Integrated Health Monitoring System Research Articles

Integrated health monitoring system for checking patients’ vitals

The increasing demand for efficient healthcare services has propelled the development of efficient health monitoring systems. This paper presents a comprehensive system designed to monitor the vital signs of patients in real-time, aimed at enhancing patient care and reducing the burden on healthcare providers. The system integrates various sensors to Data collected from these sensors are transmitted wirelessly to a central database, where it is processed and analyses using sophisticated algorithms. The results are accessible through a user-friendly interface for both healthcare professionals and patients. This health monitoring system offers several critical benefits. it provides real-time alerts for any abnormal vital signs, enabling prompt medical intervention. Secondly, the continuous monitoring allows for the early detection of potential health issues, which can significantly improve patient outcomes. The methodology involves is the analytical and systematic application of Arduino Nano Boards as microcontrollers, such as DS18B20, DHT11 and with WIFI Modula; which are driven- real time-based temperature frameworks for optimization and abstraction of accurate patient’s health data result. Results demonstrated the system's accuracy, reliability, and user satisfaction.

AI Sound Recognition on Asthma Medication Adherence: Evaluation With the RDA Benchmark Suite

Asthma is a common, usually long-term respiratory disease with negative impact on global society and economy. Treatment involves using medical devices (inhalers) that distribute medication to the airways and its efficiency depends on the precision of the inhalation technique. There is a clinical need for objective methods to assess the inhalation technique, during clinical consultation. Integrated health monitoring systems, equipped with sensors, enable the recognition of drug actuation, embedded with sound signal detection, analysis and identification from intelligent structures, that could provide powerful tools for reliable content management. Health monitoring systems equipped with sensors, embedded with sound signal detection, enable the recognition of drug actuation and could be used for effective audio content analysis. This paper revisits sound pattern recognition with machine learning techniques for asthma medication adherence assessment and presents the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Respiratory and Drug Actuation (RDA)</i> Suite <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>a</i></sup> for benchmarking and further research. The RDA Suite includes a set of tools for audio processing, feature extraction and classification procedures and is provided along with a dataset, consisting of respiratory and drug actuation sounds. The classification models in RDA are implemented based on conventional and advanced machine learning and deep networks’ architectures. This study provides a comparative evaluation of the implemented approaches, examines potential improvements and discusses on challenges and future tendencies.

New Method to Implement and Analysis of Medical System in Real Time.

The use of information technology and technological medical devices has contributed significantly to the transformation of healthcare. Despite that, many problems have arisen in diagnosing or predicting diseases, either as a result of human errors or lack of accuracy of measurements. Therefore, this paper aims to provide an integrated health monitoring system to measure vital parameters and diagnose or predict disease. Through this work, the percentage of various gases in the blood through breathing is determined, vital parameters are measured and their effect on feelings is analyzed. A supervised learning model is configured to predict and diagnose based on biometric measurements. All results were compared with the results of the Omron device as a reference device. The results proved that the proposed design overcame many problems as it contributed to expanding the database of vital parameters and providing analysis on the effect of emotions on vital indicators. The accuracy of the measurements also reached 98.8% and the accuracy of diagnosing COVID-19 was 64%. The work also presents a user interface model for clinicians as well as for smartphones using the Internet of things.

Artificial intelligence of toilet (AI-Toilet) for an integrated health monitoring system (IHMS) using smart triboelectric pressure sensors and image sensor

Smart toilet provides a feasible platform for the long-term analysis of person’s health. Common solutions for identification are based on camera or radio-frequency identification (RFID) technologies, but it is doubted for privacy issues. Here, we demonstrate an artificial intelligence of toilet (AI-toilet) based on a triboelectric pressure sensor array offering a more private approach with low cost and easily deployable software. The pressure sensor array attached on the toilet seat is composed of 10 textile-based triboelectric sensors, which can leverage the different pressure distribution of individual users' seating manner to get the biometric information. 6 users can be correctly identified with more than 90% accuracy using deep learning. The signals from pressure sensors also can be used for recording the seating time on the toilet. The system integrates a camera sensor to analyze the simulated urine by comparing with urine chart and classify the types and quantities of objects using deep learning. All information including two-factor user identification and entire seating time using pressure sensor array, and data from the urinalysis and stool analysis were automatically transferred to a cloud system and were further shown in user's mobile devices for better tracking their health status.

