Remote Healthcare System Research Articles

Addressing Service Quality Challenges in the Cloud and IoT Integration: A Case Study of Tourist Remote Healthcare System

Addressing Service Quality Challenges in the Cloud and IoT Integration: A Case Study of Tourist Remote Healthcare System

Healthcare and the Internet of Medical Things: Applications, Trends, Key Challenges, and Proposed Resolutions

In recent years, the Internet of medical things (IoMT) has become a significant technological advancement in the healthcare sector. This systematic review aims to identify and summarize the various applications, key challenges, and proposed technical solutions within this domain, based on a comprehensive analysis of the existing literature. This review highlights diverse applications of the IoMT, including mobile health (mHealth) applications, remote biomarker detection, hybrid RFID-IoT solutions for scrub distribution in operating rooms, IoT-based disease prediction using machine learning, and the efficient sharing of personal health records through searchable symmetric encryption, blockchain, and IPFS. Other notable applications include remote healthcare management systems, non-invasive real-time blood glucose measurement devices, distributed ledger technology (DLT) platforms, ultra-wideband (UWB) radar systems, IoT-based pulse oximeters, accident and emergency informatics (A&EI), and integrated wearable smart patches. The key challenges identified include privacy protection, sustainable power sources, sensor intelligence, human adaptation to sensors, data speed, device reliability, and storage efficiency. The proposed mitigations encompass network control, cryptography, edge-fog computing, and blockchain, alongside rigorous risk planning. The review also identifies trends and advancements in the IoMT architecture, remote monitoring innovations, the integration of machine learning and AI, and enhanced security measures. This review makes several novel contributions compared to the existing literature, including (1) a comprehensive categorization of IoMT applications, extending beyond the traditional use cases to include emerging technologies such as UWB radar systems and DLT platforms; (2) an in-depth analysis of the integration of machine learning and AI in IoMT, highlighting innovative approaches in disease prediction and remote monitoring; (3) a detailed examination of privacy and security measures, proposing advanced cryptographic solutions and blockchain implementations to enhance data protection; and (4) the identification of future research directions, providing a roadmap for addressing current limitations and advancing the scientific understanding of IoMT in healthcare. By addressing current limitations and suggesting future research directions, this work aims to advance scientific understanding of the IoMT in healthcare.

Healthcare System using IoT and Building Health Monitor App

An improvement in technology and miniaturization of sensors, there have been attempts to utilize the new technology in various areas to improve the quality of human life. One main area of research that has seen an adoption of the technology is the healthcare sector. The people in need of healthcare services find it very expensive this is particularly true in developing countries. As a result, this project is an attempt to solve a healthcare problem currently society is facing. The main objective of the project was to design a remote healthcare system. It’s comprised of three main parts. The first part being, detection of patient’s vitals using sensors, second for sending data to cloud storage and the last part was providing the detected data for remote viewing. Remote viewing of the data enables a doctor or guardian to monitor a patient’s health progress away from hospital premises. The Internet of Things (IoT) concepts have been widely used to interconnect the available medical resources and offer smart, reliable, and effective healthcare service to the patients. Health monitoring for active and assisted living is one of the paradigms that can use the IoT advantages to improve the patient’s lifestyle. In this project, I have presented an IoT architecture customized for healthcare applications. The aim of the project was to come up with a Remote Health Monitoring System that can be made with locally available sensors with a view to making it affordable if it were to be mass produced. Hence the proposed architecture collects the sensor data through Arduino microcontroller and relays it to the cloud where it is processed and analyzed for remote viewing. Feedback actions based on the analyzed data can be sent back to the doctor or guardian through Email and/or SMS alerts

A robust neural network for privacy-preserving heart rate estimation in remote healthcare systems

