Patient Health Monitoring Research Articles

Teknologi Aplikasi Nurse Call berbasis Client Server Pada Rumah Sakit

The credibility of a hospital has the effect of the services provided by the hospital. One of the services in the hospital is inpatient services and the role of nurses who have an important role in serving patients. Some problems in hospitalization services that occur such as manual nurse calls are done by visiting the treatment room and proposing the desired service. This method is less effective because it will require a long process in service. The application of nurse calls is done to improve services so that nurses can provide the best service and patient health can be monitored properly. The calling system for nurses is based on client-server and additional features such as heart detection, temperature detection, and emergency buttons that have the role to call nurses. The advantage of the nurse call application is that it can monitor patient health from several detection devices that will automatically call through the application. In addition to handling patients will be monitored by the hospital, the nurse's performance will also be detected. The results of this study are that the nurse call process will be automatically carried out by a detection device so that the nurse will detect the patient's condition quickly, the patient handling data will be stored on the server, therefore the history of heart rate and temperature records is more complete and can be used for doctors to handle patients.

Dry electrode made from carbon nanotubes for continuous recording of bio-signals

Health monitoring devices have received attention from individual users and clinicians. For example, the wet-type silver/silver chloride (Ag/AgCl) electrode is used to record bio-signals such as electrocardiogram (ECG) and electromyogram (EMG). However, when used for long durations, an Ag/AgCl electrode with an electrolyte gel causes signal distortion and degradation and can irritate the skin. In this study, we developed a dry-type self-adhesive electrode composed of a mixture of carbon nanotubes and adhesive-polydimethylsiloxane. This electrode can be attached to the skin by creating a vacuum between the epidermal and electrode surfaces, allowing for long-term recording of bio-signals without side effects. We analyzed the electrical and mechanical characteristics and verified the performance of the proposed electrode compared to that of an Ag/AgCl electrode by conducting ECG and EMG measurements. Moreover, the composite electrode was attached to the skin for one week, and no skin irritation, itchiness, or remarkable degradation of the bio-signals such as ECG was observed. This electrode enables long-term health monitoring of patients with chronic conditions. Additionally, this comfortable electrode can be applied to a wearable device, including for bio-signal detection.

Detecting breathing rates and depth of breath using LPCs and Restricted Boltzmann Machines

Abstract This paper presents the use of a Restricted Boltzmann Machine to develop an unsupervised machine learning approach to process breathing sounds to predict breathing rates and depth or length of breaths. Breath detection and monitoring has been the subject of several studies involving the health monitoring of patients on respirators. We are proposing to extend the use of non-invasive techniques to provide measures of physical exhaustion or activity. The level of activity or exhaustion could be used to prevent accidents or manage exposure to physically demanding environments such as firefighting or working underwater.

Sensitive and Wearable Optical Microfiber Sensor for Human Health Monitoring

AbstractSilica‐fiber‐based sensors have the advantage of being small, electrically isolated, immune to electromagnetic fields, and easily incorporated into networks. However, only few of them were used for monitoring physiological signals directly, due to their limited sensitivity, comfort, and safety. In this work, a simple architecture for sensitive and wearable photonic sensors, which are capable of strain and pressure detection for healthcare applications is reported. The proposed sensor consists of a hybrid plasmonic microfiber knot resonator embedded in a polydimethylsiloxane membrane. The mechanical sensitivity is more than one order of magnitude larger than exhibited by traditional electronic devices or other fiber devices, including Bragg gratings. In addition, the sensing of wrist pulse, respiration, and finger pulse are also experimentally demonstrated. This novel photonic‐based platform may provide a new basis for designing sensitive and wearable sensors for personalized diagnostics and physiological monitoring applications. With their potential to be embedded into networks of smart clothing and remote health monitoring of patients, these sensors possessing great biocompatibility will have a distinguished advantage in military, aerospace, and medical applications, particularly in high‐magnetic‐field environments, e.g., magnetic resonance imaging.

Software architecture pattern selection model for Internet of Things based systems

The entire world is moving towards the era of smart technology. Internet of Things (IoT) is the key factor behind this revolutionary change. IoT converts real-world objects into interactive smart systems which can be controlled from remote locations. These physical real-world objects tagged as ‘Things’ in IoT should have a unique identity. These ‘things’ can be remotely controlled and monitored from anywhere in the world through Internet. Due to this feature it has been applied in various domains like home automation, patient health monitoring, agricultural sector, environment surveillance and so on. Due to the increased use of IoT-based system, there arises a need for a well-defined software architecture. Though various IoT developers follow their own design strategies for IoT architecture, there is a need for domain specific architecture for IoT applications. The authors propose a software architecture pattern selection model which can be followed in designing IoT systems. Non-functional requirements such as scalability, availability, reliability, security and heterogeneity determine the decision making of the proposed model. Analytical results prove that this model can be used as a reference model, for IoT developers in choosing a suitable software architecture pattern for their IoT system.

