Intelligent Healthcare Monitoring Research Articles

Predicting heart disease based on an intelligent healthcare monitoring system using HPM-NIA

Predicting heart disease based on an intelligent healthcare monitoring system using HPM-NIA

A machine learning based approach to identifying malicious activity to improve privacy in IoT-based intelligent healthcare monitoring system

A machine learning based approach to identifying malicious activity to improve privacy in IoT-based intelligent healthcare monitoring system

IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN

Healthcare is undergoing a transformation due to predictive analytics, which offers clinical decision support in real-time, facilitating effective, tailored therapy. By lowering unfavorable events and raising the standard of care, this approach supports patients at every step of diagnosis, prognosis and therapy. Predictive algorithms driven by artificial intelligence (AI), machine learning (ML) and Internet-of-Things (IoT) handle enormous amounts of data, with cardiovascular illnesses as the primary cause of death worldwide. IoT devices make sophisticated deep learning (DL) analytics and remote patient monitoring possible. This paper presents a novel 5-stage disease prediction model as an IoT-cloud-based smart healthcare management framework, constructed using a seahorse optimization-driven mutated convolutional neural network (SHO-MuCNN). In stage-1, data is gathered from a variety of sources through data collection. The stage-2 of data pre-processing involves the use of standardization to get the data acceptable for additional processing. Using a customized principal component analysis (CPCA), the set of characteristics is determined in stage-3. Next, the stage-4 diseases are predicted by the suggested SHO-MuCNN methodology. According to the prediction results, physicians are educating patients in stage-5 on exactly what measures can be taken to reduce the risk. The smart healthcare monitoring measures for accuracy (98.56%), precision (96.69%), F1-score (97.86%), Recall (94.59%)and AUC (95.02), were employed in the models' performance. In addition, this study utilizes the SPSS and t-test for statistical analysis. The experiment's findings indicate that the SHO-MuCNN strategy outperformed the remaining techniques in an intelligent healthcare monitoring system.

An intelligent healthcare monitoring system-based novel deep learning approach for detecting covid-19 from x-rays images

An intelligent healthcare monitoring system-based novel deep learning approach for detecting covid-19 from x-rays images

A health monitoring system based on flexible triboelectric sensors for intelligence medical internet of things and its applications in virtual reality

The Internet of Medical Things (IoMT) is a platform that combines Internet of Things (IoT) technology with medical applications, enabling the realization of precision medicine, intelligent healthcare, and telemedicine in the era of digitalization and intelligence. However, the IoMT faces various challenges, including sustainable power supply, human adaptability of sensors and the intelligence of sensors. In this study, we designed a robust and intelligent IoMT system through the synergistic integration of flexible wearable triboelectric sensors and deep learning-assisted data analytics. We embedded four triboelectric sensors into a wristband to detect and analyze limb movements in patients suffering from Parkinson's Disease (PD). By further integrating deep learning-assisted data analytics, we actualized an intelligent healthcare monitoring system for the surveillance and interaction of PD patients, which includes location/trajectory tracking, heart monitoring and identity recognition. This innovative approach enabled us to accurately capture and scrutinize the subtle movements and fine motor of PD patients, thus providing insightful feedback and comprehensive assessment of the patients’ conditions. This monitoring system is cost-effective, easily fabricated, highly sensitive, and intelligent, consequently underscores the immense potential of human body sensing technology in a Health 4.0 society.

