Remote Healthcare Monitoring System Research Articles

IOT and AI Implementations on Remote Healthcare Monitoring System

Abstract: "IoT and AI Implementations on Remote Healthcare Monitoring System using Raspberry Pi 3" seeks to revolutionize healthcare by integrating cutting-edge technologies into a cohesive system for remote patient monitoring. Leveraging the power of Raspberry Pi 3, Internet of Things (IoT) devices will be employed to collect real-time health data from patients in diverse locations. This data will then undergo sophisticated analysis through Artificial Intelligence (AI) algorithms, enhancing the system's ability to identify anomalies, trends, and potential health risks with a high degree of accuracy. The Raspberry Pi 3 serves as a versatile and cost-effective hub, managing the connectivity and processing requirements of the IoT devices. The system aims to provide continuous and proactive healthcare support by enabling remote monitoring of vital signs and timely interventions based on AI- driven insights. The outcomes of this project not only contribute to the advancement of healthcare technology but also address the growing need for scalable and accessible healthcare solutions in a rapidly evolving digital landscape.

A Secure Blockchain-Enabled Remote Healthcare Monitoring System for Home Isolation

This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.

An efficient and secure wearable sensor based remote healthcare monitoring system using adaptive dilated transformer Bi‐LSTM with gated recurrent unit

AbstractInnovative research works in the healthcare sector keep on advancing every day. As the “Internet of Things (IoT)” keeps on evolving, the application of IoT in the medical field is prominent these days. Utilizing IoT devices, alert messages can be sent directly to medical professionals in case of an emergency. So, monitoring the health condition of an individual using IoT technology has become a popular and beneficial method in today's contemporary medical field. With the help of mobile IoT medical equipment, the technology of smart Healthcare Monitoring System (HMS) is proliferating. By utilizing deep learning and IoT technology, the medical diagnosis system has evolved from direct face‐to‐face visits to the hospital to remote telemedicine method. Most of the data generated by the IoT wearable sensors are highly correlated and may consist of outliers. The extraction of the essential attributes from these data is a complicated task. So, “deep learning and machine learning” techniques are adapted to determine the most relevant and appropriate feature required for efficient diagnosis from the unstructured data produced by the IoT devices and thus help in minimizing the redundancy of unnecessary data. Fusing deep learning methods with healthcare IoT made only the essential details to be available for diagnosis. Therefore, a deep learning‐oriented IoT‐based HMS is executed in this work. With the support of several wearable healthcare devices, the required data are acquired. The encryption of the data acquired from standard sources using Optimal Key‐based Advanced Encryption Standard (OK‐AES) is carried out next to assure the security of the sensitive medical data. The keys for AES encryption are optimally chosen with the aid of the Enhanced Heap‐Based Optimizer Algorithm (EHBOA). The encrypted data is transferred to the “cloud platform” for data storage. Once there is a need for the data, then the encrypted data is initially downloaded from the cloud platform. Then using the same AES scheme, the decryption of the data to attain the original data is carried out. From the retrieved data, the extraction of the crucial attributes is carried out. The extracted features are chosen in an optimized manner and are concatenated with the tuned weights to form the weighted feature matrix. This formulated weighted feature matrix is provided as input to the “Adaptive Dilated Transformer Bidirectional Long Short‐Term Memory (Bi‐LSTM) with Gated Recurrent Unit (GRU) (ADTBi‐LSTM‐GRU) model.” The variables in the ADTBG are optimized using the EHBOA for providing an accurate classification outcome. The classified disease outcome is obtained from the deployed ADTBi‐LSTM‐GRU model. Simulations are done to verify the efficiency and reliability of the implemented deep learning and IoT‐based HMS.

