Patient Health Monitoring Research Articles

Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity.

Health is gold, and good health is a matter of survival for humanity. The development of the healthcare industry aligns with the development of humans throughout history. Nowadays, along with the strong growth of science and technology, the medical domain in general and the healthcare industry have achieved many breakthroughs, such as remote medical examination and treatment applications, pandemic prediction, and remote patient health monitoring. The advent of 5th generation communication networks in the early 2020s led to the Internet of Things concept. Moreover, the 6th generation communication networks (so-called 6G) expected to launch in 2030 will be the next revolution of the IoT era, and will include autonomous IoT systems and form a series of endogenous intelligent applications that serve humanity. One of the domains that receives the most attention is smart healthcare. In this study, we conduct a comprehensive survey of IoT-based technologies and solutions in the medical field. Then, we propose an all-in-one computing architecture for real-time IoHT applications and present possible solutions to achieving the proposed architecture. Finally, we discuss challenges, open issues, and future research directions. We hope that the results of this study will serve as essential guidelines for further research in the human healthcare domain.

Automatic non-invasive blood pressure measurement in left ventricular assist device patients with a photoplethysmography assisted device

Non-invasive measurement of blood pressure in patients with a left ventricular assist device (LVAD) is challenging due to the mechanical properties of these circulatory support devices. Keeping blood pressure in a target range is crucial to reduce the risk of LVAD complications. As current assessment methods require trained personnel resulting in infrequent monitoring, devices for home-based measurements are sorely needed. In this paper, we report a measurement method and a custom-made monitoring device based on photoplethysmography (PPG) enabling automated measurement of the mean arterial pressure (MAP) of patients with LVAD. The method and the device were tested on 21 adult patients with LVAD, and the estimated MAP values were compared to MAP values measured simultaneously by a human expert applying the commonly used Doppler-based method. Results of the proposed method showed highly significant correlation with Doppler-based MAP values (R = 0.85 for inflation, p < 0.001; R = 0.96 for deflation, p<0.001). The mean difference of the proposed method to the Doppler-based MAP values was 1.48 ± 5.43 mmHg for inflation and -0.19 ± 2.71 mmHg for deflation. The results demonstrate that the proposed method is a promising direction to achieve accurate, automated, non-invasive BP measurement, applicable in home health monitoring for patients with LVAD.

Automated Computer Vision System for Urine Color Detection

Urine color analysis is one of the most helpful indicators of health status, and any changes in urine color might be a symptom of serious disease, dehydration of the body, or caused by drugs. To get better assistance for urine color detection in the proposed system, a urine color automatic identification has been developed based on computer vision. The proposed system uses a web camera to capture an image in real-time, analyze it, and then classify the color of urine by using the random forest (RF) algorithm and show the result via the Graphical User Interface (GUI). In addition, the proposed system can send the results to the mobile phone of the patient or care provider by using an Arduino microcontroller and GSM module. Moreover, sending a voice message about the color of urine is related to pathological conditions. The results showed that the proposed system has high accuracy (approximately about 97%) in detecting urine color under different light conditions, with low cost, short time, and easy implementation. In the comparison with the current methods the proposed system has maximum accuracy and minimum error rate. This methodology can pave the way for an additional case study in medical applications, particularly in diagnosis, and patient health monitoring.

Tongue Color Analysis and Diseases Detection Based on a Computer Vision System

The tongue reflects the abnormal condition and behavior of the internal organs of the body, such as problems of the heart, liver, pancreas, stomach, intestines, blood diseases and others, which lead to changes in some of the features and characteristics of the tongue. The most important of these is tongue color, which can be adopted as a biometric that can be used in Computerized Tongue Diagnostic Systems (CTDS). Quantitative diagnosis of the tongue requires some devices, including image acquisition devices such as cameras, light sources, filters, color checkers, image analysis and processing devices through the application of some algorithms or image processing and color correction software, as well as a computer. This study proposes a real-time imaging system to analyze tongue color and diagnose diseases using a webcam under specific conditions. The proposed system was designed in a Matlab GUI environment. After testing the system on a data set of more than 100 images, the preliminary results showed that the proposed system gives a disease diagnosis with an accuracy rate of no less than 86.667%. The proposed system contributed to the diagnosis of several diseases in real time, with an accuracy of 95.45%, with ease of use, implementation and low cost. This gives impetus to further studies to apply computerized diagnosis in medical applications, to enhance the medical reality, monitor patient health, and make an accurate diagnosis.

