IoT-Based Intelligent Healthcare Module

Saji, Meenakshi; Sridhar, M.; Rajasekaran, Anitha; Kumar, Rohan Akshai; Suyampulingam, A.; Prakash, N. Krishna

doi:10.1007/978-981-15-5029-4_66

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…The 5G-based IoT systems effectively provide a way to monitor the patient without physical contact and enable health workers to evaluate the patient’s status [ 22 ]. The framework proposed by Saji et al uses gateways to send data from biomedical sensors to the intermediate hubs for further processing [ 23 ]. The system is good at defining a gateway approach at the IoT layer, but fails to present all layers of the system.…”

Section: Related Workmentioning

confidence: 99%

Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges

Waleed,

Kamal,

et al. 2023

Sensors

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The remote monitoring of patients using the internet of things (IoT) is essential for ensuring continuous observation, improving healthcare, and decreasing the associated costs (i.e., reducing hospital admissions and emergency visits). There has been much emphasis on developing methods and approaches for remote patient monitoring using IoT. Most existing frameworks cover parts or sub-parts of the overall system but fail to provide a detailed and well-integrated model that covers different layers. The leverage of remote monitoring tools and their coupling with health services requires an architecture that handles data flow and enables significant interventions. This paper proposes a cloud-based patient monitoring model that enables IoT-generated data collection, storage, processing, and visualization. The system has three main parts: sensing (IoT-enabled data collection), network (processing functions and storage), and application (interface for health workers and caretakers). In order to handle the large IoT data, the sensing module employs filtering and variable sampling. This pre-processing helps reduce the data received from IoT devices and enables the observation of four times more patients compared to not using edge processing. We also discuss the flow of data and processing, thus enabling the deployment of data visualization services and intelligent applications.

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Section: Related Workmentioning

confidence: 99%

Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges

Waleed,

Kamal,

et al. 2023

Sensors

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“…The images obtained for face recognition can be secured further by adding face authentication algorithms and other image processing techniques [24]. The results are communicated to the user, healthcare authorities, and alerting them [25].…”

Section: Algorithmmentioning

confidence: 99%

Smart solution for reducing COVID-19 risk using internet of things

Rajeshkumar

Mathi

2022

IJEECS

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The article exposes a smart device designed for mitigating the coronavirus disease (COVID-19) risk using the internet of things. A portable smart alerting device is designed for ensuring safety in public places which can alert people when the guidelines given by the government were not followed and alert health authorities when any abnormalities found. By doing so, the spread of this fatal disease can be stopped. The modules of the proposed system include the face mask detection module, social distance alerting module, crowd detection and analysis module, health screening module and health assessment module. The proposed system can be placed in any public entrances to monitor people without human intervention. Firstly, the human face images are captured for face mask check, then the crowd analysis of the particular entrance where the person is entering is performed, thereafter health screening of the person is done and the values were imported to the health assessment module to check for any abnormalities. Finally, after all the conditions were met the door is opened automatically. The smart device can be installed and effectively used in many scenarios such as malls, stores, crowded places and campuses to avoid the risk of spread of the coronavirus.

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“…Machine learning plays an essential part in the healthcare industry by providing ease to healthcare professionals to analyze and diagnose medical data [8][9][10][11][12]. Moreover, intelligent healthcare systems are providing real-time clinical care to needy patients [13,14].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Butt

Letchmunan

Ali

et al. 2021

Journal of Healthcare Engineering

117

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The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.

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IoT-Based Intelligent Healthcare Module

Cited by 9 publications

References 11 publications

Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges

Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges

Smart solution for reducing COVID-19 risk using internet of things

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

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