An IoMT‐Based Approach for Real‐Time Monitoring Using Wearable Neuro‐Sensors

Bhatt, Mohammed Wasim; Sharma, Sparsh

doi:10.1155/2023/1066547

Cited by 10 publications

(5 citation statements)

References 59 publications

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“…This comparison is tabulated in Table 1 as follows, The comparison of the models based on accuracy is shown in Figure 7. Based on both figures, it can be observed that MAC TM [28], CA CNN [17], ResNet [2], MDACNN [25], RL PCIC [21], TENG ANN [16], QASDS [19], and BCNN [26] have better accuracy. Thus, they can be used for applications that require high accuracy.…”

Section: Discussionmentioning

confidence: 93%

“…These models must be validated for context-specific application simulations, which will assist in deploying them for different HMSM use cases. Res Net [2] 7.6 HBO LSSVM [3] 7.6 CFT [4] 5.5 AGHO [5] 6.1 SECM CA [6] 6.1 LR CoVID [7] 7.9 BHM [8] 6.3 CNN FL CoVID [9] 8.0 LAC LS-IoT [10] 6.3 NB ECG [11] 8.9 FMCW RF [12] 7.3 1D CNN [13] 5.7 CPH [14] 6.6 CNN MQTT ECG [15] 5.8 TENG ANN [16] 6.2 CA CNN [17] 6.1 PMDL [18] 6.3 QASDS [19] 8.7 FCNN [20] 5.8 RL PCIC [21] 6.8 AP3 CNN [22] 6.3 LR and XG Boost [23] 6.5 BIoT [24] 7.2…”

Section: Discussionmentioning

confidence: 99%

“…Individual models are discussed in [20,21], where the use of CNN (CNN) and Reinforcement Learning based Partial Confident Information Coverage (RL PCIC) is proposed for large-scale monitoring applications. FCNN achieves an accuracy of 85.6%, while RL showcases an accuracy of 98.5% under different monitoring scenarios.…”

Section: B Incremental Learning-basd Flexible Classification Modelsmentioning

confidence: 99%

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Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Reddy,

Debasis

2023

IJRITCC

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Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: B Incremental Learning-basd Flexible Classification Modelsmentioning

confidence: 99%

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Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Reddy,

Debasis

2023

IJRITCC

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“…A heterogeneous network setting to measure and validate the performance in distinct scenarios was presented in [12]. Yet another real-time monitoring system with the aid of wearable neuro sensors for analyzing brain signals was proposed in [13]. However, both works did not focus on the network lifetime aspect.…”

Section: Critical Review Of Literature and Identification Of Research...mentioning

confidence: 99%

Generative Deep Belief Differential Lotka Volterra-Based Data Transmission in Wsn for Real-Time Health Care Monitoring

Sachin,

Sarvagya

2024

Preprint

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Wireless Sensor Networks (WSNs) have considerable awareness owing to their extensive scope of applications, to name a few being, natural disaster prohibition, health care monitoring video surveillance and so on. Moreover, the energy consumed during communication is a substantial quantity of energy loss and the routing protocol is the most suitable means to address this issue by fine-tuning weights to conserve the energy. Therefore the main issue experienced by WSN is the routing and data transmission and hence distinct points of reference need to be addressed when designing the routing protocol for real time healthcare monitoring. Motivated by these issues, in this work a method called, Probabilistic Generative Deep Belief and Differential Game Lotka Volterra (PGDB-DGLV) based data transmission in WSN for real-time health care monitoring is proposed. The PGDB-DGLV method is split into two sections, namely, optimal route discovery and robust data transmission. To measure route stability Balanced Centrality and Probabilistic Generative Deep Belief Network algorithm is designed. In the proposed model, a fine-tuned adjacency matrix employing the Stable Balanced Centrality function is defined to determine the next hop based on certain parameters like, hop count, residual energy and link quality. These parameters are salient assessments in adjacent node selections with which routing can be performed. Also, the initialization of the adjacency matrix and its corresponding fine-tuning is performed via the Probabilistic Generative Deep Belief Network. Second, to minimize the transmission speed and therefore reduce packet loss significantly, two kinds of game models, i.e., cooperative and cooperative models are constructed. Finally, the Lotka Volterra function is applied to ensure robust data transmission. At last, the performance of the proposed PGDB-DGLV method is evaluated using different evaluation metrics they are energy consumption, throughput transmission speed and packet loss rate. Finally, the evaluated results are compared with the prevailing data transmission methods. Among these methods, the proposed PGDB-DGLV method has achieved a better transmission speed with minimum energy consumption and maximum throughput considerably.

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“…1(c), including patient and environment monitoring, therapeutics, medical diagnosis, and workflow, which involves patients' surveillance, personal assistant platforms, patient and personal identification, and other activities. Some of the examples related to on-body IOMT are been discussed in the papers like [7][8]…”

Section: Introductionmentioning

confidence: 99%

Security protocols in healthcare: A comprehensive study of AI-Enabled IoMT

Swetha Singiri,

Naga santhosh reddy vootukuri,

Siri Chandana Katari

2024

Magna Sci. Adv. Biol. Pharm.

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The Internet of Medical Things IoMT has driven a paradigm shift in the way things have been done traditionally within medical Healthcare practices and this is evidence that Smart Personal systems (Health Care) are shaping up with mighty forces. By merging these technologies, IoT has transformed data communication and provided the means to directly interconnect medical devices and sensor systems. The use of advanced technologies like Artificial Intelligence( AI), Machine Learning, Deep Learning, and Federated Learning has highly equipped and transformed the IoMT’s performance. These integrations also raise concerns on data privacy and security, and addressing these challenges become more important as without proper security and privacy measures people can face serious disruption both online and offline leading to upcoming catastrophes already waiting at its end line. This chapter looks into the intricacies of a highly secure communications system powered by AI, specifically in the age where disasters like COVID-19 [10], emphasizes the need for increased security on data transmission as well as storage. IoMT has wide applications for integrating remote patient monitoring, enhancing user satisfaction and interdimensional hospitalization rates, we delve into how IoMT works in conjunction with machine learning, particularly federated Learning intends to address any possible concerns. The chapter begins with IoMT systems, including IoMT ecosystems, implantable medical devices IMDs, and Internet-worn gadgetry. Special attention is given to the AI-powered Intrusion Detection Systems (IDSs) in IoMT environments, while network security remains the research focus helping practitioners to implement safety measures enterprise-wide; analysis, comparison, and evaluation all encompass machine learning, deep learning mechanisms as precautions against potential attacks. We will also consider another study on the services of an AI-provisioned lightweight protocol communication system that ensures data authenticity and responds to diverse security implications in IMD. The chapter makes a U-turn by alluding to internet wearable devices, pointing out federated learning and blockchain reinforcements that indicate the need for an integrated framework. The amalgamation of future technologies supports the ethical and secure integration of IoMT. Collaboration, as a guiding principle, compels healthcare practitioners and policymakers to partner efficiently to guide the future where the security of health is ever easier. In the end, a summary of modern ethical and regulatory paradigms in IoMT is provided, emphasizing that there could be an urgent need for new laws.

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An IoMT‐Based Approach for Real‐Time Monitoring Using Wearable Neuro‐Sensors

Cited by 10 publications

References 59 publications

Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Generative Deep Belief Differential Lotka Volterra-Based Data Transmission in Wsn for Real-Time Health Care Monitoring

Security protocols in healthcare: A comprehensive study of AI-Enabled IoMT

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