Smart Health Monitoring System Research Articles

Smart Health Monitoring with Power-Aware IoT Using a Wireless Body Area Network

Smart health monitoring systems have become increasingly popular in recent years due to the growth of the Internet of Things (IoT) technology and its applications in healthcare. This project proposes an IoT-based smart health monitoring system that utilises an ESP32 Microcontroller, MAX30100 Pulse Oximeter Heart Rate Sensor Module, MLX90614 ESF Non-Contact Human Body Infrared Temperature Measurement Module, LCD 16*2, 3.7v 2000mAh Lithium Battery, TP4056 1A Li-Ion Lithium Battery charging Module, and some buttons. The system is designed to monitor the user's heart rate, blood oxygen level, and body temperature in real-time and display the readings on an LCD screen as well as an IoT web page. The project aims to provide an affordable, portable, and user-friendly smart health monitoring solution that can be used at home, in hospitals, or other healthcare settings. The project methodology involved a combination of hardware and software components, including circuit design and programming using the Arduino IDE. The system was tested and evaluated for its performance, accuracy, and usability using a sample population. The results showed that the system was effective in monitoring the user's vital signs and displaying the readings in real-time. The system was found to be accurate and reliable, with a high level of user satisfaction and ease of use. Overall, the IoT-based smart health monitoring system proposed in this project has significant implications for healthcare, particularly in terms of remote monitoring, telemedicine, and personalised healthcare. The system has the potential to improve patient outcomes, reduce healthcare costs, and increase access to healthcare services. The project also highlights the potential of IoT technology in healthcare and the need for further research and development in this area

Recent Trends of Bioanalytical Sensors with Smart Health Monitoring Systems: From Materials to Applications.

Smart biosensors attract significant interest due to real-time monitoring of user health status, where bioanalytical electronic devices designed to detect various activities and biomarkers in the human body have potential applications in physical sign monitoring and health care. Bioelectronics can be well integrated by output signals with wireless communication modules for transferring data to portable devices used as smart biosensors in performing real-time diagnosis and analysis. In this review, the scientific keys of biosensing devices and the current trends in the field of smart biosensors, (functional materials, technological approaches, sensing mechanisms, main roles, potential applications and challenges in health monitoring)will be summarized. Recent advances in the design and manufacturing of bioanalytical sensors with smarter capabilities and enhanced reliability indicate a forthcoming expansion of these smart devices from laboratory to clinical analysis. Therefore, a general description of functional materials and technological approaches used in bioelectronics will be presented after the sections of scientific keys to bioanalytical sensors. A careful introduction to the established systems of smart monitoring and prediction analysis using bioelectronics, regarding the integration of machine-learning-based basic algorithms, will be discussed. Afterward, applications and challenges in development using these smart bioelectronics in biological, clinical, and medical diagnostics will also be analyzed. Finally, the review will conclude with outlooks of smart biosensing devices assisted by machine learning algorithms, wireless communications, or smartphone-based systems on current trends and challenges for future works in wearable health monitoring.

Smart Health Monitoring System

Due to the high inpatient population in hospitals, regular monitoring of inpatients' vital signs is currently a practical concern. As a solution, our proposed system manages the continuous analysis of the vital signs of every inpatient in the general wards, and informs medical professionals in any location at any time about their inpatients' current states in real-time to improve inpatients' health. The suggested system consists of the following arrangements; arrangement for acquiring health readings, identifying the on-duty reported doctors in charge of wards, arrangement for health data exhibiting unit, fall detection, and ECG acquisition. In addition to these arrangements, a website, and an android mobile application were designed to publish measured inpatient vital signs. This proposed product is both novel and different from the existent products because, it comprises of collective arrangements, and is developed in order to assess hospital wards’ inpatients, whereas other systems are designed for remote health monitoring of patients at home. This paper describes the system that was developed and tested successfully. KEYWORDS: Real-time database, Temperature, Heart rate, SpO2, ECG, Fall detection, Website, Mobile application

Cushy and Cheap Smart Health Monitoring System Incorporating a Ventilator

The handiness of mercantile mechanical ventilators is highly limited in low-income countries due to high rate in the price of its affordability. This factor affects the treatment of so many patients suffering from chronic respiratory syndrome. In this research, a low- cost and easy to use pressure ventilator coupled with a remote health monitoring system to assist the doctors check up on the patients is developed. There is need to maximize the availability of the ventilators in low-income countries at an affordable price tag. This system was developed with the aid of an Arduino nano, ESP8266 based NodeMcu microcontroller, high pressure blower, pressure transducers, humidity and temperature sensor. The developed system was evaluated and compared with existing one, using an actively breathing patient simulator as it mimics through a range of respiratory diseases. In the end, we were able to design a low-cost smart health monitoring system that incorporates ventilator and health vitals monitor. Our Design was implemented and verified to be working and equally satisfying all the specified requirements.

