Real-time Health Status Research Articles

Advances in analytical techniques for assessing volatile organic compounds in pulse crops: a comprehensive review

Pulse crops, including beans, peas, chickpeas, and lentils, are vital sources of protein, fiber, and essential nutrients worldwide. They serve not only as staple foods but also as key components of sustainable agricultural practices, contributing to soil fertility through nitrogen fixation and enhancing overall productivity. However, pulse crops face numerous abiotic and biotic stresses mainly insect pest attack and pathogen invasion, which pose significant threats to pulse crops, impacting both production and food security. To overcome these challenges, plants have evolved diverse defense mechanisms, including the emission of specific volatile organic compounds (VOCs). These volatiles play crucial roles in plant communication, protection, and real-time health status indication. Monitoring VOCs offers a promising approach for early detection of pest infestations or pathogen infections, enabling the grower to take early action and decide on the proper control measure to minimize losses. The identification of plant-emitted VOCs requires robust and sensitive analytical techniques such as gas chromatography and mass spectrometry, which are the mainly used techniques for in pulse crops studies. However, traditional methods have limitations, prompting the need for advanced, portable, and real-time detection alternatives, such as gas-sensing technologies. This paper provides a comprehensive review of VOC measuring methods, including extraction, separation, and analytical techniques, focusing on their application in pulse crops. Recent advancements in gas-sensing technologies are also discussed, highlighting their potential in enhancing crop protection and agricultural sustainability.

Implementation of Health Monitoring System of Induction Machine Using Computational Intelligence

The induction machine is a critical component in various industrial applications, and its reliable operation is paramount for uninterrupted processes. To ensure the continuous performance and prevent unexpected failures, the implementation of an effective health monitoring system is imperative. In this study, we propose a novel approach to monitor the health of induction machines using computational intelligence techniques. The proposed system integrates advanced computational intelligence algorithms, including machine learning and signal processing methods, to analyze the operational data and identify potential faults or anomalies in the induction machine. Specifically, we employ techniques such as artificial neural networks, support vector machines, and genetic algorithms to process the sensor data acquired from the induction machine. The health monitoring system operates in real-time, continuously analyzing various parameters such as current, voltage, temperature, and vibration patterns to detect deviations from normal operating conditions. By establishing baseline performance profiles and employing pattern recognition algorithms, the system can identify early signs of degradation or impending failures, allowing for proactive maintenance interventions. Moreover, the proposed system offers flexibility and scalability, enabling adaptation to different types of induction machines and operating environments. It facilitates remote monitoring capabilities, enabling maintenance personnel to access real-time health status and diagnostic information from anywhere, facilitating timely decision-making and proactive maintenance strategies.

Active Fault-Tolerant and Attack-Resilient Control for a Renewable Microgrid Against Power-Loss Faults and Data Integrity Attacks.

The next-generation power grid evolves from the development of fundamental cyber-physical energy systems called smart microgrids. In order to improve the reliability, safety, and security of smart microgrids and achieve a more cost-effective operation, innovative approaches for physical fault diagnosis and fault-tolerant control (FTC) as well as intrusion detection and attack-resilient control (ARC) should be investigated. Given that, this article considers a smart hybrid renewable-based microgrid with different types of distributed generation units, including solar photovoltaic (PV) array, wind turbines, and battery energy storage system. Novel active FTC and ARC strategies are designed for pulse-width modulation (PWM) converters at microgrid level. The proposed fault-tolerant controller is based on an optimal fuzzy gain-scheduling technique that is used to accommodate the adverse impacts of PV power-loss faults. Also, the proposed attack-resilient controller relies on the estimated values of sensor measurements during the occurrence of data integrity cyber-attacks. To access and evaluate the microgrid's real-time health status, both FTC and ARC strategies employ an integrated model-based intrusion detection and fault diagnosis (IDFD) system that is designed using a fuzzy modeling and identification technique. Finally, the effectiveness of the proposed solutions is demonstrated via a series of simulations in MATLAB/Simulink using an advanced microgrid benchmark.

A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring.

Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.

Privacy-preserving healthcare monitoring for IoT devices under edge computing

With the rapid development of the Internet of Things (IoT) technology, e-healthcare has received extensive attention because it is able to provide real-time health status feedback for users. Due to the exponential growth of health data and the expensive cost of training the healthcare monitoring model, outsourcing the healthcare monitoring service to the edge servers becomes a popular way in e-healthcare. How to accomplish healthcare monitoring without leaking users' privacy to the edge servers is the key element of e-healthcare. In this paper, we propose a privacy-preserving healthcare monitoring framework based on long short-term memory (LSTM) neural network for IoT devices, which is performed on edge servers. In our framework, to reduce the computational burden, we outsource the complicated computations to the edge servers. To protect the privacy of health data and healthcare monitoring model, we design a series of secure interactive protocols based on replicated secret sharing and use three edge servers to collaboratively implement secure inference and model training for LSTM. We demonstrate the security of the proposed protocols and give the performance evaluation. Numerical experiments show that the proposed protocols are able to produce results with fine accuracy while requiring low computation and communication overheads.

