Real-time Health Monitoring System Research Articles

Development of a Cloud-Based Real-Time Building Health Monitoring and Prediction System Using AI and IoT Sensors

In this study, a cloud-based real-time building health monitoring and prediction system using AI and IoT sensors was developed. To predict the building condition, which constitutes time-series data, statistical-based ARIMA and AI-based LSTM prediction models were designed, and the effectiveness of the proposed prediction models was experimentally verified using a 1/8-scaled miniaturized structure. The prediction accuracy in terms of MAPE (less than 1%) was experimentally confirmed to be satisfactory. Moreover, a method for analyzing dimensional structure deformation was developed by combining multiple sensor measurements, and its effectiveness was verified through the case study of a real earthquake-damaged building.

A Comprehensive Review on Wearable Health Monitoring Systems

This paper presents a comprehensive review of the wearable healthcare monitoring systems proposed by the researchers to date. One of the earliest wearable recorders, named “a silicon locket for ECG monitoring”, was developed at the Indian Institute of Technology, Bombay, in 2003. Thus, the wearable health monitoring systems, started with the acquisition of a single signal/ parameter to the present generation smart and affordable multi-parameter recording/monitoring systems, have evolved manifolds in these two decades. Wearable systems have dramatically changed in terms of size, cost, functionality, and accuracy. The early-day wearable recorders were with limited functionalities against today’s systems, e.g., Apple’s iWatch which comprises abundant health monitoring features like heart rate monitoring, breathing app, accelerometers, smart walking/ activity monitoring, and alerts. Most of the present-day smartphones are not only capable of recording various health features like body temperature, heart rate, photoplethysmograph (PPG) signal, calory consumption, smart activity monitoring, stress measurement, etc. through different apps, but they also help the user to get monitored by a family physician via GSM or even internet of things (IoT). One of the latest, state-of-the-art real-time personal health monitoring systems, Wearable IoT-cloud-based health monitoring system (WISE), is a beautiful amalgamation of body area sensor network (BASN) and IoT framework for ubiquitous health monitoring. The future of wearable health monitoring systems will be far beyond the IoT and BASN.

Review of Mobile Cloud Computing in Healthcare for Diabetics Patients Using Machine Learning Techniques

Abstract: The number of chronic diseases such as diabetes, cancer, heart disease, and others is fast increasing in our daily lives. The disadvantages of the traditional healthcare system are becoming more prevalent. One of the most important is that healthcare is only offered in hospitals. No one has access to it and no one is monitoring it. Patients' information is securely acquired from the hospital, with their consent, and monitored on a regular basis using their smart phones in mobile cloud computing. On a daily basis, a real-time mobile cloud health monitoring system is used. The patient's specifics concerning various metrics for data collection, such as blood glucose level, high/low blood pressure, high cholesterol, oxygen level, and so on, are being monitored. Diabetic patients are tracked via mobile cloud-IoT and certain wearable health tracking devices and sensors. Doctors will review the individuals' medical records and make recommendations for improving their health. In the future, it will aid in the control or recovery of diabetics. To provide improved security and performance, the proposed system can leverage advanced encryption techniques in conjunction with a machine learning classifier. Keyword: Predictive analytics; Prediction models; Machine learning; Classifications, Healthcare, Diabetes, Blood Glucose and privacy module.

Health Monitoring and Diagnosis System for a Small H-Type Darrieus Vertical-Axis Wind Turbine

As the wind power market grows rapidly, the importance of technology for real-time monitoring and diagnosis of wind turbines is increasing. However, most of the developed technologies and research mainly focus on large horizontal-axis wind turbines, and research conducted on small- and medium-sized wind turbines is rare. In this study, a novel low-cost and real-time health monitoring and diagnosis system for the small H-type Darrieus vertical axis wind turbine is proposed. Turbine operating conditions were classified into parked/idle and power production. In the case of the power production condition, abnormality diagnosis was performed using key monitoring parameters, including vibration, fundamental frequency, the bending stress of the tower and generator vibration. The turbine abnormalities were diagnosed in two stages by applying the alert and alarm limits, determined by referring to international standards and material properties and the long-term measurement data together.