Pregled integriranog sustava za praćenje stanja ovješenog mosta i preliminarna analiza rezultata

The Junshan Yangtze River Bridge, built in Wuhan, China in 2001, is a steel box girder cable-stayed bridge with a main span of 460m. Due to decades of service, the bridge suffered gradual degradation and some damage. Structural health monitoring has attracted worldwide attention due to its capacity of monitoring structural damage, assisting maintenance and management, and ensuring safe operation of bridges. To monitor the performance status of bridges and generate a timely safety alarm, an integrated health monitoring system has been designed and implemented on bridges. This paper provides a thorough account of the monitoring system used at the Junshan Yangtze River Bridge. It mainly focuses on the selection of monitoring variables and arrangement of sensor points, the data collection and transmission system, the data storage and management strategy, and the user interface system. All kinds of monitoring data collected under daily operation, such as vehicle load, deflection, displacement, and cable tension, are analysed. Monitoring data for an extreme condition, which involves two heavy trucks of 178 tons moving across the bridge, are also analysed. The results indicate that the monitoring system works well and that some local welded joints may experience fatigue damage.

Characterization of the spatial elastoresistivity of inkjet-printed carbon nanotube thin films

Next-generation lightweight-designed structures shall be able to perform self-state assessment via integrated health monitoring systems. In this article a carbon nanotube-embedded polymeric thin film is applied via inkjet-printing to perform spatial strain sensing in conjunction with using electrical impedance tomography. To gain an advanced understanding of the thin film’s spatial strain sensitivity, the elastoresistivity matrix, a fourth-order tensor correlating the strain state of a conductor into its normalized change in resistivity state, is characterized. The Montgomery method is adopted to derive the planar resistivity coefficients of the thin film, and a digital image correlation system is used to measure the planar strains. A validation test suggests that the calculated determinant of the correlated change in anisotropic resistivity shows a fairly similar result to the measured isotropic EIT reconstruction results.

Identification of sudden transitions in sensor data from rocket tests using wavelet transforms within an integrated health monitoring system

Under a project undertaken at NASA’s Stennis Space Center, an integrated framework has been developed for intelligent monitoring of smart elements. Integrated Systems Health Monitoring is an implementation of a monitoring system which is robust, user friendly, and adaptable. This paper focuses on smart sensors, and shows the advantage of utilizing an enhanced version of a previously developed intelligent system, DATA-SIMLAMT, called Enhanced DATA-SIMLAMT or EDATA-SIMLAMT. This new version contains additional properties and states for improved data interpretation. The additional properties are based on wavelets. The major advantage provided by adding wavelet analysis is the ability to detect sudden transitions as well as obtaining the frequency content using a much smaller data set then that required by the traditional Fourier transform method. Historically, sudden transitions could only be detected by a visual method or by offline analysis of the data. EDATA-SIMLAMT provides an opportunity to automatically detect sudden transitions as well as many additional data anomalies, and provide improved data-correction and sensor health diagnostic abilities. The newly developed system has been tested on actual rocket test data from NASA’s Stennis Space Center.

Applying FHIR in an Integrated Health Monitoring System

The continuous monitoring of vital signs has become an important supplement to traditional medical treatment to ensure the success of a therapy. Integrated health monitoring solutions based on existing health standards provide interoperability and enable healthcare providers and patients to exchange and access their data across institutional borders. This paper shows an integrated monitoring solution based on Continua and Integrating the Healthcare Enterprise, which has been tested by more than 130 patients and 14 healthcare institutions. According to user feedback, one recurring problem is the low battery life of smartphones due to high data traffic. Since the recently developed HL7 standard FHIR offers a resource efficient handling of web service connections, a possible approach to extend the monitoring solution to support FHIR. Comparing both solutions using data collected by 68 patients, it can be concluded that there is a significant decrease in data traffic when relying on a RESTful architecture in combination with FHIR. For productive use of the FHIR-based approach shown in this paper, security related concerns have to be taken into account in future work to ensure authenticity and authorization.