Implementation of Telemedicine through “Apoteker Keluarga Online” Application as an Effort for Rational Headache Self-Medication

Implementation of Telemedicine through “Apoteker Keluarga Online” Application as an Effort for Rational Headache Self-Medication

LTE-NBP with holistic UWB-WBAN approach for the energy efficient biomedical application.

Increased use of ultra-wideband (UWB) in biomedical applications based on wireless body area networks (WBAN) opens a variety of options in the field of biomedical research. WBAN may aid in the continuous health monitoring of patients while they go about their everyday lives. Many studies and researchers were conducted several experimentations in the same field for the performance improvement. This study covered the hybridization of UWB technology, as well as on-body, off-body, and human-body ultra-wideband communication (HB-UWB). In this paper, the parameters considered are throughput, energy consumption, energy efficiency, energy used, network survival and delay. An improved model for design and assessment of power-saving UWB-WBAN was developed in this paper. A novel protocol model was introduced in this paper, namely low-power traffic-aware emergency based narrowband protocol (LTE-NBP) to overcome the major drawbacks of emergency, critical data transmission, reliability and the power issues in UWB-WBAN. It's the emergency-based low-power traffic-aware narrowband protocol. It is based on the dual-band physical layer technology. The suggested protocol considered an aware traffic model and an emergency medium access control (MAC) protocol. The proposed model's performance was evaluated and compared with the related algorithms on different performance parameters. The improved model is found to be efficient in throughput, energy efficiency, energy consumption, and delay.

Transformer-based deep reverse attention network for multi-sensory human activity recognition

In today’s era, one of the important applications of Artificial Intelligence (AI) is Human Activity Recognition (HAR). It has a wide range of applicability in health monitoring for patients with chronic diseases, gaming consoles for gesture recognition, etc. Sensor-based HAR systems use signals collected over a period of time to label an activity. When we design an efficient sensor-based HAR system, a model requires learning an optimal association of spatial and temporal features. In this article, we propose a sensor-based HAR technique using the deep learning approach. We present a deep reverse transformer-based attention mechanism to guide the side residual features Unlike the conventional bottom-up approaches for feature fusion, we exploit a top-down feature fusion approach. The reverse attention is self-calibrated throughout the course of learning, which regularizes the attention modules and dynamically adjusts the learning rate. The overall framework outperforms several state-of-the-art methods and is shown to be statistically significant against these methods on five publicly available sensor-based HAR datasets, namely, MHEALTH, USC-HAD, WHARF, UTD-MHAD1, and UTD-MHAD2. Further, we conduct an ablation study to showcase the importance of each of the components of the proposed framework. Source code of this work is available at https://github.com/rishavpramanik/RevTransformerAttentionHAR.

IoT Based Tracking System for Patients

Abstract-Health-care Environment has developed in science and patients require ongoing monitoring of their ECG readings, body temperature, blood pressure (BP), heart rate, and other vital indicators. So, the primary goal of the working model is to use wireless technology to transmit the patient's health parameters to the doctor in case anything is abnormal. All the characteristics will be sensed, even if the patient is unconscious, and relayed to the medical professionals. So, we are proposing an innovative project to dodge such sudden death rates by using Patient Health Monitoring that uses sensor technology and uses the internet to communicate with assistance in case of problems. This system uses Temperature, Spo2, gyroscope, and heartbeat sensors for tracking patients’ health. These sensors are connected to the Arduino-Uno. A microcontroller that tracks the patient's health which is connected to an LCD display and a wireless network to transmit the information to a web server. In case of any abrupt changes in the patient’s heart rate, temperature, or blood oxygen saturation level, an alert message is sent about the patient using IoT. This tracking system also shows patients’ temperature, angular movements, and heartbeat tracked live data with timestamps over the Internetwork. Consequently, the IoT-based patient health tracking system actively monitors patient health through the internet, assists users in keeping an eye on their patients while at work, and perhaps saves lives. technology- based knowledge focused on Wireless-Sensing nodes. Key Words: Monitoring, Arduino-Uno, LCD display, temperature alert, Ubidots.

Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction.

In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data. To address theseissues, we propose a multimodal fusion model for mortality prediction that jointly models patients' time-series data as well as clinical records. We apply a fine-tuned Bert model (Bio-Bert) to the patient's clinical record to generate a holistic embedding of the text part, which is then combined with the output of an LSTM model encoding the patient's time-series data to extract valid features. The global contextual information of each modal data is extracted using the improved fusion module to capture the correlation between different modal data. Furthermore, the improved fusion module can be easily added to the fusion features of any unimodal network and utilize existing pre-trained unimodal model weights. We use a real dataset containing 18904 ICU patients to train and evaluate our model, and the research results show that the representations obtained by themodel can achieve better prediction accuracy compared to the baseline.

Remote monitoring of patient health using wireless technologies!

Remote monitoring of patient health using wireless technologies!

On PVDF composite as partially absorbable smart implants.

For various orthopedic needs, several studies have been testified on non-absorbable implants, prepared with different metals/alloys, and composites. But yet little has been stated on the partially absorbable smart implants of thermoplastic composites for online health monitoring of veterinary patients. This article highlights the in-house development of affordable, polyvinylidene fluoride (PVDF) composite-based partially absorbable smart implants (with online sensing capability) for orthopedic needs in canines. Hydroxyapatite (HAp) and chitosan (CS) nanoparticles were reinforced in the PVDF matrix by a melt processing route with various weight proportions (wt.%) to fabricate a partially absorbable smart implant for the canine. The study suggests that the 8.0 wt.% HAp and 2.0 wt.% CS in PVDF is the superlative composition/proportion of reinforcement for preparing feedstock stock filaments (for 3D printing of partially absorbable smart implants), based on rheological, mechanical, thermal, dielectric, and voltage-current-resistance (V-I-R) characteristics. For the selected composition/proportion of PVDF composite, acceptable mechanical properties (such as modulus of toughness (MoT) 2.0 MPa, Young's modulus (E) 889 MPa), and dielectric properties (dielectric constant (εr) 9.6 at room temperature (30°C) and 20 MHz) for online sensing capabilities (for health monitoring) was observed. The results are braced by attenuated total reflection Fourier transform infrared (ATR-FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) analysis.

Internet of things and cloud controlled remote health management system in telemedicine for healthcare facilities

Telemedicine, one of the most well-liked and quickly evolving medical applications, enables patients and the general public to access better and quicker medical care. To enable the process of acquiring detailed information indicative of our physical and mental health for the optimum patient health monitoring, networked sensors are either wearables or implanted in our everyday environments. This project provides a detailed explanation of the methodology used as well as a number of design factors that should be taken into account when developing a patient health monitoring system. This method records the patient's vital signs, such as their heart rate and temperature, and sends an alert to the desired smartphone app.The proposed system is primarily intended for the countryside Indian population who suffers from hypertension and hypotension. It is also programmed to detect pre-cardiac arrest situations so that people in remote areas can take precautions and seek help from a medical or healthcare unit. Biomedical and photo-electronic sensors are employed to record the input parameters, with the analytical portion being handled by a computer. Live server demonstrations of the outcomes are made.

IoT Based Covid Patient Health Monitoring System

The COVID-19 pandemic situation has made timely Health monitoring a major concern for saving a life. The busy routine life is a major challenge for doctors to regularly monitor the Health of a COVID Patient. To overcome this problem faced by the family members and doctors in this pandemic situation, we have designed a smart Health monitoring system using IoT (Internet of Things). This system immediately checks and alerts about the critical situations faced by the COVID Patients. This system uses NODEMCU ESP8266 controller, for continuous tracking of the Patient’s Blood Oxygen saturation (SpO 2 ) level. Additionally, Patient Health parameters such as Body Temperature, Heart rate (BPM), as well as Room Temperature and Humidity can also be measured. The Patient Health data are collected and stored in an IoT platform (BLYNK). These Patient Health parameters can be monitored from anywhere by any devices such as Smartphones, Laptop, PC, Smart TV’s etc… that supports browsing capabilities. If the levels of Blood Oxygen Saturation (SpO 2 ) gets lower, it sends a warning immediately to the family members, doctors and to the nearest hospital through mail, phone call and SMS (Short Message Service). By Checking the data obtained from the system, necessary actions could be taken before the problem becomes severe. Thus, the Patient’s life can be saved in time by using this proposed system.