Snake-scale stimulated robust biomimetic composite triboelectric layer for energy harvesting and smart health monitoring

Extreme frictional wear of triboelectric layers severely hinders long-term sustainability of triboelectric nanogenerators (TENGs) for efficient ambient energy harvesting. The state-of-the-art solutions necessitate sophisticated structural designs and complex packaging constraints, impeding the feasibility of TENGs for practical applications. Herein, inspired by the structures and compositions of snake scales, a biomimetic composite (BC) film is fabricated as a wear-resistant triboelectric contact layer. The successful formation of disulfide bond-facilitated cross-linkage of cysteine proteins in the keratin-based BC film promotes excellent mechanical resilience and durability, thereby solving the material abrasion challenges associated with solid-solid triboelectric layers. The BC film, with toothed microstructure, exhibits improved charge transfer, low friction, and consistent long-term stable outputs compared to commercial films. Furthermore, a self-powered BC film-based TENG is integrated into a bicycle for biomechanical energy harvesting, lightening warning signals for safer night ride with stable 300 V electrical output for at least 6 h. Moreover, a smart health monitoring system is further proposed by embedding TENG-based sensors into the bicycle saddle, accurately capturing the rider’s pelvic-saddle interactions on varying terrain slopes. By leveraging the unique biomechanical information, machine learning-assisted user identification and terrain slope data classifications are achieved with high accuracies, laying a foundation for futuristic smart healthcare applications. This work provides a strategy to reduce material abrasion by exploiting the unique characteristics of a biomimetic material, facilitating a basis for efficient environmental energy harvesting and smart sensing.

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Implementing intelligent monitoring systems for Agricultural Machinery (AM) is hindered by the intricate and costly nature of the Internet of Things (IoT) sensor technologies. The heavy reliance on cloud and fog computing, the availability of network infrastructure, and the need for expert knowledge pose challenges in rural areas that lack network connectivity. Using edge devices, such as smartphones, which possess significant computational capabilities, is a potential solution that has not yet been fully realized in the commercial sphere. Furthermore, the increasing demand from users for economically viable and user-friendly technology serves as a driving force for transitioning away from expensive and intricate sensors towards more cost-effective alternatives. In the IoT era, there is anticipated to be a widespread network connection between a vast array of AM and service centers. Using smartphone applications has increased the potential for edge computation on smartphones to significantly aid in network traffic control. The development of an Artificial Intelligence (AI) - -based data analytic method poses a significant challenge due to the need to optimize for the limited computational capabilities of smartphones. However, the users’ demand for affordable technology renders it resistant to easy penetration. This paper uses IoT and AI to propose a Smart Health Monitoring System for Agricultural Machines with Deep Learning-based Optimization (SHMAM-DLO). This paper aims to propose a Fusion Genetic Algorithm (FGA) methodology and Artificial Neural Network (ANN) for optimization during monitoring the health of AM. The proposed approach enables cost-effective utilization of smartphone end devices by leveraging their built-in microphones instead of relying on expensive IoT sensors.

Identification of Damage in a Wind Turbine Blade Using Mechanical Measurements and Artificial Neural Networks

Due to the stochastic nature of environmental loadings, a lot of interest is paid in the discovery of possible damages of the involved equipment in modern industry. In wind turbines’ blades, the development of a smart structural health monitoring system is essential. In this paper, a large-scale composite wind turbine blade model is designed and used for the detection of several damage scenarios. The process is mainly based on the development of monitoring techniques which exploit the capabilities of artificial neural networks. These techniques can provide the exact position of possible damages, under given external loading scenarios. Moreover, the use of such methods decreases significantly the need of external intervention and at the same time it increases the accuracy of the whole approach. The above processes are simulated using the finite element method. The goal is to develop a neural network which realizes the correlation of measurements with damage patterns. The goal is focused on the solution of inverse problems involving elastically deformable structures, based on remote mechanical measurements. The correlation between measurements and damages, which is much more complicated in comparison to image analysis, is studied by means of neural networks.

A Discrete Event Simulation of Patient Flow in an Assisted Reproduction Clinic With the Integration of a Smart Health Monitoring System

A Discrete Event Simulation of Patient Flow in an Assisted Reproduction Clinic With the Integration of a Smart Health Monitoring System

IntelliCare: Integrating IoT and Machine Learning for Remote Patient Monitoring in Healthcare: A Comprehensive Framework