Scalable Manufacturing of Large-Area Pressure Sensor Array for Sitting Posture Recognition in Real Time.

Pressure sensors are considered the key technology for potential applications in real-time health monitoring, artificial electronic skins, and human-machine interfaces. Despite the significant progress in developing novel sensitive materials and constructing unique sensor structures, it remains challenging to fabricate large-area pressure sensor arrays due to the involvement of complex procedures including photolithography, laser writing, or coating. Herein, a scalable manufacturing approach for the realization of pressure sensor arrays with substantially enlarged sensitive areas is proposed using a versatile screen-printing technique. A compensation mechanism is introduced into the printing process to ensure the precise alignment of conductive electrodes, insulation layers, and sensitive microstructures with an alignment error of less than 4 μm. The fully screen-printed sensors exhibit excellent collective sensing performance, such as a reasonable pressure sensitivity of -2.2 kPa-1, a fast response time of 40 ms, and superior durability over 3000 consecutive pressures. Additionally, an integrated 16 × 32 pressure sensor array with a sensing area of 190 × 380 mm2 is demonstrated to precisely recognize the sitting postures and the body weights, showing great potential in continuous and real-time health status monitoring.

A modular fault diagnosis method for rolling bearing based on mask kernel and multi-head self-attention mechanism

Data-driven methods have been applied in fault diagnosis. However, in practical engineering, workers are more concerned with the real-time health status of bearings. And it is difficult to complete the effective training of diagnostic models with insufficient labeled fault data. Therefore, this paper proposes a modular method based on a mask kernel and multi-head self-attention mechanism for rolling bearing fault diagnosis. First, the proposed method divides the diagnosis into two modules of status detection and fault recognition. The approach of sharing one backbone for both modules simplifies the optimization process. The method combines the translation invariance of the convolution kernel and the mask attention mechanism of the transformer by computing the local self-attention and superimposing the partial local attention by the mask to ensure the integrity of the information. Finally, a zero-shot training method is proposed to embed the query into the model to achieve cross-distribution fault diagnosis of bearings. The experiments on the data sets of Case Western Reserve University and machinery fault simulator are implemented to diagnose the bearings. The results show that the proposed method can obtain higher diagnostic accuracy and computational efficiency than the existing methods and can be valid for scenarios with cross-condition diagnosis or imbalanced samples.

Health Assessment Method of Equipment in Distribution Court Based on Big Data Analysis in the Framework of Distribution Network of Things

Focusing on the problem that the quantity of equipment in the distribution court is huge and the operation status is difficult to reliably control, a method of equipment health status assessment in the distribution court based on big data analysis in the distribution network of things architecture is proposed. Firstly, based on the internet of things for power distribution, the evaluation system of equipment status in the distribution court is designed to ensure the efficient analysis of massive data through the cooperation of cloud center and edge computing. Then, at the edge of the system, the grey correlation analysis algorithm and the Granger hypothesis method are used to obtain the correlation and causality of the failure rate of equipment components and the influencing factors so as to understand the accurate failure rate of equipment components. Finally, the weight of factors affecting the equipment failure rate is identified by using the dynamic variable weight analytic hierarchy process, and it is corrected in the cloud center; and the overall health degree of the equipment in the distribution court is obtained through transformation. Based on the selected station area model, the proposed method is experimentally demonstrated. The results show that it can accurately obtain the real-time health status of the court equipment and the evaluation accuracy is close to 98%, which provides theoretical support for the operation and maintenance of the distribution network.

