Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time

Sensors, Journal Year: 2023, Volume and Issue: 23(11), P. 5204 - 5204

Published: May 30, 2023

With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care reducing healthcare costs. The Internet of Things (IoT) recently drawn much interest as a potential remedy. IoT-based systems can gather analyze wide range physiological data, including blood oxygen levels, heart rates, body temperatures, ECG signals, then provide real-time feedback medical professionals so they may take appropriate action. This paper proposes system for early detection problems in home clinical settings. comprises three sensor types: MAX30100 measuring level rate; AD8232 module signal data; MLX90614 non-contact infrared temperature. collected data is transmitted server using the MQTT protocol. A pre-trained deep learning model based on convolutional neural network with attention layer used classify diseases. detect five different categories heartbeats: Normal Beat, Supraventricular premature beat, Premature ventricular contraction, Fusion ventricular, Unclassifiable beat from fever or non-fever Furthermore, provides report patient's rate level, indicating whether are within normal ranges not. automatically connects user nearest doctor further diagnosis if any abnormalities detected.

Language: Английский

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Classification of Alzheimer’s disease using MRI data based on Deep Learning Techniques

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2024, Volume and Issue: 36(2), P. 101940 - 101940

Published: Jan. 24, 2024

Alzheimer's Disease (AD) is a worldwide concern impacting millions of people, with no effective treatment known to date. Unlike cancer, which has seen improvement in preventing its progression, early detection remains critical managing the burden AD. This paper suggests novel AD-DL approach for detecting AD using Deep Learning (DL) Techniques. The dataset consists pictures brain magnetic resonance imaging (MRI) used evaluate and validate suggested model. method includes stages pre-processing, DL model training, evaluation. Five models autonomous feature extraction binary classification are shown. divided into two categories: without Data Augmentation (without-Aug), CNN-without-AUG, (with-Aug), CNNs-with-Aug, CNNs-LSTM-with-Aug, CNNs-SVM-with-Aug, Transfer learning VGG16-SVM-with-Aug. main goal build best accuracy, recall, precision, F1 score, training time, testing time. recommended methodology, showing encouraging results. experimental results show that CNN-LSTM superior, an accuracy percentage 99.92%. outcomes this study lay groundwork future DL-based research identification.

Language: Английский

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Reducing communication overhead in the IoT-edge-cloud continuum: A survey on protocols and data reduction strategies

Internet of Things, Journal Year: 2025, Volume and Issue: unknown, P. 101553 - 101553

Published: March 1, 2025

Language: Английский

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Intelligent Digital Twins for Personalized Migraine Care

Journal of Personalized Medicine, Journal Year: 2023, Volume and Issue: 13(8), P. 1255 - 1255

Published: Aug. 13, 2023

Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts data can be collected to produce actionable information. These powerful tools are constructed with aid artificial intelligence, machine learning, deep learning; Internet Things; cloud computing collect a diverse range (e.g., from patient journals, wearable sensors, digitized equipment or processes), which provide information on conditions therapeutic responses physical twins. data-driven clinical decision making advance realization personalized care. Migraines highly prevalent complex neurological disorder affecting people all ages, genders, geographical locations. It is ranked among top disabling diseases, substantial negative personal societal impacts, but current treatment strategies suboptimal. Personalized care for migraines has been suggested optimize treatment. The implementation intelligent migraine theoretically beneficial in supporting patient-centric management. also expected that will reduce costs long run enhance effectiveness. This study briefly reviews concept available literature disorders such as diseases. Based these, potential construction utility then presented. challenges when implementing future management discussed.

Language: Английский

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Effective Identification and Authentication of Healthcare IoT Using Fog Computing with Hybrid Cryptographic Algorithm

Symmetry, Journal Year: 2024, Volume and Issue: 16(6), P. 726 - 726

Published: June 11, 2024

Currently, Internet of Things (IoT)-based cloud systems face several problems such as privacy leakage, failure in centralized operation, managing IoT devices, and malicious attacks. The data transmission between the healthcare needs trust secure Electronic Health Records (EHRs). IoT-enabled equipment is seen hospitals that have been implementing technology for many years. Nonetheless, medical agencies fail to consider security risk associated with which are readily compromised cause potential threats authentication encryption procedures. Existing computing methods like homomorphic elliptic curve cryptography unable meet security, identity, authentication, devices. majority conventional algorithms lack transmission. Therefore, fog introduced overcome device verification, identification scalable data. In this research manuscript, includes a hybrid mathematical model: Elliptic Curve Cryptography (ECC) Proxy Re-encryption (PR) Enhanced Salp Swarm Algorithm (ESSA) identification, EHRs. ESSA incorporated into PR algorithm determine optimal key size parameters algorithm. Specifically, ESSA, Whale Optimization (WOA) integrated (SSA) enhance its global local search processes. primary objective proposed model further sharing real time services. extensive experimental analysis shows approximately reduced 60 Milliseconds (ms) 18 milliseconds processing improved 25% 3% reliability, compared traditional cryptographic algorithms. Additionally, obtains communication cost 4260 bits memory usage 680 bytes context analysis.

