Smart Healthcare Monitoring System Research Articles

IoT Based Smart Healthcare Monitoring System for the Paralysis and Mute Person

Flex sensors are used by the IOT-based smart healthcare monitoring system for paralyzed and silent people to identify their requirements. The system includes temperature and pulse rate monitoring, and it transmits and displays real-time data updates via Internet of Things to an LCD. The brain of the system is an Arduino or CPU, which gathers and processes input from several sensors and coordinates all of the system's functions. It displays vital sign data, interpreted requirements, and any alarms or messages that the patient or caregivers may observe in real time. Because of its Internet of Things (IOT) characteristics, this system can send gathered data to distant servers or devices over the internet. This makes it possible for caregivers or healthcare professionals to view the patient's data remotely for tracking and prompt action. In the event that any sensor readings exceed predetermined thresholds indicating critical circumstances, an alarm system is included to rapidly contact caretakers or medical personnel. The buzzer functions as an auditory alert that requires quick attention. Caregivers are alerted by a buzzer when high sensor readings are detected. The method uses audio playback to convey requirements that have been discovered in order to improve communication with patients who are silent. By delivering timely information and promoting efficient communication, this integrated approach improves treatment for people with paralysis and those who are silent. It also makes the healthcare environment more responsive and patient-centered.

IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN

Healthcare is undergoing a transformation due to predictive analytics, which offers clinical decision support in real-time, facilitating effective, tailored therapy. By lowering unfavorable events and raising the standard of care, this approach supports patients at every step of diagnosis, prognosis and therapy. Predictive algorithms driven by artificial intelligence (AI), machine learning (ML) and Internet-of-Things (IoT) handle enormous amounts of data, with cardiovascular illnesses as the primary cause of death worldwide. IoT devices make sophisticated deep learning (DL) analytics and remote patient monitoring possible. This paper presents a novel 5-stage disease prediction model as an IoT-cloud-based smart healthcare management framework, constructed using a seahorse optimization-driven mutated convolutional neural network (SHO-MuCNN). In stage-1, data is gathered from a variety of sources through data collection. The stage-2 of data pre-processing involves the use of standardization to get the data acceptable for additional processing. Using a customized principal component analysis (CPCA), the set of characteristics is determined in stage-3. Next, the stage-4 diseases are predicted by the suggested SHO-MuCNN methodology. According to the prediction results, physicians are educating patients in stage-5 on exactly what measures can be taken to reduce the risk. The smart healthcare monitoring measures for accuracy (98.56%), precision (96.69%), F1-score (97.86%), Recall (94.59%)and AUC (95.02), were employed in the models' performance. In addition, this study utilizes the SPSS and t-test for statistical analysis. The experiment's findings indicate that the SHO-MuCNN strategy outperformed the remaining techniques in an intelligent healthcare monitoring system.

A non-invasive smart healthcare monitoring system based on the Internet of things

<p>A worldwide increase in healthcare problems and disorders underscored the significance of across the globe healthcare monitoring (HCM). Several countries have increased lifespans due to technological medical developments, government health initiatives, and personal cleanliness. Globalization has led to a gradually rising elderly population and a decrease in fertility, which might result in problems with socioeconomic status. In order to assist older adults, HCM technologies must be cost-effective and simple to implement. Autonomous HCM could be feasible, incorporating wearable devices with sensors, actuators, and communication. The above approach enables practical and efficient personal medical care for older people, minimizing their requirement for costly hospital treatment. The entire study emphasizes the non-invasive blood glucose monitoring (NIBGM). The idea for the project includes developing a non-invasive medical device with RFID tags login, data storage, and smart sensors that allow the monitoring of several organs. The small in size, interconnected sensor monitors body temperature (BT), blood glucose level (BGL), blood pressure (BP), heart rate (HR), and oxygen saturation (OS). The internet-based monitoring device permits healthcare providers to keep track of patient’s health metrics in real-time and send data to remote places. The new method allows healthcare professionals to present rapid assistance and reinforcement, enhancing the results for patients.</p>

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.

