Artificial Intelligence-driven Real-time Monitoring of Cardiovascular Conditions with Wearable Devices: A Scoping Review (Preprint) DOI
Ali Abedi, Anshul Verma, Dherya Jain

et al.

Published: March 18, 2025

BACKGROUND Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for 18 million deaths annually. Early detection and prediction cardiovascular conditions are essential timely intervention improved patient outcomes. Wearable devices offer a promising, non-invasive solution continuous monitoring signals, vital signs, physical activity. However, large data volumes generated by these rapid fluctuations in signals necessitate advanced Artificial Intelligence (AI) techniques real-time analysis effective clinical decision-making. OBJECTIVE The objective this scoping review is to identify main challenges AI-driven platforms condition with wearable explore potential solutions. Additionally, aims examine how AI algorithms deployment pipelines optimized enable monitoring. METHODS A comprehensive search was conducted following electronic databases: MEDLINE(R) ALL (Ovid), Embase Cochrane Central Register Controlled Trials Web Science Core Collection (Clarivate), IEEE Xplore, ACM Digital Library, yielding 2,385 unique records. Inclusion criteria focused on studies that utilized participant collection applied detect or predict events diseases. After title abstract screening, 153 articles remained, full-text review, 19 met inclusion criteria. RESULTS findings indicate despite promise devices, research remains limited lacks validation. Most relied publicly available datasets rather than real-world validation recruited participants community settings. Studies deployed frequently failed report operational characteristics challenges. ECG-based sensors were most used primarily hospital variety techniques, ranging from traditional machine learning lightweight deep algorithms, either via cloud-based processing. CONCLUSIONS Robust, interdisciplinary needed harness full AI-driven, health management using devices. This includes development scalable solutions community-based deployment. Furthermore, such as compliance, hardware connectivity constraints, model optimization must be carefully addressed.

Language: Английский

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity DOI Creative Commons
Raihan Uddin, Insoo Koo

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(5), P. 1876 - 1876

Published: Feb. 25, 2024

This comprehensive review paper explores the intricate integration of biosensors with multi-hop Internet Things (IoT) systems, representing a paradigm shift in healthcare through real-time remote patient monitoring. The strategic deployment different locations medical facilities, intricately connected to multiple microcontrollers, serves as cornerstone establishment robust IoT networks. highlights role this network, which efficiently facilitates seamless transmission vital health data centralized server. Crucially, utilization cloud connectivity emerges linchpin integration, providing secure and scalable platform for cloud-based approach not only improves accessibility critical information but also transcends physical limitations, allowing providers monitor patients from any location. transformative potential overcoming traditional limitations

Language: Английский

Citations

25
Smart and sustainable nano-biosensing technologies for advancing stress detection and management in agriculture and beyond DOI Creative Commons

Melina Sarabandi,

Meisam Zargar, Abazar Ghorbani

et al.

Industrial Crops and Products, Journal Year: 2025, Volume and Issue: 226, P. 120713 - 120713

Published: Feb. 26, 2025

Language: Английский

Citations

2
Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation DOI Creative Commons

Md Reazul Islam,

Khondokar Oliullah, Md. Mohsin Kabir

et al.

Journal of Agriculture and Food Research, Journal Year: 2023, Volume and Issue: 14, P. 100880 - 100880

Published: Nov. 23, 2023

Agriculture plays a vital role in feeding the growing global population. But optimizing crop production and resource management remains significant challenge for farmers. This research paper proposes an innovative ML-enabled IoT device to monitor soil nutrients provide accurate recommendations. The utilizes FC-28 sensor, DHT11 JXBS-3001 sensor collect real-time data on composition, moisture, humidity, temperature, nutrient levels. collected is transmitted server using MQTT protocol. Machine learning algorithms are employed analyze generate customized recommendations, including possible high-yielding list, fertilizer names, its amount based requirements nutrients. Furthermore, applied fertilizers treatments field during stored database. As result, it has become determine quality of produce at consumer level through mobile app. system's effectiveness evaluated experiments, comparing performance with traditional methods. results demonstrate device's ability enhance productivity optimize utilization, promoting sustainable agricultural practices food security. contributes IoT-enabled agriculture, demonstrating potential ML techniques improving management, facilitating informed decision-making about fertilizers, assessing produced crops level.

Language: Английский

Citations

32
Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications DOI Open Access
Abdussalam Elhanashi, Pierpaolo Dini, Sergio Saponara

et al.

Electronics, Journal Year: 2023, Volume and Issue: 12(24), P. 4925 - 4925

Published: Dec. 7, 2023

The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating interconnected and intelligent devices across multifarious domains. proliferation IoT resulted in an unprecedented surge data, presenting formidable challenges concerning efficient processing, meaningful analysis, informed decision making. Deep-learning (DL) methodologies, notably convolutional neural networks (CNNs), recurrent (RNNs), deep-belief (DBNs), have demonstrated significant efficacy mitigating these by furnishing robust tools for learning extraction insights from vast diverse IoT-generated data. This survey article offers comprehensive meticulous examination recent scholarly endeavors encompassing the amalgamation deep-learning techniques within landscape. Our scrutiny encompasses extensive exploration models, expounding on their architectures applications domains, including but not limited to smart cities, healthcare informatics, surveillance applications. We proffer into prospective research trajectories, discerning exigency innovative solutions that surmount extant limitations intricacies deploying methodologies effectively frameworks.

Language: Английский

Citations

29
Healthcare Monitoring Using an Internet of Things-Based Cardio System DOI Creative Commons
Galya Georgieva-Tsaneva, Krasimir Cheshmedzhiev,

Yoan-Aleksandar Tsanev

et al.

IoT, Journal Year: 2025, Volume and Issue: 6(1), P. 10 - 10

Published: Feb. 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.

