Estimation of quality of experience (QoE) in e-Health ecosystems

Rojas-Mendizabal, Veronica; Serrano-Santoyo, Arturo; Conte-Galvan, Roberto; Villarreal-Reyes, Salvador; Rivera-Rodríguez, Raúl

doi:10.15446/ing.investig.v37n2.57664

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…If the patient is within a range that implies risk to his health, the monitoring system will call him or his relatives and will alert health professionals, who should take the measures necessary to minimize this health risk. 17,18 From the moment the monitoring system receives the blood pressure reading from a patient, medical staff will be able to access this information at any time to check and analyze the blood pressure history of patients. 19 This way, it is possible to follow up all patients and, if necessary, to modify the frequency at which patients need to take their blood pressure, considering the time frames of days or weeks required to conduct a more extensive study.…”

Section: Methodsmentioning

confidence: 99%

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Automatized follow-up and alert system for patients with chronic hypertension

Urrea

Venegas

2020

Health Informatics J

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This article presents the development and implementation of a monitoring system for patients with chronic hypertension. Technological advances in wireless communication are increasingly used today to send and receive information through smartphones. This also applies to devices for measuring blood pressure, which can be efficiently integrated with smartphones. Telemedicine is used in a variety of health fields, and in the past 5 years, it has extended its reach to the online monitoring of patients. The objective of this study is to create an integrated system capable of conducting the follow-up, through mobile communication (smartphones), of patients with chronic diseases such as hypertension. An iHealth equipment certified by the Food and Drug Administration is used. The blood pressure values from users are uploaded via Internet and stored in an integral system for processing. The monitoring system developed not only informs users about their disease status but also sends them alerts generated during monitoring. This work uses the telecommunication technology existing through smartphones. The integrated system developed ensures the follow-up of the blood pressure of a large number of users. In addition, this system can be further applied to diseases such as diabetes and metabolic syndrome. The system developed was easy to use and efficient to monitor patients with chronic diseases such as high blood pressure.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automatized follow-up and alert system for patients with chronic hypertension

Urrea

Venegas

2020

Health Informatics J

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“…Utilizing decision trees, these modules suggest QoE remedies and improvements. The e-Health sector employs fuzzy logic for QoE estimation [14]. A framework evaluates the relationship between technology and human factors in e-Health, albeit challenges remain in defining QoD and application.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Quality of Experience Evaluation Framework for RPL Protocol in Mobile IoT Environments under Manhattan Grid Mobility

Kablian K

2023

IJRITCC

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This research paper introduces a novel and advanced framework for assessing the Quality of Experience (QoE) implications associated with the utilization of the Routing protocol for low power and lossy networks (RPL) in the context of the Internet of Things (IoT). The study undertakes a thorough investigation into RPL's performance under the influence of Manhattan grid (MG) mobility scenarios, addressing a notable gap in current research. By meticulously incorporating essential Quality of Service (QoS) metrics like average packet delivery ratio (PDR), average power consumption (Avg_P), and average inter-packet time (Avg_IPT), the framework enables an in-depth evaluation of QoE. The uniqueness of this work lies in its incorporation of a comprehensive dependency matrix (DM) and the subsequent application of a dependency function (DF) that comprehensively captures the multifaceted aspects of the system's perceived quality. Beyond its methodological innovation, this research enhances our comprehension of RPL's adaptability through the shift from static to dynamic environments. Furthermore, the study systematically explores various scalability levels, contributing novel insights into how RPL performs across diverse IoT scenarios.Based on the Quality of Experience (QoE) analysis, it can be deduced that the network effectively maintains the performance of a static model even under conditions of MG scenarios. In the static model, the RPL's performance was measured at 67.73%. However, when exposed to the MG mobility model, its performance decreased to 42.93%. Given that RPL is primarily optimized for static models and considering its static model performance as a reference benchmark, it manages to retain approximately 63.38% of the static model's performance when subjected to the MG mobility model.

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“…The different elements of the e-Health ecosystem, involving human, technology and socio-economic aspects, are described in Figure 4. Rojas-Mendizabal explains in [18] the characteristics of the agents and their attributes in such ecosystem. The human aspects involve the participation of both the patient and the medical specialist.…”

Section: Telemedicine Networkmentioning

confidence: 99%

Telemedicine Networks: Integrating Different Health Technologies towards a Common Goal

Conte-Galvan¹,

Serrano-Santoyo²,

Rojas-Mendizabal³

2019

RCS

Self Cite

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People like to live, work, travel and vacation in all kinds of places and weathers. Our active and inquisitive nature takes us to the most extreme locations, and our gregarious behavior allows us to coexist in both isolated as well as overcrowded communities. In all these scenarios, the new paradigm of healthcare becomes significant. The objective of this paper is to highlight the essential elements of both the current and emerging technologies that may constitute a ubiquitous, universal network of healthcare networks, providing an efficient and effective health service distributed globally. Our aim is to describe the current health ecosystem in order to identify the key agents and basic interactions that may contribute to conform a conceptual framework to provide ubiquitous health services using technology as an enabler but not the central objective. We propose an integral scheme that includes technology applications, regulation strategies and collaborative networks in order to provide efficient healthcare services anytime, anywhere, to anyone.

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Estimation of quality of experience (QoE) in e-Health ecosystems

Cited by 4 publications

References 13 publications

Automatized follow-up and alert system for patients with chronic hypertension

Automatized follow-up and alert system for patients with chronic hypertension

Analysis of Quality of Experience Evaluation Framework for RPL Protocol in Mobile IoT Environments under Manhattan Grid Mobility

Telemedicine Networks: Integrating Different Health Technologies towards a Common Goal

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