Healthcare Monitoring System Research Articles

Energy-efficient multisensor adaptive sampling and aggregation for patient monitoring in edge computing based IoHT networks

The need for remote healthcare monitoring systems that utilize limited resources’ biosensors is growing. These biosensors increase the amount of transmitted data across the Internet of Healthcare Things (IoHT) network. Therefore, it is necessary to decrease the transmitted data and make a decision at the edge gateway to save the energy of the biosensors and produce a quick response for the medical staff. This paper proposes an energy-efficient multisensor adaptive sampling and aggregation (EMASA) for patient monitoring in edge computing-based IoHT networks. In the edge-based IoHT network, EMASA operates on two levels: biosensors and the edge gateway. Each biosensor removes the redundant sensed data using the local emergency detection and sampling rate adaptation algorithms. In the edge gateway, it implements a machine learning-based Support Vector Machine (SVM) model to provide a suitable decision about the status of the monitored patient. We accomplished various examinations using real data from the patients’ biosensors. According to the simulation results, EMASA reduced the size of transmitted data from 93.5% to 99% and saved 78.35% of energy when compared to a previous study. It keeps the whole score with a good representation at the Edge gateway and provides accurate and fast decisions based on the patient’s condition.

ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness.

INVESTIGATION ANALYSIS ON E-HEALTHCARE MONITORING SYSTEM WITH IOT AND BIG DATA BY TSLR-CHSC MODEL

Internet of Things (IoT) technology helped the development of E-Health care Monitoring System from direct visit to virtual Monitoring (Telemedicine). Smart health care system in IoT environment monitored the basic health signs such as heartbeat rate, body temperature and blood pressure in real-time applications. IoT and big data is a prominent challenge in smart healthcare system. In this health monitoring system big data is employed to analyze the large volume of data and to determine the Normal and Abnormal patient condition. Numerous issues like accuracy, time and error have yet to be conveyed to generate a ductile system for health care monitoring. To address these issues, I proposed a method called Theil Sen Linear Regression and Canopy Hopkins Statistics Clustering (TSLR-CHSC) for IoT based Health monitoring system is proposed. This method splits into three sections such as Data collection, Feature Selection and Clustering. First, Cardiovascular disease dataset is acquired from sensors are collected. Second, appropriate features can be selected by using Theil Sen regression feature. Third, clustering is performed based on the cluster tendency by using canopy algorithms. Through this way I deployed an efficient E-Health Monitoring System with minimum time consumption. For evaluation, a cardiovascular disease dataset is obtained from various medical sensor devices are analyzed to identify the disease severity. Keyword: IoT, Big Data, Theil Sen Linear Regression, Canopy Hopkins Statistic Clustering, Health Care Monitoring.

Strain‐Driven Negative Resistance Switching of Conductive Fibers with Adjustable Sensitivity for Wearable Healthcare Monitoring Systems with Near‐Zero Standby Power (Adv. Mater. 36/2023)

Strain‐Driven Negative Resistance Switching of Conductive Fibers with Adjustable Sensitivity for Wearable Healthcare Monitoring Systems with Near‐Zero Standby Power (Adv. Mater. 36/2023)

Iot based health care monitoring system using raspberry pi

Iot based health care monitoring system using raspberry pi

A Prototype Robotics Wheelchair with An IOT Based Paralyzed Patient Health Care Monitoring System

This project presents an implementation of wearable, portable, low power consumption, real-time remote bio-signals monitoring system based on the internet of thing technology. This internet technology along with modern electronic devices could offer promising solutions in this field. Based on that, this project utilizes a web or mobile application as an IoT platform to monitor remotely the live pulse rate signal, BPM and the body temperature of patients. The signals are measured and processed by using a microcontroller-based device (Arduino). The main contribution of this paper is sending an electrocardiogram to a web or mobile phone to be watched by a doctor. If some abnormal conditions are detected, the IoT alert send to doctor and relations, This assists in heart diseases diagnosing before the worst case can happen. Finally, the obtained results of this project are illustrated on both smartphone and personal computer (PC) as well. Index Terms— Microcontroller, LCD, flex sensor, remote sensor, ultrasonic sensor ,temperature sensor , cloud connectivity, data processing ,IoT