Can We Improve Physical Health Monitoring for Patients Taking Antipsychotics on a Mental Health Inpatient Unit?

Patients with severe mental illness are at risk of medical complications, including cardiovascular disease, metabolic syndrome, and diabetes. Given this vulnerability, combined with metabolic risks of antipsychotics, physical health monitoring is critical. Inpatient admission is an opportunity to screen for medical comorbidities. Our objective was to improve the rates of physical health monitoring on an inpatient psychiatry unit through implementation of an electronic standardized order set. Using a clinical audit tool, we completed a baseline retrospective audit (96 eligible charts) of patients aged 18 to 100 years, discharged between January and March 2012, prescribed an antipsychotic for 3 or more days. We then developed and implemented a standard electronic admission order set and provided training to inpatient clinical staff. We completed a second chart audit of patients discharged between January and March 2016 (190 eligible charts) to measure improvement in physical health monitoring and intervention rates for abnormal results. In the 2012 audit, thyroid-stimulating hormone (TSH), blood pressure, blood glucose, fasting lipids, electrocardiogram (ECG), and height/weight were measured in 71%, 92%, 31%, 36%, 51%, and 75% of patients, respectively. In the 2016 audit, TSH, blood pressure, blood glucose, fasting lipids, ECG, and height/weight were measured in 86%, 96%, 96%, 64%, 87%, and 71% of patients, respectively. There were statistically significant improvements (P < 0.05) in monitoring rates for blood glucose, lipids, ECG, and TSH. Intervention rates for abnormal blood glucose and/or lipids (feedback to family doctor and/or patient, consultation to hospitalist, endocrinology, and/or dietician) did not change between 2012 and 2016. Electronic standardized order set can be used as a tool to improve screening for physical health comorbidity in patients with severe mental illness receiving antipsychotic medications.

Design and Development of Patient Health Monitoring System Using IoT

Design and Development of Patient Health Monitoring System Using IoT

Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions.

Wearable motion sensors are assumed to be correctly positioned and oriented in most of the existing studies. However, generic wireless sensor units, patient health and state monitoring sensors, and smart phones and watches that contain sensors can be differently oriented on the body. The vast majority of the existing algorithms are not robust against placing the sensor units at variable orientations. We propose a method that transforms the recorded motion sensor sequences invariantly to sensor unit orientation. The method is based on estimating the sensor unit orientation and representing the sensor data with respect to the Earth frame. We also calculate the sensor rotations between consecutive time samples and represent them by quaternions in the Earth frame. We incorporate our method in the pre-processing stage of the standard activity recognition scheme and provide a comparative evaluation with the existing methods based on seven state-of-the-art classifiers and a publicly available dataset. The standard system with fixed sensor unit orientations cannot handle incorrectly oriented sensors, resulting in an average accuracy reduction of 31.8%. Our method results in an accuracy drop of only 4.7% on average compared to the standard system, outperforming the existing approaches that cause an accuracy degradation between 8.4 and 18.8%. We also consider stationary and non-stationary activities separately and evaluate the performance of each method for these two groups of activities. All of the methods perform significantly better in distinguishing non-stationary activities, our method resulting in an accuracy drop of 2.1% in this case. Our method clearly surpasses the remaining methods in classifying stationary activities where some of the methods noticeably fail. The proposed method is applicable to a wide range of wearable systems to make them robust against variable sensor unit orientations by transforming the sensor data at the pre-processing stage.

Recent Patient Health Monitoring Platforms Incorporating Internet of Things-Enabled Smart Devices.

Synergistic integration of the Internet of Things (IoT), cloud computing, and big data technologies in healthcare have led to the notion of “smart health.” Smart health is an emerging concept that refers to the provision of healthcare services for prevention, diagnosis, treatment, and follow-up management at any time or any place by connecting information technologies and healthcare. As a significant breakthrough in smart healthcare development, IoT-enabled smart devices allow medical centers to carry out preventive care, diagnosis, and treatment more competently. This review focuses on recently developed patient health monitoring platforms based on IoT-enabled smart devices that can collect real-time patient data and transfer information for assessment by healthcare providers, including doctors, hospitals, and clinics, or for self-management. We aimed to summarize the available information about recently approved devices and state-of-the-art developments through a comprehensive, systematic literature review. In this review, we also discuss possible future directions for the integration of cloud computing and blockchain, which may offer unprecedented breakthroughs in on-demand medical services. The combination of IoT with real-time, remote patient monitoring empowers patients to assert more control over their care, thereby allowing them to actively monitor their particular health conditions.