In this study, we propose a computationally-light and robust neural network for estimating heart rate in remote healthcare systems. We develop a model that can be trained on consumer-grade graphics processing units (GPUs), and can be deployed on edge devices for swift inference. We propose a hybrid model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architectures for estimating heart rate from Electrocardiogram (ECG) and Photoplethysmography (PPG) signals. Considering the sensitive nature of the ECG signals, we ensure a formal privacy guarantee, differential privacy, for the model training. We perform a tight accounting of the overall privacy budget of our training algorithm using the Rényi Differential Privacy technique. We demonstrate that our model outperforms state-of-the-art networks on a benchmark dataset for both ECG and PPG signals despite having a much smaller number of trainable parameters and, consequently, much smaller training and inference times. Our CNN-BiLSTM architecture can also provide excellent heart rate estimation performance even under strict privacy constraints. We develop a prototype Arduino-based data collection system that is low-cost, efficient, and useful for providing access to modern healthcare services to people living in remote areas.

Optimization and Application of Cloud Storage Solutions in Remote Healthcare Data Management in India

Optimization and Application of Cloud Storage Solutions in Remote Healthcare Data Management in India

Blinder Oaxaca and Wilk Neutrosophic Fuzzy Set-based IoT Sensor Communication for Remote Healthcare Analysis

In the remote healthcare industry data analytics denotes the computerization of collection, processing, and exploring complicated data to acquire finer perceptions and validate healthcare practitioners to produce familiar decisions. Healthcare basics in the modern age are vital challenges specifically in developing countries owing to the shortfall of difficult hospitals and medical professionals. As fuzzy systems have reformed several areas of work, health has also made the most of it. In this paper, the purpose of the study is to introduce a novel and intelligent remote healthcare system based on modern technologies like the Internet of things (IoT) and Neutrosophic fuzzy systems to ensure precise data analysis with lesser time and energy consumption. In this study, a novel method called, Blinder Oaxaca-based Shapiro Wilk Neutrosophic Fuzzy (BO-SWNF) data analytics for remote healthcare is designed. Data collection is performed with the WESAD dataset. Duplicated data are eliminated by Blinder Oaxaca Linear Regression-based Preprocessing model. With the application of the Blinder Oaxaca function, energy efficiency is enhanced. Finally, the Shapiro Wilk Neutrosophic Fuzzy algorithm is applied for ensuring robust data analysis. The experimental results of the proposed BO-SWNF envisage the data for finer comprehension of attribute distribution. The result is conducted by using PYHTON application to analyze stress detection with the WESAD dataset. The proposed BO-SWNF method achieved an overall accurate data analysis of 12% with minimum time ensuring 56%improvement and minimizing energy consumption by 54%.

Access Control, Key Management, and Trust for Emerging Wireless Body Area Networks.

Wireless Body Area Networks (WBANs) are an emerging industrial technology for monitoring physiological data. These networks employ medical wearable and implanted biomedical sensors aimed at improving quality of life by providing body-oriented services through a variety of industrial sensing gadgets. The sensors collect vital data from the body and forward this information to other nodes for further services using short-range wireless communication technology. In this paper, we provide a multi-aspect review of recent advancements made in this field pertaining to cross-domain security, privacy, and trust issues. The aim is to present an overall review of WBAN research and projects based on applications, devices, and communication architecture. We examine current issues and challenges with WBAN communications and technologies, with the aim of providing insights for a future vision of remote healthcare systems. We specifically address the potential and shortcomings of various Wireless Body Area Network (WBAN) architectures and communication schemes that are proposed to maintain security, privacy, and trust within digital healthcare systems. Although current solutions and schemes aim to provide some level of security, several serious challenges remain that need to be understood and addressed. Our aim is to suggest future research directions for establishing best practices in protecting healthcare data. This includes monitoring, access control, key management, and trust management. The distinguishing feature of this survey is the combination of our review with a critical perspective on the future of WBANs.

Leveraging telemedicine in gastroenterology and hepatology: a narrative review.