Monitoring and alerting the physicians related to trauma cases using behavioural DL models

Presently Every nation places a high value on healthcare due to the rise in trauma instances. The ideal approach in this case is thought to be an Internet of Things (IoT) based health monitoring system. A novel approach in internet technology, the Internet of Things (IoT) is an increasing study area, mainly in the field of healthcare. The rising use of wearable sensors and cellphones has allowed these remote health care monitoring systems to grow at such a rapid rate. IoT health monitoring helps patients receive the appropriate care for their present level of health even when a physician is far away. The notion of the IoT and deep learning are combined in this study to present a systematic method to identify trauma instances more effectively. By utilizing some of the biological information from the patient's body, such as temperature, heart rate, and other factors, this approach presents an overview of the IoT and is also used to monitor health status and identify symptoms in the human body. For fall detection using sensor nodes, the Deep Convolutional Neural Network (DCNN) analyses individuals' motion and architecture. The suggested method may be put in place for less money but has significant potential to identify symptoms by giving patients and suspected cases the care they need and reacting to life-or-death circumstances. Metrics such as precision, recall, F-measure, and accuracy are utilized to evaluate the results when applying classification algorithms. When compared to techniques like the Decision Tree (DT), Support Vector Machine (SVM), and K Nearest Neighbour (k-NN), the findings show that it has been very successful.

Edge-Based Health Care Monitoring System: Ensemble of Classifier Based Model

Health Monitoring System (HMS) is an excellent tool that actually saves lives. It makes use of transmitters to gather information and transmits it wirelessly to a receiver. Essentially, it is much more practical than the large equipment that the majority of hospitals now employ and continuously checks a patient's health data 24/7. The primary goal of this research is to develop a three-layered Ensemble of Classifier model on Edge based Healthcare Monitoring System (ECEHMS) and Gauss Iterated Pelican Optimization Algorithm (GIPOA) including data collection layer, data analytics layer, and presentation layer. As per our ECEHMS-GIPOA, the healthcare dataset is collected from the UCI repository. The data analytics layer performs preprocessing, feature extraction, dimensionality reduction and classification. Data normalization will be done in preprocessing step. Statistical features (Min/Max, SD, Mean, Median), improved higher order statistical features (Skewness, Kurtosis, Entropy), and Technical indicator based features were extracted during Feature Extraction step. Improved Fuzzy C-means clustering (FCM) will be used for handling the Dimensionality reduction issue by clustering the appropriate feature set from the extracted features. Ensemble model is introduced to predict the disease stage that including the models like Deep Maxout Network (DMN), Improved Deep Belief Network (IDBN), and Recurrent Neural Network (RNN). Also, the enhancement in prediction/classification accuracy is assured via optimal training. For which, a GIPOA is introduced. Finally, ECEHMS-GIPOA performance is compared with other conventional approaches like ASO, BWO, SLO, SSO, FPA, and POA.

Energy efficient selection of spreading factor in LoRaWAN-based WBAN medical systems

Recently, the Internet of Things has been leading the technological revolution in several fields and applications. This revolution affected healthcare services adding more value to the concept of e-health. To improve the quality of these services, remote monitoring allows healthcare professionals to observe and diagnose their patients without being physically present. Since the spreading factor affects the energy consumption, the data rate, the receiver sensitivity and the time on air. A monitoring healthcare system using LoRa technology is proposed on this work to adopt the data transmission in a reliable and energy-efficient way. We propose a method to select the most convenient spreading factor based on the patients medical state. This method takes in consideration the distance to gateway, the packet error rate and the energy consumption requirements.We first determine the criticality level of the patient using the Early Warning Score system. Second, a decision about the optimal spreading factor is taken based on this level. We use a method based on the technique for order preference by similarity to an ideal solution to select a spreading factor. We use real data to show that the results reflect an acceptable increase in energy consumption while applying our selection method. In contrast, the results show that the selection method reduces the error rate especially for patients with a high critical level.

