ArticleFrontiers in physiology2023
A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms.
Article in Frontiers in physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 70 citations in OpenAlex.
- IoT-aware extreme machine learning for efficient health monitoring.BMC health services research · 2026Article
- Towards advanced AI-based solutions for securing IoMT in smart health information systems.Scientific reports · 2026Article
- The B-Health Box: A Standards-Based Fog IoT Gateway for Interoperable Health and Wellbeing Data Collection.Sensors (Basel, Switzerland) · 2025Article
- Adaptive resource aware and privacy preserving federated edge learning framework for real time internet of medical things applications.Scientific reports · 2025Article
- PhysioDimClassifier-imbalance data classifier model for IoMT-based remote patient monitoring systems.MethodsX · 2025Article
- Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data.Scientific reports · 2025Article
- Enhancing human activity recognition with machine learning: insights from smartphone accelerometer and magnetometer data.PeerJ. Computer science · 2025Article
- Enhanced lightweight and compromised-resilient image encryption for resource constrained environments.PloS one · 2025Article
- Fusing convolutional learning and attention-based Bi-LSTM networks for early Alzheimer's diagnosis from EEG signals towards IoMT.Scientific reports · 2024Article
- Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects.Heliyon · 2024Review
- Oscillometry in Lung Function Assessment: A Comprehensive Review of Current Insights and Challenges.Cureus · 2023Review
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generally, cloud computing is integrated with wireless sensor network to enable the monitoring systems and it improves the quality of service. The sensed patient data are monitored with biosensors without considering the patient datatype and this minimizes the work of hospitals and physicians. Wearable sensor devices and the Internet of Medical Things (IoMT) have changed the health service, resulting in faster monitoring, prediction, diagnosis, and treatment. Nevertheless, there have been difficulties that need to be resolved by the use of AI methods. The primary goal of this study is to introduce an AI-powered, IoMT telemedicine infrastructure for E-healthcare. In this paper, initially the data collection from the patient body is made using the sensed devices and the information are transmitted through the gateway/Wi-Fi and is stored in IoMT cloud repository. The stored information is then acquired, preprocessed to refine the collected data. The features from preprocessed data are extracted by means of high dimensional Linear Discriminant analysis (LDA) and the best optimal features are selected using reconfigured multi-objective cuckoo search algorithm (CSA). The prediction of abnormal/normal data is made by using Hybrid ResNet 18 and GoogleNet classifier (HRGC). The decision is then made whether to send alert to hospitals/healthcare personnel or not. If the expected results are satisfactory, the participant information is saved in the internet for later use. At last, the performance analysis is carried so as to validate the efficiency of proposed mechanism.
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