Visualizing Collaboration Characteristics and Topic Burst on International Mobile Health Research: Bibliometric Analysis.

BackgroundIn the last few decades, mobile technologies have been widely adopted in the field of health care services to improve the accessibility to and the quality of health services received. Mobile health (mHealth) has emerged as a field of research with increasing attention being paid to it by scientific researchers and a rapid increase in related literature being reported.ObjectiveThe purpose of this study was to analyze the current state of research, including publication outputs, in the field of mHealth to uncover in-depth collaboration characteristics and topic burst of international mHealth research.MethodsThe authors collected literature that has been published in the last 20 years and indexed by Thomson Reuters Web of Science Core Collection (WoSCC). Various statistical techniques and bibliometric measures were employed, including publication growth analysis; journal distribution; and collaboration network analysis at the author, institution, and country collaboration level. The temporal visualization map of burst terms was drawn, and the co-occurrence matrix of these burst terms was analyzed by hierarchical cluster analysis and social network analysis.ResultsA total of 2704 bibliographic records on mHealth were collected. The earliest paper centered on mHealth was published in 1997, with the number of papers rising continuously since then. A total of 21.28% (2318/10,895) of authors publishing mHealth research were first author, whereas only 1.29% (141/10,895) of authors had published one paper. The total degree of author collaboration was 4.42 (11,958/2704) and there are 266 core authors who have collectively published 53.07% (1435/2704) of the total number of publications, which means that the core group of authors has fundamentally been formed based on the Law of Price. The University of Michigan published the highest number of mHealth-related publications, but less collaboration among institutions exits. The United States is the most productive country in the field and plays a leading role in collaborative research on mHealth. There are 5543 different identified keywords in the cleaned records. The temporal bar graph clearly presents overall topic evolutionary process over time. There are 12 important research directions identified, which are in the imbalanced development. Moreover, the density of the network was 0.007, a relatively low level. These 12 topics can be categorized into 4 areas: (1) patient engagement and patient intervention, (2) health monitoring and self-care, (3) mobile device and mobile computing, and (4) security and privacy.ConclusionsThe collaboration of core authors on mHealth research is not tight and stable. Furthermore, collaboration between institutions mainly occurs in the United States, although country collaboration is seen as relatively scarce. The focus of research topics on mHealth is decentralized. Our study might provide a potential guide for future research in mHealth.

Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes

Internet of Things (IoT) technology provides a competent and structured approach to handle service deliverance aspects of healthcare in terms of mobile health and remote patient monitoring. IoT generates an unprecedented amount of data that can be processed using cloud computing. But for real-time remote health monitoring applications, the delay caused by transferring data to the cloud and back to the application is unacceptable. Relative to this context, we proposed the remote patient health monitoring in smart homes by using the concept of fog computing at the smart gateway. The proposed model uses advanced techniques and services, such as embedded data mining, distributed storage, and notification services at the edge of the network. Event triggering-based data transmission methodology is adopted to process the patient’s real-time data at fog layer. Temporal mining concept is used to analyze the events adversity by calculating the temporal health index of the patient. In order to determine the validity of the system, health data of 67 patients in IoT-based smart home environment was systematically generated for 30 days. Results depict that the proposed Bayesian belief network classifier-based model has high accuracy and response time in determining the state of an event when compared with other classification algorithms. Moreover, decision making based on real-time healthcare data further enhances the utility of the proposed system.

Perspectives of Nurses Toward Telehealth Efficacy and Quality of Health Care: Pilot Study.