Intelligent Health Monitoring Based on Pervasive Technologies and Cloud Computing

The proper management of patient data and their accessibility are still remaining issues that prevent the full deployment and usage of pervasive healthcare applications. This paper presents an integrated health monitoring system based on mobile pervasive technologies. The system utilizes Cloud Computing for providing robust and scalable resources for sensor data acquisition, management and communication with external applications like health information systems. A prototype has been developed using both mobile and wearable sensors for demonstrating the usability of the proposed platform. Initial results regarding the performance of the system, the efficiency in data management and user acceptability have been quite promising.

An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process

Efficient asset management is of paramount importance, particularly for systems with costly downtime and failure. As in energy and capital-intensive industries, the economic loss of downtime and failure is huge, the need for a low-cost and integrated health monitoring system has increased significantly over the years. Timely detection of faults and failures through an efficient prognostics and health management (PHM) framework can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize the overall maintenance cost of the systems. This paper aims at practical challenges of online diagnostics and prognostics of mechanical systems under unobservable degradation. First, the elements of a multistate degradation structure are reviewed and then a model selection framework is introduced. Important dynamic performance measures are introduced, which can be used for online diagnostics and prognostics. The effectiveness of the result of this paper is demonstrated with a case study on the health monitoring of turbofan engines.

Automated manufacture of 3D reinforced aerospace composite structures

PurposeThe purpose of this paper is to focus on exploring an innovative combination of cutting‐edge technologies to be implemented within automated processes for composite parts manufacturing. The objective is the design of a production route for components with tailored fibre orientation and ply lay‐up, with improved damage tolerance thanks to through‐the‐thickness reinforcement and integrated health monitoring systems based on optical fibres technology. This study is part of the FP7 project ADVITAC.Design/methodology/approachThe proposed technologies are described in detail and their compatibility and potential for integration are discussed.A set up for on‐line monitoring of infusion and curing processes of carbon/epoxy laminates preformed by dry fibre placement technology is proposed, and a preliminary study of their mechanical performance is presented. The possibility of reinforcing through‐the‐thickness preforms manufactured with dry slit tapes automatically laid‐up and consolidated by laser heating is investigated.FindingsImproved knowledge was obtained of interaction/compatibility between the discussed technologies and scope for application.Research limitations/implicationsThe paper reports the technical potential and practical feasibility of the proposed integrated production process. Limited quantitative evaluations on the materials performance are provided. The analysis of the technologies involved represents the early outcome of the ongoing ADVITAC project.Practical implicationsThis study contributes to the identification of a new generation of composite architecture which allows production cost and weight savings while retaining the level of quality suitable for demanding structural applications, with particular relevance to the aerospace field.Originality/valueThis paper investigates for the first time the practical possibility of designing a single automated process involving dry fibre placement, tufting and optical fibre sensor monitoring for the production of complex composite components.

SmartSync: An Integrated Real-Time Structural Health Monitoring and Structural Identification System for Tall Buildings

This study introduces a unique prototype system for structural health monitoring (SHM), SmartSync, which uses the building’s existing Internet backbone as a system of virtual instrumentation cables to permit modular and largely plug-and-play deployments. Within this framework, data streams from distributed heterogeneous sensors are pushed through network interfaces in real time and seamlessly synchronized and aggregated by a centralized server, which performs basic data acquisition, event triggering, and database management while also providing an interface for data visualization and analysis that can be securely accessed. The system enables a scalable approach to monitoring tall and complex structures that can readily interface a variety of sensors and data formats (analog and digital) and can even accommodate variable sampling rates. This study overviews the SmartSync system, its installation/operation in the world’s tallest building, Burj Khalifa, and proof-of-concept in triggering under dual excitations (wind and earthquake).