Field monitoring and prediction on temperature distribution of glass curtain walls of a super high-rise building

Temperature action has a significant effect on the behavior of a supertall structure but has rarely been investigated due to lack of knowledge on the actual temperature measurements. It would be of great significance to predict the time-pace temperature distribution of a high-rise building by using the general environmental monitoring data which is more easily acquired and has a close relation with structural thermal action. In this study, the actual temperature distribution of the exterior glass curtain wall of Shanghai Tower is investigated using the field monitoring data achieved by the integrated real-time Structural Health Monitoring (SHM) system installed on the tower. The data recorded during 2018.1.1–2018.10.13 (283 days) are employed. A data-driven model is developed based on Artificial Neural Network (ANN), which is capable of predicting the temperature distribution of the curtain wall by using the environmental monitoring data including the wind velocity and direction measurements as well as the public weather information. Compared with the discrete temperature measurements at a few limited locations, the proposed model provides continuous time-space temperature distribution of the curtain wall, which adds insights into the knowledge of temperature actions for the maintenance of the Shanghai Tower and the design of glass curtain wall of a supertall structure.

Internet of things-enabled real-time health monitoring system using deep learning.

Smart healthcare monitoring systems are proliferating due to the Internet of Things (IoT)-enabled portable medical devices. The IoT and deep learning in the healthcare sector prevent diseases by evolving healthcare from face-to-face consultation to telemedicine. To protect athletes’ life from life-threatening severe conditions and injuries in training and competitions, real-time monitoring of physiological indicators is critical. In this research work, we present a deep learning-based IoT-enabled real-time health monitoring system. The proposed system uses wearable medical devices to measure vital signs and apply various deep learning algorithms to extract valuable information. For this purpose, we have taken Sanda athletes as our case study. The deep learning algorithms help physicians properly analyze these athletes’ conditions and offer the proper medications to them, even if the doctors are away. The performance of the proposed system is extensively evaluated using a cross-validation test by considering various statistical-based performance measurement metrics. The proposed system is considered an effective tool that diagnoses dreadful diseases among the athletes, such as brain tumors, heart disease, cancer, etc. The performance results of the proposed system are evaluated in terms of precision, recall, AUC, and F1, respectively.

Real-time structural health monitoring system based on streaming data

In this paper, a novel real-time structural health monitoring (SHM) system based on streaming data is proposed. In contrast to a traditional SHM system, the proposed system implements a series of optimizations for data transmission and processing to reduce the latency and better satisfy the real-time requirement. The concept of the watermark in the streaming system is adopted to address the problem of when to trigger the time window calculation under the real-time requirement. Moreover, a well-designed parallel mechanism is used to satisfy the multistage computation requirement in the parallel data stream. A case study in which the proposed system is applied to the Shanghai Tower is presented. The peak picking method is used as an example in the test environment to track the latency of each main operation under different parallelism schemes. The results show that computing in parallel effectively reduces the latency and provides a reference for integrating the random decrement technique (RDT), stochastic subspace identification (SSI), or other more complex but effective algorithms in parallel into the system in the future. The total latency under the test environment from data generation to data transmission to the web server is approximately only 200-400 ms, which indicates the excellent real-time performance of the system.

Patient health monitoring system using IoT

Healthcare monitoring systems have been one of the most important system in the last decade, and they have become increasingly technological. Humans are facing various issue and untimely death due to multiple illness, due to a lack of medical treatment for patients. To overcome this issue a real time health monitoring system has proposed based on IOT. The proposed system consists of mobile application and GSM for continuous wireless monitoring of patients. The main aim is to create a dependable patient management system based on IoT so that healthcare professionals can monitor their patients who are either hospitalized or at home using an IOT based integrated healthcare system to ensure quality patient care. Sensors are used to track vital parameters, and the data collected by the sensors is sent to the cloud via a WiFi module. A wireless healthcare monitoring system has created that can provide real time online information about a patients conditions. The system is made up of sensors, a data acquisition unit, a microcontroller (i.e. ESP32) and software. The patients temperature, heart beat rate, ECG, blood pressure and SPO2 are regularly monitored, displayed and stored by the system and the same has been sent to the doctors mobile containing the application. Additionally a message is sent to the doctors mobile if any of the parameter crosses the threshold value. Thus, IOT based real time health monitoring system systematically monitor the condition of patients health and save their life on time.