Lightweight Authentication Scheme for Healthcare With Robustness to Desynchronization Attacks

Remote healthcare monitoring systems are gaining a lot of interest as they enable doctors to use public channels to get real-time data from the sensors placed in/on the patient’s body. This necessitates the implementation of a robust authentication scheme to ensure secure communication between trusted healthcare providers and sensors which are usually low in resources. To address these issues, in 2021, Masud et al. presented a lightweight anonymous user authentication scheme for securely obtaining patient’s real-time data. Their protocol is considered practical for deployment on sensor nodes as it only utilizes hash functions and does not require any public key cryptography. In this work, we demonstrate how their protocol loses synchronization when a message is blocked/jammed and how in some scenarios, the protocol is exposed to the risk of session key disclosure and cannot ensure forward secrecy. To overcome these threats, we propose, a Lightweight mutual Authentication Protocol that provides Robustness to Desynchronization attacks. The proposed scheme uses a one-way hash chain technique to ensure forward secrecy and enable resynchronization between the protocol entities in the event of a desynchronization attack. also achieves user and sensor node anonymity, thus ensuring privacy of the communicating entities. With the demonstration of both formal and informal analysis, the proposed protocol is ensured to withstand the identified attacks in Masud et al.’s scheme. The comparative analysis in terms of security and performance with relevant protocols indicates that the proposed protocol ensures higher security with considerably low computation and communication overheads, making it suitable for practical implementation in a lightweight healthcare environment.

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.

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.

IOT based Remote Healthcare Monitoring System

IOT based Remote Healthcare Monitoring System

1415 NOVEL APPROACHES TO POST DISCHARGE CARE. REMOTE HEALTHCARE MONITORING SYSTEMS FOLLOWING TRAUMATIC BRAIN INJURY IN OLDER ADULTS

Abstract Introduction Major trauma including Traumatic Brain Injury (TBI) is an increasingly common cause of hospitalisation in older adults (OAs). We studied post-discharge recovery from TBI using a remote healthcare monitoring system that captures data on activity and sleep. We aim to assess the feasibility and acceptability of this technology to monitor recovery at home following a significant acute health event in OAs. Methods We installed Minder, a remote healthcare monitoring system, in recently discharged patients >60 years with moderate-severe TBI. We present descriptive analyses of post-discharge recovery for two males, corroborating data from Minder against verified activities and events. We recorded semi-structured interviews assessing acceptability. Both participants have similar household set-up, multimorbidity profiles and clinical frailty scores; however, one participant has prior cognitive impairment (PAT1), and one does not (PAT2). Results We present 10 weeks of sleep and activity data from Minder and feedback from interviews. Data observed from PAT1 revealed habitual patterns of activity and sleep. These remained stable, despite discrete clinical events. Conversely, PAT2's data revealed irregular sleep patterns that became increasingly fragmented. Activity was detected in multiple rooms throughout the house at night, consistent with carer reports of night-time wandering. Increased overnight activity coincided with multiple falls, prompting increased care provision. Initial feedback from interviews was the technology helped participants and those involved in their care feel supported. Conclusions As pressure on services mounts, novel approaches to post-discharge care are of increasing importance. Remote healthcare monitoring can provide high temporal resolution data offering ‘real world’ insights into the effects of significant health events in OAs. Our provisional results support our hypothesis that use of this technology is feasible and acceptable for frail, multimorbid participants and highlights the substantial potential of this technology to help clinicians improve community-based care and more effectively monitor interventions and chronic conditions.

An efficient compression technique for Foetal phonocardiogram signals in remote healthcare monitoring systems

Remote Healthcare Monitoring Systems (RHMs) that employ fetal phonocardiography (fPCG) signals are highly efficient technologies for monitoring continuous and long-term fetal heart rate. Wearable devices used in RHMs still face a challenge that decreases their efficacy in terms of energy consumption because these devices have limited storage and are powered by batteries. This paper proposes an effective fPCG compression algorithm to reduce RHM energy consumption. In the proposed algorithm, the Discrete Orthogonal Charlier Moment (DOCMs) is used to extract features of the signal. The householder orthonormalization method (HOM) is used with the Charlier Moment to overcome the propagation of numerical errors that occur when computing high-order Charlier polynomials. The proposed algorithm’s performance is evaluated in terms of CR, PRD, SNR, PSNR, and QS and provides the average values 18.33, 0.21, 48.85, 68.86, and 90.88, respectively. The results of the comparison demonstrate the proposed compression algorithm’s superiority over other algorithms. It also tested in terms of compression speed and computational efficiency. The results indicate that the proposed algorithm has a high Compression speed (218.672 bps) and high computational efficiency (21.33). Additionally, the results reveal that the proposed algorithm decreases the energy consumption of a wearable device due to the transmission time decreasing for data by 3.68 s.