Over the years, telemedicine has played a prominent role in delivering healthcare to patients. Due to its flexibility and many benefits, telemedicine confers physicians the ability to guide and promote medical care remotely. The advent of the coronavirus disease 2019 (COVID-19) pandemic has changed the landscape of medicine and has accelerated the usage of digital and remote healthcare systems for clinical care. Herein, we provide an overview of telemedicine, its applications in managing inflammatory bowel disease (IBD), celiac disease (CD), and liver diseases, its advantages and limitations, and its use in educating the next generation of gastroenterologists. We conducted a review of scientific articles published in PubMed and Google Scholar. Articles were selected based on the search terms included in the search strategy summary. The language of the articles was restricted to English only. We report that telemedicine has the potential to streamline and improve patient care in gastroenterology (GI) and hepatology while also limiting health care expenses. Additionally, we noted the importance of tele-education for training the next generation of physicians who intend on practicing in rural settings. Furthermore, we identified barriers to telemedicine care that exacerbate health inequities and potential solutions to achieving digital health equity. Lastly, we briefly discuss the role of artificial intelligence (AI) in remote patient monitoring. Although telemedicine has existed for many decades, over the past decade there have been many advancements in telemedicine applications in GI and hepatology. Despite its broad benefits, further research needs to be done to alleviate barriers to telemedicine care.

Optimizing Deep Learning Model Parameters Using Socially Implemented IoMT Systems for Diabetic Retinopathy Classification Problem

Diabetic retinopathy (DR) is on the increase nowadays due to the high sugar level in the blood, and it is the reason for blindness that mainly occurs in middle-aged people. Furthermore, the Internet of Medical Things (IoMT) enabled computer-aided diagnostic (CAD) systems record DR-related data online and give patients with reassuring information. The internet allows for the interconnection of a variety of smart devices, enabling remote healthcare systems based on the IoMT to link patients with medical professionals. The proper diagnosis of diabetic patients and detection of DR severity in earlier stages help in preventing blindness. Therefore, the basic aim of this study is to prevent the diabetic patient from losing vision by detecting and classifying the severity of DR fundus images using the IoMT-enabled CAD system. This article designed a novel diabetic retinopathy classification (DRC) system by hybridizing the DL model with optimization algorithms to classify the DR images based on severity. This system begins with preprocessing phase for removing the noise from edges. Next, the proposed K-mean cluster-based growing region segmentation is employed to extract the useful region from the images. Then, pretrained convolutional neural network (CNN) model, i.e., RESnet with the proposed hybrid genetic and ant colony optimization (HGACO) algorithm, is applied to extract the features from the region of interest (ROI) and classify them into four severity levels. Performance indices such as AUC, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$</tex-math> </inline-formula> -measure, accuracy, sensitivity, and specificity are analyzed to evaluate the performance on MESSIDOR dataset. The performance of the proposed DRC system is compared with state-of-the-art classification systems. The proposed HGACO algorithm is also compared with Adam and gradient descent (GD) optimizers. The system is also evaluated by employing different parameters of CNN and HGACO. Additionally, for the determination of the classification accuracy of the DRC system, the confidence interval statistical test is implemented considering various parameters and configurations of the neural network. The results revealed that the proposed DR system provides higher classification results by achieving 95.78%, 91.98%, 97.7%, and 94.56% AUC, sensitivity, accuracy, and specificity rate, respectively. CNN alleviates the difficulty of developing image features, whereas the HGACO algorithm-based technique automates CNN hyperparameter design.

Human Health Monitoring System Using IOT

Abstract: With an improvement in technology and miniaturization of sensors, there have been attempts to utilize the new technology in various areas to improve the quality of human life. One main area of researchthat has seen an adoption of the technology is the healthcare sector. The people in need of healthcare services find it very expensive this is particularly true in developing countries. As a result, this project is an attempt to solve a healthcare problem currently society is facing. The main objective of the project wasto design a remote healthcare system. It’s comprised of three main parts. The first part being, detection of patient’s vitals using sensors, second for sending data to cloud storage and the last part was providing the detected data for remote viewing. Remote viewing of the data enables a doctor or guardian to monitor a patient’s health progress away from hospital premises.

