TasLA: An innovative Tasmanian and Lichtenberg optimized attention deep convolution based data fusion model for IoMT smart healthcare

Khadidos, Alaa O.; Khadidos, Adil O.; Shitharth, S.; Mirza, Olfat M.

doi:10.1016/j.aej.2023.08.010

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…A broader range of sensors is essential to cover such features. Moving forward, it will be important to increase the number of sensor types and leverage sensor-fusion methodologies ( Khadidos et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Ogasawara,

Mukaino,

Matsunaga

et al. 2024

Front. Bioeng. Biotechnol.

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Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices.Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies.Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman’s rank correlation coefficient (R) of 0.56 and −0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics.Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements.

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Section: Discussionmentioning

confidence: 99%

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Ogasawara,

Mukaino,

Matsunaga

et al. 2024

Front. Bioeng. Biotechnol.

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“…Although there are many evaluation models intended for the security of IoT devices in different fields, the central emphasis in the proposed study is to investigate the existing literature only for the evaluation frameworks, models, and methods employed for the IoT-based systems in the healthcare area. These models often use MCDM-driven methods 8 – 12 and Artificial intelligent approaches 13 , 14 for the assessment purpose. However, the literature study is restricted to highlighting only those research works that are targeted to perform security assessments in healthcare environments using multi-criteria decision-making (MCDM) techniques.…”

Section: Related Workmentioning

confidence: 99%

Modeling security evaluation framework for IoHT-driven systems using integrated decision-making methodology

Khan,

Ali

2024

Sci Rep

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The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts’ groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems

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“…One way to ensure high security for IoT applications is by utilizing networks to create a series of computational tasks. Moreover, the transfer of computational tasks is accomplished by employing a transaction procedure for offloading, which involves a limited number of computational nodes (22) (23) , as explained in Equation (1).…”

Section: Mathematical Approachmentioning

confidence: 99%

A BlockChain-Based IoT Data Securing and Sharing System for Attendance Management

Ul Islam,

Das,

Deka

et al. 2024

IJST

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Objective: An Internet of Things (IoT) network has constraints on power consumption, processing power and security of end devices in the network. On the other hand, blockchain technology has some difficulties in storage capacity, data privacy, the ability to revoke consent, energy usage, scalability, and speed. To overcome the individual limitations of these two technologies, we integrate these two to propose a new design with an intention to authenticate, secure and share data pertaining to attendance system. In this present work the primary implementation details of our proposed work regarding coding are analyzed and a straightforward solution is offered by using indexing. Methods: Through our proposed work we introduce a multifaceted authentication system for user device substantiation, a Public Key Infrastructure (PKI) and RSA (Rivest-Shamir-Adleman) based blockchain implementation for data sharing and a rule-based access control scheme used for accessing data. Findings: The system can work with unidirectional and bidirectional IoT networks, and the data is stored on blockchain, providing more security to the data generated on the IoT network. Blockchain technology is utilized to offer a solution for certain problems encountered within a widespread network. Furthermore, a multi-factor authentication scheme is introduced and a rule-based access control scheme to access the data. Novelty: The blockchain is emerging which can be used to decentralize management and protect sensitive data. In a blockchain based attendance system, no administrative authority is permitted to modify or erase data. The individual who adds a data entry to the blockchain cannot later deny their involvement in the action. On the blockchain, all of the data and history are accessible to every participant. The blockchain based attendance system needs to offer a database that can be trusted, secure, and impossible to manipulate. Keywords: Blockchain (BC), Internet of Things (IoT), Student Attendance Management (SAM), User Authentication Key (UAK), Data Sharing

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TasLA: An innovative Tasmanian and Lichtenberg optimized attention deep convolution based data fusion model for IoMT smart healthcare

Cited by 7 publications

References 33 publications

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Modeling security evaluation framework for IoHT-driven systems using integrated decision-making methodology

A BlockChain-Based IoT Data Securing and Sharing System for Attendance Management

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