Application Protocol adapted to Health Awareness for Smart Healthcare Service

Lee, Byung Mun; Ouyang, Jinsong

doi:10.14257/astl.2013.43.21

Cited by 14 publications

(16 citation statements)

References 5 publications

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“…In addition, Vilallonga et al [11] presented a study conducted on a group of obese patients having undergone surgery, who found it very motivating to observe, easily and quickly, a consistent graphic representation of their activities. Mun-Lee and Ouyang [12] presented a study that sought to identify correlations between the risks of developing certain diseases and used healthcare devices in the context of the IoT. By contrast, Zaragozá et al [13] presented a platform that uses intercommunicated sensors to monitor the activities of children with obesity problems.…”

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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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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“…One of the characteristics of an open healthcare service system is that all devices or services are designed in user-centered ways [19][20][21][22]. For example, suppose that a user purchases a medical device compatible with an open platform at a nearby convenience store.…”

Section: Figure 1 Two Different Network Model For U-healthmentioning

confidence: 99%

Registration Protocol for Health IoT Platform to Share the Use of Medical Devices

Lee

2015

IJBSBT

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“…As the concept of IoT (Internet of Things) was recently introduced, researches which attempt to apply the IOT model in different fields are being progressed [4,5,6]. If IoT technique is applied to UH, then UHD will break away from the simple functions of indicating measured data and sending them to the server and execute autonomous information exchange with neighboring systems (UHDs, gateway, ser ver) and provide comprehensively assessed feedback immediately to the patient [4]. For instance, a blood pressure which is above 140mmHg is generally assessed as hypertension [7].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Healthcare Service by using Collaborations between IoT Personal Health Devices

Lee¹,

Ouyang²

2014

IJBSBT

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Management of chronic diseases is important to self-management for health. The IoT concept plays a significant role in self-management for health. In order to accomplish it, personal health devices need two functions such as application network protocol and intelligent service. But, most of them have only simple function such as indicating measured data and storing data temporarily. In this research, we proposed an intelligent service model for healthcare which gives an effective feedback to an individual. In order to do this, we introduced the collaboration protocol which transfers risk factors between IoT personal health devices. In addition to this, we proposed intellectualized service application algorithm which will be operated in the personal health device. Finally, based on the findings of the experiment, the effectiveness was confirmed on proposed model.

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Application Protocol adapted to Health Awareness for Smart Healthcare Service

Cited by 14 publications

References 5 publications

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

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

Registration Protocol for Health IoT Platform to Share the Use of Medical Devices

Intelligent Healthcare Service by using Collaborations between IoT Personal Health Devices

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