Self‐Adhesive Polydimethylsiloxane Foam Materials Decorated with MXene/Cellulose Nanofiber Interconnected Network for Versatile Functionalities

AbstractPolydimethylsiloxanes (PDMS) foam as one of next‐generation polymer foam materials shows poor surface adhesion and limited functionality, which greatly restricts its potential applications. Fabrication of advanced PDMS foam materials with multiple functionalities remains a critical challenge. In this study, unprecedented self‐adhesive PDMS foam materials are reported with worm‐like rough structure and reactive groups for fabricating multifunctional PDMS foam nanocomposites decorated with MXene/cellulose nanofiber (MXene/CNF) interconnected network by a facile silicone foaming and dip‐coating strategy followed by silane surface modification. Interestingly, such self‐adhesive PDMS foam produces strong interfacial adhesion with the hybrid MXene/CNF nano‐coatings. Consequently, the optimized PDMS foam nanocomposites have excellent surface super‐hydrophobicity (water contact angle of ≈159o), tunable electrical conductivity (from 10−8 to 10 S m−1), stable compressive cyclic reliability in both wide‐temperature range (from −20 to 200 oC) and complex environments (acid, sodium, and alkali conditions), outstanding flame resistance (LOI value of >27% and low smoke production rate), good thermal insulating performance and reliable strain sensing in various stress modes and complex environmental conditions. It provides a new route for the rational design and development of advanced PDMS foam nanocomposites with versatile multifunctionalities for various promising applications such as intelligent healthcare monitoring and fire‐safe thermal insulation.

A computationally efficient learning model to classify audio signal attributes

<p>The era of machine learning has opened up groundbreaking realities and opportunities in the field of medical diagnosis. However, it is also observed that faster and proper diagnosis of any diseases/medical conditions require proper analysis and classification of digital signal data. It indicates the proper identification of tumors in the brain. Brain magnetic resonance imaging (MRI) data has to be appropriately classified, and similarly, pulse signal analysis is required to evaluate the human heart operating condition. Several studies have used machine learning (ML) modeling to classify speech signals, but very few studies have explored the classification of audio signal attributes in the context of intelligent healthcare monitoring. The study thereby aims to introduce novel mathematical modeling to analyze and classify synthetic pulse audio signal attributes with cost-effective computation. The numerical modeling is composed of several functional blocks where deep neural network-based learning (DNNL) plays a crucial role during the training phase, and also it is further combined with a recurrent structure of long-short term memory (R-LSTM) feedback connections (FCs). The design approaches further experiment in a numerical computing environment in terms of accuracy and computational aspects. The classification outcome of the proposed approach shows that it attains approximately 85% accuracy, which is comparable to the baseline approaches and execution time.</p>

Ultrasensitive capacitive tactile sensor with heterostructured active layers for tiny signal perception

The development of capacitive tactile sensors with both ultrasensitivity and ultrafast response/relaxation is crucial for the development of intelligent healthcare monitoring technology. Despite the significant improvement in the sensing performance presented by the introduction of microstructures into the dielectric or electrode layers, meeting the demands of intellectualization remains a major challenge. Herein, a strategy to simultaneously introduce microstructures in both the dielectric and electrode layers is developed. The hierarchical sea-urchin TiO2 particle-in-micropore (HSP-MP) structure in the dielectric layer induces stress concentration near the micropore regions, significantly enhancing the sensitivity and toughness of the device. The sparsely spaced large microcone improves the sensitivity of the hierarchical microcone (HM) structure in the electrode layer, while the small microcone reduces the hysteresis caused by interfacial adhesion. With the synergistic effect of the HSP-MP&HM structure, the proposed tactile sensor achieves ultrasensitivity of 10.5 kPa−1, ultrafast response/relaxation time of 5.6/5.6 ms, and ultralow limit of detection of 0.1 Pa. These sensing properties are demonstrated in practical applications, including tiny signal perception of the human fingertip pulse, tiny muscle motion perception for Morse code transmission, and high-resolution flexible perception array with pressure mapping capability.