A Review on existing IoT Architecture and Communication Protocols used in Healthcare Monitoring System

Nowadays, due to modernization or advancement in the Internet of Things (IoT) especially in the Healthcare area, we want to take care of our elders with some monitoring equipment, and the Internet of Things can play a significant role in it. The motivation of writing this paper is to collect the information of various existing Internet of Things Architecture and Communication Techniques used in Healthcare Monitoring System to observe that how efficiently, different researchers have used it. So we have studied different real-time health monitoring system based on diseases which are common in elderly people like diabetes, blood pressure, heart disease, sleep apnea, and cancer, etc. In this real-time health monitoring system, researchers introduced many new measures, communication techniques like ZigBee, Long-Range Wide Area Network (LoRawan), Radio Frequency Identification (RFID). Apart from this, it was also observed that remote monitoring system in Healthcare is incomplete without data processing and early prediction in such diseases. Though, Machine learning provides efficient techniques to extract knowledge from diagnostic medical datasets collected from the patients. That is why we highlighted the current role of various Machine Learning algorithms like Support Vector Machine, K-Nearest Neighbor, Random Forest, etc., for processing of Healthcare data and also helpful to predict the output more precisely.

Securing smart healthcare system with edge computing

Nowadays, the healthcare system faces new challenges due to the increasing growth of sensitive patient data. There is a need for a Smart Healthcare System (SHS) that makes the healthcare system smarter to protect the data and maintain privacy. An SHS contains several IoT devices, sensors, and mobile nodes that collect the data from the patient’s body and store it remotely. The most commonly used approach for storing data in the SHS is cloud computing-based storage. This type of system is very popular and provides common solutions to all users because it allows accessing the data remotely. The security and privacy of remotely located data is another important concern because the solution is available to all types of users. Remotely located data enables a lack of control over the data. The real-time health monitoring system based on cloud computing is not feasible because the response time is comparatively high. It is essential to provide some solution that reduces this response time. The edge computing approach reduces the response time in real-time cases as well as needs low bandwidth.This paper proposed a smart healthcare system based on edge computing architecture. This architecture consists of an intermediary layer called an edge computing layer responsible for maintaining the network latency and preserving the privacy of the patient data. This edge computing layer has handled the encryption and privacy of the patient data with the help of the Privacy-Preserving Searchable Encryption (PPSE) technique. The access control mechanism is also implemented to restrict unauthorized access to the remote stored patient data. The proposed model’s implementation, performance, and security analysis show high security, low latency, low transfer time, low power, and low energy compared to a similar approach. The computed results demonstrated transfer time (64.24%), power consumption (69.03%), and energy consumption (69.56%) is reduced using the edge computing approach.

SME 4.0: Machine learning framework for real-time machine health monitoring system

Over the past ten years, manufacturers and consumers have become increasingly interested in the applications of smart, sustainable, and autonomous systems in the industry and in everyday life. Due to the recent industrial revolution (Industry 4.0), most of the existing Small and Medium-sized Enterprises (SMEs) also want to adapt their work environment into the smart system by the applications of these technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI) techniques, Machine Learning Algorithm (MLA), Internet Communication Technology (ICT), and Cyber-Physical System (CPS). Because they are very much interested in maximizing productivity, machine availability, reliability, and customer satisfaction in this competitive industrial world. This research study particularly focuses on the Predictive Maintenance (PdM) activity of critical machines and their components in the SME based on the maintenance history dataset through the application of the supervised machine learning algorithm such as Logistics Regression (LR) and K-Means (K-Nearest Neighbor) approaches. In accordance, the real-time case study is presented in SMEs in the southern region of Tamil Nadu, India with two-phase activities. Initially, the optimal failure rate of the machines is predicted by the utilization of LR trained models. Then trigger the man-machine communication and suitable decision-making process of service and maintenance activity through the application of the K-Mean approaches. The main objective of this research study is to organize the smart PdM activity of the smart factory systems in SMEs with the application of MLA based on the real-time maintenance history dataset.

A Raspberry Pi-Based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram.

Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16–64 s epochs for TBI vs. control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems.

Smart Wearable Sensors Based on Triboelectric Nanogenerator for Personal Healthcare Monitoring.