Multibiosensor Data Sampling and Transmission Reduction With Decision-Making for Remote Patient Monitoring in IoMT Networks

The rise in chronic diseases and the aging of the population led to an increase in the demand for remote healthcare systems that employ biosensors to monitor people’s health status. The increasing need for these automated systems has led to the emergence of the Internet of Medical Things (IoMT) networks. In the IoMT networks, the biosensor devices collect vital signs and transmit them to the gateway for further analysis and fusion. In light of the limited biosensor device resources (power, storage, and computation) and the periodical transmission of a large amount of data, it is necessary to optimize the transmission of data in order to conserve power while maintaining data quality at the gateway. Also, it became important to have a decision-making-based machine learning model at the gateway to evaluate a patient’s health and make a quick, accurate decision in case of an emergency. This paper proposes Multibiosensor Data Sampling and Transmission Reduction with Decision-making (MuDaSaTReD) for Remote Patient Monitoring in the IoMTs Networks. The MuDaSaTReD achieves this goal on two levels: biosensors and a fog gateway. It uses an Energy-saving Lightweight Data Transmission (ELiDaT) algorithm to get rid of the repeated data and then adapts the sampling rate of each biosensor using an Adaptive Data Sampling (ADaS) algorithm. The MuDaFuDeC implements the machine learning model at the fog gateway to learn and decide the situation of the patient according to the received data from the biosensors. The performance evaluation shows that the MuDaFuDeC outperforms other approaches in terms of the data reduction percentage and energy consumption. It keeps a good representation of all the scores at the fog gateway and makes automated, fast, and accurate decisions based on the patient’s condition.

Self-Assembled Porous-Reinforcement Microstructure-Based Flexible Triboelectric Patch for Remote Healthcare

HighlightsThe porous-reinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge.With the excellent performance both for tiny pressure and large mechanical stimuli, the flexible triboelectric patch can be used to monitor pulse wave and plantar pressure.A remote healthcare system for real-time physiological signal monitoring is proposed.

Paper-based wearable ultra-sensitive strain sensors for fitness monitoring

Emerging technologies that exhibit features of biodegradability and eco-friendliness are potential game changers. Paper is a cost-effective and easily available material that has proved itself a promising candidate for manufacturing strain sensors due to its easy integration into flexible electronics. In this work, an ultra-sensitive and highly stable strain sensor is presented. Easily available materials are used for sensors development and very easy and simple fabrication. The modified interdigital capacitor structure is fabricated on a simple printing paper, whereas copper tape is used as an electrode material. In the anticipated work, the milestones of high sensitivity, durability, and fast response time are achieved. The proposed flexible strain sensor is eco-friendly, bio-degradable, inexpensive, and responds well to applied strain. The proposed work exhibits a fast response time of 0.56 s. The high gauge factor value of 3040.26 and stability up to 5000 cycles. The proposed prototype has been also implemented on the human body for monitoring physical activities and fitness exercises. The proposed strain sensor is an outstandingly good option for assimilation into wearable systems like remote healthcare systems, electromechanical sensing, and human physical monitoring.

TeleRehabilitation of Social-Pragmatic Skills in Children with Autism Spectrum Disorder: A Principal Component Analysis.

In many therapeutic settings, remote health services are becoming increasingly a viable strategy for behavior management interventions in children with autism spectrum disorder (ASD). However, there is a paucity of tools for recovering social-pragmatic skills. In this study, we sought to demonstrate the effectiveness of a new online behavioral training, comparing the performance of an ASD group carrying out an online treatment (n°8) with respect to a control group of demographically-/clinically matched ASD children (n°8) engaged in a traditional in-presence intervention (face-to-face). After a 4-month behavioral treatment, the pragmatic skills language (APL test) abilities detected in the experimental group were almost similar to the control group. However, principal component analysis (PCA) demonstrated that the overall improvement in socio-pragmatic skills was higher for ASD children who underwent in-presence training. In fact, dimensions defined by merging APL subscale scores are clearly separated in ASD children who underwent in-presence training with respect to those performing the online approach. Our findings support the effectiveness of remote healthcare systems in managing the social skills of children with ASD, but more approaches and resources are required to enhance remote services.