Monitoreo de la Salud de los Ocupantes de Vehículos Inteligentes: Una Revisión

Technological innovations have pursued to improve the quality of life of people. These innovations have led to the development of high-tech smart equipments, vehicles, monitoring systems, and so on. Smart healthcare monitoring systems have become an important tool for monitoring the health of patients, company employees, athletes, among others. This work is a review of the scientific works and studies of the last ten years, the state of the art regarding the techniques employed for monitoring the health of occupants of smart vehicles. Through a systematic review of the studies reported in the literature, we quantified articles published and identified the most relevant works with the use of the Proknow-C technique. The databases employed for our review were ScienceDirect, Web of Science, Scopus and Google Scholar. Approximately 1337 works were found. Once selected the 16 most relevant papers, we applied the conceptual maps technique to construct a comprehensive overview linking the contributions of works and correlating them to the main research approaches adopted in the literature. This review brings meaningful contributions to help the scientific community to have a comprehensive understanding of the subject and to make advances.

A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients

An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medical assistance. Such human-centered smart healthcare systems can not only provide continuous medical assistance to diabetes patients but can also reduce overall medical expenses. In the last decade, machine learning has been successfully implemented to design more accurate and precise medical applications. In this paper, a smart sensing technologies-based architecture is proposed that uses AI and the Internet of Things (IoT) for continuous monitoring and health assistance for diabetes patients. The designed system senses various health parameters, such as blood pressure, blood oxygen, blood glucose (non-invasively), body temperature, and pulse rate, using a wrist band. We also designed a non-invasive blood sugar sensor using a near-infrared (NIR) sensor. The proposed system can predict the patient’s health condition, which is evaluated by a set of machine learning algorithms with the support of a fuzzy logic decision-making system. The designed system was validated on a large data set of 50 diabetes patients. The results of the simulation manifest that the random forest classifier gives the highest accuracy in comparison to other machine learning algorithms. The system predicts the patient’s condition accurately and sends it to the doctor’s portal.

An Improved AI-Based Secure M-Trust Privacy Protocol for Medical Internet of Things in Smart Healthcare System

Medical Internet of Things (MIoT) is a rapidly growing field that promises to revolutionize health care. The ability to connect devices and collect data from them has the potential to transform the way it monitors and treat patients. However, the security and privacy of this data is a major concern. The cryptography and other conventional methods for security in resource constrained MIoT has several shortcomings. First, it does not provide adequate protection for patient data. Second, it is not designed to work with the large number of devices and huge volumes of data that are typically generated in MIoT applications. In this paper, Secure M-Trust Privacy protocol (SMP) has been designed to address these issues. The SMP protocol uses a combination of trust, cryptographic and Machine Learning techniques to provide security and privacy for data in transit. The SMP protocol has been designed to work with the Smart Health Care Monitoring System to provide a secure and private communication channel between devices in the system. The SMP protocol is an improvement over the existing security and privacy protocols for medical data. The simulation results proves that the SMP protocol is more efficient and scalable than the existing protocols. The SMP protocol is a valuable addition to the MIoT landscape and can help improve the privacy of data exchanged between medical devices.

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 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.

Advancement of Smart Healthcare Monitoring Systems in an Internet of Things-based Environment

Advancement of Smart Healthcare Monitoring Systems in an Internet of Things-based Environment

Blockchain-enabled healthcare monitoring system for early Monkeypox detection.

The recent emergence of monkeypox poses a life-threatening challenge to humans and has become one of the global health concerns after COVID-19. Currently, machine learning-based smart healthcare monitoring systems have demonstrated significant potential in image-based diagnosis including brain tumor identification and lung cancer diagnosis. In a similar fashion, the applications of machine learning can be utilized for the early identification of monkeypox cases. However, sharing critical health information with various actors such as patients, doctors, and other healthcare professionals in a secure manner remains a research challenge. Motivated by this fact, our paper presents a blockchain-enabled conceptual framework for the early detection and classification of monkeypox using transfer learning. The proposed framework is experimentally demonstrated in Python 3.9 using a monkeypox dataset of 1905 images obtained from the GitHub repository. To validate the effectiveness of the proposed model, various performance estimators, namely accuracy, recall, precision, and F1-score, are employed. The performance of different transfer learning models, namely Xception, VGG19, and VGG16, is compared against the presented methodology. Based on the comparison, it is evident that the proposed methodology effectively detects and classifies the monkeypox disease with a classification accuracy of 98.80%. In future, multiple skin diseases such as measles and chickenpox can be diagnosed using the proposed model on the skin lesion datasets.