Internet of Things Gateway Edge for Movement Monitoring in a Smart Healthcare System

Over the past two decades, there has been a notable and swift advancement in the field of healthcare with regards to the Internet of Things (IoT). This progress has brought forth a substantial prospect for healthcare services to enhance performance, transparency, and cost effectiveness. Internet of Things gateways, such as local computational facilities, mobile devices, or custom miniature computational embedded electronics like the Raspberry Pi (RPi), are crucial in facilitating the required processing and data compression tasks as well as serving as front-end event detectors. Numerous home-based healthcare monitoring systems are currently accessible; however, they have several limitations. This paper examines the role of the Raspberry Pi gateway in the healthcare system, specifically in the context of pre-operative prehabilitation programs (PoPPs). The IoT remote monitoring system employed a Microduino integrated with various supporting boards as a wearable device. Additionally, a Raspberry Pi was utilised as a base station or mobile gateway, while ThingSpeak served as the cloud platform. The monitoring system was developed with the purpose of assisting healthcare personnel in real time, remotely monitoring patients while engaging in one or more of the nine typical physical activities that are often prescribed to individuals participating in a prehabilitation program. Furthermore, an alert notification system was designed to notify the clinician and patient if the values were abnormal (i.e., the patient had not been active for many days). The integration of IOT and Raspberry Pi technology into a pre-operative prehabilitation program yielded a promising outcome with a success rate of 78%. Consequently, this intervention is expected to facilitate the resolution of challenges encountered by healthcare providers and patients, including extended waiting periods and constraints related to staffing and infrastructure.

Retracted: A Novel Smart Healthcare Monitoring System Using Machine Learning and the Internet of Things

Retracted: A Novel Smart Healthcare Monitoring System Using Machine Learning and the Internet of Things

An effective healthcare monitoring system in an IoMT environment for heart disease detection using the HANN model

The proposed work aims to develop an automated machine learning based network model for heart disease prediction with better accuracy. In the pre-processed data, the most significant features are selected using the White Shark Optimization based Linear Discriminant Analysis (WSO-LDA) technique, reducing computational complexity. Finally, the selected features are fed to the Hybrid Artificial Neural Network (HANN) with a Multi-Objective Spotted Hyena optimization (MOSHO) based classification stage. This stage classifies heart disease with minimized processing time.

Portable and Real-Time IoT-Based Healthcare Monitoring System for Daily Medical Applications

Remote healthcare and telemedicine technology have witnessed a large and rapid development in the last decade with the large development of the Internet of Things (IoT) technology, where various types of medical sensors are aggregated for measuring medical parameters and transmitting them anywhere. Smart portable products can now be used to monitor different medical aspects to track human health. Also, they can be used in the prediagnosis of various diseases and in detecting abnormalities of organ functionality. In this article, we design and implement a multifunction and portable health monitoring system, which can help in daily medical inspections. The developed system monitors various medical aspects: heart rate (HR), blood oxygen saturation level (SpO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{2})$</tex-math> </inline-formula> , body temperature, photoplethysmography (PPG) signal, electrocardiography (ECG) signal, room temperature, and room humidity. The obtained measurements are displayed on the built-in display or transmitted over Wi-Fi to either a mobile application, in the local mode, or to the cloud storage for remote monitoring. The developed system can be used to keep an eye on the people we need to care about, while keeping them in their normal daily life. The maximum error percentage of the proposed system is reported as 2.67%, 2.04%, and 1.58% for HR, SpO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{2}$</tex-math> </inline-formula> , and body temperature, respectively, compared to commercial devices. In addition, statistical tests were performed and they showed a high level of agreement between the observed and the reference measurements. The results indicate the high accuracy and effectiveness of the proposed system to be used in daily medical applications.