An RHMIoT Framework for Cardiovascular Disease Prediction and Severity Level Using Machine Learning and Deep Learning Algorithms

Cardiovascular disease is one of the deadliest diseases in the world. Accurate analysis and prediction for real-time heart disease are highly significant. To address this challenge, a novel IoT-based automated function monitoring system to promote the e-healthcare system is proposed. The proposed remote healthcare monitoring system uses an IoT framework (RHMIoT) using deep learning and auto encoder-based machine learning algorithms to accurately predict the presence of heart disease. The RHMIoT framework contains two phases: the first phase is to monitor the severity level of the heart disease patient in real-time, and the second phase is used in the medical decision support system to predict the accuracy level of heart disease. To train and test the open-access Framingham dataset, various deep learning and auto encoder-based machine learning techniques are used. The proposed system obtains an accuracy of 0.8714% using the auto encoder-based kernel SVM algorithm compared to other approaches.

Smart Health Monitoring System

As we Know that Health is More Important for Human’s to maintain good Health essential is Health care system and also to overcome from the disease. The health care is more importance in now a day in the occurrence of the corona virus. As view of now for corona virus, an IOT based health monitoring system is better solution for pandemic. The new revolution of internet is IOT is which is the expanding research area especially in the health care System. This is remote health care monitoring system where we can monitor from desire location.

Remote Health Care Monitoring System Using IoT SaaS

Remote Health Care Monitoring System Using IoT SaaS

Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises.

Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client–server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.

Lightweight Privacy and Confidentiality Preserving Anonymous Authentication Scheme for WBANs

The recent developments in the wireless body area networks (WBAN) play a vital role in the modern remote health care monitoring system. In WBAN, the deployed intelligent, resource-limited body sensors will collect the patient's biological information (BI) and communicate the same to the doctor for further action through the Internet. But during the communication among the WBAN entities, the privacy, security of the BI, and user's personal data need to be protected against various security threats. To address these security flaws, in this article, a computationally efficient privacy-preserving anonymous authentication scheme is proposed for resource-limited WBAN. Also, it preserves the physical security of sensors and the confidentiality of BI and provides conditional privacy to the WBAN users. The comprehensive security and performance evaluation phase ensures that the proposed scheme is efficient and secure in terms of computational and communication complexity when compared with the other conventional schemes.

Man-in-the-Middle Attack Mitigation in Internet of Medical Things

The Internet of Medical Things are susceptible to Man-in-the-Middle (MitM) attack, which can identify healthcare emergency of monitored patients and replay normal physiological data to prevent the system from raising an alarm. In this article, we propose a framework to prevent a MitM from disrupting the operations and prohibiting the raise of alarms by the remote healthcare monitoring system. To reduce energy consumption for normal data transmission, and preserve the privacy of health data, our framework transmits a smaller size signature derived from acquired data with message authentication code, where the key is derived from received signal strength indication. Our experimental results for emergency detection show that our approach can achieve a high detection accuracy with a low false alarm rate of 3%.

An Effective Data Science Technique for IoT-Assisted Healthcare Monitoring System with a Rapid Adoption of Cloud Computing.

Patients are required to be observed and treated continually in some emergency situations. However, due to time constraints, visiting the hospital to execute such tasks is challenging. This can be achieved using a remote healthcare monitoring system. The proposed system introduces an effective data science technique for IoT supported healthcare monitoring system with the rapid adoption of cloud computing that enhances the efficiency of data processing and the accessibility of data in the cloud. Many IoT sensors are employed, which collect real healthcare data. These data are retained in the cloud for the processing of data science. In the Healthcare Monitoring-Data Science Technique (HM-DST), initially, an altered data science technique is introduced. This algorithm is known as the Improved Pigeon Optimization (IPO) algorithm, which is employed for grouping the stored data in the cloud, which helps in improving the prediction rate. Next, the optimum feature selection technique for extraction and selection of features is illustrated. A Backtracking Search-Based Deep Neural Network (BS-DNN) is utilized for classifying human healthcare. The proposed system's performance is finally examined with various healthcare datasets of real time and the variations are observed with the available smart healthcare systems for monitoring.