The development of smart health monitoring systems has emerged as a consequence of the integration of Internet of Things (IoT) and Machine Learning (ML) technologies within the healthcare sector. This transformation has significantly reshaped patient care methodologies, shifting from traditional approaches to electronic healthcare systems. Leveraging IoT technology fosters a contemporary medical device ecosystem, fostering seamless communication among healthcare professionals, patients, and medical devices. Through the deployment of IoT devices, encompassing sensors and transmitters, coupled with Machine Learning algorithms, various applications have arisen, spanning from remote patient monitoring to real-time health assessment during ambulance transit to medical facilities. This proposed framework aims to monitor essential physiological parameters including body temperature, blood pressure, heart rate, sweat analysis, glucose levels, ECG, EEG, and pulse oximetry, transmitting pertinent data for tailored processing and analysis. Implantable IoT devices serve as conduits for wireless communication, data storage, centralized computation, and portable processing, facilitating connectivity among sensors, GPS-enabled ambulances, medical practitioners, and patients. To mitigate potential health risks, sensors are equipped with Machine Learning capabilities to promptly assess illness severity and recommend appropriate interventions, potentially triggering automated alerts to healthcare providers. This seamless exchange of information via IoT and wireless networks enables rapid communication between doctors and patients, facilitating personalized medical recommendations, prescription management, and hospital selection based on individual health profiles.

IoT-Based Smart Health Monitoring System: Investigating the Role of Temperature, Blood Pressure and Sleep Data in Chronic Disease Management

IoT-Based Smart Health Monitoring System: Investigating the Role of Temperature, Blood Pressure and Sleep Data in Chronic Disease Management

Verifying trust over IoT-ad hoc network-based applications under uncertainty

The rapid integration of the Internet of Things (IoT) with ad hoc networks offers significant advantages for revolutionizing smart environments. However, ensuring trust and reliability within these interconnected systems remains a significant challenge. To address this challenge, this paper introduces an innovative three-valued (3v) trust model customized specifically for IoT-ad hoc systems. The model employs three-valued logic to evaluate trust in social commitments within ambiguous scenarios, where commitments serve as the foundation of the business logic. Our aim is to enhance the effectiveness and economic feasibility of IoT in smart environments. To advance this initiative, we introduce the 3v-TCTLC, a distinctive modeling language that extends the conventional two-valued logic by introducing a third value to accommodate uncertainty. This novel approach facilitates the assessment of trust in commitments within uncertain IoT-ad hoc environments. Additionally, we improve the functionality of the “MACMAS-interactor” tool, incorporating new features to support our 3v-TCTLC logic. Through two case studies in the domains of smart health monitoring and smart home systems, we validate our model against specific requirements in uncertain contexts. These case studies highlight the robustness and practicality of our proposed tools and methodologies. Compared to prevalent trust management strategies employed in IoT and ad-hoc networks, our methodology stands out distinctly. While many current solutions propose trust-centered protocols, with some even harnessing advanced technologies, they frequently overlook the crucial element of model checking. Our approach not only incorporates this critical component but also ensures the integrity of the system. Furthermore, even though the field of multi-valued model checking has seen advancements like χCTL, our research contributes significantly by integrating trust and commitment verification specifically designed for IoT-ad hoc environments. Our empirical assessments in the domains of smart health and home systems confirm that our tool and strategies demonstrate superior performance in terms of time and space usage and better adaptability and scalability in uncertain scenarios, representing a noteworthy advancement over existing techniques and tools.

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.

Retracted: IoT-Based Smart Health Monitoring System for COVID-19 Patients

Retracted: IoT-Based Smart Health Monitoring System for COVID-19 Patients

The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data

The technology, which plays a significant role in our lives, has made it possible for many of the appliances and gadgets we use on a daily basis to be monitored and controlled remotely. Health and fitness data is collected by wearable devices attached to patients' bodies. A number of parties could benefit from this technology, including doctors, insurers, and health providers. This technology, including smartwatches, smart ring, smart cloth wristbands, and GPS shoes, is frequently used for fitness and wellness since it allows users to track their day-to-day health. Devices that compute the sleep characteristics by storing sleep movements fall within the category of wearables worn on the wrist. In order to lead a healthy lifestyle, sleep is crucial. Inadequate sleep can harm one's physical, mental, and emotional well-being and increase the risk of developing a number of ailments, including stress, heart disease, high blood pressure, insulin resistance, and other conditions. Deep learning (DL) models have recently been used to forecast sleep-quality based on wearables information from the awake hours. Deep learning has been demonstrated to be capable of predicting sleep efficiency based on wearable data obtained during awake periods. In this regard, this study creates a novel deep learning model for wearables-enabled smart health monitoring system (DLM-WESHMS) for the prediction of sleep quality. The wearables are initially able to collect data linked to sleep-activity using the described DLM-WESHMS approach. The data is then put through pre-processing to create a standard format. Using the DLM-WESHMS, sleep quality is predicted using the deep belief network (DBN) model. The DBN model uses the auto-encoders algorithm (AEA) to predict popularity, which improves the accuracy of its predictions of sleep quality. The experimental outcomes of the DLM-WESHMS approach are investigated using several metrics. The DLM-WESHMS model performs significantly better than other models, according to a thorough comparison analysis.

