Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

Discover Internet of Things, Год журнала: 2025, Номер 5(1)

Опубликована: Янв. 13, 2025

Язык: Английский

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Healthcare Monitoring Using an Internet of Things-Based Cardio System

IoT, Год журнала: 2025, Номер 6(1), С. 10 - 10

Опубликована: Фев. 6, 2025

This study describes an IoT-based health monitoring system designed to notify attending physicians when necessary. The developed IoT incorporates sensors for ECG, PPG, and temperature; a gyroscope/accelerometer; microcontroller. A critical analysis of existing components in these areas was conducted ensure the system’s good performance, reliability, suitability continuous cardiac data processing. addresses challenge activity patients with arrhythmias, focusing on differences heart rate variability (HRV) parameters between healthy individuals those extrasystolic arrhythmia. purpose this research is evaluate effectiveness systems using PPG ECG registration HRV analysis. leverages time domain frequency methods assess states autonomic nervous system. Significant were observed parameters, such as SDNN, SDANN, RMSSD, LF/HF ratio. results demonstrated that both provide comparable measurements, despite PPG’s higher susceptibility noise. concludes integration can reliably detect arrhythmias offer real-time care.

Язык: Английский

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Analysis of integration of IoMT with blockchain: issues, challenges and solutions

Discover Internet of Things, Год журнала: 2024, Номер 4(1)

Опубликована: Окт. 23, 2024

The incorporation of Artificial Intelligence (AI) into the fields Neurosurgery and Neurology has transformed landscape healthcare industry. present study describes seven dimensions AI that have way providing care, diagnosing, treating patients. It exhibited unparalleled accuracy in analyzing complex medical imaging data expediting precise diagnoses neurological conditions. also enabled personalized treatment plans by harnessing patient-specific genetic information, promising more effective therapies. For instance, AI-powered surgical robots brought precision remote capabilities to neurosurgical procedures, reducing human error. In AI, machine learning models predict disease progression, optimizing resource allocation patient whereas wearable devices with provide continuous monitoring, enable early intervention for chronic accelerated drug discovery vast datasets, potentially leading breakthrough Chatbots virtual assistants powered enhance engagement adherence plans. holds promise further personalization augmented decision-making, earlier intervention, development groundbreaking treatments. mainly focuses on blockchain technology provides a reasonable understanding associated issues challenges along its solutions. will allow professionals advance field contribute towards improvement an individual's well-being when facing challenges.

Язык: Английский

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IoMT landscape: navigating current challenges and pioneering future research trends

Deleted Journal, Год журнала: 2024, Номер 7(1)

Опубликована: Дек. 18, 2024

Technological advancement drives the growth of Internet Things (IoT) applications in many fields, such as smart homes, cities, grids, and healthcare. IoT healthcare is called Medical (IoMT), which provides remote patient treatment using information communications technology. This new telemedicine technology simplifies regular effective communication between medical computing devices. Critical motivations for adopting IoMT are reduced cost, increased quality life, timely intervention. significant because it enables continuous, real-time monitoring during routine everyday activities a variety wearables sensors. With big data, makes excellent use Machine Learning (ML) to support disease detection health condition prediction, alerting patients providers. Many research studies have been conducted explore several aspects its real world. However, challenging comprehend all techniques solutions proposed by community. Therefore, this survey sheds light on some crucial explores potential gaps directions community could tackle. The examines discusses characteristics standards, protocols, types. It then delves into layers distinguishes them fog edge. published under each type were explored, limitations these works highlighted. approaches also findings directions, further endeavors be carried out address issues existing IoMT.

Язык: Английский

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Building an intelligent diabetes Q&A system with knowledge graphs and large language models

Frontiers in Public Health, Год журнала: 2025, Номер 13

Опубликована: Фев. 20, 2025

This paper introduces an intelligent question-answering system designed to deliver personalized medical information diabetic patients. By integrating large language models with knowledge graphs, the aims provide more accurate and contextually relevant guidance, addressing limitations of traditional healthcare systems in handling complex queries. The combines a Neo4j-based graph Baichuan2-13B Qwen2.5-7B models. To enhance performance, Low-Rank Adaptation (LoRA) prompt-based learning techniques are applied. These methods improve system's semantic understanding ability generate high-quality responses. performance is evaluated using entity recognition intent classification tasks. achieves 85.91% precision 88.55% classification. integration structured significantly improves accuracy clinical relevance, enhancing its responses for diabetes management. study demonstrates effectiveness graphs systems. proposed approach offers promising framework advancing management other applications, providing solid foundation future interventions.

