, the energy storage system is typically positioned close to the ground under the passenger cell. Thus, the crossing of obstacles or typical load scenarios affecting the underbody play a crucial role for vehicle safety. Currently, such battery protection structures usually consist of thick-walled aluminum, steel or titanium constructions, which results in increased weight and manufacturing costs [1]. Higher moving masses in battery electric vehicles lead to high resource consumption in their life cycle. In this context, battery protection structures represent lightweight structures that are safety critical parts protecting the battery system from impact events and medial influences. Additionally, these parts are in general large-scale parts, which show a high potential for mass reduction and functional integration of a conditions monitoring system. Therefore, the focus of the presented paper is on the development of a functionally integrative lightweight battery protection structure, which consist of a sandwich lay-up of glass fiber reinforced plastic (GRP) cover sheets and polymeric foam in the middle. In addition to the purely mechanical protection of the energy storage unit, this structure should be able to detect and classify any damage occurring of the battery module above it caused by impact events. Thus, a structure-integrated sensor system is also focused, which shall be used for the determination of the extent of damage, which means that service activities or component replacements will only be carried out when necessary and not within predefined maintenance intervals. Based on a detailed requirement profile for the structure, a comparison of selected sensor concepts is carried out. For further investigations, three sensor types (pressure sensor, strain sensor, inductive sensors) are selected and examined in more detail. Thus, investigations in regard to the sensor positioning in the composite lay-up and the ability for detecting different load cases were planned and carried out. A further issue is the clarification of possible integration options in terms of manufacturing technology. For this purpose, the so-called e-preforming technology is used, which enables an automated application of functional elements. Therefore, different types of conductor track manufacturing and contacting of electronic components within the sandwich structure are investigated. The results of these initial investigations in the case of automotive applications show a high potential to be transferred to applications in the aviation or aeronautical industry. In particular, these also influence the approaches for the design and development of structural health monitoring components and systems.

Non-Crack-Growth Acoustic Emission Observed in Controlled-Stress-Intensity-Factor High-Cycle-Fatigue Tests

Acoustic emission (AE) was monitored during stress intensity factor (SIF)-controlled high-cycle fatigue (HCF) tests on an aluminum 2024-T3 specimen with a fatigue crack growing at its center. The SIF control was implemented in such a manner that crack growth could be slowed down and even inhibited while the fatigue experiment continued. In the beginning, a specific type of AE signal was observed while the crack was allowed to grow to up to approximately 9.4 mm in length. Subsequently, the load was reduced in order to control the SIF value at the crack tip and to inhibit the crack growth. AE signals were recorded even when the crack stopped growing, although the specific signature of these AE signals was different from those observed when the crack was growing, as discussed in the text. The gist of the phenomenon reported in this article is that strong AE signals could still be observed even when the crack stopped growing. These latter AE signals could be due to rubbing and clapping of the crack faying surfaces. Travel analysis was consistently performed to ensure that these AE signals were originating from the crack, though not necessarily from the crack tip. In addition, absorbing clay wave dams were built around the crack region to inhibit boundary reflections and grip noise. Fast Fourier Transform (FFT) and Choi–Williams Transform (CWT) analysis were performed to classify the AE signals. It was observed that the AE signals related to crack growth were clearly different from the AE signals originating from the crack while the crack was not growing. Strong S0-mode Lamb wave components were observed in the crack-growth AE signals, whereas strong A0-mode Lamb wave components dominated the non-crack-growth AE signals. Pearson correlation clustering analysis was performed to compare the crack-growth and non-crack growth AE signals. We propose that the fatigue-crack faying surfaces may undergo rubbing and/or clapping during fatigue cyclic loading and thus produce strong AE signals that are registered by the AE system as hits, although the crack is not actually growing. The understanding of this phenomenon is very important for the design of the structural health monitoring (SHM) system based on AE-hit signal capture and interpretation.

Design of a Structural Health Monitoring System and Performance Evaluation for a Jacket Offshore Platform in East China Sea

Offshore platform plays an important role in ocean strategy, and the construction of structural health monitoring (SHM) system could significantly improve the safety of the platform. In this paper, complete SHM system architecture design for offshore platform is presented, including the sensor subsystem, data reading and transferring subsystem, data administration subsystem, and assessment subsystem. First, the sensor subsystem is determined to include the structure information, component information, and vibration information monitoring of the offshore platform. Based on the monitoring target, three sensor types including incline sensor, acceleration sensor, and strain sensor are initially selected. Second, the assessment subsystem is determined to include safety monitoring and early warning evaluation using static measurements, overall performance evaluation based on frequency variation, and damage identification based on strain modal using strain monitoring. Overall performance evaluation based on frequency variation and damage identification based on Strain modal are illustrated. Finally, an offshore platform in the East China Sea is selected to establish a finite-element model to discuss the application and feasibility of the SHM system, the frequency variation due to scouring, corrosion, the growth of marine organisms, and temperature variation was investigated, and the overall performance of the platform was also evaluated. This work can provide a reference for installation and implementation of SHM system for offshore platform.