Smart Healthcare Monitoring System

Nowadays, extreme priority is focused on health, due to the out- break of Corona virus, to ensure precautions before any illness. In many circumstances, people tend to forget the importance of health and fail to monitor the health parameters, such as temperature, heart rate, and so on, due to work and busy schedules. Therefore, a smart healthcare system has been proposed in this research, wherein five sensors such as MAX30100 sensor, DS18B20 sensor, DHT11 sensor, MQ9, and MQ135 sensor which sense the patient and patient’s room conditions, are used. The obtained values are displayed in the web server, through which the patient’s condition is communicated to the hospital staff, where they can detect and evaluate the current health status of the patient. This approach will enhance the current healthcare system, by potentially saving many lives. Furthermore, medication should also be taken at the right time to improve the state of health which is too difficult for old patients and those who give less preference to health than work. Hence, a pill reminder system which uses IR sensor and a buzzer to remind the patients regarding the proper intake of medication is also designed.

Data Security Mechanisms, Approaches, and Challenges for e-Health Smart Systems

In the new era, the trend of using wearable devices and smart accessories gained considerable popularity and become a necessity utility for human life due to their major role to keep monitoring health conditions and providing healthcare services. The combination of IoT networks with edge computing paradigms develops an intelligent e-health system that aims to monitor different real-time scenarios. The deployment of an e-health system exposes several challenges regarding the security and privacy aspects, particularly in the case of dealing with an enormous quantity of medical data and the risk presented by exchanging operations with external entities. In this paper a comprehensive presentation covered the basic topics of e-health system layers thus the advantages and limitations in terms of existing challenges has been mentioned, subsequently, adapted to the exposed cyber risk through the traditional systems in exchanging medical data, a discussion of the blockchain technology come over for new application opportunities, where this approach efficiently ensure the security of data transactions over the network, in addition, an overview outlined the main research works related to this technology. Therefore, a presentation study of diverse works reveals different security framework solutions related to e-health system’s layers, furthermore, uncovering the benefits of integrating intelligent technologies such as Machine Learning (supervised, and unsupervised types), Deep Learning, and Reinforcement Learning as well as introducing a comparison analysis of multiple AI algorithm models based on their efficiency for future deployment related security purposes to provide a smart healthcare monitoring system that meets patient needs. The end of this review highlighted further research directions and the actual open challenges regarding the e-Health system’s limitations.

IoT-Deep Learning Based Activity Recommendation System

The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community. Health professionals are increasingly making use of the benefits of these technologies, resulting in a major improvement in health care both in and out of clinical settings. The Internet of Things (IoT) is a new internet revolution that is a rising research area, particularly in health care. Healthcare Monitoring Systems (HMS) have progressed rapidly as the usage of Wearable Sensors (WS) and smartphones have increased. The existing framework of conventional telemedicine’s store-and-forward method has some issues, including the need for a nearby health centre with dedicated employees and medical devices to prepare patient reports. Patients’ health can be continuously monitored using advanced WS that can be fitted or embedded in their bodies. This research proposes an innovative and smart HMS, which is built using recent technologies such as the IoT and Machine Learning (ML). In this study, we present an innovative and intelligent HMS based on cutting-edge technologies such as the IoT and Deep Learning (DL) + Restricted Boltzmann Machine (RBM). This DL + RBM model is clever enough to detect and process a patient’s data using a medical Decision Support System (DSS) to determine whether the patient is suffering from a major health problem and treat it accordingly. The recommended system’s behavior is increasingly investigated using a cross-validation test that determines various demographically relevant standard measures. Through a healthcare DSS, this framework is clever enough to detect and analyze a patient’s data. Experiment results further reveal that the proposed system is efficient and clever enough to deliver health care. The data reported in this study demonstrate the notion. This device is a low-cost solution for people living in distant places; anyone can use it to determine if they have a major health problem and seek treatment by contacting nearby hospitals.

IoT Based Patient’s Smart Healthcare Monitoring and Recording Using GSM Module

An IoT based smart healthcare monitoring system is presented in this study. A local database, sensors, a microcontroller, and a GSM module make up the suggested system. Here, we’re using an Arduino Uno to attach sensors, which then sense the environment and record data that is sent over the internet using the GSM protocol. The health of patients in isolated or rural locations without access to medical services can be tracked using this project. The importance of offering patients who were anticipated to decrease travel and direct contact with others remote patient monitoring services became even more clear when the epidemic struck. When there is an abnormal activity identified, healthcare team will get a message and they would make a call and prescribe the activities which need to be do for emergency and even it doesn’t work ambulance is immediately depart to that particular patient’s location since we have the patient’s personal info about his location and his/her relative emergency mobile number.