IoT Enabled Ubiquitous Healthcare System using Predictive Analytics

The recent pandemic has shown its different shades across various solicitations, especially in the healthcare sector. It has a great impact on transforming the traditional healthcare architecture which is based on the physical approaching model into the modern or remote healthcare system. By utilizing multiple modern technological paradigms like AI, Cloud Computing, Feature Learning, the Internet of Things, etc. the remote healthcare approach is quite achievable now. A quite feasible and approachable framework has evolved through this work with respect to hardware development and predictive analysis. Multiple hardware modules have been discussed during the hardware development process. After receiving the sensor-acquired data it will pass through the various machine learning models deployed in the cloud. Finally, detailed analytics will be generated out of the meticulous analysis of the procured cardinal, essential and fundamental data of the patients and notified to the concerned medical actors for the necessary actions.

Fall Detection With Wrist-Worn Watch by Observations in Statistics of Acceleration

It is common for older people to live alone, which can have tragic consequences if they have an accident and can’t call for help in time. This is particularly acute in an aging society where falling is one of the most common accidents. According to the CDC, 1/4 of people over the age of 65 in the United States fall each year. The development of IoT and MEMS has made it possible to detect falls in time and automatically call for help. The presented fall detection system focuses on the walk-fall-still pattern, collects accelerations through the wrist-worn M5StickC-Plus watch, analyses the data locally in the watch, detects falls using an algorithm based on observations in the statistics of acceleration in one second, and then transmits the alarm signal to a remote healthcare system in real-time via WIFI. The lightweight algorithm has been proven to be 90% accurate in detecting falls, and the system can notify service staff of accidents within 1 second. The features of comfort, lightness, and timeliness make the device more practical than similar products. The low-cost, non-intrusive device can be used in care homes and is also suitable for elderly people living alone.

Provably secure certificateless protocol for wireless body area network

Wireless body area networks are gaining popularity due to their innovative applications such as timely analysis, remote monitoring of patients’ health, and high patient care quality. However, these healthcare systems that carry patient’s physiological data need special attention for the security and privacy of information. Due to the openness of transmitted data, the healthcare system gets prone to several adverse attacks. In this paper, a provably secure remote healthcare system is proposed based on the elliptic curve cryptosystem. The goal is to enable confidentiality and privacy of sensitive information by designing a certificateless authenticated key agreement protocol with low computational cost and higher security. The proposed scheme achieves anonymity, resistance to key escrow problems, mutual authentication between the sensor nodes attached to patients and the application provider. Furthermore, the protocol undergoes formal security analysis using the random oracle model, and the soundness of the proposed scheme is validated using ProVerif. Finally, the performance analysis depicts that the proposed scheme is efficient compared to existing methods.

ML algorithms to estimate data reliability metric of ECG from inter-patient data for trustable AI-based cardiac monitors