Insights on assessing image processing approaches towards health status of plant leaf using machine learning

<p class="Abstract"><span lang="EN-US">With the advancement of digital image processing in agriculture and crop cultivation, imaging techniques are adopted to acquire real-time health status. Out of all the parts of plants, the leaf is the direct indicator of its health status, and hence applying various image processing approaches could benefit the process of yielding informative cases of plant health. At present, there are various approaches, e.g., feature extraction, segmentation, identification, the classification being evolved up with more dependencies being found in using machine learning; the studies show many contributions towards this challenge. However, it is not yet conclusive to understand the optimal approach. Hence, this paper highlights an explicit strength and weakness associated with the existing approaches existing imaging processing techniques to identify the disease condition from an input of plant leaves' image. The study also contributes to highlighting open-end research problems to have conclusive remarks about effectiveness. </span></p>

A Multilayered and Multifactorial Health Assessment Method for Launch Vehicle Engine under Vibration Conditions

Sixty percent of the failures of launch vehicles in the ascending phase occur in the propulsion system. Among them, the vibration generated by the engine is an important factor in the occurrence of failure. At present, health assessment methods in the aerospace field are mostly for specific equipment, and scholars mostly assess the real-time health status of launch vehicle engines which can only reflect the current health status of the launch vehicle. Existing methods cannot be applied to different equipment, and there is a lack of research on health assessments of fuzzy and complex mechanical systems. In this article, we propose a multi-layer and multi-factor predictive evaluation method for a fuzzy and complex system and conduct experiments on real vibration data of rockets. First, we divide the health assessment level according to the vibration data that affect the normal operation of the rocket. Secondly, we obtain the future trend of vibration signals based on five data prediction methods and calculate the health status interval of the rocket engine’s working conditions based on the boxplot method. At the same time, we calculate the single health evaluation set of every vibration signal. We obtain the weights of each level and factor for the health value based on an analytic hierarchy process (AHP). The optimization of this step avoids an over-reliance on expert experience. Finally, we complete a fuzzy comprehensive evaluation of the engine system from the bottom up to obtain the final health value. The minimum evaluation error is 0.0193% on the test data of the Long March series launch vehicle engine, which shows that the proposed method can successfully predict and evaluate the launch vehicle engine.

An Intelligent Healthcare System Using IoT in Wireless Sensor Network.

The Internet of Things (IoT) uses wireless networks without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental factors. There are a variety of WSN uses, and some well-known application factors include energy consumption and lifespan duration for routing purposes. The sensors have detecting, processing, and communication capabilities. In this paper, an intelligent healthcare system is proposed which consists of nano sensors that collect real-time health status and transfer it to the doctor's server. Time consumption and various attacks are major concerns, and some existing techniques contain stumbling blocks. Therefore, in this research, a genetic-based encryption method is advocated to protect data transmitted over a wireless channel using sensors to avoid an uncomfortable data transmission environment. An authentication procedure is also proposed for legitimate users to access the data channel. Results show that the proposed algorithm is lightweight and energy efficient, and time consumption is 90% lower with a higher security ratio.

Real-time comprehensive health status assessment of hydropower units based on multi-source heterogeneous uncertainty information

An effective health status assessment (HSA) method of hydropower unit (HU) is the key to obtaining the real status of HU and providing technical support for safety operation decisions. Thus, a real-time comprehensive HSA method for HUs is proposed. Firstly, a multi-scale data cleaning method based on Gaussian mixture model and density-based spatial clustering of applications with noise (GMM-DBSCAN) is proposed to eliminate outliers and dense anomalies while considering for the variation of operating conditions. Secondly, the correlation analysis method is used to select important measurement points reflecting HU operation status to create multi-source evaluation indices. Then, a novel health indicator (HI) based on Gaussian cloud model (GCM) is proposed to analyze HU condition changes, considering the uncertainty of monitoring signal changes. Further, a hybrid weighting calculation method is proposed to establish a real-time comprehensive health index (RCHI) for assessing HU status. Finally, the proposed approach is verified by a case study using data from a hydropower station in China. The results show that the method improved the stability and smoothness of the state assessment curve compared to other studies.

A Method for Satellite Component Health Assessment Based on Multiparametric Data Distribution Characteristics

This research presents a novel data-based multi-parameter health assessment method to meet the growing need for the in-orbit health assessment of satellite components. This method analyzed changes in component health status by calculating distribution deviations and variation similarities in real-time operational data. Firstly, a single-parameter health state description method based on data distribution characteristics was presented. Secondly, the main health characteristic parameters were selected by mechanistic analysis and expert experience. The CRITIC method and the entropy weighting method were fused to assign reasonable weights and establish a multi-parameter component health assessment model. Then, the feasibility of a component health assessment algorithm based on data distribution characteristics was verified using real telemetry data from satellites. Finally, to verify the rationality of the presented health assessment algorithm, the results were compared with the pre-processed original data using empirical mode decomposition. The experimental results show that the method can accurately describe the change trend of the health status of the components. It proves that the method can be effectively used for the real-time health condition assessment and monitoring of satellite components.