Язык: Английский

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An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors

Sensors, Год журнала: 2025, Номер 25(5), С. 1552 - 1552

Опубликована: Март 2, 2025

Counting fetal movements is essential for assessing health, but manually recording these can be challenging and inconvenient pregnant women. This study presents a wearable device designed to detect across various settings, both within outside medical facilities. The integrates accelerometer gyroscope sensors with Internet of Things (IoT) technology accurately differentiate between non-fetal movements. Data were collected from 35 women at Suranaree University Technology (SUT) Hospital. evaluated ten signal extraction methods, six machine learning algorithms, four feature selection techniques enhance classification performance. utilized Particle Swarm Optimization (PSO) Extreme Gradient Boosting (XGB) PSO hyper-tuning. It achieved sensitivity 90.00%, precision 87.46%, an F1-score 88.56%, reflecting commendable results. IoT-enabled facilitated continuous monitoring average latency 423.6 ms. ensured complete data integrity successful transmission, the capability operate continuously up 48 h on single charge. findings substantiate efficacy proposed approach in detecting movements, thereby demonstrating practical valuable movement detection applications.

Язык: Английский

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Internet of Medical Things Integrating IoT with Healthcare for Remote Monitoring and Diagnosis

ITM Web of Conferences, Год журнала: 2025, Номер 76, С. 03004 - 03004

Опубликована: Янв. 1, 2025

The Internet of Medical Things (IoMT): It is changing the healthcare sector in various ways by coupling crucial aspects IoT to monitor and diagnose patients remotely. Existing literature regarding IoMT applications has identified high security vulnerabilities, unrealized real-world implementations, poor scalability, latency, but there are no proposed solutions these challenges. presents a robust (IoMT) architecture which real-time, secure, scalable, enables remote health monitoring. By leveraging edge computing, AI, blockchain-based security, framework improves data privacy, reduces increases energy efficiency. In contrast earlier studies that discuss specific conditions, current work generalizes for variety ailments, enabling personalized through artificial intelligence (AI)–driven analytics. addition, system designed be interoperable such it supports seamless integration across different devices. Using predictive analytics, this facilitates early disease detection preventative action, fostering better patient outcomes fewer hospital visits. This study also design an energy-efficient network prolong lifetime viability conclusion, research expands on future providing privacy real-time decision-making challenges, thereby developing robust, future-proof adaptable smart applications.

Язык: Английский

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Enhancing Connected Health Ecosystems Through IoT-Enabled Monitoring Technologies: A Case Study of the Monit4Healthy System

Sensors, Год журнала: 2025, Номер 25(7), С. 2292 - 2292

Опубликована: Апрель 4, 2025

The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. system's modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, photoplethysmography, EKG, allow for the remote gathering evaluation information. In order decrease network load enable quick identification abnormalities, edge computing used filtering feature extraction. Flexible transmission based on context available bandwidth provided through hybrid approach that includes Bluetooth Low Energy Wi-Fi. Under typical scenarios, laboratory testing shows reliable connectivity ongoing battery-powered operation. appropriate scalable deployment connected ecosystems portable due its responsive power management approaches structured transmission, which improve resiliency system. ensures reliability signals whilst lowering latency volume comparison conventional cloud-only systems. Limitations include requirement profiling, distinctive hardware miniaturizing, sustained real-world validation. By integrating context-aware flexible design, effective communication, complements existing IoT solutions promotes better integration clinical smart city healthcare environments.

Язык: Английский

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Biomedical Sensor Networks and Remote Patient Monitoring

Advances in computer and electrical engineering book series, Год журнала: 2025, Номер unknown, С. 221 - 252

Опубликована: Фев. 7, 2025

In contemporary healthcare, integrating advanced technologies such as data analytics and wearable sensor networks is revolutionizing patient monitoring disease management. Effective analysis essential for improving health outcomes, enabling early diagnosis, supporting personalized treatment plans. This process begins by examining the role of in processing vast amounts healthcare data, including EHRs, medical imaging, from sensors. chapter provides an in-depth exploration these their impact on delivery. It explains how body-fitted wireless (BF-WSNs) enable continuous vital sign collection transmission to professionals, facilitating real-time monitoring, prompt intervention, detection. Additionally, it discusses biomedical (BSNs), which offer remote without requiring in-person hospital visits. The benefits include enhanced engagement, cost savings, improved outcomes.

Язык: Английский

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Energy-Efficient Solutions in Medical IoT Devices

Advances in medical technologies and clinical practice book series, Год журнала: 2025, Номер unknown, С. 541 - 572

Опубликована: Фев. 14, 2025

Real-time monitoring, diagnostics, and personalized care through mHealth devices are important for medical IoT, but the challenge of energy consumption persists. The most thing in this respect is efficient usage to increase lifespan devices, reduce maintenance costs, improve patient care. present chapter will cover hardware optimization, energy-saving algorithms, advanced power management, harvesting technologies like solar kinetic IoT devices. It also discusses low-power communication protocols context AI 5G further enhance efficiency. Practical case studies, regulatory issues, future innovations be discussed highlight path toward more sustainable, energy-efficient solutions, supporting a greener effective healthcare system.

Язык: Английский

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