Prediction of health monitoring with deep learning using edge computing

Today's modern computing environment provides a smart healthcare monitoring system for early prediction of fall detection. The Internet of Things-based health model plays a significant role in the health care service area and helps to improve the processing of data and its prediction. Transferring reports or data from one place to another takes too much time and energy, and it will cause high latency and energy issues. To handle these kinds of hazards, edge computing provides solutions. In this paper w presents smart healthcare system issues, services, and applications. Furthermore, propose a CNN-based prediction model with the use of edge computing and IoT paradigms. Edge computing is a distributed environment framework that enables rapid resource availability and response time through local edge servers computed at the end of IoT devices. The CNN model is used to analyse the health data collected by IoT devices. Furthermore, the role of edge devices is to provide doctors and patients with timely health-prediction reports via edge servers. The proposed mechanism can be analysed using accuracy and error rate performance parameters. In the proposed mechanism, the accuracy is 99.23% in comparison with other techniques.

Development of Deep Learning Technique of Features for the Analysis of Clinical Images Integrated with CANN.

Computer tomography is an extensively used method for the detection of the disease in the subjects. Basically, computer-aided tomography depending on the artificial intelligence reveals its significance in smart health care monitoring system. Owing to its security and the private issue, analyzing the computed tomography dataset has become a tedious process. This study puts forward the convolutional autoencrypted deep learning neural network to assist unsupervised learning technique. By carrying out various experiments, our proposed method produces better results comparative to other traditional methods, which efficaciously solves the issues related to the artificial image description. Hence, the convolutional autoencoder is widely used in measuring the lumps in the bronchi. With the unsupervised machine learning, the extracted features are used for various applications.

IoMT-based smart healthcare monitoring system using adaptive wavelet entropy deep feature fusion and improved RNN

IoMT-based smart healthcare monitoring system using adaptive wavelet entropy deep feature fusion and improved RNN

Design of in-and-out warehouse information encryption system for large precision instruments based on smart healthcare monitoring system

The traditional in-and-out warehouse information encryption system of large-scale precision instruments can not guarantee the security of the Therefore, the design of in-and-out warehouse information encryption system for large precision instruments based on speech recognition technology is proposed. Therefore, the design of in-and-out warehouse information encryption system for large precision instruments based on speech recognition technology is proposed. On the basis of speech recognition technology, according to the hardware structure diagram of in-and-out warehouse information encryption system for large precision instruments, the information collector, information processor and information storagefor large precision instruments are designed, and the hardware design of the system is completed. By compressing the redundancy of large precision instrument information, in-and-out warehouse information encryption process of large precision instrument is designed, and the software design of the system is completed. The encryption of in-and-out warehouse information of the large precision instruments is realized. The experimental results show that the encryption system based on speech recognition technology can not only improve the encryption effect, but also improve the completeness of the information.

Hybrid cluster made content forwarding and light weight sign encryption IoT based Named Data Network using African Buffalo optimization algorithm

Information Centric Network (ICN) is a newer technology in handling web content distribution that has recently emerged in order to tackle the risk of data security. For handling content distribution, ICN provides data security via a name-based approach. Named Data Networking (NDN) is an ongoing ICN realisation that was incorporated recently. Named Data Networking (NDN) has recently grown in popularity and significance as a new internet design that solves certain limitations in traditional internet communications. NDN is perfectly adapted for the Internet of Things (IoT), which is today dominated by huge, and emerging applications. In this work, we propose an IoT enabled hybrid cluster-based routing protocol with mitigation of content poisoning attack for information-centric Wireless Sensor Network (WSN)-NDN. In this method, hybrid K-medoids clustering is used with African Buffalo Optimization Algorithm (ABOA), which is to find an optimal shortest path between the cluster heads, and light weight encryption. It is developed by using Hyperelliptic Curve Cryptography (HCC) to mitigate content poisoning. Our proposed system has effective data security as it has encrypted data in the cluster head. The smart health care monitoring system has been used for our proposed method. The proposed method has been subjected to extensive analysis by comparing with other existing methods that should improve performance justified in terms of several metrics by introducing the malicious nodes (10%, 20%, and 30%).