Real-time Health Monitoring System Research Articles

A Novel Framework of Health Monitoring Systems

IoT plays an important role in the healthcare domain for improving the quality of patient care. To analyze the patients' healthcare data, a real-time health-monitoring system is required. The proposed framework in this work is cable of such monitoring and sending alerts on critical circumstances. In this framework, the use of IoT devices makes it possible. This is very helpful in taking care of especially old wards and children in the absence or their caretakers. The function of alerting the caretakers and to inform hospital in critical condition makes this system one of its kind. Readings of patient pulse rates are taken from the pulse rate sensor and the body temperature is measured by MAX30205, a temperature sensor. The data is collected through sensors and sent over the cloud servers. Linear regression is used for further analysis and prediction of pulse and temperature trend lines. Corresponding health repots will be sent to the nearby hospitals and registered mobile numbers. The framework is validated with real-time patient data, and prediction is made regarding the trends.

Direct Ink Writing Method for Manufacturing Electronic Circuits Using Multiwalled Carbon Nanotubes and Polyvinyl Alcohol Composites

Abstract Usage of conductive materials for manufacturing electronic circuits using 3D printing methods plays a prominent role in sensing applications and has achieved significant growth in recent years. This growth has led to the development of stretchable, flexible electronics and wearable electronics that are useful as sensing applications in the fields of real-time health monitoring systems. In this paper, a multiwalled carbon nanotube (MWCNT)-based electronic circuit was developed using the 3D printing technique called direct ink writing (DIW). Polyvinyl alcohol (PVA) was used as the binder material to make the MWCNT ink for printing. Different concentrations of MWCNT ink were prepared using 5, 10, and 15 wt. % PVA solutions. The viscosity of MWCNT inks was measured using a rheometer (cone and plate type). The ink exhibited shear thinning behavior, which is suitable for DIW. Finally, the printing of a designed electronic circuit was carried out using a pneumatic extrusion–based 3D printer. The strain sensor circuit—measuring 20 mm long, 9.2 mm wide, and 0.8 mm thick—is manufactured by the 3D printer. Electrical resistivity of strain sensor circuit is evaluated by adopting a two-probe method; the observed resistivity value was 1.7 × 10−2 Ωm for MWCNT ink made with 10 wt. % PVA solution and 1.97 × 10−2 Ωm for MWCNT ink made with 15 wt. % PVA solution.

Real-time Health Monitoring System Using Wireless Body Area Network

Wireless body area network (WBAN) being a sub-domain of wireless sensor network (WSN) is a new emerging technology for healthcare applications. A WBAN consists of low-power tiny wireless nodes placed on or around the human body that continuously observe vital health signs of a patient. These sensors are capable of sending information of physiological parameters taken from human body to other devices for diagnosis procedures and prescription. WBAN provides ubiquitous healthcare services and enables greater mobility without restricting human normal activities, as the medical personnel can observe the patient health conditions based on the data received through the wireless network. This research work provides a WBAN based healthcare monitoring system that can provide the electrocardiogram (ECG), heartbeat, and human body temperature information. The wireless transmission of the received data from human body is performed by using Zigbee IEEE802.15.4 communication standard. The physiological data will be communicated to remote medical server where data is stored and analyzed. In case any disease is diagnosed, medical personnel can provide immediate assistance to the patients.

Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress

Continuous wearable sensor data in high resolution contain physiological and behavioral information that can be utilized to predict human health and wellbeing, establishing the foundation for developing early warning systems to eventually improve human health and wellbeing. We propose a deep neural network framework, the Locally Connected Long Short-Term Memory Denoising AutoEncoder (LC-LSTM-DAE), to automatically extract features from passively collected raw sensor data and perform personalized prediction of self-reported mood, health, and stress scores with high precision. We enabled personalized learning of features by finetuning the general representation model with participant-specific data. The framework was evaluated using wearable sensor data and wellbeing labels collected from college students (total 6391 days from N=239). Sensor data include skin temperature, skin conductance, and acceleration; wellbeing labels include self-reported mood, health and stress scored 0 - 100. Compared to the prediction performance based on hand-crafted features, the proposed framework achieved higher precision with a smaller number of features. We also provide statistical interpretation and visual explanation to the automatically learned features and the prediction models. Our results show the possibility of predicting self-reported mood, health, and stress accurately using an interpretable deep learning framework, ultimately for developing real-time health and wellbeing monitoring and intervention systems that can benefit various populations.