5G Technology in Healthcare and Wearable Devices: A Review.

Wearable devices with 5G technology are currently more ingrained in our daily lives, and they will now be a part of our bodies too. The requirement for personal health monitoring and preventive disease is increasing due to the predictable dramatic increase in the number of aging people. Technologies with 5G in wearables and healthcare can intensely reduce the cost of diagnosing and preventing diseases and saving patient lives. This paper reviewed the benefits of 5G technologies, which are implemented in healthcare and wearable devices such as patient health monitoring using 5G, continuous monitoring of chronic diseases using 5G, management of preventing infectious diseases using 5G, robotic surgery using 5G, and 5G with future of wearables. It has the potential to have a direct effect on clinical decision making. This technology could improve patient rehabilitation outside of hospitals and monitor human physical activity continuously. This paper draws the conclusion that the widespread adoption of 5G technology by healthcare systems enables sick people to access specialists who would be unavailable and receive correct care more conveniently.

Sinusoidal model-based diagnosis of the common cold from the speech signal

Background and objective:Developing speech signal-based non-invasive diagnosis techniques is an emerging research field in biomedical signal processing. Detecting the common cold and other illnesses with similar symptoms may be important to prevent the spread of these viral infections and remotely monitor patient health. This study aims to develop a method that achieves higher performance with fewer features to identify a subject with a common cold from their speech. Method:The spectrum of cold and non-cold speech is distinct from one another. To capture these spectral variations, this study proposed three features: normalized harmonic peak with respect to the first harmonic peak (NHPF), normalized harmonic peak with respect to the maximum value of harmonic peak (NHPM), and successive harmonic peak ratio (SHPR). The NHPF and NHPM feature gives information about the relative magnitudes of all harmonics with respect to the first and maximum harmonics, respectively. The SHPR feature gives information on the relative harmonic magnitude with respect to the magnitude of its neighboring harmonic. The classifiers derive frame-level confidence scores for cold and non-cold classes. For utterance level categorization, confidence scores of all frames are summed, and a class with a higher confidence score is assigned to that utterance. Results:The analysis shows that the proposed features effectively classify cold and non-cold speech. We achieved 69.16% and 66.90% UAR on the develop and test set of the URTIC database. Conclusion:The proposed features efficiently capture the spectral difference between cold and non-cold speech and can be utilized to automatically diagnose common cold and related disorders.

VISION ENABLED CONTAGIOUS WARD ROBOTIC NURSE

The objective of the project is to develop a wireless robotic vehicle, which has vision using high resolution web-cam, capable of delivering medicines and monitoring patient health in contagious ward. It can provide indication alarm at every patient’s bed for monitoring body temperature and medicine delivery. The approach is to use low-cost solution for robotic nurse, with high efficiency. Android application is also developed for controlling the robot and vision monitoring. Project is developed using 8-bit Atmega328p microcontroller.

The 24-Hour Movement Paradigm: An integrated approach to the measurement and promotion of daily activity in cancer clinical trials

Increased physical activity (PA), improved sleep, and decreased sedentary behavior (SB) are essential components of supportive care for cancer survivors. However, researchers and health care professionals have achieved limited success in improving these behaviors among cancer survivors. One potential reasoning is that, over the past two decades, guidelines for promoting and measuring PA, sleep, and SB have been largely siloed. With greater understanding of these three behaviors, health behavior researchers have recently developed a new paradigm: the 24-Hour movement approach. This approach considers PA, SB, and sleep as movement behaviors along a continuum that represent low through vigorous intensity activity. Together these three behaviors form the sum of an individual's movement across a 24-hour day. While this paradigm has been studied in the general population, its usage is still limited in cancer populations. Here, we seek to highlight (a) the potential benefits of this new paradigm for clinical trial design in oncology; (b) how this approach can allow for greater integration of wearable technology as a means of assessing and monitoring patient health outside the clinical setting, improving patient autonomy through self-monitoring of movement behavior. Ultimately, implementation of the 24-Hour movement paradigm will allow health behavior research in oncology to better promote and assess critical health behaviors to support the long-term well-being for cancer patients and survivors.