Improvement in remote healthcare systems has been conducted utilizing cyber-physical systems (CPS), although data reliability is still the biggest challenge in unsupervised settings. Cardiac monitoring remotely using wearable sensors and implementing trustable artificial intelligence (AI) algorithms are one of the most challenging problems in Smart Health (sHealth). Remote cardiac monitoring using wearable electrocardiogram (ECG) devices is prone to noise and artifacts, which can make the data unreliable as false detection, can happen due to the presence of noise. Checking the reliability of data manually is time-consuming and not possible for long-term recorded data from unsupervised settings. To mitigate the data reliability issue in remote cardiac monitoring, we propose a novel Data Reliability Metric (DReM), where we predict the reliability of inter-patient ECG data using machine learning (ML) algorithms from data statistics itself. We predicted DReM on a scale of 0–1, where 0 means an extremely noisy signal and 1 means a noise-free signal. In this work, we have used Lasso regression (LR), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR) algorithms. To evaluate the model performance R 2 , Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) was used. Decision tree regression was able to predict the DReM with a high correlation (R 2 = 1) and low prediction error. We tested our model using different patients’ data and observed that DTR performed best in all cases. Our proposed data reliability approach can be beneficial in cardiac disease detection by identifying false predictions from unreliable data. • A novel Data Reliability Metric (DReM) is proposed to estimate the reliability of inter-patient ECG data on a scale of 0–1. • Both time-series features (statistical, spectral, and temporal) and morphological features were extracted from the noise-contaminated ECG data. • For evaluating model performance R 2 , RMSE, and MAE are used as analysis indices. • Decision tree regression (DTR) is the best-performed model that achieved R 2 value 1. • Our proposed automated method can be useful in remote cardiac monitoring to identify false alarms due to the less reliable data in ECG.

The effect of eConsult on the provision of orthopaedic services in Nunavut

ABSTRACT To assess the effect of eConsultation in providing Orthopaedic Surgery specialist service to patients in Nunavut. A cross-sectional study of 161 Orthopaedic Surgery consultations received from primary care providers (PCPs) in Nunavut via the Champlain Building Access to Specialist service through eConsult (BASETM) service over the 2-year period from January 2017 to December 2018. Data captured were: reason for consultation, impact of advice on referral, perceived value to the PCPs and time spent. eConsult avoided unnecessary in-person consultation 62% of the time while catching 5% of the referrals that would have otherwise been missed. PCP referral behaviour was modified 48% of the time. 94% of eConsults were rated as valuable to PCPs in their practice and 100% of eConsults resulted in actionable advice. Further, eConsults took an average of 15.4 minutes of specialist time to complete, and the mean time from referral to response was 1.4 days. eConsultation spares unnecessary consultation to Orthopaedic Surgery, catches important referrals that would have otherwise been missed, decreases wait time, and may reduce cost in remote healthcare systems such as Nunavut.

An Effective Self-Configurable Ransomware Prevention Technique for IoMT.

Remote healthcare systems and applications are being enabled via the Internet of Medical Things (IoMT), which is an automated system that facilitates the critical and emergency healthcare services in urban areas, in addition to, bridges the isolated rural communities for various healthcare services. Researchers and developers are, to date, considering the majority of the technological aspects and critical issues around the IoMT, e.g., security vulnerabilities and other cybercrimes. One of such major challenges IoMT has to face is widespread ransomware attacks; a malicious malware that encrypts the patients' critical data, restricts access to IoMT devices or entirely disable IoMT devices, or uses several combinations to compromise the overall system functionality, mainly for ransom. These ransomware attacks would have several devastating consequences, such as loss of life-threatening data and system functionality, ceasing emergency and life-saving services, wastage of several vital resources etc. This paper presents a ransomware analysis and identification architecture with the objective to detect and validate the ransomware attacks and to evaluate its accuracy using a comprehensive verification process. We first develop a comprehensive experimental environment, to simulate a real-time IoMT network, for experimenting various types of ransomware attacks. Following, we construct a comprehensive set of ransomware attacks and analyze their effects over an IoMT network devices. Furthermore, we develop an effective detection filter for detecting various ransomware attacks (e.g., static and dynamic attacks) and evaluate the degree of damages caused to the IoMT network devices. In addition, we develop a defense system to block the ransomware attacks and notify the backend control system. To evaluate the effectiveness of the proposed framework, we experimented our architecture with 194 various samples of malware and 46 variants, with a duration of sixty minutes for each sample, and thoroughly examined the network traffic data for malicious behaviors. The evaluation results show more than 95% of accuracy of detecting various ransomware attacks.