The Art of Advanced Healthcare Applications in Big Data and IoT Systems

Ifrim, Claudia; Pintilie, Andreea-Mihaela; Apostol, Elena-Simona; Dobre, Ciprian; Pop, Florin

doi:10.1007/978-3-319-45145-9_6

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…Firstly, a draft extraction scheme was created by one of the researchers independently. The H-IoT data-related challenge items proposed in this draft scheme were based on synthesizing existing H-IoT review studies (Mavrogiorgou et al , 2018; Kang et al , 2019; Maksimovic, 2018; Ifrim et al , 2017). These draft elements are:Acquisition challenges include high-quality data access, real-time data collection, health-related data collection from multiple sources in a remote, secure and safe manner;Processing challenges include data transmission, integration, aggregation, validation and data interoperability;Storage emphasizes challenges related to data persistence, data backup and recovery;Management challenges derived from data standards and data sharing perspectives;Privacy and security challenges include identity privacy, location privacy, owner privacy.…”

Section: Methodsmentioning

confidence: 99%

Issues and challenges of user and data interaction in healthcare-related IoT

Zou

Liang

2020

LHT

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PurposeThe Internet of Things (IoT), which enables smart objects to collect and exchange data, has a variety of application domains used in everyday life including healthcare. As a set of promising next-generation technologies in the healthcare domain, Healthcare-related Internet of Things (H-IoT) promises to facilitate better healthcare by offering data-driven insights. While effective in practice at large, emerging data concerns arise because of the inscrutable black-box systems. Inspired by the notion of human data interaction, this paper seeks to understand how people engage with the H-IoT data that is about and produced by themselves and to elucidate the main data issues and challenges involved in the development of H-IoT.Design/methodology/approachThis work conducted a comprehensive survey and integrated the method of content analysis by systematically review the recently published H-IoT research work in the healthcare domain.FindingsThis study thoroughly surveyed more than 300 research studies published in the last decades and classified seven H-IoT end-user groups, and three H-IoT data types that are important to H-IoT comprehension. Attention to human data interaction, our study also highlights several critical issues associated with this notion in the context of H-IoT.Originality/valueThis study will support H-IoT research by characterizing the data issues and challenges exist in the context of H-IoT user and data interaction. The findings will provide insights in designing for effective interactions with data in the H-IoT.

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Section: Methodsmentioning

confidence: 99%

Issues and challenges of user and data interaction in healthcare-related IoT

Zou

Liang

2020

LHT

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“…In addition, Camara [42] described future trends in wireless communication with a focus on 5G networks, in which the benefits for the IoT and e-health are notable. Further, Ifrim et al [43] presented a study focusing on the use of IoT in e-Health, along with future guidelines and the evolution of the IoT in the field of health. This analysis of related work has shown that there are tools or applications in the IoT seeking to reduce the prevalence of overweight or obesity.…”

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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“…A literature survey with main challenges (i.e. data management, privacy, data mining, security, chaos of data) on this topic is done by [5]. The IoT can also be adapted to the manufacturing market, which can be a great advantage as described in [6].…”

Section: Research Analysismentioning

confidence: 99%

Big IoT Data Analytics by Combining Batch and Stream Processing Technologies

Kharole¹

2019

IJRASET

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The Internet of things is a current major developing technology, which is a network of everyday physical objects that enhances the quality of lifestyle. Application of the internet of things encounters dealing with huge amount of data. One of the directions of big data is this huge amount of data with respect to the internet of things. As the name implies, big data refers to the data that cannot be analyzed by a traditional data processing software. The key challenge of this phenomenon is to use a proper way to analyse, which can provide useful features from the data absorbed by the perception layer of the internet of things in order to provide feedback to end users, which helps them in better decision making and improves the performance of the corresponding internet of things network. Analysis of big data in the internet of things is obviously a hard task. Data storages are distributed and there should be parallel data processing. Transmission of the data across the network can slow down because of the massive amount of data. In this regard, this paper focuses on how to analyze the massive and heterogeneous data of the internet of things in a proper way. At first, the internet of things and the big data are discussed separately with architectures, applications, challenges etc. Since these two technologies are interrelated, data analysis in the internet of things is discussed with various methodologies and challenges. Finally, the study discusses a proper framework that can analyze the big data in the internet of things (IOT) in an efficient way.

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The Art of Advanced Healthcare Applications in Big Data and IoT Systems

Cited by 12 publications

References 15 publications

Issues and challenges of user and data interaction in healthcare-related IoT

Issues and challenges of user and data interaction in healthcare-related IoT

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

Big IoT Data Analytics by Combining Batch and Stream Processing Technologies

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