Bridge Safety Monitoring System using IOT

In this study, Bridge safety monitoring system using IOT is developed using the Wireless technology.With the help of Advancements in sensor technology have brought the automated real-time bridge health monitoring system. This system will help prevention in disaster management and recovery.IOT-based bridge safety monitoring system is developed using the Wireless Technology. By the use of wireless sensor nodes, various types of data can be collected like vibration, waterlevel and Bridge weight. Thease data would also be useful for monitoring and surveillance. The main moto of this paper is to develop a system that can prevent accidents or structural disasters of flyovers and bridges. This study gives the survey of various techniques used to monitor the conditions of the bridges and proposed a system for monitoring continuous structures and an ultrasonic sensor for monitoring the water level in the river to avoid traffic from a bridge in flood conditions using Kalman’s Filter algorithm. In case of emergency situations the gates of the Bridge will be automatically closed. The obtained data are transmitted to the server and database for admins to have real-time monitoring of the bridge conditions via mobile telecommunication devices.

Internet of things sensor assisted security and quality analysis for health care data sets using artificial intelligent based heuristic health management system

Abstract The developments in the medical systems, especially in health care management systems, play a vital role in patients. The effective management of health records leads to an increase in the importance of the healthcare management system all over the world. A real-time health monitoring system is a key zone for the Internet of Things (IoT) sensor technology in human services using Big Data Analytics. The major challenge that has to do with the health care data sets is security and privacy. In this paper, an artificial intelligence-based heuristic health management system has been designed and developed. This system is exceptionally close to improve the security and privacy of the live datasets of patients and the association of medicinal services over its different viewpoints. These services include the capacity for specialists, experts, attendants, and staff to settle on better decisions faster. Moreover, security and quality of data by configuration should be a part of any IoT use case, task or arrangement. Utilizing IoT assisted artificial intelligent based heuristic health management system intends to improve and minimize the security risk on health care data sets with assisted IoT sensors. The experimental results show promising outcomes in terms of various performance factors. The system attains precision as 99.75%, error rate as 0.0646 and predicted positive condition rate as 98.46%, Informedness as 98.6% and accuracy as 99.66%. The system is implemented using the MATLAB program.

Smart orthopaedic implants: A targeted approach for continuous postoperative evaluation in the spine

Real-time health monitoring systems are emerging in diverse medical fields, tracking biological and physiological signals for direct feedback to the user. Orthopaedics is yet to adapt to innovative trends in health monitoring. Despite an evident entry point during orthopaedic surgeries, clinicians remain unable to objectively examine the structural integrity and biomechanics in the operated region through implantable sensors. As such, postoperative advice can be non-specific and poorly guided. This perspective discusses the clinical need for load-sensing implants that address biomechanical postoperative monitoring, taking the example of spinal interbody cages. Research has attempted to establish sensing approaches in different orthopaedic settings; however, they fail to meet mechanical sensing requirements or lack in vivo translatability, especially in the spine. Polymeric flexible sensors and Microelectromechanical Systems (MEMS) have favourable attributes aligned to the required features for in vivo load-sensing, although these approaches are yet to be tested extensively in orthopaedics. While inductive powering is promising, wireless energy transfer and telemetry are areas of ongoing research. This perspective proposes a thorough understanding of the relevant biomechanics to identify the pertinent sensing parameters, concurrent treatment of sensing and powering aspects, and utilisation of energy harvesting for sensing and data transmission. While sensing advancements have contributed to the rise of real-time health monitoring in other fields of medicine, orthopaedics has so far been overlooked. It is the application of these innovations that will lead to the development of a new generation of ‘smart’ implants for continuous postoperative evaluation.