An Energy-Efficient Compression Algorithm of ECG Signals in Remote Healthcare Monitoring Systems

Remote Healthcare Monitoring Systems (RHMs) that use ECG signals are very effective tools for the early diagnosis of various heart conditions. However, these systems are still confronted with a problem that reduces their efficiency, such as energy consumption in wearable devices because they are battery-powered and have limited storage. This paper presents a novel algorithm for the compression of ECG signals to reduce energy consumption in RHMs. The proposed algorithm uses discrete Krawtchouk moments as a feature extractor to obtain features from the ECG signal. Then the accelerated Ant Lion Optimizer (AALO) selects the optimum features that achieve the best-reconstructed signal. Our proposed algorithm is extensively validated using two benchmark datasets: MIT-BIH arrhythmia and ECG-ID. The proposed algorithm provides the average values of compression ratio (CR), percent root mean square difference (PRD), signal to noise ratio (SNR), Peak Signal to noise ratio (PSNR), and quality score (QS) are 15.56, 0.69, 44.52, 49.04 and 23.92, respectively. The comparison demonstrates the advantages of the proposed compression algorithm on recent algorithms concerning the mentioned performance metrics. It also tested and compared against other existing algorithms concerning Processing Time, compression speed and computational efficiency. The obtained results show that the proposed algorithm extremely outperforms in terms of (Processing Time = 6.89 s), (compression speed = 4640.19 bps) and (computational efficiency = 2.95). The results also indicate that the proposed compression algorithm reduces energy consumption in a wearable device by decreasing the wake-up time by 3600 ms.

Compression Techniques of Biomedical Signals in Remote Healthcare Monitoring Systems: A Comparative Study

Compression Techniques of Biomedical Signals in Remote Healthcare Monitoring Systems: A Comparative Study

Conturarea unui scenariu optimizat de monitorizare la distanță a sănătății vizând atenuarea presiunii induse de pandemia COVID-19

COVID-19 pandemic has had a major impact on most aspects of everyday life and that has led to a significant faster evolution in the digitalization of medical services. Telemedicine and eHealth continue to provide solutions to mitigate this pandemic's effects, with the aim of helping isolated coronavirus patients, as well as people with chronic conditions. This paper presents how COVID-19 pandemic has been influencing the development of remote healthcare monitoring systems as a result of the need to maintain a remote communication between patient and medical specialist and to provide reliable care. In order to demonstrate how a remote healthcare monitoring scenario can be optimized and personalized according to the needs, specificities and the evolving health status of a patient, the building of the Healthcare Monitoring Component from the ongoing RO-SmartAgeing system is presented. This component, based on a series of medical sensors, connected to an Arduino board that collects and transmits data, through a Wi-Fi network, to the Fog/Edge level, offers both the means to real-time monitor the patient's vital signs and to alert healthcare specialists in case of an abnormal measured value. The gathered information is filtered and then sent to a database in the Cloud allowing further analysis and health data-based applications to be provided. The benefits of this solution that allows the real time visualization of medical data, as well as its storage, processing and subsequent analysis are highlighted and correlated with the specificities induced by COVID-19 pandemic.

Definition of a lightweight cryptographic solution to secure health data on IoT and cloud

The Internet of Things (IoT) and the Cloud Computing are among the main technologies influencing the quality and efficiency of our social and economic activities. Their importance is quite notable in several areas such as remote medical care and healthcare monitoring systems. These later face difficulties most of them are the expensive costs for the chronic diseases and the hospitals charges specially during the pandemic time. All these issues have led to the creation of new solutions for enhancing the quality of the healthcare systems. In this paper, we focus on the network architecture security including healthcare monitoring systems based on IoT and Cloud Computing. We present a recent review on the research field and we propose a secure solution based on lightweight cryptography in accordance with the limited resource environments within which health data are exchanged. The proposed solution is guaranteeing data confidentiality and ensuring integrity and authenticity.