IoT Based Patient Healthcare Monitoring System

Abstract: The need for a health care monitoring system that provides instant sensing and precise predictions with prompt response is highly required. Recently, health care sensors are playing a key role in hospitals and home care for patients. The patient monitoring systems has become one of the most advanced technologies because of its ability to provide a higher level of information about the patient's condition The prototype model designed for the healthcare monitoring system is based on three main objectives: to monitor various physical parameters of patients, to detect if any emergency has occurred to the patient and to reduce the workload on the hospital staff. Physical parameters of the patients like pulse per minute and body temperature are sensed using biosensors like pulse rate sensor and LM35 sensor respectively. The sensor values are displayed on the LCD for constant monitoring. A buzzer is turned ON and an alert notification is sent to the caretaker, if any of the values crosses the threshold, indicating an emergency. A graphical representation is also displayed on the IoT platform for past analysis. An accelerometer is usedin this system to sense the movements of the patients and detect any fall or injury for senior citizens who prefer to stay at home and live independently, the system can communicate with caretakers to request for help if needed.

Implementation of Smart Health Monitoring System Using Edge Computing

The last ten years have seen a shift in the healthcare monitoring systems, making them one of the most important systems. Because of a lack of timely medical attention for individuals suffering from various illnesses, humans are struggling with the issue of untimely mortality. The main objective was to create a dependable IoT-based patient monitoring system that would enable healthcare providers to keep track of their patients, whether they are in a hospital or at home, in order to provide better treatment. Sensors, a data collecting unit, and a microprocessor make up the majority of a mobile device-based wireless healthcare monitoring system that was built in order to offer real-time online information about a patient's physiological status. The Internet of Things (IoT), edge computing, cloud computing, and other emerging information technologies have all contributed to the development of smart healthcare. The Internet of Things (IoT) concepts have been widely applied to connect the available medical resources and provide patients with intelligent, dependable, and efficient healthcare. One of the paradigms that can harness the benefits of the IoT to improve the patient's lifestyle is health monitoring for active and supported living. We have introduced an IoT architecture that has been specially designed for healthcare applications in this project. Hence the proposed architecture collects the sensor data through Node MCU microcontroller&#x0D;

Strain-Driven Negative Resistance Switching of Conductive Fibers with Adjustable Sensitivity for Wearable Healthcare Monitoring Systems with Near-Zero Standby Power.

Recently, one of the primary concerns in e-textile-based healthcare monitoring systems for chronic illness patients has been reducing wasted power consumption, as the system should be always-on to capture diverse biochemical and physiological characteristics. However, the general conductive fibers, a major component of the existing wearable monitoring systems, have a positive gauge-factor (GF) that increases electrical resistance when stretched, so that the systems have no choice but to consume power continuously. Herein, a twisted conductive-fiber-based negatively responsive switch-type (NRS) strain-sensor with an extremely high negative GF (resistance change ratio ≈ 3.9× 108 ) that can significantly increase its conductivity from insulating to conducting properties is developed. To this end, a precision cracking technology is devised, which could induce a difference in the Young's modulus of the encapsulated layer on the fiber through selective ultraviolet-irradiation treatment. Owing to this technology, the NRS strain-sensors can allow for effective regulation of the mutual contact resistance under tensile strain while maintaining superior durability for over 5000 stretching cycles. For further practical demonstrations, three healthcare monitoring systems (E-fitness pants, smart-masks, and posture correction T-shirts) with near-zero standby power are also developed, which opens up advancements in electronic textiles by expanding the utilization range of fiber strain-sensors.