Design of intelligent IoT for smart healthcare monitoring system using optimal neural network (IONN) model

One of the most important aspects of human life is health. Every society is paying more attention to and implementing technology in the areas of health and healthcare. In the healthcare sector, Artificial Intelligence (AI) is frequently employed to deliver quick and reliable results. Early disease predictions assist doctors in making timely decisions to save patients' lives. The Internet of Things (IoT) is assisting AI applications in healthcare by acting as a catalyst. The data of the patients is gathered by IoT sensor, and the data is analysed using machine learning algorithms. The IONN model, an innovative and intelligent healthcare monitoring system based on modern technologies such as the IoT, optimization techniques, and machine learning, is introduced in this study to detect various diseases early and precisely. For people who reside in rural places, this system gives a low-cost solution. The accuracy of the proposed IONN model has enhanced by 4% to 15% when compared to existing approaches. Furthermore, as compared to ANNs that use the BPN algorithm, the IONN model reduces the overall training time by 15% to 52%.

Six‐arm Stellat Dendritic‐PbS Flexible Infrared Photodetector for Intelligent Healthcare Monitoring

AbstractThe flexible photodetector plays an important role in improving human medical health status. However, the narrow spectral detection range, poor stress stability, and non‐degradation of traditional flexible photodetectors greatly hinder the further development of wearable medical devices. In this paper, a novel flexible infrared photodetector is proposed for intelligent healthcare monitoring using high purity lead sulfide (PbS) nanoparticles on paper‐based flexible substrate synthesized by hydrothermal method and physical friction. The excellent performance of the detector is attributed to the 1.01 eV band gap and six‐arm stellate dendritic structure of PdS, a good combination between PbS and paper substrate via physical friction. As a result, our photodetector demonstrates wide‐spectrum detection capabilities from 365 to 1550 nm. The photodetector at 980 nm (50.4 μW cm−2) shows responsivity of 6.45 mA W−1, detectivity of 6.4 × 1010 Jones, response recovery time of 0.36 s/0.41 s, with good mechanical stability. By comparison, our detector has a wider detection range, better weak signal detection performance, and shorter response time than the performance of the former paper‐based detector. Furthermore, the paper‐based PbS photodetector as a dual‐wavelength photoplethysmography sensor is applied to analyze the oxygen saturation and develop an intelligent bandage to monitor wound healing. This paper‐based PbS photodetector has tremendous potential in the field of wearable medical devices and intelligent medical applications are expected.

An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

Human Activity Recognition (HAR) plays an important role in life care and health monitoring since it involves examining various activities of patients at homes, hospitals, or offices. Hence, the proposed system integrates Human-Human Interaction (HHI) and Human-Object Interaction (HOI) recognition to provide in-depth monitoring of the daily routine of patients. We propose a robust system comprising both RGB (red, green, blue) and depth information. In particular, humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency map. To track the movement of humans, we proposed orientation and thermal features. A codebook is generated using Linde-Buzo-Gray (LBG) algorithm for vector quantization. Then, the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network (ANN) while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for classification. There are two publicly available datasets used for experimentation on HHI recognition: Stony Brook University (SBU) Kinect interaction and the University of Lincoln's (UoL) 3D social activity dataset. Furthermore, two publicly available datasets are used for experimentation on HOI recognition: Nanyang Technological University (NTU) RGB-D and Sun Yat-Sen University (SYSU) 3D HOI datasets. The results proved the validity of the proposed system.

A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection

A sustainable healthcare focuses on enhancing and restoring public health parameters thereby reducing gloomy impacts on social, economic and environmental elements of a sustainable city. Though it has uplifted public health, yet the rise of chronic diseases is a concern in sustainable cities. In this work, a sustainable lung cancer detection model is developed to integrate the Internet of Health Things (IoHT) and computational intelligence, causing the least harm to the environment. IoHT unit retains connectivity continuously generates data from patients. Heuristic Greedy Best First Search (GBFS) algorithm is used to select most relevant attributes of lung cancer data upon which random forest algorithm is applied to classify and differentiates lung cancer affected patients from normal ones based on detected symptoms. It is observed during the experiment that the GBFS-Random forest model shows a promising outcome. While an optimal accuracy of 98.8 % was generated, simultaneously, the least latency of 1.16 s was noted. Specificity and sensitivity recorded with the proposed model on lung cancer data are 97.5 % and 97.8 %, respectively. The mean accuracy, specificity, sensitivity, and f-score value recorded is 96.96 %, 96.26 %, 96.34 %, and 96.32 %, respectively, over various types of cancer datasets implemented. The developed smart and intelligent model is sustainable. It reduces unnecessary manual overheads, safe, preserves resources and human resources, and assists medical professionals in quick and reliable decision making on lung cancer diagnosis.