BackgroundTelehealth nursing, or the delivery, management, and coordination of nursing care services provided via telecommunications technology, is one of the methods of delivering health care to patients in the United States. It is important to assess the service quality of the involved health professionals as well as the telehealth nursing process. The focus of this study is the innovative model of telehealth care delivery by nurses for managing patients with chronic disease while they are living in their own residence.ObjectiveThe primary objective of this pilot study was to examine whether telehealth technology impacts the perceived level of internal service quality delivered by nurses within a telehealth organization. To address this research goal, the notion of telehealth nursing service quality (TNSQ) is empirically tested and validated with a survey instrument.MethodsData were collected from nurses belonging to a home care agency based on interview questions inquiring about facilitators and inhibitors to TNSQ. A survey to measure TNSQ based on the SERVQUAL instrument was completed by adjusting descriptions of the original instrument to suit the context. Follow-up interviews were conducted to validate questions on the revised instrument.ResultsThe findings of this survey research were positive, based on mean differences between expectations and perceptions of TNSQ. This indicates satisfaction with TNSQ and shows that the quality of the service is higher than what the respondents expect. The Wilcoxon signed-rank test using the P value for the test, which is .35, did not show a statistically significant change between the median differences of perception and expectation. The total number of respondents was 13. Results indicate that overall perceived service quality is a positive value (0.05332). This means the perceptions of the level of service are slightly higher than what they expect, indicating there is satisfaction with TNSQ.ConclusionsThe responses to the interview questions and data gathered from the survey showed overall satisfaction with TNSQ. The SERVQUAL instrument was a good framework to assess TNSQ. In a nutshell, the study highlighted how the telehealth process provides daily monitoring of patient health, leading to the benefits of immediate feedback for patients, family, and caregivers as well as convenience of scheduling.

Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data.

This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.

Managing Patient-Generated Health Data Through Mobile Personal Health Records: Analysis of Usage Data

BackgroundPersonal health records (PHRs) and mHealth apps are considered essential tools for patient engagement. Mobile PHRs (mPHRs) can be a platform to integrate patient-generated health data (PGHD) and patients’ medical information. However, in previous studies, actual usage data and PGHD from mPHRs have not been able to adequately represent patient engagement.ObjectiveBy analyzing 5 years’ PGHD from an mPHR system developed by a tertiary hospital in South Korea, we aimed to evaluate how PGHD were managed and identify issues in PGHD management based on actual usage data. Additionally, we analyzed how to improve patient engagement with mPHRs by analyzing the actively used services and long-term usage patterns.MethodsWe gathered 5 years (December 2010 to December 2015) of log data from both hospital patients and general users of the app. We gathered data from users who entered PGHD on body weight, blood pressure (BP), blood glucose levels, 10-year cardiovascular disease (CVD) risk, metabolic syndrome risk, medication schedule, insulin, and allergy. We classified users according to whether they were patients or general users based on factors related to continuous use (≥28 days for weight, BP, and blood glucose, and ≥180 days for CVD and metabolic syndrome), and analyzed the patients’ characteristics. We compared PGHD entry counts and the proportion of continuous users for each PGHD by user type.ResultsThe total number of mPHR users was 18,265 (patients: n=16,729, 91.59%) with 3620 users having entered weight, followed by BP (n=1625), blood glucose (n=1374), CVD (n=764), metabolic syndrome (n=685), medication (n=252), insulin (n=72), and allergy (n=61). Of those 18,256 users, 3812 users had at least one PGHD measurement, of whom 175 used the PGHD functions continuously (patients: n=142, 81.14%); less than 1% of the users had used it for more than 4 years. Except for weight, BP, blood glucose, CVD, and metabolic syndrome, the number of PGHD records declined. General users’ continuous use of PGHD was significantly higher than that of patients in the blood glucose (P<.001) and BP (P=.03) functions. Continuous use of PGHD in health management (BP, blood glucose, and weight) was significantly greater among older users (P<.001) and men (P<.001). In health management (BP, weight, and blood glucose), overall chronic disease and continuous use of PGHD were not statistically related (P=.08), but diabetes (P<.001) and cerebrovascular diseases (P=.03) were significant.ConclusionsAlthough a small portion of users managed PGHD continuously, PGHD has the potential to be useful in monitoring patient health. To realize the potential, specific groups of continuous users must be identified, and the PGHD service must target them. Further evaluations for the clinical application of PGHD, feedback regarding user interfaces, and connections with wearable devices are needed.

MRI brain image enhancement using brightness preserving adaptive fuzzy histogram equalization

AbstractIn this article, fuzzy logic based adaptive histogram equalization (AHE) is proposed to enhance the contrast of MRI brain image. Medical image plays an important role in monitoring patient's health condition and giving an effective diagnostic. Mostly, medical images suffer from different problems such as poor contrast and noise. So it is necessary to enhance the contrast and to remove the noise in order to improve the quality of a various medical images such as CT, X‐ray, MRI, and MAMOGRAM images. Fuzzy logic is a useful tool for handling the ambiguity or uncertainty. Brightness Preserving Adaptive Fuzzy Histogram Equalization technique is proposed to improve the contrast of MRI brain images by preserving brightness. Proposed method comprises of two stages. First, fuzzy logic is applied to an input image and then it's output is given to AHE technique. This process not only preserves the mean brightness and but also improves the contrast of an image. A huge number of highly MRI brain images are taken in the proposed method. Performance of the proposed method is compared with existing methods using the parameters namely entropy, feature similarity index, and contrast improvement index and the experimental results show that the proposed method overwhelms the previous existing methods.