A reference free non-negative adaptive learning system for health care monitoring and adaptive physiological artifact elimination in brain waves

Electroencephalogram (EEG), also referred to as brain wave (BW), is a physiological phenomenon that depicts how the human brain functions. Brain wave analysis is fundamental in applications like brain-computer interference (BCI), beamforming, sleep analysis, epilepsy detection, and emotion recognition. In real-time applications, the brain wave encounters many physiological and non-physiological artifacts during acquisition. Due to this phenomenon, the analysis method is complicated and obscures the brain wave’s tiny features. This study proposes an intelligent signal enhancement unit (SEU) for processing EEG signals to enable decision-making under certainty. The proposed SEU enables healthcare professionals to analyze high-resolution brain wave components for various applications. A new singular spectrum decomposition (SSD) based on score reconstruction (score RC) is used in the first phase of the SEU to generate the artifact nature, which is then used as a reference signal in the adaptive artifact cancellation (AAC) method. The SSD performs the embedding, decomposition, grouping, and reconstruction procedures to provide the reference signal. A modified Logarithmic Non-Negative Adaptive Learning Algorithm (MLNNAL) is employed in the second stage of the SEU to improve the EEG signal. With the help of this proposed adaptive learning, a system with lower computing complexity which is stable and has non-negative weights can be realized. The adaptive learning algorithm’s weight recursion continually reweights the weight vector for each iteration to eliminate artifacts from contaminated brain waves. Excess mean square error (EMSE), signal to noise ratio improvement (SNRI) and computational cost of the adaptive learning algorithm are used to evaluate how the proposed SEU performs.

Wearable triboelectric nanogenerator with micro-topping structures via material jet printing method

With the development of human-machine interactions, human healthcare monitoring and Internet of Things, it is of vital importance to explore revolutionary fabrication methods to provide sustainable and flexible energy harvesters for massive personnel information. In this work, we developed the micro-topping structures enhanced triboelectric nanogenerator (MT-TENG) via unique jet printing method with the advantages of high precision, high efficiency and excellent uniformity. Regular micro-topping structure array could be tuned conveniently and rapidly through adjusting the ink properties, printing parameters and substrate characteristics in jet printing process. The results show that with micro-topping structures, TENG has high specific surface for contact electrification. MT-TENG can be applied in the real-time monitoring and self-powered sensing of body physiological signal and human joint movement. This work provides a previously unexplored strategy for shortening individual design cycle and promoting the practicality of TENG in mass manufacturing, and also possess a potential application of wearable electronics and healthcare monitoring system.

An In-Depth Analysis of Smart Healthcare Monitoring Systems for Humans Based on IoT

Smart healthcare monitoring systems make use of IoT, the most recent technology. As a result, IoT incorporates one of its most critical applications, the health monitoring and control system. Within those systems, individuals commonly use pulse rate sensing devices, ECG sensor systems, and blood pressure sensor with various embedded systems to collect observations from the identification modules and send them to a database machine so that records can be transferred with the patient and the physician in an Android app. This investigation makes it easier to imagine how IoT could be used in difficult medical procedures. The remote health monitoring management system (HMS) is a key IoT app that allows users to connect to smart sensors, people, healthcare professionals, networks, and other connected devices. Clinicians can now constantly monitor their own patients from afar thanks to the tried method, the internet of things.

IoT-based patient monitoring system for predicting heart disease using deep learning

Motivation: Chronic diseases include diabetes, cancer, heart disease (HD), and chronic respiratory diseases are the main causes of mortality globally. It is quite challenging to identify heart diseases when the symptoms or characteristics vary. In the contemporary digital environment, the healthcare sector produces a considerable volume of patient data. For doctors, manually processing these created data becomes exceedingly challenging. The Internet of Things is handling the generated data quite well. It provides continuous communication between individuals and devices, and its fusion with the Cloud enhances the quality of life. Materials and Methods: Deep learning, a branch of machine learning, has the transformational ability to rapidly and reliably analyse massive amounts of data, produce insightful conclusions, and effectively resolve complex problems. Massive volumes of data were collected by the IoT, and because of deep-learning algorithms, it is now possible to identify and diagnose diseases. The suggested approach collects information from IoT devices, and electronic medical evidence connected to patient histories that are stored in the cloud are sent to predictive analytics. Results and Conclusion: The Long Short Term Memory (LSTM) and Recurrent Neural Network based smart healthcare system for monitoring and precisely forecasting heart diseases obtains an accuracy of 99.99%, which is substantially superior to the current smart heart disease prediction systems such as traditional methods.