Realtime health monitoring system design for children with cerebral palsy using internet of things

The realtime health monitoring system is a typical application of wearable computing in the medical field. However, existing technology does not consider to design monitoring system for supervision for people with cerebral palsy. The advances in internet innovation have made possible techniques for the conveyance of healthcare. The objective of this research was to design and develop innovative technological solutions to offer a more proactive and reliable medical care environment. The short-term and primary goal was to construct IoT4CP. We also describe an experimental model designed for checking the health condition of the children with cerebral palsy based on IoT architecture. The framework depends on sensor shield associated with a cloud platform that gathers the data from the sensors. The sensors measure various parameters, such as a heart rate and muscle contraction which are transmitted via microcontroller by a gateway to a cloud storage platform by google firebase. This research presents the first realtime analyses to be activated within this framework and can send data smoothly with total average 1.77 seconds.

A secure and intelligent real-time health monitoring system for remote cardiac patients

A secure and intelligent real-time health monitoring system for remote cardiac patients

Energy Efficient Fog-Based Healthcare Monitoring Infrastructure

Recent advances in mobile technologies and cloud computing services have inspired the development of cloud-based real-time health monitoring systems. However, the transfer of health-related data to the cloud contributes to the burden on the networking infrastructures, leading to high latency and increased power consumption. Fog computing is introduced to relieve this burden by bringing services to the users' proximity. This study proposes a new fog computing architecture for health monitoring applications based on a Gigabit Passive Optical Network (GPON) access network. An Energy-Efficient Fog Computing (EEFC) model is developed using Mixed Integer Linear Programming (MILP) to optimize the number and location of fog devices at the network edge to process and analyze the health data for energy-efficient fog computing. The performance of the EEFC model at low data rates and high data rates health applications is studied. The outcome of the study reveals that a total energy saving of 36% and 52% are attained via processing and analysis the health data at the fog in comparison to conventional processing and analysis at the central cloud for low data rate application and high data rate application, respectively. We also developed a real-time heuristic; Energy Optimized Fog Computing (EOFC) heuristic, with energy consumption performance approaching the EEFC model. Furthermore, we examined the energy efficiency improvements under different scenarios of devices idle power consumption and traffic volume.

HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics

Electroencephalography (EEG) is immediate and sensitive to cortical impairment resulting from ischemic stroke and is considered as the potential predictive tool of stroke onset, and post-stroke clinical management. Brainwave monitoring outside the heavily equipped clinical environment demands a low-cost, portable, and wearable EEG system. This study aims to assess the feasibility of using an ambulatory EEG system to classify the stroke patient group with neurological changes due to ischemic stroke and the control healthy adult group. HealthSOS, a real-time health monitoring system for stroke prognostics, is proposed here, which consists of an eye-mask embedded portable EEG device, data analytics, and medical ontology based health advisor service. This system was investigated with 37 stroke patients (mean age 71.6 years, 61% male) admitted in the emergency unit of a hospital and 36 healthy elderly volunteers (mean age 76 years, 28% male). EEG was recorded in resting-state using the portable device with frontal cortical electrodes (Fp1, Fp2) embedded in an eye-mask within 120 h after the onset of symptoms of ischemic stroke (confirmed clinically). The EEG data acquisition of the left and right brain hemispheres was done for at least 15 minutes in the awake resting state while subjects laid down on the bed. The statistical result shows that the revised brain symmetry index (rsBSI), the delta-alpha ratio, and the delta-theta ratio of the stroke group differ significantly from those of the healthy control group. In the machine learning analysis, the support vector machine (SVM) model shows the highest accuracy (Overall accuracy: 92%) and the highest Gini coefficient (95%) in classification performance. This study will be useful for early stroke prognostics and the management of post-stroke treatment.