Healthcare Monitoring System for Remote Areas

Abstract: Doctors now place a high importance on ongoing patient health monitoring since it gives them the chance to save a patient's life. So, the primary objective is to develop a patient monitoring system that can monitor a patient's various physiological data when they are in a remote location and provide the doctor with this information in real time. The information is made public online so that any doctor in the globe can access it. The necessity for a patient to visit the doctor is lessened via remote patient monitoring. The Raspberry Pi employed here is not only a sensor node but also a CPU, and IOT plays a significant part in this complete system by delivering several apps and services. This data can be sensed, gathered, and published online by an intelligent gadget. The paper suggests a general health monitoring system utilising a neural network-based HDPS (Heart Disease Prediction System). The HDPS system forecasts a patient's risk of developing heart disease. The technique uses medical parameters like sex, blood pressure, age, height, and weight for prediction. as an improvement over the work done in this area up until now.

An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients

Background:During the COVID-19 pandemic, there is a global demand for intelligent health surveillance and diagnosis systems for patients with critical conditions, particularly those with severe heart diseases. Sophisticated measurement tools are used in hospitals worldwide to identify serious heart conditions. However, these tools need the face-to-face involvement of healthcare experts to identify cardiac problems. Objective:To design and implement an intelligent health monitoring and diagnosis system for critical cardiac arrhythmia COVID-19 patients. Methodology:We use artificial intelligence tools divided into two parts: (i) IoT-based health monitoring; and (ii) fuzzy logic-based medical diagnosis. The intelligent diagnosis of heart conditions and IoT-based health surveillance by doctors is offered to critical COVID-19 patients or isolated in remote locations. Sensors, cloud storage, as well as a global system for mobile texts and emails for communication with doctors in case of emergency are employed in our proposal. Results:Our implemented system favors remote areas and isolated critical patients. This system utilizes an intelligent algorithm that employs an ECG signal pre-processed by moving through six digital filters. Then, based on the processed results, features are computed and assessed. The intelligent fuzzy system can make an autonomous diagnosis and has enough information to avoid human intervention. The algorithm is trained using ECG data from the MIT-BIH database and achieves high accuracy. In real-time validation, the fuzzy algorithm obtained almost 100% accuracy for all experiments. Conclusion:Our intelligent system can be helpful in many situations, but it is particularly beneficial for isolated COVID-19 patients who have critical heart arrhythmia and must receive intensive care.

I-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals.

In this article, we propose a smart bedsheet-i-Sheet-for remotely monitoring the health of COVID-19 patients. Typically, real-time health monitoring is very crucial for COVID-19 patients to prevent their health from deteriorating. Conventional healthcare monitoring systems are manual and require patient input to start monitoring health. However, it is difficult for the patients to give input in critical conditions as well as at night. For instance, if the oxygen saturation level decreases during sleep, then it is difficult to monitor. Furthermore, there is a need for a system that monitors post-COVID effects as various vitals get affected, and there are chances of their failure even after the recovery. i-Sheet exploits these features and provides the health monitoring of COVID-19 patients based on their pressure on the bedsheet. It works in three phases: 1) sensing the pressure exerted by the patient on the bedsheet; 2) categorizing the data into groups (comfortable and uncomfortable) based on the fluctuations in the data; and 3) alerting the caregiver about the condition of the patient. Experimental results demonstrate the effectiveness of i-Sheet in monitoring the health of the patient. i-Sheet effectively categorizes the condition of the patient with an accuracy of 99.3% and utilizes 17.5 W of the power. Furthermore, the delay involved in monitoring the health of patients using i-Sheet is 2 s which is very diminutive and is acceptable.