Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

Uppal, Mudita; Gupta, Deepali; Juneja, Sapna; Sulaiman, Adel; Rajab, Khairan; Rajab, Adel; Elmagzoub, M. A.; Shaikh, Asadullah

doi:10.3390/su141811667

Cited by 25 publications

(3 citation statements)

References 62 publications

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“…Faults can be classified into a gain fault, stuck-at-fault, offset fault, and out-ofbounds fault. Uppal et al [71] developed a different machine learning model to predict the faults in real-time sensor data monitoring in the hospital environment. The result analysis shows that the machine learning modes applied over IoT-based sensors are proficient in monitoring the automation process of the hospital process.…”

Section: Fault Detectionmentioning

confidence: 99%

Survey on Sensors and Smart Devices for IoT Enabled Intelligent Healthcare System

Chopade

Gupta

Dutta

2023

Wireless Pers Commun

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The Internet of Things (IoT) in the healthcare system is rapidly changing from the conventional hospital and concentrated specialist behavior to a distributed, patient-centric approach. With the advancement of new techniques, a patient needs sophisticated healthcare requirements. IoT-enabled intelligent health monitoring system with sensors and devices is a patient analysis technique to monitor the patient 24 h a day. IoT is swapping the architecture and has improved the application of different complex systems. Healthcare devices are one of the most remarkable applications of the IoT. Many patient monitoring techniques are available in the IoT platform. This review presents an IoT-enabled intelligent health monitoring system by analyzing the papers reported between 2016 and 2023. This survey also discusses the concept of big data in IoT networks and the IoT computing technology known as edge computing. This review concentrated on sensors and smart devices used in intelligent IoT based health monitoring systems with merits and demerits. This survey gives a brief study based on sensors and smart devices used in IoT smart healthcare systems.

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Section: Fault Detectionmentioning

confidence: 99%

Survey on Sensors and Smart Devices for IoT Enabled Intelligent Healthcare System

Chopade

Gupta

Dutta

2023

Wireless Pers Commun

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“…They gathered the related data from the process and equipment sensors of centrifugal pumps to generate fault prediction alerts properly in decision support systems for operatives. In another study [70], the authors reported a fault prediction model with the aim of real-time tracking of sensor data in an IoT-enabled cloud environment for a hospital by machine learning. They applied the DT, KNN, NB, and RF techniques for controlling unanticipated losses produced by different faults.…”

Section: Machine Learning-based Fault Predictionmentioning

confidence: 99%

Logistic Model Tree Forest for Steel Plates Faults Prediction

et al. 2023

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Fault prediction is a vital task to decrease the costs of equipment maintenance and repair, as well as to improve the quality level of products and production efficiency. Steel plates fault prediction is a significant materials science problem that contributes to avoiding the progress of abnormal events. The goal of this study is to precisely classify the surface defects in stainless steel plates during industrial production. In this paper, a new machine learning approach, entitled logistic model tree (LMT) forest, is proposed since the ensemble of classifiers generally perform better than a single classifier. The proposed method uses the edited nearest neighbor (ENN) technique since the target class distribution in fault prediction problems reveals an imbalanced dataset and the dataset may contain noise. In the experiment that was conducted on a real-world dataset, the LMT forest method demonstrated its superiority over the random forest method in terms of accuracy. Additionally, the presented method achieved higher accuracy (86.655%) than the state-of-the-art methods on the same dataset.

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“…Screen time has a clear connection with high blood pressure irrespective of the physique of any human. Hindering passive sitting with computer use (light walking or basic exercises) may effectively lessen blood pressure [ 75 , 76 ].…”

Section: Proposed Modelmentioning

confidence: 99%

Analysis of IoT-Related Ergonomics-Based Healthcare Issues Using Analytic Hierarchy Process Methodology

Upadhyay¹,

Juneja²,

Muhammad

et al. 2022

Sensors

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The objective of the present work is for assessing ergonomics-based IoT (Internet of Things) related healthcare issues with the use of a popular multi-criteria decision-making technique named the analytic hierarchy process (AHP). Multiple criteria decision making (MCDM) is a technique that combines alternative performance across numerous contradicting, qualitative, and/or quantitative criteria, resulting in a solution requiring a consensus. The AHP is a flexible strategy for organizing and simplifying complex MCDM concerns by disassembling a compound decision problem into an ordered array of relational decision components (evaluation criteria, sub-criteria, and substitutions). A total of twelve IoT-related ergonomics-based healthcare issues have been recognized as Lumbago (lower backache), Cervicalgia (neck ache), shoulder pain; digital eye strain, hearing impairment, carpal tunnel syndrome; distress, exhaustion, depression; obesity, high blood pressure, hyperglycemia. “Distress” has proven itself the most critical IoT-related ergonomics-based healthcare issue, followed by obesity, depression, and exhaustion. These IoT-related ergonomics-based healthcare issues in four categories (excruciating issues, eye-ear-nerve issues, psychosocial issues, and persistent issues) have been compared and ranked. Based on calculated mathematical values, “psychosocial issues” have been ranked in the first position followed by “persistent issues” and “eye-ear-nerve issues”. In several industrial systems, the results may be of vital importance for increasing the efficiency of human force, particularly a human–computer interface for prolonged hours.

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Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

Cited by 25 publications

References 62 publications

Survey on Sensors and Smart Devices for IoT Enabled Intelligent Healthcare System

Survey on Sensors and Smart Devices for IoT Enabled Intelligent Healthcare System

Logistic Model Tree Forest for Steel Plates Faults Prediction

Analysis of IoT-Related Ergonomics-Based Healthcare Issues Using Analytic Hierarchy Process Methodology

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