Real-Time Health Monitoring System with Hybrid Wearable Suit

Real-Time Health Monitoring System with Hybrid Wearable Suit

An Energy Efficient Health Monitoring System using Relay Node in Wireless Body Area Networks

INTRODUCTION: In today's world, Wireless Body Area Networks (WBAN) has emanated as a budding field within the healthcare scenario for monitoring, detecting, predicting, and diagnosing various fatal diseases such as cancer, cardiovascular, diabetes and other infectious diseases such as tuberclosis, pneumonia and ebola. It is an assemblage of bio-sensors which could be put on or embedded in a patient's body to surveil the functionality of various body organs.OBJECTIVES: The purpose of that proposed protocol is to select the relay node and record the various physiological parameters like body temperature, blood pressure, rate of blood sugar of human being. Energy consumption is a major hurdle in WBAN as replacing as well as recharging the nodes pinioned to human body may bring about discomfort to the patient. Therefore, in this work, the emphasis is given to improve the performance of WBAN i.e. network lifetime, greater mobility, stability and energy efficiency.METHODS: To find the shortest energy-efficient route and avoid any kind of relay node failure, a novel routing scheme called Enhanced Opportunistic Protocol using Relay nodes (E-OPR) is proposed for data routing.CONCLUSION: Finally, an enhanced opportunistic protocol using relay node in WBAN has been proposed for cost effective real time health monitoring system. The proposed solution seems to give better results as compare single-hop, multi-hop routing protocols in terms of throughput, network lifetime, energy consumption and path loss.

A hierarchical, scalable architecture for a real-time monitoring system for an electrocardiography, using context-aware computing.

Heart failure is one of the most common cause of death in the world. The real-time health monitoring system with the advent of the Internet of things has attracted growing attention in the health care industry, which can help reducing the death rate of heart failure. Despite the recent success of these efforts, there have been some limitations, such as response time, scalability, latency and fault tolerance. To address these issues, in this paper, we propose a hierarchical architecture with four layers to develop health care systems. In the proposed model, the vital signs of a patient are measured by means of a body sensor network and sent to a smart health care system. Each of these layers is related to a certain level of heart failure. Therefore, in the proposed model, simple and low-risk heart failure can be detected quickly before it reaches to a dangerous level. Empirical results confirm a significant improvement in terms of response time and scalability in comparison with the state of the art techniques.

RETRACTED ARTICLE: A fuzzy model of wearable network real-time health monitoring system on pharmaceutical industry

With the introduction of wearable network technologies, smart healthcare is gradually being realized. Real-time health care monitoring system should be safe, effective, and patient-centered. This paper aims to analyze the opinions and emotions of medical and health products, and to design and develop monitoring systems by crawling multiple websites and reviews in a variety of formats. Data preprocessing is used to preprocess the quantized image, and finally, it is recommended in combination with the analysis results. This system combines a large amount of products’ comment data, combined with MATLAB and other tools to achieve the visualization of big data analysis, by the use of the maximum entropy algorithm for comprehensive analysis and comparison. Support vector machine (SVM) algorithm is used to analyze and compare various parts of the medical image in detail. It is suitable for current medical applications. It can provide advice for consumers to purchase drugs and achieve real-time health monitoring objectives.

Structural Health Monitoring of Underground Structures in Reclamation Area Using Fiber Bragg Grating Sensors.

The long-term structural performance of underground structures in reclamation areas is very sensitive to the vibrations caused by vehicles passing above the structures and environmental factors (e.g., tide levels, rainfall and temperature). In the present study, an integrated remote real-time structural health monitoring system using fiber Bragg grating sensors was developed to assess the structural performance of underground structures. Using a composite road box-type structure project in a reclamation area in Southern China as a case study, the developed real-time system was implemented to investigate the effects of changes in tide levels, rainfall, temperature and vehicle induced vibrations on crack propagation in the structure. The results show that the change in tide levels has little influence on the change in crack width in the structure, whereas variations in temperature could significantly influence the crack width with an average Pearson correlation of around 0.8. In addition, the crack width generally decreases with an increase in rainfall. Furthermore, a relatively low frequency (<25 Hz) induced by the traffic could result in a relatively larger crack width.