Language: Английский

Citations

1
Prediction and detection of terminal diseases using Internet of Medical Things: A review DOI

Akeem Temitope Otapo,

Alice Othmani, Ghazaleh Khodabandelou

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 188, P. 109835 - 109835

Published: Feb. 24, 2025

Language: Английский

Citations

1
Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach DOI Creative Commons
Ahmad Raza, Mohsin Ali, Muhammad Khurram Ehsan

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(17), P. 7456 - 7456

Published: Aug. 27, 2023

The rapid technological advancements in the current modern world bring attention of researchers to fast and real-time healthcare monitoring systems. Smart is one best choices for this purpose, which different on-body off-body sensors devices monitor share patient data with personnel hospitals quick decisions about patients' health. Cognitive radio (CR) can be very useful effective smart systems send receive patient's health by exploiting primary user's (PU) spectrum. In paper, tree-based algorithms (TBAs) machine learning (ML) are investigated evaluate spectrum sensing CR-based required sets TBAs created based on probability detection (Pd) false alarm (Pf). These used train test system using fine tree, coarse ensemble boosted medium bagged RUSBoosted optimizable tree. Training testing accuracies all calculated both simulated theoretical sets. comparison training classifiers presented numbers received signal samples. Results depict that tree gives accuracy results minimum classification error (MCE).

Language: Английский

Citations

17
Digital transformation for sustainable health and well-being: a review and future research directions DOI Creative Commons

Khizar Hameed,

Ranesh Kumar Naha,

Faisal Hameed

et al.

Discover Sustainability, Journal Year: 2024, Volume and Issue: 5(1)

Published: May 30, 2024

Abstract Ensuring good health and well-being is one of the crucial Sustainable Development Goals (SDGs) that aims to promote healthy lives for people all ages. This involves providing affordable environmentally friendly medical services public fairly equitably. Good goals include achieving fair outcomes strong healthcare systems. It also highlights importance integrating sustainable considerations into policy frameworks developing countries, which are established address social factors influence health. Regarding reform, Information Communication Technologies (ICTs) play a pivotal role as key enablers improve patient access, treatment quality, system efficiency. shift in focus significance fostering digital accessibility, sustainability, inventiveness, cybersecurity, leadership. Nevertheless, incorporating progressively advancing ICT technology systems, sometimes called transformation, not simple. However, some challenges arise integration, application design, security measures. While numerous studies have been suggested tackle technologies these had limited scope considered several factors. Therefore, there pressing need an extensive research study focusing on integration technologies, design challenges, privacy areas, potential positive negative effects. this paper contributes literature covering important SDG, “Good well-being,” its along with summarising our findings detailed taxonomical way. First, we analyze all-encompassing taxonomy prior well-being, emphasizing healthcare, specifically applications associated Electronic Health (E-Health), future avenues exploration. Then, explore transformation significant components, highlight E-Health’s benefits, categorize challenges. Next, determine Blockchain Technology today’s leading E-Health. We discuss characteristics, describe possible types Blockchain-based E-Health use cases. Furthermore, compare impacts identify open issues directions, strengthening researchers solutions.

Language: Английский

Citations

8
Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN DOI Creative Commons

HongYuan Lu,

XinMiao Feng,

Jing Zhang

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: July 16, 2024

Abstract This research study demonstrates an efficient scheme for early detection of cardiorespiratory complications in pandemics by Utilizing Wearable Electrocardiogram (ECG) sensors pattern generation and Convolution Neural Networks (CNN) decision analytics. In health-related outbreaks, timely diagnosis such is conclusive reducing mortality rates alleviating the burden on healthcare facilities. Existing methods rely clinical assessments, medical history reviews, hospital-based monitoring, which are valuable but have limitations terms accessibility, scalability, timeliness, particularly during pandemics. The proposed commences deploying wearable ECG patient’s body. These collect data continuously monitoring cardiac activity respiratory patterns patient. collected raw then transmitted securely a wireless manner to centralized server stored database. Subsequently, assessed using preprocessing process extracts relevant important features like heart rate variability rate. preprocessed used as input into CNN model classification normal abnormal patterns. To achieve high accuracy abnormality trained labeled with optimized parameters. performance evaluated gauged different scenarios, shows robust detecting sensitivity 95% specificity 92%. Prominent observations, highlight potential interventions include subtle changes preceding distress. findings show significance technology improving pandemic management strategies informing public health policies, enhances preparedness resilience face emerging threats.

Language: Английский

Citations

5
An IoT Healthcare System With Deep Learning Functionality for Patient Monitoring DOI
Ali Hamzah Najim,

Kareem Ali Malalah Al-Sharhanee,

Istabraq M. Al‐Joboury

et al.

International Journal of Communication Systems, Journal Year: 2024, Volume and Issue: 38(4)

Published: Oct. 22, 2024

ABSTRACT Currently, healthcare systems operate under conventional management practices and entail storing processing substantial medical data. Integrating the Internet of Things (IoT) wireless sensor networks (WSNs) technologies has facilitated development IoT‐enabled healthcare, which possesses advanced data capabilities extensive storage. This paper proposes a WSN IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), intelligent health system was developed using technology to monitor person's blood pressure, heart rate, oxygen level, temperature. Furthermore, helps elderly being critical cases their homes communicate update condition with hospital, especially cases, be treated as soon possible, remote areas. The experimental results showed superiority effectiveness proposed system. Moreover, relying ANNs extract basic features, accuracy reached 96%. implemented practically, were displayed real time compared commercial devices. Maximum relative errors are rate (2.19), body temperature (2.94), systolic pressure (3.4), diastolic (2.89), SpO2 (1.05). On other hand, is much faster than communication methods, regardless detection quality.

Language: Английский

Citations

4