An Overview on the Internet of Things for Health Monitoring Systems

Ahmed, Mobyen Uddin; Björkman, Mats; Čaušević, Aida; Fotouhi, Hossein; Lindén, María

doi:10.1007/978-3-319-47063-4_44

Cited by 32 publications

(14 citation statements)

References 14 publications

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“…The communications between the devices are done using Wi-fi/Bluetooth/Zigbee and HTTPS web services. Thus, to support all of these facilities in sensor data management a generic system-level framework for a self-served health monitoring system through the Internet of Things (IoT) is proposed and presented in [25], [26]. Here, the paper elaborates the overview of the system together with the system-level framework and its individual components.…”

Section: Vsummary and Discussionmentioning

confidence: 99%

An Intelligent Healthcare Serviceto Monitor Vital Signs in Daily Life – A Case Study on Health-IoT

Ahmed¹

2017

IJERA

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Vital signs monitoring for elderly in daily life environment is a promising concept that efficiently can provide medical services to people at home. However, make the system self-served and functioning as personalized provision makes the challenge even larger. This paper presents a case study on a Health-IoT system where an intelligent healthcare service is developed to monitor vital signs in daily life. Here, a generic Health-IoT framework with a Clinical Decision Support System (CDSS) is presented. The generic framework is mainly focused on the supporting sensors, communication media, secure and safe data communication, cloud-based storage, and remote accesses of the data. The CDSS is used to provide a personalized report on persons" health condition based on daily basis observation on vital signs. Six participants, from Spain (n=3) and Slovenia (n=3) have been using the proposed healthcare system for eight weeks (e.g. 300+ health measurements) in their home environments to monitor their health. The sensitivity, specificity and overall accuracy of the DSS"s classification are achieved as 90%, 97% and 96% respectively while k=2 i.e., top 2 most similar retrieved cases are considered. The initial user evaluation resultdemonstrates the feasibility and performance of the implemented system through the proposed framework.

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Section: Vsummary and Discussionmentioning

confidence: 99%

An Intelligent Healthcare Serviceto Monitor Vital Signs in Daily Life – A Case Study on Health-IoT

Ahmed¹

2017

IJERA

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“…2019, 9,3037 4 of 23 study on the effectiveness of an experimental intervention based on diet recommendations given by means of a smart application for weight loss in overweight and obese patients over 18 years of age. Ahmed et al [25] presented an overview of existing health monitoring systems, taking into account the IoT approach, and discussed recent trends and the development of health monitoring systems in terms of health parameters and frameworks, wireless communication, and security issues, while identifying limitations and advantages.…”

Section: Related Workmentioning

confidence: 99%

PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

et al. 2019

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Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables.

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“…Ahmed et al [14] presentaron una visión general de los sistemas existentes de monitoreo de la salud, teniendo en cuenta el enfoque del IoT, además de las tendencias recientes y el desarrollo de sistemas de monitoreo de la salud en términos de parámetros y marcos de salud, comunicación inalámbrica y problemas de seguridad, así como identificar limitaciones y ventajas.…”

Section: Trabajos Relacionadosunclassified

“…Como se muestra en la Tabla 1 (integrada por los elementos: a. wearable, b. conectividad, c. interoperabilidad, d. integración, e. parámetros médicos, y f. recomendación médica), los trabajos [5,6,7,13,14] cuentan con la inclusión de parámetros médicos, pero no proporcionan recomendaciones médicas.…”

Section: Trabajos Relacionadosunclassified

Diseño de una aplicación Web para el seguimiento y control del sobrepeso u obesidad a través del paradigma del IoT

Ramos-Deonati¹,

Alor-Hernández²,

Machorro-Cano³

et al. 2018

RCS

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Resumen. En la actualidad, el sobrepeso y la obesidad son dos problemas de salud globales que afectan la calidad de vida de las personas y, por lo tanto, su vida laboral. El Internet de las cosas (IoT, por sus siglas en inglés) es un nuevo paradigma que está ganando terreno rápidamente en el escenario de la tecnología inalámbrica moderna. La idea básica de este concepto es la presencia en el entorno de diversas cosas u objetos interconectados, tales como los identificadores de radiofrecuencia (RFID), sensores, actuadores, teléfonos inteligentes, entre otras cosas u objetos. En este trabajo se presenta el diseño de una aplicación Web para contribuir en el seguimiento y control del sobrepeso u obesidad, bajo el paradigma del IoT, incorporando técnicas de inteligencia artificial aplicadas al cuidado de la salud. Adicionalmente, se presenta una arquitectura de componentes y el prototipo inicial, el cual está dividido en cinco capas principales (capa de presentación, capa de integración, capa de servicios en el IoT, capa analítica de datos y capa de datos) que integran el modelo interno del sistema, así como el aspecto de la aplicación. Además, se describen las principales funcionalidades de la aplicación Web, su diseño y un análisis de distintas plataformas de aprendizaje automático para el soporte del sistema de recomendaciones médicas, considerando utilizar el algoritmo de clasificación C4.5 para analizar la información obtenida a través de los dispositivos wearables.

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An Overview on the Internet of Things for Health Monitoring Systems

Cited by 32 publications

References 14 publications

An Intelligent Healthcare Serviceto Monitor Vital Signs in Daily Life – A Case Study on Health-IoT

An Intelligent Healthcare Serviceto Monitor Vital Signs in Daily Life – A Case Study on Health-IoT

PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

Diseño de una aplicación Web para el seguimiento y control del sobrepeso u obesidad a través del paradigma del IoT

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