A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition

With the rapid development of artificial intelligence and mobile Internet, the new requirements for human-computer interaction have been put forward. The personalized emotional interaction service is a new trend in the human-computer interaction field. As a basis of emotional interaction, emotion recognition has also introduced many new advances with the development of artificial intelligence. The current research on emotion recognition mostly focuses on single-modal recognition such as expression recognition, speech recognition, limb recognition, and physiological signal recognition. However, the lack of the single-modal emotional information and vulnerability to various external factors lead to lower accuracy of emotion recognition. Therefore, multimodal information fusion for data-driven emotion recognition has been attracting the attention of researchers in the affective computing filed. This paper reviews the development background and hot spots of the data-driven multimodal emotion information fusion. Considering the real-time mental health monitoring system, the current development of multimodal emotion data sets, the multimodal features extraction, including the EEG, speech, expression, text features, and multimodal fusion strategies and recognition methods are discussed and summarized in detail. The main objective of this work is to present a clear explanation of the scientific problems and future research directions in the multimodal information fusion for data-driven emotion recognition field.

Analysis of Bearing Faulty Cage using Non-Intrusive Condition Monitoring Techniques

The cage is an important element of the bearing. The cage is used to support the bearing balls and it maintains the fix distance between rotating balls. The defected cage could create defects in the balls and race ways of the bearing. It could lead to premature failure of the bearing which causes machine down time, production loss and huge maintenance cost. Traditionally, periodic checks of the bearing health are performed in industry but they did not avoid the bearing cage failures. Thus, it is essential to develop some real-time health monitoring system to analyze the status of bearing cage. Recent research developments on the bearing fault diagnosis have that non-intrusive stator current and power analysis techniques are most economical and simple as compared to intrusive vibration analysis technique. However, a limited research has been conducted to check and confirm the effectivity of stator current and power analysis techniques for the diagnosis of cage faults. Thus, the aim of this paper is to analyze cage faults using non-intrusive stator current and power analysis. The comparison of both techniques have been performed experimentally and it has been concluded that power analysis is a most suitable technique to diagnose cage faults. Keywords: Cage Faults, Non-Intrusive Condition Monitoring, Harmonics Analysis, LabVIEW based Signal Processing.

An IoT System for Remote Health Monitoring in Elderly Adults Through a Wearable Device and Mobile Application.

With the increase in global life expectancy and the advance of technology, the creation of age-friendly environments is a priority in the design of new products for elderly people healthcare. This paper presents a proposal for a real-time health monitoring system of older adults living in geriatric residences. This system was developed to help caregivers to have a better control in monitoring the health of their patients and have closer communication with their patients’ family members. To validate the feasibility and effectiveness of this proposal, a prototype was built, using a biometric bracelet connected to a mobile application, which allows real-time visualization of all the information generated by the sensors (heart rate, body temperature, and blood oxygenation) in the bracelet. Using these data, caregivers can make decisions about the health status of their patients. The evaluation found that the users perceived the system to be easy to learn and use, providing initial evidence that our proposal could improve the quality of the adult’s healthcare.

Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health

Remote health monitoring is an effective method to enable tracking of at-risk patients outside of conventional clinical settings, providing early-detection of diseases and preventive care as well as diminishing healthcare costs. Internet-of-Things (IoT) technology facilitates developments of such monitoring systems although significant challenges need to be addressed in the real-world trials. Missing data is a prevalent issue in these systems, as data acquisition may be interrupted from time to time in long-term monitoring scenarios. This issue causes inconsistent and incomplete data and subsequently could lead to failure in decision making. Analysis of missing data has been tackled in several studies. However, these techniques are inadequate for real-time health monitoring as they neglect the variability of the missing data. This issue is significant when the vital signs are being missed since they depend on different factors such as physical activities and surrounding environment. Therefore, a holistic approach to customize missing data in real-time health monitoring systems is required, considering a wide range of parameters while minimizing the bias of estimates. In this paper, we propose a personalized missing data resilient decision-making approach to deliver health decisions 24/7 despite missing values. The approach leverages various data resources in IoT-based systems to impute missing values and provide an acceptable result. We validate our approach via a real human subject trial on maternity health, in which 20 pregnant women were remotely monitored for 7 months. In this setup, a real-time health application is considered, where maternal health status is estimated utilizing maternal heart rate. The accuracy of the proposed approach is evaluated, in comparison to existing methods. The proposed approach results in more accurate estimates especially when the missing window is large.