Accurate monitoring of motion and sleep states is critical for human health assessment, especially for a healthy life, early diagnosis of diseases, and medical care. In this work, a smart wearable sensor (SWS) based on a dual-channel triboelectric nanogenerator was presented for a real-time health monitoring system. The SWS can be worn on wrists, ankles, shoes, or other parts of the body and cloth, converting mechanical triggers into electrical output. By analyzing these signals, the SWS can precisely and constantly monitor and distinguish various motion states, including stepping, walking, running, and jumping. Based on the SWS, a fall-down alarm system and a sleep quality assessment system were constructed to provide personal healthcare monitoring and alert family members or doctors via communication devices. It is important for the healthy growth of the young and special patient groups, as well as for the health monitoring and medical care of the elderly and recovered patients. This work aimed to broaden the paths for remote biological movement status analysis and provide diversified perspectives for true-time and long-term health monitoring, simultaneously.

Security Framework for IoT Based Real-Time Health Applications

The amazing fusion of the internet of things (IoT) into traditional health monitoring systems has produced remarkable advances in the field of e-health. Different wireless body area network devices and sensors are providing real-time health monitoring services. As the number of IoT devices is rapidly booming, technological and security challenges are also rising day by day. The data generated from sensor-based devices need confidentiality, integrity, authenticity, and end-to-end security for safe communication over the public network. IoT-based health monitoring systems work in a layered manner, comprising a perception layer, a network layer, and an application layer. Each layer has some security, and privacy concerns that need to be addressed accordingly. A lot of research has been conducted to resolve these security issues in different domains of IoT. Several frameworks for the security of IoT-based e-health systems have also been developed. This paper introduces a security framework for real-time health monitoring systems to ensure data confidentiality, integrity, and authenticity by using two common IoT protocols, namely constrained application protocol (CoAP) and message query telemetry transports (MQTT). This security framework aims to defend sensor data against the security loopholes while it is continuously transmitting over the layers and uses hypertext transfer protocols (HTTPs) for this purpose. As a result, it shields from the breach with a very low ratio of risk. The methodology of this paper focuses on how the security framework of IoT-based real-time health systems is protected under the tiers of CoAP and HTTPs. CoAP works alongside HTTPs and is responsible for providing end-to-end security solutions.

IoT-based GPS assisted surveillance system with inter-WBAN geographic routing for pandemic situations

BackgroundWorldwide pandemic situations drive countries into high healthcare costs and dangerous conditions. Hospital occupancy rates and medical expenses increase dramatically. Real-time remote health monitoring and surveillance systems with IoT assisted eHealth equipment play important roles in such pandemic situations. To prevent the spread of a pandemic is as crucial as treating the infected patients. The COVID-19 pandemic is the ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). MethodsWe propose a surveillance system especially for coronavirus pandemic with IoT applications and an inter-WBAN geographic routing algorithm. In this study, coronavirus symptoms such as respiration rate, body temperature, blood pressure, oxygen saturation, heart rate can be monitored and the social distance with ‘mask-wearing status’ of persons can be displayed with proposed IoT software (Node-RED, InfluxDB, and Grafana). ResultsThe geographic routing algorithm is compared with AODV in outdoor areas according to delivery ratio, delay for priority node, packet loss ratio and bit error rate. The results obtained showed that the geographic routing algorithm is more successful for the proposed architecture. ConclusionThe results show that the use of WBAN technology, geographic routing algorithm, and IoT applications helps to achieve a realistic and meaningful surveillance system with better statistical data.

An Intelligent and Energy-Efficient Wireless Body Area Network to Control Coronavirus Outbreak.