Cloud-based platform for intelligent healthcare monitoring and risk prevention in hazardous manufacturing contexts

This paper presents an intelligent cloud-based platform for workers healthcare monitoring and risk prevention in potentially hazardous manufacturing contexts. The platform is structured according to sequential modules dedicated to data acquisition, processing and decision-making support. Several sensors and data sources, including smart wearables, machine tool embedded sensors and environmental sensors, are employed for data collection, comprising information on offline clinical background, operational and environmental data. The cloud data processing module is responsible for extracting relevant features from the acquired data in order to feed a machine learning-based decision-making support system. The latter provides a classification of workers’ health status so that a prompt intervention can be performed in particularly challenging scenarios.

An intelligent based healthcare security monitoring schemes for detection of node replication attack in wireless sensor networks

Due to the increased growth of elderly people in recent years, healthcare systems face many challenges on the money spent for those people. Both quality and affordability has to be provided by the new technology which is the today’s need. When applying WSN technologies, the advantages such as continuous monitoring with alert mechanisms and relative information are to be satisfied. Among the other challenges, due to the deployed environment, security is a key challenge. As gateway connects to the wireless networks, it is the target area for many adversaries to launch various attacks. Initially, attacker launches node compromise attack which leads to node replication attack. The introduced security methods for intelligent healthcare monitoring system effectively detect replication attack and provide protection to the system. The potential application of proposed methods namely Exponential Moving Average based Replica Detection (EMABRD), Secured Ant Colony Optimization (SACOP) and Fingerprint based Zero Knowledge Authentication (FZKA) is applied to a real time environment. While comparing three algorithms, SACOP has higher detection probability of malicious nodes at the expense of increased storage and communication overheads over EMABRD and FZKA. FZKA performs better compared to EMABRD in terms of detection probability but at the cost of increased overheads. So, among the three algorithms, EMABRD is better in terms of overheads and SACOP is better in terms of detection probability.

An intelligent healthcare monitoring framework using wearable sensors and social networking data

Wearable sensors and social networking platforms play a key role in providing a new method to collect patient data for efficient healthcare monitoring. However, continuous patient monitoring using wearable sensors generates a large amount of healthcare data. In addition, the user-generated healthcare data on social networking sites come in large volumes and are unstructured. The existing healthcare monitoring systems are not efficient at extracting valuable information from sensors and social networking data, and they have difficulty analyzing it effectively. On top of that, the traditional machine learning approaches are not enough to process healthcare big data for abnormality prediction. Therefore, a novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy. The proposed big data analytics engine is based on data mining techniques, ontologies, and bidirectional long short-term memory (Bi-LSTM). Data mining techniques efficiently preprocess the healthcare data and reduce the dimensionality of the data. The proposed ontologies provide semantic knowledge about entities and aspects, and their relations in the domains of diabetes and blood pressure (BP). Bi-LSTM correctly classifies the healthcare data to predict drug side effects and abnormal conditions in patients. Also, the proposed system classifies the patients’ health condition using their healthcare data related to diabetes, BP, mental health, and drug reviews. This framework is developed employing the Protégé Web Ontology Language tool with Java. The results show that the proposed model precisely handles heterogeneous data and improves the accuracy of health condition classification and drug side effect predictions.