Language: Английский

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Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

Pharmaceutics, Journal Year: 2024, Volume and Issue: 16(2), P. 260 - 260

Published: Feb. 9, 2024

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. widespread machine learning (ML) and mathematical modeling (MM)-based techniques widely acknowledged. These two approaches have fueled the advancement in cancer research eventually led uptake telemedicine care. For diagnostic, prognostic, treatment purposes concerning different types research, vast databases varied information with manifold dimensions are required, indeed, all this can only be managed by an automated system developed utilizing ML MM. In addition, MM being used probe relationship between pharmacokinetics pharmacodynamics (PK/PD interactions) anti-cancer substances improve treatment, also refine quality existing models incorporated at steps development related routine patient This review will serve as a consolidation benefits special focus on area prognosis anticancer therapy, leading identification challenges (data quantity, ethical consideration, data privacy) yet fully addressed current studies.

Language: Английский

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The Convergence of Cutting-Edge Technologies: Leveraging AI and Edge Computing to Transform the Internet of Medical Things (IoMT)

Published: July 6, 2023

This research used a wearable sensor to gather photoplethysmography (PPG) signals from 15 healthy subjects. The dataset includes 7,308 PPG segments, each containing 8 seconds of data and corresponding labels indicating the type physical activity subject performed. article proposes convolutional neural network (CNN) model classify signals. proposed several layers: batch normalization, convolutional, max-pooling, dropout, fully connected. output layer uses softmax activation function compute probabilities class. Regarding performance, suggested CNN outperforms conventional models like SVM with RBF kernel, Decision Tree, Random Forest. also suggests techniques optimize further, which can be beneficial for developing IoMT applications such as recognition vital signs monitoring.

Language: Английский

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Deep Learning Applications in ECG Analysis and Disease Detection: An Investigation Study of Recent Advances

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 126258 - 126284

Published: Jan. 1, 2024

Language: Английский

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Applications of Fog Computing in Healthcare

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: July 10, 2024

Fog computing is a decentralized infrastructure that processes data at or near its source, reducing latency and bandwidth usage. This technology gaining traction in healthcare due to potential enhance real-time processing decision-making capabilities critical medical scenarios. A systematic review of existing literature on fog was conducted. The included searches major databases such as PubMed, IEEE Xplore, Scopus, Google Scholar. search terms used were "fog healthcare," "real-time diagnostics computing," "continuous patient monitoring "predictive analytics "interoperability "scalability issues "security challenges healthcare." Articles published between 2010 2023 considered. Inclusion criteria encompassed peer-reviewed articles, conference papers, articles focusing the applications healthcare. Exclusion not available English, those related applications, lacking empirical data. Data extraction focused diagnostics, continuous monitoring, predictive analytics, identified interoperability, scalability, security. significantly enhances diagnostic by facilitating analysis, crucial for urgent stroke detection, closer source. It also improves during surgeries enabling vital signs physiological parameters, thereby enhancing safety. In chronic disease management, collection analysis through wearable devices allow proactive management timely adjustments treatment plans. Additionally, supports telemedicine communication remote specialists patients, improving access specialist care underserved regions. offers transformative healthcare, precision, personalized treatment. Addressing security will be fully realizing benefits leading more connected efficient environment.

Language: Английский

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Heart Rate Monitoring System

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Gateway computing is a critical paradigm for health monitoring systems which has enabled operations and/or processing and analysis of data to occur at/distributed in the edge system. By methods focused on real-time, its advantages, addressing latency issues efficiency, scalability benefits, while user privacy/confidentiality with computational resources moved closer where generated. This review paper provides an exhaustive examination based computing, including introduction, architectures, potential security implications, future direction. Apart from access controls currently implemented secure sensitive medical collected by systems, we also discuss variety devices used devices, sensors, gateways. The purpose this explore current area emerging trends knowledge advances lens clinical system transformation benefits.

Language: Английский

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