Towards collaborative intelligent IoT eHealth: From device to fog, and cloud

Farahani, Bahar; Barzegari, Mojtaba; Aliee, Fereidoon Shams; Shaik, Khaja Ahmad

doi:10.1016/j.micpro.2019.102938

Cited by 94 publications

(64 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Using the fog, we can overcome the fundamental barriers described above for IoT-Cloud systems, by processing data on the edge of the network and obtaining immediate feedback from the local community [6]. Excellent and successful cooperation between fog computing and IoTenabled technology can support various advantages such as improved service quality (QoS) in terms of data traffic reduction, low response time, scalability, location awareness, more exceptional user experience, and fewer bandwidth requirements [7].…”

Section: Introductionmentioning

confidence: 99%

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

2020

View full text Add to dashboard Cite

Throughout recent years, the progress of telemonitoring and telediagnostics devices for evaluating and tracking Parkinson's (PD) disease has become increasingly important. The early detection of PD increases the consistency of the treatment of patients and ultimately allows it possible to achieve a rapid diagnostic decision from an experienced clinician. In this paper, a proposed fog-based ANFIS+PSOGWO model provided for Parkinson's disease prediction. The proposed model exploits the advantages of the grey wolf optimization (GWO) and the particle swarm optimization (PSO) for adjusting the adaptive neuro-fuzzy inference system (ANFIS) parameters with the use of chaotic tent map for the initialization. The fog processing utilized for gathering and analyzing the data at the edge of the gateways and notifying the local community instantly. Compared to other optimization methods, many evaluation metrics used like the root mean square error (RMSE), the mean square error (MSE), the standard deviation (SD), and the accuracy and five standard datasets from repository of UCI machine learning that demonstrated the superiority of the model proposed against the grey wolf optimization (GWO), the particle swarm optimization (PSO), the differential evolution (DE), the genetic algorithm (GA), the ant colony optimization (ACO), and the standard ANFIS model. Moreover, the proposed ANFIS+PSOGWO applied for Parkinson's disease prediction and achieved an accuracy of 87.5%. The proposed ANFIS+PSOGWO compared in producing positive outcomes better than PSO, GWO, GA, ACO, DE, and some recent literature for Parkinson's disease prediction. The proposed model produced accuracy for the Parkinson's disease prediction has outperformed its closest competitors in all algorithms by 7.3%.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

2020

View full text Add to dashboard Cite

show abstract

“…Low cost and easily implementable IoT based health monitoring systems that offer accurate measurement of heart rate, blood pressure, glucose level and other health parameters of patients are also proposed in, Moghadas et al 14 and Savaridass et al 15 Increasing demands of current IoMT-driven applications require leveraged solutions for effective health monitoring, decision making and data storage. 16 In such a context, the highly dynamic and real-time nature of IoMT systems can be enhanced by AI-based methods which can endow IoMT systems with several degrees of intelligence 17 in decision making and provide a higher degree of robustness and accessibility. 18 Moreover, the advantages of AI-driven innovation can extend the boundaries of healthcare outside of hospital settings by transforming the hospital-centric to patient-centric ecosystem.…”

Section: Research Backgroundmentioning

confidence: 99%

Incorporating patient concerns into design requirements for IoMT-based systems: The fall detection case study

Νικολαϊδου¹,

Kotronis²,

Routis³

et al. 2021

Health Informatics J

View full text Add to dashboard Cite

Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes.

show abstract

“…The common ways are using wireless access points, cellular network to transmit the data and Bluetooth media gateways. All can be use at the same time to transport the data to the fog layer [20]. Fog layer is the middleware between sensor and sensor gateway's layer and the cloud layer.…”

Section: Tx Powermentioning

confidence: 99%

A Development of LBS by Using Beacon BLE Technology in Healthcare

Mesbah¹,

E²

2021

Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 Feb

View full text Add to dashboard Cite

Wireless sensor networks are becoming popular in health-care systems day by day with the rapid growth of new technologies and application, including Location-based aware systems. Following the comprehensive health-care reforms in past years, it motivates researchers to improve approachability to health-care specially in hospitals and health centers. The main purpose of this research is to develop a model for using of Location-based systems through iBeacon and Bluetooth Low Energy technologies in health-care. The proposed model is developed for hospitals as one of the active places, where time is critical. One of the major problems in the hospital environment is often the waiting time, procedures and finding appropriate departments, which are because of large population and the size of a hospital. To endeavor this dilemma, we developed a model that easily can help for the centers and places to visited by patients through their smartphone devices and examine it in a real indoor environment. The results show that, the proposed model can significantly improve hospital information management and patient guidance, and keep them up-to-date and provide detailed patient database, patient's information and location to keep them track of doctor's appointment, and receiving a medication. With using different sensors, BLS, iBeacon and BLE, we can align patient, doctor, nurse, and pharmacy vendor to the specific purpose of satisfying patient's needs.

show abstract

Towards collaborative intelligent IoT eHealth: From device to fog, and cloud

Cited by 94 publications

References 21 publications

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Incorporating patient concerns into design requirements for IoMT-based systems: The fall detection case study

A Development of LBS by Using Beacon BLE Technology in Healthcare

Contact Info

Product

Resources

About