3D-assembled microneedle ion sensor-based wearable system for the transdermal monitoring of physiological ion fluctuations

Monitoring human health is of considerable significance in biomedicine. In particular, the ion concentrations in blood are important reference indicators related to many diseases. Microneedle array-based sensors have enabled promising breakthroughs in continuous health monitoring due to their minimally invasive nature. In this study, we developed a microneedle sensing-array integrated system to continuously detect subcutaneous ions to monitor human health status in real time based on a fabrication strategy for assembling planar microneedle sheets to form 3D microneedle arrays. The limitations of preparing 3D microneedle structures with multiple electrode channels were addressed by assembling planar microneedle sheets fabricated via laser micromachining; the challenges of modifying closely spaced microneedle tips into different functionalized types of electrodes were avoided. The microneedle sensing system was sufficiently sensitive for detecting real-time changes in Ca2+, K+, and Na+ concentrations, and it exhibited good detection performance. The in vivo results showed that the ion-sensing microneedle array successfully monitored the fluctuations in Ca2+, K+, and Na+ in the interstitial fluids of rats in real time. By using an integrated circuit design, we constructed the proposed microneedle sensor into a wearable integrated monitoring system. The integrated system could potentially provide information feedback for diseases related to physiological ion changes.

Multi-Mode Electric Wheelchair with Health Monitoring and Posture Detection Using Machine Learning Techniques

Patients with cognitive difficulties and impairments must be given innovative wheelchair systems to ease navigation and safety in today’s technologically evolving environment. This study presents a novel system developed to convert a manual wheelchair into an electric wheelchair. A portable kit has been designed so that it may install on any manual wheelchair with minor structural changes to convert it into an electric wheelchair. The multiple modes include the Joystick module, android app control, and voice control to provide multiple features to multiple disabled people. The proposed system includes a cloud-based data conversion model for health sensor data to display on an android application for easy access for the caretaker. A novel arrangement of sensors has been applied according to the accurate human body weight distribution in a sitting position that has greatly enhanced the accuracy of the applied model. Furthermore, seven different machine learning algorithms are applied to compare the accuracy, i.e., KNN, SVM, logistic regression, decision tree, random forest, XG Boost, and NN. The proposed system uses force-sensitive resistance (FSR) sensors with prescribed algorithms incorporated into wheelchair seats to detect users’ real-time sitting positions to avoid diseases, such as pressure ulcers and bed sores. Individuals who use wheelchairs are more likely to develop pressure ulcers if they remain in an inappropriate posture for an extended period because the blood supply to specific parts of their skin is cut off owing to increased pressure. Two FSR configurations are tested using seven algorithms of machine learning techniques to discover the optimal fit for a high-efficiency and high-accuracy posture detection system. Additionally, an obstacle detection facility enables one to drive safely in unknown and dynamic environments. An android application is also designed to provide users of wheelchairs with the ease of selecting the mode of operation of the wheelchair and displaying real-time posture and health status to the user or caretaker.

Physiological Big Data Mining Through Machine Learning and Wireless Sensor Networks

With the improvement of living standards, the requirements for medical care and daily healthcare quality have become higher and higher. However, the traditional medical diagnosis mode cannot provide patients with all-round, real-time, and accurate health status. With the aggravation of the aging population, the scale of physiological data will increase in a blowout manner. The traditional medical diagnosis model for monitoring, which is based at the central hospital, has been unable to meet the current real-time monitoring needs for families and individuals. In order to solve this issue, this paper establishes a wireless sensor network based medical platform, which implements sleep monitoring by mining electroencephalogram signals. The wireless sensor network-based medical platform adopts the end-edge-cloud architecture. The experiments and simulations show the effectiveness of the proposed end-edge-cloud architecture-based medical platform.

Air-to-Ground Transmission and Near Real-Time Visualization of FBG Sensor Data via Cloud Database

During the last decades, innovative aircraft health management systems have been receiving increasing interest from original equipment manufacturers (OEMs) and aircraft operators. Their implementation could lead to substantial benefits: drastic cuts in turnaround time, operation costs, and life cycle costs (LCCs) as well as sharp increases in system availability, safety, and reliability. An interconnectivity step-up is, hence, needed to guarantee a seamless data transfer. In this article, an integrated open-source solution for reliable data transmission and near real-time graphical visualization is proposed. After a comprehensive calibration and verification campaign performed on a test stand, the overall system has been successfully validated on structural data measured using a network of fiber Bragg gratings (FBGs) mounted on a radio-controlled model aircraft. The result is an effective and robust system able to monitor near real-time critical parameters and health status of structures. With this system, the temperature and displacements of the structure can be displayed on a heat map arranged on a 3-D model and visualized through a computer application on the ground. The proposed methodology can be applied to heterogeneous scenarios, ranging from maintenance planning activities to performance checks, providing an all-in-one solution for flight data management as well as other applications in the structural monitoring domain.