The coronaviruses are a deadly family of epidemic viruses that can spread from one individual to another very quickly, infecting masses. The literature on epidemics indicates that the early diagnosis of a coronavirus infection can lead to a reduction in mortality rates. To prevent coronavirus disease 2019 (COVID-19) from spreading, the regular identification and monitoring of infected patients are needed. In this regard, wireless body area networks (WBANs) can be used in conjunction with machine learning and the Internet of Things (IoT) to identify and monitor the human body for health-related information, which in turn can aid in the early diagnosis of diseases. This paper proposes a novel coronavirus-body area network (CoV-BAN) model based on IoT technology as a real-time health monitoring system for the detection of the early stages of coronavirus infection using a number of wearable biosensors to examine the health status of the patient. The proposed CoV-BAN model is tested with five machine learning-based classification methods, including random forest, logistic regression, Naive Bayes, support vector machine and multi-layer perceptron classifiers, to optimize the accuracy of the diagnosis of COVID-19. For the long-term sustainability of the sensor devices, the development of energy-efficient WBAN is critical. To address this issue, a long-range (LoRa)-based IoT program is used to receive biosensor signals from the patient and transmit them to the cloud directly for monitoring. The experimental results indicate that the proposed model using the random forest classifier outperforms models using the other classifiers, with an average accuracy of 88.6%. In addition, power consumption is reduced when LoRa technology is used as a relay node.

Structural monitoring and remaining fatigue life estimation of typical welded crack details in the Manhattan Bridge

Manhattan Bridge is a suspension structure that connects Manhattan to Downtown Brooklyn. This bridge is over 110 years old and subjected to daily repeated axles of transit trains and other vehicular traffic. Visual inspections of the bridge revealed manifestation of many cracks in the welded details due to the out-of-plane forces at the connections between transit and floor beams. The research reported herein, pertains to the fatigue life evaluation and reliability assessment of typical cracked welded details. The bridge was instrumented with a real-time structural health monitoring system based on FBG sensors for remote monitoring of the cracked connections. Both deterministic and probabilistic approaches were employed for prediction of the remaining fatigue life of typical cracked details. For the same fatigue reliability index of 2.3, the deterministic and probability based methods yielded remaining fatigue life of 369 and 120 years, respectively, for the typical welded connections details of the bridge.

A Working Prototype Using DS18B20 Temperature Sensor and Arduino for Health Monitoring

Due to the successful emergence of internet of things, sensor-based real-time health monitoring is getting popularized. A usable health-monitoring system is required for prolonged monitoring of the patient with reduced cost. This paper describes a working prototype system for real-time health-monitoring system using DS18B20 temperature sensor, Arduino Nano with micro-controller ATmega328 where Zigbee module is used for wireless communication. In this prototype sensor data gets acquired and analyzed to give proper feedback to the patient with or without mobility support at indoor. The sensor vitals are collected and sent to the computing device using shielded cable and ZigBee, i.e., through wired and wireless communication, respectively. Analysis of patient vitals based on medical definitions gives patient’s real-time health condition so that if condition is not normal, then timely preventive measures can be taken to avoid further complication. Per user data can be saved in the system database for further reference.

Smart real time health monitoring system using Arduino and Raspberry Pi

The common problem we face these days is the lack of accurate health care for the elderly. There are many home-based health care monitoring systems but they are limited due to various setbacks. This work proposes a design of a health care monitoring system by using Arduino and Raspberry PI, to monitor the parameters i.e., heartbeat, body temperature, heart rhythm, and electrical activity. GPS module is used for tracking the location and camera for capturing live feed and an alert notification system that will notify the hospital and family if the values are abnormal and we can also check the real-time data in an app or a website that is incorporated into this module.

Real-time Machine Health Monitoring System using Machine Learning with IoT Technology

Machine health monitoring is the main focal point for now as many industries are evolving to industry 4.0. Industry 4.0 is the revolution in industrial that involve the Internet of Things (IoT) and artificial intelligence toward automation and data sharing for production efficiency improvement. The existing established methods for machine health monitoring were not in real-time and there was no real-time correction of data from the load and processing of data on the computer. In tracking machine health efficiency this approach wasn’t very successful. Real-time machine health monitoring can improve overall equipment effectiveness (OEE), reduce electricity consumption, minimize unplanned downtime, and extend machine lifetime. In this research paper, we propose to design a real-time machine health monitoring system using machine learning with IoT technology that can analyze the supply balancing condition on a 3-phase system. This system is built with compact physical hardware and can capture the electrical data from the load then send it to the server. The server will progress data and train the data using machine learning. The system was installed on a blender machine in a factory. In this research, a system which is able to monitor the machine operation and classify the operation stages of the machine was developed. Besides that, the system also capable to monitor the load balancing condition of the machine.