Bio-radar performance evaluation for different antenna designs

Cardiopulmonary monitoring without any physical contact with the human body has several applications, such as health monitoring of bedridden patients, monitoring sleeping, or even for rescuing people from collapsed buildings. The Bio-Radar system can accurately measure vital signals by using the principle of the Doppler effect, which relates the properties of the received signal to the distance change between the radar antennas and the person's chest wall. In this paper, a mathematical model of the Bio-Radar is presented. The algorithm used for extracting breathing rate is explained, and an analysis of the influence of the radiation pattern of the antenna on the quality of the received signal is presented. Moreover, we show that an antenna with a narrow beam leads to a better signal-to-noise ratio (SNR). The full performance of the developed prototype was also evaluated by using a certified measuring system to monitor vital signals simultaneously with the Bio-Radar. The signals extracted using both acquisition methods are presented, and a comparison is made in order to prove the accuracy of the prototype.

INTERNET OF THINGS BASED HEALTH MONITORING SYSTEM : OPPORTUNITIES AND CHALLENGES

Internet of Things (IoT) is a new and fast growing technology in which everything ( smart objects and smart devices) are connected to the internet for effective communication between these connected things. Internet of things serves as a catalyst for the healthcare and plays very important role in wide range of healthcare monitoring applications. Networked sensors devices, either worn on the body or embedded in living environments, make possible the gathering of rich information to evaluate physical and mental health condition of the patient by collecting body temperature, blood pressure, sugar level etc. Communicating this collected information to the doctor, making accurate decision on the data collected and notifying the patient is the challenging task in the Internet of things. In this paper author focus on review of IoT based Healthcare System and outline about Opportunities and Challenges for Internet of Things based Patient Health Monitoring System.

Patient Health Monitoring System based on IoT using Raspberry Pi

Patient Health Monitoring System based on IoT using Raspberry Pi

IoT Based Health Care Monitoring System using Wireless sensor Networks

The improvement in the technology in health care monitoring system it is necessary to constantly monitor the patient's physiological parameter. In the advanced in the health care environment by the usage of IOT technology bring convenience to the patient and the physician. Since they are used in various medical areas. The internet of things (IOT) has been broadly used to interconnect the available medical resources and offers the smart, reliable and effective health care services. A sensor node has been attached on the surface of the body the sensor node will gather the information and sends them to the wireless sensor node. The sensor nodes which are arranged on the patient body can sense the heart beat, temperature of surrounding. The main objective of this system is to transmit the patient's health monitoring system through wireless communication in an emergence situation. We propose a secure IOT based healthcare monitoring system to check the heartbeat rate, temperature of the surrounding and saline level of the patient. In the proposed system of health care monitoring system the heart beat rate, temperature of surrounding and MEMS accelerometer is used for when a patient's body will fall down on the foor then it will send the SMS alert to the predefned mobile number and by using IOT we can send the data to the server.

Commentary and Reflection Related to the Perspectives of Nurses Toward Telehealth Efficacy and Quality of Health Care

Telehealth nursing enabling communication with chronic disease patients has shown to improve medical outcomes for patients suffering from diabetes mellitus, chronic obstructive pulmonary disease (COPD), and chronic heart disease. Studies have been conducted to examine whether telehealth technology impacts the perceived level of internal service quality (SQ) delivered by nurses within a telehealth organization. Overall, the perspectives toward telehealth SQ researched through case studies showed positive results. The innovative role of telehealth nursing and related technology toward patient care was highlighted. Literature establishes how telehealth provides daily monitoring of patient health, which has the benefits of peace of mind, immediate feedback for patients, family, and caregivers, as well as the convenience of scheduling. This commentary not only reflects but also aims to analyze the effectiveness of the telehealth nursing interventions and reasons for the delayed implementation across United States. Improving and implementing telehealth methods in the case of chronic patients suffering from diabetes, COPD, and heart disease can lead to reductions in health-care errors and readmissions. Telehealth nursing has the potential of decreasing the length of hospital stay, at the same time improving satisfaction of care teams, while the patient management continues at home.