Retracted: Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases.

[This retracts the article DOI: 10.1155/2022/3564482.].

SECURING IOT HEALTHCARE APPLICATIONS AND BLOCKCHAIN: ADDRESSING SECURITY ATTACKS

The Internet of Things (IoT) describes the connection of bodily devices as "things" that can communicate with other systems and devices through the Internet and exchange statistics (data or information), facilitating the exchange of data with other systems and devices. These devices have sensors, software, and various components designed to exchange data seamlessly within the IoT network. Securing and protecting the data transmitted over the Internet from unauthorized access is imperative to ensuring the integrity and confidentiality of the information. IoT Smart health monitoring systems, integral components of the IoT landscape, are susceptible to various attacks. These include denial of service (DoS), fingerprint, router, select, forwarding, sensor, and replay attacks, all of which pose significant threats to the security of these systems. As such, there is a pressing need to address and mitigate the vulnerabilities associated with IoT healthcare applications. This paper aims to explore the significant role of IoT devices in healthcare systems and provide an in-depth review of attacks that threaten the security of IoT healthcare applications. The study analyses the existing literature on the vulnerabilities present in smart health monitoring systems and the potential application of blockchain technology as a robust solution to enhance the security of IoT healthcare applications. This research reveals critical vulnerabilities in IoT healthcare applications and highlights blockchain's effectiveness in mitigating them, providing insights for robust security measures and strategic decision-making in secure healthcare systems. This paper provides valuable insight and recommendations for policymakers, researchers, and practitioners involved in the domain of the IoT healthcare system.

Computer-aided feature recognition of CFRP plates based on real-time strain fields reflected from FBG measured signals

Condition monitoring of critical and expensive carbon fiber reinforced polymer (CFRP) composite infrastructures is extremely essential task for uninterrupted operation and the durability of structures. Therefore, accurate and robust monitoring techniques and correlated damage identification algorithms need to be developed to characterize the health status of the CFRP structures. However, the effectiveness of existing methods is highly dependent on the structural properties of the CFRP plates. To identify the random damages caused by static or dynamic forces, the concept to establish the real-time strain fields of the structures based on measured signals is proposed. A moving surface spline interpolation algorithm based on Green's function has been developed to map the real-time strain fields of the CFRP plate using strain measured by surface-attached FBG sensors. A few static and impact loadings have been applied to a sample CFRP plate and the strain-field prediction by the proposed algorithm has been evaluated by a finite element model. Time and frequency domain analysis has also been conducted to recognize the dynamic response of the CFRP plates. Results indicate that the proposed algorithm can establish real-time strain field predictions with high precision, which can be used to recognize the static and dynamic responses of CFRP plate. Most importantly, the method is independent on the structural properties of the plate and the sensor layout, which is particularly significant for constructing the smart health monitoring system of CFRP.

Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient’s activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.

An investigation on applicability of Ad-hoc routing protocols in Fog-assisted smart health care system

Cloud computing is widely adopted as it provides real-time services to users on a respectable scale in alignment with pay-per-use models. The heterogeneity and lively characteristics of the cloud network's components make failure inevitable. The failure also has an impact on the cloud service's dependability and accessibility. A computer paradigm called fog brings cloud computing services closer to edge hardware. Fog computing is being used more and more in applications that require faster real-time services, and the healthcare industry has embraced it heavily due to the urgent need to save lives. This work is aimed to investigate the suitable ad-hoc routing protocol for fog-assisted smart health monitoring system. By simulating with the network simulator NS-2 and using throughput, latency, and packet delivery ratio (PDR) as assessment criteria, the effectiveness of routing protocols is assessed. The protocols Ad-hoc on-demand Distance Vector (AODV), Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), and Optimized Link State Routing (OLSR) are being tested. The AODV exhibits encouraging results with other comparative protocols.

IoT Based Animal Health Monitoring System using Raspberry Pi

IoT has bought many changes in the existing technologies, where its applications are numerous in the field of industry automation, bio-medical, agriculture, transport, smart cities etc. The key benefits of IoT enabled livestock management in real time is enabling farmers to quickly treat animals and prevent the spread of illness or disease, track gazing animals to prevent loss and to identify grazing patterns, gather and analyze historical data to identify trends in cattle health or to track the spread of illness, monitor readiness to give birth , preventing the loss of new calves and optimizing breeding practices. In this paper a smart animal health monitoring system is developed for monitoring the parameters such as body temperature, heart rate and rumination pattern has been monitored. Different kind of sensors are mounted on the body of animals and related information is gathered which can be accessed by the authorized person through internet. Raspberry Pi is used in this work, where different information is gathered from the sensors and placed on to the cloud so that the information can be accessed from the mobile using internet.