Comprehensive Database Creation for Potential Fish Zones Using IoT and ML with Assimilation of Geospatial Techniques

The framework for aqua farming database collection and the real-time monitoring of different working functions of aqua farming are essential to enhance and digitalize aqua farming. Data collection and real-time monitoring are attained using cutting-edge technologies, and these cutting-edge technologies are useful for the conservation and advancement of traditional aquatic farming, particularly in hilly areas with sustainable development goals (SDGs). Geo-tagging and geo-mapping of the aqua resources will play an important role in monitoring the species in the aquatic environment and can track the real-time health status, movement, and location, and monitor the foraging behaviors, of aquatic species. This study proposed an architecture with the IoT to manage the aqua resource for eco-sustainability with geospatial data. This study also discussed the geo information systems (GIS)- and geo positioning system (GPS)-based web-based framework for the fisheries sector and the creation of a database for aqua resource management. In the study, the results of database generation for the aqua resource management and the results of the fishpond in the cloud server are presented in detail. Machine learning (ML) is integrated with the framework to analyze the sensor data and geo-spatial data for the identification of any degradation in the water quality. This will provide real-time information to the policymakers for their critical decisions for the further development of aquatic species for enhancing the economy of the state as well as aqua farmers.

Soldier health and position monitoring system using GPS and IOT technology

The fighter Wellbeing and Position Global Positioning Framework allows the military to track the current GPS position of officers and also monitors their health status, including internal temperature and heartbeat. This system also includes an additional feature that enables a soldier to request physical assistance or send a distress signal to the military if needed. The GPS modem transmits the latitude and longitude positions in a connected pattern, aiding the military in tracking the officer's current location. The system is highly beneficial for obtaining real-time health status information and providing immediate assistance to soldiers. In today's world, all nations prioritize their security, and soldiers, as the backbone of any armed forces, often lose their lives due to lack of medical aid during emergencies. Furthermore, soldiers engaged in missions or special operations might become disoriented on war fields, losing contact with authorities. To address these concerns, we developed this project. By utilizing a Wireless Body Area Sensor Network (WBANS) consisting of temperature and heartbeat sensors, we can monitor the soldiers' health status whenever necessary. Additionally, through GPS, we can track the precise location of the soldiers as needed. The use of an oxygen level sensor enables us to monitor the environmental conditions so that necessary aid can be provided by medical professionals. Communication between the soldiers and medical personnel is established via the Internet of Things (IoT). Any anomalies detected by the Remote Body Area Sensor Network (WBASNs) act as triggers for the IoT device to establish a connection between the soldier and the base unit, transmitting current location and health status to the receiver. To effectively convey this data, we have employed this equipment to create a cost-effective, lightweight, portable, and accurate defense system for soldiers.

AI-Based Low-Cost Real-Time Face Mask Detection and Health Status Monitoring System for COVID-19 Prevention

The outbreak of COVID-19 had brought a great challenge for the World Health Organization (WHO) in preventing the spreading of SARS-CoV-2. The Ministry of Health (MOH) of Malaysia introduced the MySejahtera mobile application for health monitoring and contact tracing. Wearing a face mask in public areas had been made compulsory by the Government. The overhead cost incurred in hiring the extra manpower to ensure all the visitors wear a face mask, check-in through MySejahtera and the status in MySejahtera is healthy before entering a premise. A low-cost solution is urgently needed to reduce the heavy overhead cost. An AI-Based Low-Cost Real-Time Face Mask Detection and Health Status Monitoring System (AFMHS) is proposed to perform real-time detection for the face mask and MySejahtera Check-In tickets by using artificial intelligence. MobileNetV2 was used for the detection and recognition of face and face masks. YOLOv3 was used for the detection of the region of interest for the MySejahtera Check-In ticket to locate the health and vaccination status of the visitor. Optical character recognition (OCR) is a technique that is used to detect the text captured in an image and encode the recognized text. OCR is implemented to recognize the text extracted from the ticket. Tesseract is used as the OCR engine in AFMHS. Raspberry-Pi-4B (Raspberry Pi Generation 4 Model B) with 4 GB RAM is used as the processing unit of AFMHS. The total cost of the AFMHS is only USD220. Extensive experimental tests were carried out to evaluate the performance of AFMHS. The optimum operation setup conditions are proposed to achieve 100% accuracy. The optimum operating distance for the face mask detector and MySejahtera Check-In ticket detector are 1.5m and 15cm respectively.