Physical and Virtual Intelligent Sensors for Integrated Health Management Systems

Abstract This paper describes the development of intelligent sensors as part of an integrated systems approach. The integrated systems approach treats the sensor as a complete system with its own sensing hardware (the traditional sensor), A/D converter, processing and storage capabilities, software drivers, self-assessment algorithms and communication protocols. The immediate application is the monitoring of rocket test stands, but the technology should be generally applicable to the Integrated Systems Health Monitoring (ISHM) vision. This paper outlines progress made in the development of intelligent sensors by describing the work done to date on Physical Intelligent Sensors (PIS) and Virtual Intelligent Sensors (VIS). The PIS as discussed here consists of a thermocouple used to read temperature in an analog form which is then converted into digital values. A microprocessor collects the sensor readings and runs numerous embedded event detection routines on the digital data. If any event, i.e. spike, drift, noise, is detected, it is reported, stored and sent to a remote system through an Ethernet connection. Hence the output of the PIS is data coupled with a confidence factor in the reliability of the data. The VIS discussed here is a virtual implantation of the PIS in C++. The VIS is designed to mirror the operations of the PIS; however, the VIS works on a computer at which digital data is provided as the input and is thus portable to any sensor system. This work lays the foundation for the next generation of smart devices that have embedded intelligence for distributed decision making capabilities.

An Integrated Health Monitoring System for Fission Surface Power

Abstract Based on such criteria as safety and mission success, programmatic risk, affordability, and extensibility/flexibility, the National Aeronautics and Space Administration (NASA) has chosen fission surface power (FSP) as the primary energy source for building a sustained human presence on the Moon, exploring Mars, and extremely long-duration space missions. The current benchmark FSP system has a mission life of at least 8 years during which time there is no opportunity for repair, sensor calibrations, or periodic maintenance tasks that are normally performed on terrestrial-based nuclear power plants during scheduled outages. Current technology relies heavily on real-time human interaction, monitoring and control. However; due to the long communication times between the Earth and Moon, or Mars, real-time human control is not possible, resulting in a critical need to develop autonomous health monitoring technology for FSP systems.This paper describes the design and development of an autonomous health monitoring system that will (1) provide on-line calibration monitoring, (2) reduce uncertainties in sensor measurements, and (3) provide sensor validation and fault detection capabilities for the control systems of various FSP subsystems. The health monitoring system design integrates a number of signal processing algorithms and techniques such as cross-calibration, empirical modeling using neural networks, and physical modeling under a modular signal processing platform that will enable robust sensor and system monitoring without the need for human interaction. Prototypes of the health monitoring system have been tested and validated on data acquired from preliminary subsystem testing of NASA's FSP Technology Demonstration Unit (TDU) as well as simulated laboratory data. Results from this testing have demonstrated the utility and benefits that such autonomous health monitoring systems can provide to FSP subsystems and other potential applications within NASA such as launch vehicle systems, other nuclear power systems, and ground operations.

Piezoelectric Fiber Composites as Sensor Elements for Structural Health Monitoring and Adaptive Material Systems

Piezoelectric Active Fiber Composites (AFC) and Macro Fiber Composites (MFC) have the potential to provide various sensor functions for nondestructive test methods. AFC have been integrated into fiber-reinforced laminates as a first step towards structures with sensing capability. These developments constitute initial stages for developing adaptive composite structures or structures with integrated health monitoring system. So far, the use of AFC and MFC has been explored in selected nondestructive tests for defect detection in model composite systems on laboratory scale with e.g., Acoustic Emission, Acousto-Ultrasonics, and Electromechanical Impedance testing. The present article will focus on limitations and current prospects for structural health monitoring with AFC or MFC and discuss selected concepts and approaches.

Comparison between Non-Destructive Evaluation Techniques and Integrated Fiber Optic Health Monitoring Systems for Composite Sandwich Structures

Non-Destructive Evaluation (NDE) methods have been adapted to composite sandwich structures in order to detect damage, in particular impact induced damage. Using three coupons representative of radome structures, several techniques were tested: ultrasonics, the Electromagnetic/InfraRed method (EMIR), stimulated shearography and stimulated infrared thermography. The extension of the measured damaged area was taken as a criterion, showing that stimulated shearography and ultrasound are the most sensitive methods. Moreover, two integrated Health Monitoring Systems (HMS) were tested, based on the one hand on microbending sensitive fibres and Optical Time Domain Reflectometry (OTDR), and on the other hand on Fibre Bragg Grating (FBG) sensors. The experiments demonstrated that HMS can detect damage with sensitivity comparable to that of the external NDE techniques.