Data Analytics Assisted Internet of Things Towards Building Intelligent Healthcare Monitoring Systems

The rise in technology has had a tremendous impact in the field of healthcare. Moreover, the Internet of Things and ubiquitous computing when applied to the field of healthcare can ease the process by helping doctors better monitor their patients thereby enabling a speedy recovery. This paper proposes three systems to demonstrate the wide array of fields in healthcare and everyday life where Internet of Things can be applied. The first is a system to monitor infants while they sleep to detect crying. Once detected the system swings the cradle and also alerts the parents. The second system aims to help in rehabilitation of patients undergoing corrective knee surgery or other knee related procedures. This is done with the help of accelerometers attached to the joint which interfaces with a microcontroller and constantly monitors the knee movements. The last system aims to help the blind wear the right combination of clothing. This is achieved using image and pattern recognition.

An outdoor intelligent healthcare monitoring device for the elderly

The current paper presents an outdoor monitoring system for elderly people, which can transmit information on physiological signals and falling events to a healthcare center at any time and from any place. To detect simultaneously the occurrence of any falling event, as well as the relative electrocardiogram (ECG) signal of the user, a multi-thread method is proposed with the objective of enhancing the response time and the accuracy of detection. A healthcare box is used to determine the relative position of the patient through a global positioning system for fall detection; moreover, an ECG signal acquisition thread is adopted to increase the precision of the fall detection system. Integrating a precise map into the monitoring system facilitates understanding of a client user of the correct location and surrounding environment using the portable display. According to experimental results based on 4,000 samples, successful detection time with the multi-thread method was reduced by 38%, thereby increasing rescue opportunities for elderly patients who are at risk1.

An intelligent elderly healthcare monitoring system using fiber‐based sensors

It is common to see elderly people requiring round‐the‐clock care from caregivers due to declines in physical and mental abilities. Elderly people who are frail and disabled spend most of their time in bed. In this paper, we present a smart Fiber Bragg Grating (FBG) monitoring system in which the FBG sensors are embedded into the bed. The optical sensor system will allow safe long term continuous monitoring of elderly people's vital signs such as respiration and heart rate, monitoring of posture and movement and bed occupancy status. Use of FBG sensors offers many advantages over conventional electrical sensors, such as being passive, immune to EMI, able to multiplex, etc. But the negative side is the cost of the optical interrogator system. To create a cost‐effective solution for institutions, we try to support multiple beds using a single optical interrogator. This paper presents a multi‐bed monitoring system and the algorithms used for adding intelligence to the system ‐ calculation of pulse, respiration rates, pressure distribution plots and detection of bed occupancy. The system is designed for real deployment and trial in hospitals and the algorithms have been chosen to support an easy deployment.

SAPHIRE: intelligent healthcare monitoring based on semantic interoperability platform: pilot applications

As a response to the challenge of providing high-quality healthcare services with reasonable costs while the elderly population and the associated chronic diseases increase, SAPHIRE architecture provides an intelligent healthcare monitoring architecture. The monitoring of patients is achieved through a clinical decision support system based on clinical guidelines. SAPHIRE provides the necessary interoperability layers to access the patient's vital signs from wireless medical sensors and the electronic healthcare records of the patient in order to exploit them in the decision process seamlessly. This architecture is presented through two pilot applications: one for the bedside monitoring of cardiac patients at hospitals, and the other for homecare monitoring of the cardiac patients rehabilitated after a revascularisation therapy.

A Bluetooth-enabled mobile intelligent remote healthcare monitoring system: analysis and design issues

Many studies have shown that mobile phones are in great demand; smart phones in particular have an ever-growing market. It is evident that with greater number of mobile users, people can be facilitated with mobile accessible applications. Of many beneficial applications one such application is providing remote healthcare on mobile devices. Much work has been done in this area of research; however using Bluetooth enabled smart phones with Wireless Application Protocol (WAP) technology implementing an intelligent remote healthcare system has been proposed. A rule based expert system can identify diagnosis for remotely located patients to off location expert physicians and subsequent analysis can be delivered to the patient relieving her from frequent visits to healthcare service provider thus reducing the load of patients on existing infrastructure. This paper presents design and implementation of Mobile Intelligent Remote Healthcare Monitoring System (MIRHMS) and discusses issues.